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# Prevention of type 2 diabetes among women with prior gestational diabetes mellitus

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### Projections of Global Mortality and Burden of Disease from 2002 to 2030

(2006)
Introduction As part of the groundbreaking Global Burden of Disease (GBD) study for 1990, Murray and Lopez [1,2] prepared projections of mortality and burden of disease by cause to 2000, 2010, and 2020 under three alternative scenarios. These projections have been widely used and continue to be widely quoted to provide information on likely future trends in global health, for example in the Atlas of Heart Disease and Stroke [3]. However, these projections were based on the GBD 1990 estimates and on projections of HIV/AIDS, smoking, income, and human capital from 1990 to 2020, and are now outdated. The HIV/AIDS projections in particular substantially underestimate the spread of the HIV epidemic and the level of HIV/AIDS mortality around 2000. We have thus prepared updated projections of mortality and burden of disease from 2002 to 2030 using methods similar to those of the original GBD study and new input information. An earlier version of these projections has already been extensively used in the recently released World Health Organization (WHO) global report “Preventing chronic diseases: A vital investment” [4]. There is a substantial literature on the projection or forecasting of all-cause mortality rates and mortality rates for specific diseases. The methods used fall into two broad groups. First are those methods based on time-series analysis of historical trends in mortality rates. These “aggregate models,” whether for all-cause mortality or for specific causes, use the previous trend of the variable of interest as the basis for predicting its future value. By their data requirements, such methods are generally limited to high-income countries with good death registration data (see, for example, Lee and Carter [5,6]). Second are the “structural models,” which are based on relationships between mortality and a set of independent variables, and are necessarily projections of those independent variables. To the extent that the structural model identifies the important components—and the relationships among them—of the “system” that determines the variable of interest, they offer the potential for more robust predictions. When the underlying system is complex and sensitive to one or more of its components, a shift in some of the system variables can introduce large changes in the outcome that may be missed by extrapolation (such as the discovery of antibiotics and infectious disease trends or the change in tuberculosis mortality after the HIV epidemic). Aggregate models, in contrast, require considerably less knowledge of the system components and the relationships among them. These models can therefore provide more reliable estimates when such information is not available, especially when the system is not very sensitive to its inputs in time intervals that are in the order of the prediction time. The GBD projections fall into the structural models class, as do a number of more sophisticated and data-demanding risk factor–based models for specific causes [7–10]. Methods also vary as to whether mortality is projected for all age groups simultaneously or separate models are developed for each age–sex group, and as to whether separate cause-specific projections are carried out. To our knowledge, the original GBD projections represent the only attempt to date to project cause-specific mortality, rather than all-cause mortality, for a complete set of causes at global and regional levels. The GBD projections use separate structural models for each of a small set of major cause groups and cause-compositional models for detailed causes within these groups. The latter are a special case of more general cause-compositional models that relate the proportional distributions of causes of death to the levels of all-cause mortality [11]. Recently, both types of projection model have been extended to allow incorporation of prior knowledge and enforce coherence in the resulting mortality estimates across age groups, causes, and regions. Czado et al. [12] extended the Lee-Carter Poisson log-bilinear model into a Bayesian model, in which the prior data impose smoothness of age and period effects in the projections. Girosi and King [13] developed a forecasting model that models future values of log-mortality as functions of covariates known to affect risk of death. They also used a Bayesian approach to allow different country–age–group cross-sections to “borrow strength” from each other in order to overcome the serious data limitations faced for many countries. Their model also constrains the projections to vary smoothly over time and across age groups in accordance with known age profiles for specific causes of death. The detailed application of such methods may result in substantially improved forecasts of global mortality and burden of disease trends in the next few years. In the meantime, these updated projections provide a comprehensive update of the original GBD projections, with some methodological improvements and extensions. Methods The methods used in the present study for the projections of mortality and burden of disease are similar to those used in the original GBD study [2]. Separate projections models for males and females and for seven age groups (0–4, 5–14, 15–29, 30–44, 45–59, 60–69, and 70 y and older) were developed to produce parsimonious equations for ten cause-of-death clusters. Baseline, pessimistic, and optimistic projections of covariates in the regression equations were developed and used to project mortality rates for the cause clusters. Separate projection models were developed for HIV/AIDS, tuberculosis, lung cancer, diabetes mellitus, and chronic respiratory diseases. Separate regression equations were developed relating age- and sex-specific mortality rates for 132 detailed causes to the age- and sex-specific mortality rates from corresponding cause clusters, based on International Classification of Diseases Ninth Edition (ICD-9) coded vital registration data from 98 countries. Whereas the original GBD study carried out projections for eight regions of the world, we carried out these projections at country level, but aggregated the results into regional or income groups for presentation of results. Four income groups were defined based on World Bank estimates of GDP per capita in 2001 (see Table S1 for definitions of the regional and income groups). Baseline estimates at country level for 2002 were derived from the GBD analyses for 2002 as published in the World Health Report 2004 [14]. Data sources and methods for the 2002 estimates were comprehensively documented by Mathers et al. [15], together with an analysis of uncertainty levels of the results [16]. Mortality and burden of disease trends from 1990 to 2001 were recently analyzed by Lopez et al. [17]. Projections of burden of disease measured in disability-adjusted life years (DALYs) were then developed for the three scenarios based on projected mortality rates and alternative assumptions for those causes with little or no mortality. Population projections were developed based on United Nations Population Division projections of fertility and net migration and our projected mortality rates. We describe the main features of the methods, assumptions, and input data in this section, with a particular emphasis on revisions and changes to the original methods used by Murray and Lopez. Protocol S1 and Tables S1–S7 provide full details. Regressions for Major-Cause Clusters Disease and injury causes of death are classified in the GBD using a tree structure in which the first level comprises three broad cause groups: Group I (communicable, maternal, perinatal, and nutritional conditions), Group II (noncommunicable diseases), and Group III (injuries) [15]. We projected age- and sex-specific death rates at country level for ten major-cause clusters: Group I conditions excluding HIV/AIDS (communicable, maternal, perinatal, and nutritional conditions as defined in Table S2), malignant neoplasms (excluding lung cancer), diabetes mellitus, cardiovascular diseases, digestive disorders, chronic respiratory conditions, other Group II diseases, road traffic accidents, other unintentional injuries, and intentional injuries. In contrast to the original GBD projections, diabetes mellitus was treated as a separate cause category from other Group II diseases, since the available evidence suggests that its dominant risk factor, overweight and obesity, is becoming more prevalent over time in both developed and developing regions and is projected to continue to rise [18]. Rather than attempt to model the effects of the many separate direct determinants or risk factors for disease from the limited data that are available, the GBD methodology considered a limited number of socioeconomic variables: (1) average income per capita, measured as gross domestic product (GDP) per capita; (2) the average number of years of schooling in adults, referred to as “human capital”; and (3) time, a proxy measure for the impact of technological change on health status. This latter variable captures the effects of accumulating knowledge and technological development, allowing the implementation of more cost-effective health interventions, both preventive and curative, at constant levels of income and human capital [1]. These socioeconomic variables show clear historical relationships with mortality rates, and may be regarded as indirect, or distal, determinants of health, although it is not necessary for the purposes of projection to know whether these associations are causal. In addition, a fourth variable, tobacco use, was included in the projections for cancers, cardiovascular diseases, and chronic respiratory diseases, because of its overwhelming importance in determining trends for these causes. Tobacco use was measured in terms of “smoking impact”—that component of observed lung cancer mortality attributable to tobacco smoking [19]. This indirect measure of the accumulated hazards provides a better measure than do current smoking rates for the overall health impact of tobacco, taking into account lag times as well as important aspects of exposure such as duration, type, amount, and mode of smoking (see Protocol S1 for more details). We used a regression equation of the same form as in the original GBD projections: where Ca,k,i is a constant term, Ma,k,i is the mortality level for age group a, sex k, and cause i, and Y, HC, and T denote GDP per capita, human capital, and time, respectively. The log of the smoking impact SI is included in the equation only for malignant neoplasms, cardiovascular diseases, and respiratory diseases. This basic functional relationship makes no specific assumptions about the relationships between these more distal socioeconomic factors and more proximate determinants of mortality rates such as environment, lifestyle, and physiological risk factors. Nevertheless, our regression results indicate that a considerable proportion of the variance in age-, sex-, and cause-specific mortality rates can be explained by this limited set of distal determinants. Murray and Lopez used a variety of econometric approaches to estimate the equations (Equation 1), including methods that take into account auto-correlation and heteroscedasticity. Because these methods substantially restricted the subset of panel data that could be used, and in particular resulted in much loss of information for moderate- to high-mortality populations, they chose to use ordinary least-squares regression based on the entire dataset for the final set of parameter estimates. We followed the same approach and estimated equations (of the form illustrated by Equation 1) separately for each age–sex–cause group. Death registration data from 106 countries for 1950–2002 [20] were used to estimate the regression equations. In total, 2,605 country-years were available, almost double those used for the original projections by Murray and Lopez. Although the dataset is extensive, it does not include many observations from populations with high rates of mortality. Whereas the original GBD projections applied a single set of models based on all observed death registration data for projections in all regions, the new projections for low and lower-middle-income countries (income in international dollars less than $3,000 per capita in 2002) were based on the observed relationships for a dataset consisting of 1,734 country-years of observation where income per capita was less than$10,000 per year. Additionally, observed regional trends in child mortality from 1990 to 2002 were compared with those predicted by the projection model for low-income countries conditional on the observed time series for the model covariates for that period. As a result, the regression coefficient for time was set to zero for sub-Saharan Africa, and to 25% of its original value for other low-income countries. Final parsimonious regression results are listed in Tables S3 and S4. The low-income regression results give somewhat more conservative declines for Group II (noncommunicable) diseases in low-income regions. In general, the final regression equations are broadly similar to those estimated for the original projections. Apart from injuries, they explain a surprising proportion of the variance for many age–sex–cause categories. For males and females aged 70 y and above, the R 2 for many cause clusters was generally lower than for other age groups, probably reflecting poorer quality of coding of causes of death or the smaller range of variation in mortality rates between countries at these older ages, as well as the inherent reductions in the explanatory power of covariates with increasing death rates with age. The lower proportions of variance explained for injuries may reflect lower levels of temporal variation in these causes as well as more heterogeneous injury causes that may be less well-correlated with income and human capital. For cause clusters in age–sex groups in which the R 2 was less than 10%, we carefully examined the consistency of the projections with other age–sex groups, and made a choice between using the final parsimonious regression equation, an alternative assumption such as stable (constant) rates, or a separate prediction model based on projections for the major relevant risk factor (diabetes mellitus and chronic respiratory diseases). Model predictions of age-, sex-, and cause-specific mortality rates in 2002 for each of the ten clusters of causes were compared for each country with the results of the GBD study for that year. A series of scalars were then derived so that projected values for 2002 were identical to the 2002 GBD results. It was then assumed that these scalars would remain constant over the period 2002–2030. Diabetes and Chronic Respiratory Conditions Initial regression analysis for diabetes mellitus found inconsistent trends between males and females, probably reflecting the large variations across countries and inaccuracies in recording diabetes as the underlying cause of death in many death registration systems. While overall trends for diabetes mortality showed no consistent relationship across the sexes or levels of development, the regression analysis found significant betas for T (year) with death rates increasing with time. Since a substantial proportion of diabetes mortality is attributable to overweight and obesity [21], a separate projection model for diabetes mortality was developed using WHO projection of trends in body mass index distributions from 2000 to 2010 (see Protocol S1 for more details). Initial projections for chronic respiratory diseases resulted in substantially increasing rates for high-income countries, although smoking is the main risk factor and smoking impact has been generally decreasing. It is likely that the initial projections may reflect increasing propensity to code chronic obstructive pulmonary disease (COPD) as the underlying cause of death with time. Projections of mortality for chronic respiratory diseases were thus adjusted for projected changes in smoking impact (see Protocol S1 for more details). Projections of Income, Human Capital, and Smoking Impact Revised country-level projections for income per capita, human capital, and smoking impact were developed for the baseline, pessimistic, and optimistic projections. Income per capita was measured using average GDP per capita expressed in international dollars (purchasing power parity adjusted). Country-specific and regional income growth forecasts by the World Bank were used to project GDP per capita for all WHO member states. Beyond 2015, projected growth rates for most regions approach 3% per annum under the baseline scenario, with somewhat lower growth rates in sub-Saharan Africa, the Middle East, and high-income countries (see Protocol S1 for more details). Revised estimates and projections of human capital for WHO member states were prepared for the period 1950–2030 drawing on previous estimates by Barro and Lee for 98 countries [22] and observed relationships between growth in human capital and growth in GDP per capita. Smoking impact was calculated for the historical mortality country–year observations by subtracting nonsmoker lung cancer rates from observed total lung cancer mortality rates in the data. Higher nonsmoker lung cancer mortality rates were used in China and Southeast Asian countries, to reflect the higher levels of lung cancer in nonsmokers due to indoor air pollution from exposure to smoke from solid fuels [23,24]. Ezzati and Lopez developed projections of smoking intensity for 14 sub-regions of the WHO regions for the Comparative Risk Assessment (CRA) project, based on an analysis of past and current age–sex-specific smoking prevalence calibrated to regional characteristics of the tobacco epidemic for different sub-regions [23,24]. These regional projections were used to develop country-specific projections drawing also on more recent World Bank data on trends in adult per capita consumption of cigarettes and recent observed trends in age–sex-specific lung cancer mortality (see Protocol S1 for more details). For four high-income countries, recent projections were used based on an age–period–cohort model estimated using historical data on age–sex-specific tobacco consumption, average tar content, and adult tobacco consumption per capita [25]. Regression Equations for Detailed Causes To project death rates for specific causes within the major-cause clusters, we followed the original GBD methodology in estimating the relationship between trends in age–sex-specific mortality for each specific cause with the trend in the age–sex-specific mortality for the major-cause cluster to which the specific cause belongs. To avoid biasing the results due to changes in specific cause death rates associated with use of different versions of ICD, we restricted the analysis to country-years for which deaths were coded using ICD-9. The regression parameters were estimated on 1,357 country-year observations for 98 countries. For each individual regression, observations were excluded from analysis if they related to fewer than 50 deaths. Regression results were used only where the relationship was reasonably strong, as measured by an R 2 greater than 0.25 and a p-value less than 0.001 for the regression coefficient. The resulting coefficients are shown in Table S5. The major-cause cluster regression equation for intentional injury predicted generally falling death rates in middle- and low-income countries and rising death rates in high-income countries. For homicide, this is the opposite of the observed trends in the larger high-income countries. For this reason, homicide death rates were assumed to remain constant under the baseline scenario, with some modifications for country-specific observed trends. Similarly, war deaths were assumed to remain constant under the baseline scenario. For countries with good death registration data and populations of 5 million or more, trends in mortality rates were estimated for ischaemic heart disease, cerebrovascular disease, tuberculosis, suicide, and homicide. These were used to adjust the initial years of relevant country-specific projections to match the recent observed trends. Projections for HIV/AIDS and Tuberculosis We used separate projections for HIV/AIDS mortality under several scenarios derived from existing models. The Joint United Nations Programme on HIV/AIDS (UNAIDS) and WHO [26] have prepared projections of HIV/AIDS mortality under a range of assumptions about the future of the HIV epidemics in all regions and with varying treatment scale-up assumptions for both adult and children using a transmission model previously used to assess the impact of preventive interventions [10,27]. The model includes underlying regional demography, acquisition of HIV and other sexually transmitted infections, and progression from HIV infection to AIDS and death. For countries with generalized epidemics (mostly in sub-Saharan Africa), the model variables were estimated from sentinel site prevalence data. For countries with epidemics concentrated in groups with higher-risk behaviour, the size and HIV prevalence was estimated for each of these groups, and prevalence in low-risk populations was estimated by allowing for transmission from high-risk to low-risk groups via sexual mixing. Projections of these epidemics were based on assumptions about degree of saturation for each of the high-risk groups, time to saturation, and spread from high-risk to low-risk populations over time (see Protocol S1 for further information). Our baseline projections for HIV/AIDS age–sex-specific mortality rates were based on the UNAIDS and WHO “medium” scenario for treatment scale-up. Under this scenario, antiretroviral therapy (ART) coverage will reach 80% by 2012 in all regions, remaining constant beyond that year. The treatment scenarios assumed no effect of treatment on transmission and incidence rates, and no additional prevention efforts resulting in reduction of transmission and incidence rates. Our pessimistic projections for HIV/AIDS age–sex-specific mortality rates were based on the UNAIDS and WHO “slow” scenario for treatment scale-up. Under this scenario, ART coverage will reach 60% by 2012 in all regions except Latin America, where it reaches 70% in 2013. HIV/AIDS mortality rates in high-income countries were assumed to remain constant as in the baseline scenario. Salomon et al. [28] have modelled scenarios for sub-Saharan Africa combining treatment and additional prevention efforts. In one scenario, ART coverage is scaled up and optimal assumptions are made about treatment impact on transmissibility and patient behaviour. A second scenario assumes that an emphasis on treatment leads to less effective implementation of prevention, resulting in only 25% attainment of the maximum potential impact of prevention efforts. For our optimistic projection of HIV/AIDS mortality, we used a third unpublished scenario prepared by Salomon et al. that is approximately halfway between their two published mixed treatment-prevention scenarios. For low- and middle-income countries outside sub-Saharan Africa, we assumed similar proportional reductions in incidence rates for HIV infection due to increased prevention, as estimated for sub-Saharan Africa (see Protocol S1 for more details). Because of the powerful interaction between tuberculosis and HIV infections in regions such as sub-Saharan Africa, Murray and Lopez modified the original projections from 1990 to 2020 for tuberculosis death rates. As part of the Global Plan to Stop TB covering the period 2006–2015, the Stop TB Partnership has prepared three projection scenarios for tuberculosis incidence, prevalence, and mortality based on different assumptions about the pace of scale-up and coverage of interventions to achieve the UN's Millennium Development Goals for tuberculosis [29]. The “Global Plan” scenario assumes massive scale-up in tuberculosis control activities, achieving case detection levels of more than 70% and using the WHO DOTS (directly observed therapy, short-course) treatment strategy to reach cure rates of more than 85%. The “Sustained DOTS” scenario assumes that case detection and treatment success rates increase until 2005 and then remain constant to 2015. A third “No DOTS” scenario assumes that the DOTS treatment strategy was never introduced in any region, so case detection, treatment, and cure rates would continue as they were pre-DOTS (see Protocol S1 for more details). The projected annual regional trends in tuberculosis death rates for HIV-negative cases under these three scenarios were used to project tuberculosis death rates for countries in each region as follows. Annual projected trends under the “Sustained DOTS” scenario were used for the baseline projections, and those under the “Global Plan” scenario were used for the optimistic projections. For the pessimistic projections, annual trends in rates were estimated as halfway between those projected under the “sustained DOTS” scenario and those projected under the “No DOTS” scenario from 2006 onwards. For 2016–2030, annual trends from 2015 to 2030 were assumed to converge on the regional projected trends for Group I causes excluding HIV/AIDS under the baseline scenario. For the optimistic and pessimistic scenarios, annual regional trends were assumed to remain constant from 2015 to 2030. Projections of Tobacco-Caused Deaths As part of the WHO's CRA project, Ezzati and Lopez [23] estimated the total mortality attributable to tobacco smoking in 2000 for the world and for WHO sub-regions. Current levels of smoking impact ratio (SIR) were used as an indirect indicator of accumulated smoking risk based on excess lung cancer mortality. SIR measures the absolute excess lung cancer mortality due to smoking in the study population, relative to the absolute excess lung cancer mortality in lifelong smokers of the reference population. SIR values were calculated from the projected smoking impact variables for individual countries for age groups 30–44 y and older. Projected mortality attributable to tobacco was estimated using the CRA methods for lung cancer, upper aerodigestive cancer, all other cancers, COPD, other respiratory diseases, cardiovascular diseases, tuberculosis, and selected other disease causes. Projecting the Burden of Disease and Injury The WHO has undertaken new assessments of the GBD for 2000–2002, with consecutive revisions and updates published annually in WHO's World Health Reports. These assessments use a health gap measure, the DALY, developed by Murray and Lopez [30] to quantify the equivalent years of full health lost due to diseases and injury in WHO member states. The data sources and methods used for the GBD 2002 are documented elsewhere [15–17], and summary results for 14 regions of the world are published in the World Health Report 2004 [14] and available at http://www.who.int/evidence/bod. The burden of disease estimates for 2002 were used as a base for projections of burden of disease to 2030. Years of life lost (YLL) were calculated from the projections of mortality by cause, age, and sex, according to the GBD method. To project DALYs, it is also necessary to project years lived with disability (YLD). Given the lack of good information on trends in disability and health state distributions, the approach used here is an elaboration of the methods and assumptions used by Murray and Lopez in the original GBD projections [1]. YLD projections were generally derived from the YLL projections by applying the ratio of YLD to YLL for 2002. For ischaemic heart disease and stroke, future incidence rates were assumed to decline at 50% of their mortality rate declines reflecting declining case fatality rates as well as incidence rates. For causes where there is little or no mortality, age–sex-specific YLD rates per capita were generally assumed to remain constant into the future. For certain mental disorders, musculoskeletal conditions, and hearing loss, disability weights were assumed to decline somewhat with improvements in income per capita reflecting increasing treatment coverage. YLD rates for nonfatal communicable diseases and nutritional deficiencies were assumed to decline at between 50% and 100% of the mortality rate declines for Group I causes. Projections of Population and Numbers of Deaths and DALYs Our projections of mortality rates, together with UN medium variant assumptions for fertility rate projections and projected migration rates [31], were also used to prepare consistent population projections for all regions. The projected global population in 2015 was 7.1 billion compared to the UN medium variant projection of 7.2 billion, reflecting somewhat higher adult death rates in our mortality projections. Projected death and DALY rates for 192 WHO member states for 2003–2030 were applied to the projected populations to generate projected numbers of deaths and DALYs for each of the three scenarios. The resulting country projections were added back into regional groups for presentation of results (see Table S1).The choices and assumptions incorporated in each of the baseline, pessimistic, and optimistic scenarios are summarized in Table S7. Results This section provides an overview of the projections. Detailed results for 2002, 2015, and 2030 are presented in Dataset S1 for deaths and Dataset S2 for DALYs. These tables include baseline, pessimistic, and optimistic projected totals for each cause category, as well as a sex and age breakdown for the baseline category. Figure 1 shows projected life expectancies at birth in 2030 under the three scenarios, for World Bank regions. Life expectancy at birth is projected to increase in all World Bank regions, with the largest increases in the African region and the South Asian regions. However, under the baseline scenario, male life expectancy in the African region will still remain less than 55 y. In all regions except the European region, life expectancy increases are greater for females than for males. Life expectancy for women in the high-income countries may reach 85.0 y by 2030, compared with 79.7 y for men. The highest projected life expectancy in 2030 is for Japanese women at 88.5 y (with a range of 87.7 to 89.2 across the pessimistic and optimistic scenarios). The female–male difference in life expectancy at birth is projected to narrrow from 5.9 y in 2002 to 5.3 y in 2030 in the high-income countries, whereas the gap will more than double in low-income countries to 5.2 y in 2030. Figure 1 Projected Life Expectancy at Birth in 2030 by World Bank Region and Sex: Baseline, Optimistic, and Pessimistic Scenarios Compared with 2002 Estimates Projected global deaths in 2030 ranged from 64.9 million under the optimistic scenario to 80.7 million under the pessimistic scenario, variations of −11% to +10% relative to the baseline projection of 73.2 million. Figure 2 shows the projected global numbers of deaths in 2030 by age in the three scenarios, compared with the numbers of deaths by age in 2002. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages. The risk of death for children younger than 5 y is projected to fall substantially in the baseline scenario, by almost 25% between 2005 and 2015, and by more than 40% between 2005 and 2030. These rates of decline are similar to those projected between 2000 and 2015 in the original GBD projections. The vertical bars attached to the points for 2030 in Figure 2 represent the range of deaths projected under the optimistic and pessimistic scenarios. Figure 2 Global Numbers of Deaths by Age and Sex: Baseline, Optimistic, and Pessimistic Scenarios for 2030 Compared with 2002 Estimates Trends in Cause-Specific Mortality Table 1 summarizes the projected annual average changes in age-standardized death rates for selected major causes for the baseline projections for the period 2002–2020. For all the Group I and Group II cause groups in which the projections were based on the major-cause cluster regression equations, age-specific and age-standardized death rates are projected to decline over the next 20 years. The average annual rate of decline is greater (at about 3%) for Group I causes than for Group II causes. The HIV/AIDS projections, discussed in more detail below, have a substantial projected average annual rate of increase of 3% for males and 2% for females. Other causes with projected increases in age-standardized rates include lung cancer, diabetes, chronic respiratory diseases, road traffic accidents, violence, and war. Table 1 Projected Average Annual Rates of Change in Age-Standardized Death Rates for Selected Causes: World, 2002–2020 The Group I causes, excluding HIV/AIDS and tuberculosis, decline with average annual rates of change typically about one-third slower than the original GBD projections of Murray and Lopez. To some extent, this reflects the more conservative projections for low-income countries, where the coefficient of the time factor was reduced or set to zero. Average rates of decline for Group II causes (excluding lung cancer and chronic respiratory conditions) are similar for females to those in the original projections. However, the differential between males and females in the original projections has disappeared in the current projections, with males having a greater average annual rate of decline for Group II conditions than previously projected. Figure 3 summarizes the contributions of major causes to global trends in numbers of deaths for the three major cause groups. Large declines in mortality between 2002 and 2030 are projected for all of the principal Group I causes, with the exception of HIV/AIDS. Under the baseline scenario involving scale-up of ART coverage to 80% by 2012, but not additional prevention efforts, HIV/AIDS deaths increase from 2.8 million in 2002 to 6.5 million in 2030. Total deaths due to other Group I causes decline from 15.5 million in 2002 to 9.0 million in 2030. Unfortunately, this is substantially offset by the projected rise in HIV/AIDS deaths. Under the optimistic scenario involving additional HIV prevention activity, 3.7 million HIV/AIDS deaths are projected for 2030, so that total deaths due to Group I conditions would decline from 32% of all deaths in 2002 to 14% of all deaths in 2030. Figure 3 Baseline Projections of Deaths from Group I, Group II, and Group III Causes, World, 2002–2030 Although age-specific death rates for most Group II conditions are projected to decline, ageing of the population will result in significantly increasing total deaths due to most Group II conditions over the next 30 years (Figure 3). Global cancer deaths are projected to increase from 7.1 million in 2002 to 11.5 million in 2030, and global cardiovascular deaths from 16.7 million in 2002 to 23.3 million in 2030. Overall, Group II conditions will account for almost 70% of all deaths in 2030 under the baseline scenario. The projected 40% increase in global deaths due to injury between 2002 and 2030 are predominantly due to the increasing numbers of road traffic accident deaths, together with increases in population numbers more than offsetting small declines in age-specific death rates for other causes of injury. Road traffic accident deaths are projected to increase from 1.2 million in 2002 to 2.1 million in 2030, primarily due to increased motor vehicle fatalities associated with economic growth in low- and middle-income countries. Projections of HIV/AIDS Mortality and Other Selected Causes Figure 4 summarizes projected HIV/AIDS deaths by income group for the three scenarios. The declining death rates for 2005–2010 in the baseline and pessimistic scenarios, followed by increasing death rates, reflect the effects of the assumed treatment scale-up scenarios. Rapidly increasing levels of ART coverage result in postponement of deaths for a number of years, but once the ART coverage plateaus at its final level, numbers of deaths continue to rise, reflecting largely the underlying growth in population. HIV incidence rates essentially remain constant in the baseline scenario for sub-Saharan Africa, and the global growth in incident cases and in deaths is largely driven by population growth in sub-Saharan Africa. Figure 4 Projections of Total AIDS Deaths (Thousands) by Income Group for Three Scenarios Scenarios are indicated: baseline (solid lines), optimistic (dotted lines), and pessimistic (dashed lines). Under the baseline scenario, the total deaths from HIV/AIDS over the 25-y period 2006–2030 are projected to be 117 million. Under the optimistic scenario, in which additional prevention efforts result in a long-term 3% decline in incidence rates, the projected total deaths over 2006–2030 are 89 million, a saving of 28 million lives. The ranges defined by the optimistic and pessimistic projections differ substantially by cause (Figure 5). For example, the range for cardiovascular disease before age 70 y is much wider than that for cancers before age 70 y or for road traffic accidents. Group I deaths (excluding HIV/AIDS) have a wider range than most other causes, although the total deaths decline substantially in all three scenarios. Figure 5 Projections of Global Deaths (Millions) for Selected Causes, for Three Scenarios: Baseline, Optimistic, and Pessimistic, 2002–2030 Comparison with GBD 1990 Projections Figure 6 compares projected global deaths for each of the three major cause groups for 2002–2030 with the corresponding projections for 1990–2020 from the original GBD study [1]. Projections for Group I causes are substantially different, mainly because of the very large difference in projected HIV/AIDS mortality. Total global deaths for Group II and Group III causes are projected to increase at a somewhat slower rate than the original GBD projections, and in addition, the base levels for these causes in 2002 are somewhat lower than the levels projected by the original GBD study. Figure 6 Comparison of Baseline Projections 2002–2030 with the Original GBD Projections 1990–2020: Global Deaths for All Causes, and Major Cause Groups Despite these differences for all three major cause groups, and differences in projected global population numbers, the projections of total global all-cause deaths are almost identical to those in the original GBD study. Global deaths in 2020 under the baseline scenario are 66.5 million, compared with 68.3 million projected by Murray and Lopez from the 1990 base, and the overall trend is almost identical. Projected trends for global deaths due to most Group I causes other than HIV/AIDS are broadly similar. The new baseline projections for lung cancer give lower global numbers and a lower rate of increase. Trends in global respiratory deaths are similar for the two sets of projections, but the projected rates of increase in global deaths due to cancers, cardiovascular disease, and digestive disorders are all somewhat slower for the current projections. Increases for road traffic accidents are somewhat slower in the current projections, whereas trends for other unintentional causes are quite similar. There are some substantial differences in trends for intentional causes, reflecting different assumptions and decisions concerning use of the regression parameters. Leading Causes of Death Table 2 lists the 15 leading causes of death according to the baseline scenario for males, females, and both sexes combined as projected for 2030 globally. Table 3 provides similar lists of the ten leading causes of death according to the baseline scenario for the four income groups of countries. The four leading causes of death in all scenarios are projected to be ischaemic heart disease, cerebrovascular disease (stroke), HIV/AIDS, and COPD, although HIV/AIDS moves from third to fourth position in the optimistic scenario. Table 2 Changes in Rankings for 15 Leading Causes of Death, 2002 and 2030 (Baseline Scenario) Table 3 Ten Leading Causes of Death, by Income Group, 2030 (Baseline Scenario) Table 2 also summarises the changes in rank order of deaths between 2002 and 2030 for the 15 leading causes. Lower-respiratory infections, perinatal conditions, diarrhoeal diseases, malaria, and measles are all projected to decline substantially in importance. On the other hand, diabetes mellitus, lung cancer, stomach cancer, and liver and colorectal cancer are all projected to move up three or more places in the rankings. Decomposition The results discussed so far have described projected changes in mortality in terms of the absolute (and relative) numbers of deaths expected under the various scenarios. These changes may be due to changes in age-specific disease and injury mortality risks, or due to demographic change that alters the size and age composition of the population, or both. Because mortality risks are strongly age dependent for most causes, changes in the age structure of a population may result in substantial changes in the number of deaths, even when the age-specific risks remain unchanged. We further analysed the relative impact of demographic and epidemiological change on the projected numbers of deaths by cause by calculating three hypothetical alternatives. In the first, we calculated the expected number of deaths in 2030 given the 2030 projected age-specific rates under the baseline scenario and the 2002 population. The difference between this and the 2002 mortality estimates is a measure of the change in mortality expected solely on the basis of changing age-specific mortality rates, and is labelled “epidemiological change” in Figure 7. Figure 7 Decomposition of Projected Change in Numbers of Deaths into Demographic and Epidemiological Components, by Broad Cause Group and Income Group, 2002–2030 Second, we calculated the expected number of deaths in 2030 by taking the 2002 age-specific death rates and applying them to the 2030 projected population. The difference between this and the 2002 mortality estimates is a measure of the change in mortality expected solely on the basis of changing demography (including size and age composition of the population). We then repeated this calculation, but applied the 2002 age-specific death rates to projected population numbers for 2030 that matched the total projected male and female population numbers for each country but retained the 2002 age distribution of the population. The difference between this and the 2002 mortality estimates is a measure of the change in mortality expected solely on the basis of population growth excluding changes in age composition. The difference between the total change in mortality due to demography and this latter estimate gives a measure of the effect of the change in age composition of the population alone. These two components of demographic change are labelled “population growth” and “population ageing” in Figure 7. The total projected change in numbers of deaths between 2002 and 2030 is the sum of the population growth, population ageing, and epidemiological change components. In almost all cases, demographic and epidemiological factors are operating in opposing directions in determining mortality in 2030. The major exception is HIV/AIDS, where demographic and epidemiological change are acting in the same direction to increase total HIV/AIDS deaths to 6.5 million deaths in 2030 under the baseline scenario. Demographic change dominates, as the majority of HIV/AIDS deaths are in sub-Saharan Africa, where population growth is highest and where HIV/AIDS incidence rates are assumed to remain largely constant under the baseline scenario. For Group I conditions other than HIV/AIDS for which substantial declines in mortality rates are projected, the effect of these declines will be attenuated in most regions by demographic change leading to an increase in the child population most at risk for these conditions. Population growth and population ageing act in opposite directions for Group I mortality excluding HIV/AIDS in low-income countries, but not in other income groups. If future fertility rates are higher than projected, then the higher child population numbers will further offset the projected reductions in death rates for Group I conditions. For Group II (noncommunicable diseases), demographic changes in all regions will tend to increase deaths substantially, with offsetting reductions in projected death rates in all regions. Population growth and population ageing both act to increase Group II deaths in all regions, although the impact of population ageing is generally much more important than population growth. Population growth has the largest relative impact for low-income countries, and the smallest for lower-middle-income countries. The latter group includes Eastern European populations such as Russia that will experience negative population growth. For Group III (injuries), demographic change similarly dominates the epidemiological change. The latter is small at group level in most regions, because the projected increase in road traffic fatalities is offset by projected decreases in death rates for other unintentional injuries. Tobacco-Attributable Deaths Figure 8 summarises the projected number of tobacco-attributable deaths for the world, and for high-income and low- and middle-income countries. Under the baseline scenario, total tobacco-attributable deaths will rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030. Projected deaths for 2030 range from 7.4 million in the optimistic scenario to 9.7 million in the pessimistic scenario. Tobacco-attributable deaths are projected to decline by 9% between 2002 and 2030 in high-income countries, but to double from 3.4 million to 6.8 million in low- and middle-income countries. Figure 8 Projected Numbers of Tobacco-Caused Deaths for the World and for High-Income and Middle- plus Low-Income Countries, Three Scenarios, 2002–2030 Table 4 divides the projected 2015 global mortality due to smoking into leading causes: cancers are responsible for one-third of the deaths, followed by cardiovascular diseases and chronic respiratory diseases, each responsible for 30% of the deaths. According to our baseline projection, smoking will kill 50% more people in 2015 than HIV/AIDS, and will be responsible for 10% of all deaths globally. Table 4 Projected Global Tobacco-Caused Deaths, by Cause, 2015 Baseline Scenario Burden of Disease Global DALYs lost are projected to increase from 1.48 billion in 2002 to 1.54 billion in 2030, an overall increase of only 3%. Since the population increase is projected to be 27% during the same period, there is actually a decrease in the global per capita burden. Unlike deaths, where the overall global death rate is projected to increase by 1%, the DALY rate decreases because the increasing number of deaths is offset by the shift in age at death to older ages, associated with fewer lost years of life. Even with the assumption that the age-specific burden for most nonfatal causes remains constant into the future, and hence that the overall burden for these conditions increases with the ageing of the population, there is still an overall projected decrease in the global burden of disease per capita of 19% from 2002 to 2030. The proportional contribution of the three major cause groups to the total disease burden is projected to change substantially, however. Group I causes are projected to account for 30% of total DALYs lost in 2030, compared with more than 40% in 2002. In low-income countries, the decline is even greater, from 56% in 2002 to 41% in 2030, even including the doubling of the HIV/AIDS burden. The noncommunicable disease (Group II) burden is projected to increase to 57% in 2030, and to represent a greater burden of disease than Group I conditions in all income groups, including low-income countries. Table 5 lists the 15 leading causes of DALYs globally in 2002 and in 2030 according to the baseline scenario, and Table 6 provides similar lists of the ten leading causes of burden of disease for high-, middle-, and low-income country groups. The three leading causes of DALYs in the baseline and pessimistic scenarios are projected to be HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease. Road traffic accidents become the third leading cause under the optimistic scenario, ahead of ischaemic heart disease and cerebrovascular disease. Perinatal conditions are the fourth leading cause under the pessimistic scenario and the fifth leading cause under the baseline scenario, after road traffic accidents. HIV/AIDS becomes the leading cause of burden of disease in middle-income countries, as well as low-income countries, by 2015. Table 5 Changes in Rankings for 15 Leading Causes of DALYs, 2002 and 2030 (Baseline Scenario) Table 6 Ten Leading Causes of DALYs, by Income Group and Sex, 2030 (Baseline Scenario) Table 5 also illustrates the changes in rank order of DALYs between 2002 and 2030 for the 15 leading causes globally. Lower-respiratory infections, perinatal conditions, diarrhoeal diseases, malaria, measles, tuberculosis, and congenital anomalies are all projected to decline substantially in importance. On the other hand, ischaemic heart disease, diabetes mellitus, road traffic accidents, self-inflicted injuries, COPD, hearing loss, and cataracts are all projected to move up three or more places in the rankings. Hearing loss is projected to be among the top ten causes of burden of disease in high- and middle-income countries, and Alzheimer disease and other dementias and alcohol-use disorders among the top four causes in high-income countries in 2030. In low-income countries in 2030, Group I conditions continue to account for five of the ten leading causes of burden of disease. These are HIV/AIDS, perinatal conditions, diarrhoeal diseases, malaria, and acute lower-respiratory infections. Discussion We have addressed the need for updated projections of mortality and burden of disease using methods similar to those of the original GBD study. Our intent was not to undertake major new methodological developments, but rather to provide updated projections for the first 30 years of the 21st century, using as much relevant new information as is available. We project that life expectancy will increase around the world for all three scenarios, fewer children younger than 5 y will die (a 50% decline under the baseline scenario), and the proportion of people dying from non-communicable diseases will increase (from 59% in 2002 to 69% in 2030 under the baseline scenario). Although deaths from infectious diseases will decrease overall, HIV/AIDS deaths will continue to increase; the exact magnitude of the increase will depend to a limited extent on how many people have access to antiretroviral drugs and much more on whether there are increased prevention efforts. Although a projected 6.5 million people will die from HIV/AIDS under the 2030 baseline projection, an even larger number will die from disease attributable to tobacco smoking (8.3 million). By 2030, the three leading causes of burden of disease will be HIV/AIDS, depression, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. These updated projections of mortality and burden of disease have been prepared using a similar methodology to that of the original GBD study, but with some changes described above and with updated inputs and an updated base set of estimates for 2002. We have incorporated a number of methodological improvements and changes. These include carrying out the projections at country rather than regional level, use of separate regression equations for low-income countries, incorporation of information from death registration datasets on recent observed trends for selected causes, and calibration of the regression equations through a comparison of back-projections with observed child mortality trends from 1990 to 2002. Additionally, separate risk factor–based projection models were developed for diabetes mellitus and chronic respiratory diseases. Population projections also included UN projections for net migration rates. Baseline estimates of deaths, disability, and DALYs lost in 2002 have been projected into the future based on an explicit set of assumptions and methods, and the results are nothing more than the numerical consequences of the assumptions and methods employed. In this paper and the accompanying Protocol S1 and Tables S1–S7, we have tried to explicitly describe and summarize all inputs and assumptions for the three scenarios to enable replication of these projections for different scenarios. Additional information and detailed datasets are available from the authors on request. We do not claim that the scenarios presented here necessarily represent the best predictions of future global and regional health trends, and it is possible that more sophisticated causal models incorporating projections of important determinants may provide better predictions for specific diseases. However, we do believe that these projections, as with the previous GBD projections of Murray and Lopez, represent the most comprehensive and consistent set of health status projections available today. Comparisons with Other Projections We are not aware of any projections of global and regional mortality, apart from the previous GBD projections, that have included a comprehensive set of causes of death. A review of other published studies containing global and regional projections for specific single causes identified relatively few studies that did not either apply base estimates of age–sex-specific death rates (assumed not to change in the future) to UN population projections, or were based on the previous GBD projections, or were earlier versions of the projections by WHO and/or UNAIDS for HIV/AIDS and tuberculosis used here. Our projected global population in 2015 under the baseline scenario was 7.1 billion, compared with the UN medium variant projection of 7.2 billion [32], a difference of only −1.6% (see Protocol S1 for details of projection methods). By 2030, the difference between WHO and UN baseline projections of population grew to −3.5%, and the range from pessimistic to optimistic scenarios to 7.75 billion to 8.07 billion. The largest difference in the population growth projections is for the European region, where we project the total population to decline at a faster rate than the UN projections. Projected global deaths in 2030 ranged from 64.9 million under the optimistic scenario to 80.7 million under the pessimistic scenario, with a baseline projection of 73.2 million. Projected global deaths in 2030 under the UN medium variant projections were 1% higher, at 74.0 million [32]. Our global projections for all-cause mortality are remarkably close to the UN projections, given that our projections are the sum of independent projections for 13 separate cause groups, whereas the UN projections are based on estimated trends in all-cause mortality, with adjustments for projected HIV/AIDS mortality. Our baseline global projection for all-cause mortality in 2020 (66.5 million deaths) is also remarkably similar to the original GBD baseline projection for 2020 of 68.3 million deaths, although the projected numbers of deaths for each of the three major cause groups differ quite significantly, as do projections for HIV/AIDS and tuberculosis. The congruence in numbers and trends in rates for all-cause mortality is almost certainly a coincidence, since the global total death projections are derived from summing separate projections across 13 cause groups, and almost all of the inputs to those projections have changed significantly from those used in the original projections. Under our baseline projection, mortality of children younger than 5 y will decline globally by 29% from 2002 to 2015 (range 44% to 14% under the optimistic and pessimistic scenarios). For sub-Saharan Africa the decline is projected to be 25%. In contrast, child mortality declined by only 12% globally and 7% in sub-Saharan Africa between 1990 and 2001 [17]. The more-than doubling of the decline in child mortality for sub-Saharan Africa projected for the next decade is largely determined by the relatively high projected economic growth for this region according to the World Bank (about 2% annual increase in income per capita). Income per capita growth averaged only 0.5% in sub-Saharan Africa during the period 1990–2002. Despite this, our back-projections to 1990 predicted a larger decline in child mortality than observed. The major regression coefficients were adjusted for low-income countries, and in particular for sub-Saharan Africa, to match observed trends (see Protocol S1 for details). Even with this adjustment, we are predicting more than double the decline in child mortality in the next decade compared to that observed in the 1990s. This reflects the higher levels of economic growth projected for the next decade in sub-Saharan Africa. Trends in tuberculosis death rates projected by the Stop TB Partnership for deaths of HIV-negative persons were used here, as the GBD classifies deaths of HIV-positive persons as due to HIV/AIDS. The GBD base estimates of tuberculosis deaths for 2002 are higher than the Stop TB Partnership 2002 estimates for deaths in sub-Saharan Africa and substantially lower in Southeast Asia (see Protocol S1). As a result of these differences in base estimates, our baseline projection gives 960,000 tuberculosis deaths in 2015, compared to the 888,000 projected by the Stop TB Partnership under the “Sustained DOTS” scenario. Both these estimates are substantially lower than the approximately 2.2 million tuberculosis deaths projected for 2015 by Murray and Lopez in the original GBD study [1,2]. Bray and Møller have published global projections for cancer incidence in 2020 [33]. They projected that total cancer incidence would rise from 11 million new cases in 2002 to 16.5 million in 2020, assuming that 2002 age–sex-specific incidence rates remained unchanged. Taking into account projected changes in age–sex–site-specific incidence rates, we project global incidence for all types of cancer to be 15.5 million cases in 2020, 6% lower than Bray and Møller's estimate. They also estimated global incidence in 2010 of 1.5 million new breast cancer cases and 1 million new prostate cancer cases, taking into account current trends in incidence rates. These are higher than our projections of 1.3 million and 0.85 million, respectively, mainly reflecting differences in the base incidence estimates for 2002. Wilde et al. [34] estimated that the global diabetes prevalence would rise from 171 million in 2000 to 366 million in 2030, assuming that age–sex-specific diabetes prevalence rates remain unchanged within urban and rural populations in each region, but taking into account projected increases in urbanization to 2030. Their estimate is somewhat higher than our projection of 328 million diabetes cases in 2030. Our projections took into account projected trends for overweight and obesity, which may capture some of the impact of urbanization but did not explicitly account for trends in urbanization. Projections based on prevalence trends have been made for diabetes in Canada and the US [35,36]. These studies estimated that diabetes prevalence would reach 2.4 million for Canada in 2016, and 19.9 million for the US in 2025. Our projections of diabetes prevalence for the same years are one-third higher for Canada (3.2 million) and slightly higher for the US (20.9 million). Projected tobacco-attributable deaths in 2030 under the baseline scenario are lower than the 10 million predicted by Peto et al. [37] to occur “sometime in the 2020s or early 2030s.” This earlier prediction is fairly consistent with our projection of 9.7 million tobacco-attributable deaths under the pessimistic scenario. Murray and Lopez projected 8.4 million tobacco-attributable deaths in 2020, also somewhat higher than our baseline projection of 7.0 million. Our lower projection reflects our downward adjustment of projected lung cancer mortality to reflect recent trends in tobacco consumption. It must be emphasized that there is very substantial uncertainty in the tobacco-attributable mortality projections, as the trends in apparent consumption of tobacco do not necessarily account for changes in duration, type, per capita amount, and mode of smoking. A recent World Bank report projected deaths due to road traffic accidents from 1990 to 2020 using an economic model that related death rates to changes in fatalities per motor vehicle and motor vehicle ownership per person due to economic growth [38]. An overall growth of 66% was projected for road traffic fatalities from 2000 to 2020, somewhat higher than our projection of a 51% increase in global deaths due to road traffic accidents from 2002 to 2020 (Figure 9). Projected regional increases were generally similar, although the World Bank projected a substantially greater increase in South Asia than we did, and a small growth for Europe and Central Asia, rather than a decline. In terms of absolute numbers of deaths, the projections are quite different, reflecting large differences in base estimates. The World Bank study estimated global road fatalities in 2000 as 723,000, compared with our estimate for 2002 of 1.2 million. The World Bank base estimates were derived from police statistics, unadjusted for under-reporting. Police statistics are known to generally substantially underestimate deaths due to road traffic accidents. For example, the World Bank estimate of 32,000 deaths for Europe and Central Asia in 2000 is 60% lower than the GBD estimate of 82,000 for 2002, based on reasonably complete death registration data. Figure 9 Projected Growth in Road Traffic Fatalities, 2002–2020: A Comparison of World Bank Projections with GBD Projections Uncertainties and Limitations There have been substantial improvements in the data and methods available for the assessment of global and regional mortality by cause. The GBD 2002 estimates of deaths by cause, age, and sex were carried out separately for 226 countries and territories, drawing on a total of 770 country-years of death registration data, as well as 535 additional sources of information on levels of child and adult mortality, and in excess of 2,600 datasets providing information on specific causes of death in regions not well covered by death registration systems [15]. There remain, however, a large number of methodological and empirical challenges to more reliably estimating global, regional, and national disease burden. For regions with limited death registration data, such as the Eastern Mediterranean region, sub-Saharan Africa, and parts of Asia and the Pacific, there is considerable uncertainty in estimates of deaths by cause. Uncertainty ranges for all-cause deaths in 2001 were estimated by Mathers et al. [16] to increase from around ±1% for high-income countries to around ±20% for sub-Saharan Africa. For most specific causes, uncertainty ranges are greater than those of the all-cause mortality estimates, since there is additional uncertainty associated with cause attribution. For example, the relative uncertainty ranges for ischaemic heart disease in 2001 were estimated to range from about 12% for high-income countries to around ±30% for sub-Saharan Africa. The uncertainty range for HIV/AIDS deaths in sub-Saharan Africa was narrower at ±15%, reflecting the substantial data base for these estimates. The projections of burden are not intended as forecasts of what will happen in the future but as projections of current and past trends, based on certain explicit assumptions. The methods used base the disease burden projections largely on broad mortality projections driven by World Bank projections of future growth in income and WHO projections of increases in human capital in different regions of the world, together with a model relating these to cause-specific mortality trends based on the historical observations in countries with death registration data from the past 50 years. The results depend strongly on the assumption that future mortality trends in developing countries will have a similar relationship to economic and social development as has occurred in the more developed countries. If this assumption is not correct, then the projections for low-income countries will be over-optimistic in the rate of decline of communicable diseases and the speed of the epidemiological transition. The predictions of the projections model were compared with historical trends in child mortality from 1990 to 2002, and as a result, certain regression coefficients were modified for low-income countries. As a result, our revised projections assume that projected rates of change for cause-specific mortality rates over time, given levels of constant income and human capital, will be slower than those observed in the mainly high- and middle-income countries with death registration data from the past 50 years. This has reduced the projected rates of decline in Group I conditions for low-income countries compared to the original GBD projections, and it is entirely possible that this adjustment may be too conservative. On the other hand, the many problems facing low-income countries in improving and sustaining access to effective health interventions, and in scaling up health systems to cost-effectively address these challenges, may mean that the low-income countries do not experience the temporal pace of health improvement at constant levels of income and human capital that have been seen in the high-income countries in the past 50 years. The projections have also not taken explicit account of trends in major risk factors apart from tobacco smoking, and, to a limited extent, overweight and obesity. If broad trends in risk factors are for worsening of risk exposures with development, rather than the improvements observed in recent decades in many high-income countries, then again the projections for low- and middle-income countries presented here will be too optimistic. There is a need to develop much more comprehensive projection models that take explicit account of available information on trends in a wide range of risk factors. The HIV/AIDS baseline projections in particular assume that transmission probabilities will remain largely unchanged in the future and there will not be substantial reductions in risk factors for HIV. A projections exercise such as this by its nature involves substantial assumptions about the similarity of future trends to past trends, and about the future trends in broad drivers of health improvement. There are thus wide uncertainty ranges around future projections. Nevertheless, there are some aspects of the projections that clearly involve more uncertainty than others. For example, the projections of HIV/AIDS mortality are strongly affected by the assumptions made about the levels of additional prevention effort that occur over the next two decades. Additionally, there are substantial uncertainties about the future trends in chronic respiratory disease mortality for non-smokers, and diabetes mortality for persons not overweight. Also, the evidence on the associations of injury mortality with income and human capital was weaker than for Group I and Group II conditions, and stronger assumptions were thus required for injury projections for some external causes. In the absence of any realistic approach to forecasting future war deaths, rates for these were assumed to remain constant over time in the baseline scenario. This may be too conservative, given the substantial decrease in the number of wars and civil conflicts in the past decade or two. Finally, as did Murray and Lopez, we recognize that the approach taken to projecting YLD is extremely crude, and that the projections of DALYs are likely to be even more uncertain than the projections of deaths. It may be the case that case fatality rates for many diseases decline during the next 30 years, so that YLD become an increasing proportion of the total DALYs for these causes. On the other hand, improvements in risk factors and/or health interventions may lead to decreases in burden for some nonfatal conditions. Substantial research remains to develop robust and unbiased methods for measuring trends in case fatality rates, survival times, and disability due to specific causes, let alone collecting such data across all regions of the world. Conclusions Despite these uncertainties, projections provide a useful perspective on population health trends and health policies, provided that they are interpreted with a degree of caution. Projections enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, and the continuation of the epidemiological transition in developing countries. These projections represent a set of three visions of the future for population health, under an explicit set of assumptions and for specific projections of income, human capital, and of future trends in tobacco smoking, HIV/AIDS transmission and survival, and overweight and obesity. If the future is not like the past—for example, through sustained and additional efforts to address the Millennium Development Goals, or through major scientific breakthroughs—then the world may well achieve faster progress than projected here, even under the optimistic scenario. On the other hand, if economic growth in low-income countries is lower than the forecasts used here, then the world may achieve slower progress and widening of health inequalities. Supporting Information Alternative Language Text S1 Translation of the Article into French (37 KB DOC) Click here for additional data file. Dataset S1 Deaths by Cause, Scenario, Sex, and Age for 2002, 2015, and 2030 (1 MB XLS) Click here for additional data file. Dataset S2 DALYs by Cause, Scenario, Sex, and Age for 2002, 2015, and 2030 (1 MB XLS) Click here for additional data file. Protocol S1 Technical Appendix (200 KB PDF) Click here for additional data file. Table S1 Country Classifications Used for Reporting Results: World Bank Regional Groups and World Bank Income Groups (69 KB DOC) Click here for additional data file. Table S2 GBD Cause Categories and ICD Codes (184 KB DOC) Click here for additional data file. Table S3 Parsimonious Regression Equations for Nine Major Cause Clusters Based on the Full Country Panel Dataset, 1950–2002 (334 KB DOC) Click here for additional data file. Table S4 Parsimonious Regression Equations for Nine Major Cause Clusters Based on the Low-Income Country Panel Dataset, 1950–2002 (335 KB DOC) Click here for additional data file. Table S5 Results of Regressions of Age–Sex-Specific Mortality for Detailed Causes on the Respective Cause Cluster Based on the Full Country Panel Dataset, 1950–2002 (765 KB DOC) Click here for additional data file. Table S6 Results of Log-Linear Poisson Regressions for Deaths Due to Selected Causes, by Age and Sex, for Countries with Complete Death Registration Data and Population of More Than 5 Million (1.2 MB DOC) Click here for additional data file. Table S7 Summary of Assumptions and Inputs for Baseline, Optimistic, and Pessimistic Projection Scenarios (67 KB DOC) Click here for additional data file.
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### Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.

(2001)
Type 2 diabetes mellitus is increasingly common, primarily because of increases in the prevalence of a sedentary lifestyle and obesity. Whether type 2 diabetes can be prevented by interventions that affect the lifestyles of subjects at high risk for the disease is not known. We randomly assigned 522 middle-aged, overweight subjects (172 men and 350 women; mean age, 55 years; mean body-mass index [weight in kilograms divided by the square of the height in meters], 31) with impaired glucose tolerance to either the intervention group or the control group. Each subject in the intervention group received individualized counseling aimed at reducing weight, total intake of fat, and intake of saturated fat and increasing intake of fiber and physical activity. An oral glucose-tolerance test was performed annually; the diagnosis of diabetes was confirmed by a second test. The mean duration of follow-up was 3.2 years. The mean (+/-SD) amount of weight lost between base line and the end of year 1 was 4.2+/-5.1 kg in the intervention group and 0.8+/-3.7 kg in the control group; the net loss by the end of year 2 was 3.5+/-5.5 kg in the intervention group and 0.8+/-4.4 kg in the control group (P<0.001 for both comparisons between the groups). The cumulative incidence of diabetes after four years was 11 percent (95 percent confidence interval, 6 to 15 percent) in the intervention group and 23 percent (95 percent confidence interval, 17 to 29 percent) in the control group. During the trial, the risk of diabetes was reduced by 58 percent (P<0.001) in the intervention group. The reduction in the incidence of diabetes was directly associated with changes in lifestyle. Type 2 diabetes can be prevented by changes in the lifestyles of high-risk subjects.
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### Standards of Medical Care in Diabetes—2013

(2013)
Diabetes mellitus is a chronic illness that requires continuing medical care and ongoing patient self-management education and support to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires multifactorial risk reduction strategies beyond glycemic control. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payers, and other interested individuals with the components of diabetes care, general treatment goals, and tools to evaluate the quality of care. Although individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. Specifically titled sections of the standards address children with diabetes, pregnant women, and people with prediabetes. These standards are not intended to preclude clinical judgment or more extensive evaluation and management of the patient by other specialists as needed. For more detailed information about management of diabetes, refer to references (1–3). The recommendations included are screening, diagnostic, and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A large number of these interventions have been shown to be cost-effective (4). A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E. Table 1 ADA evidence grading system for clinical practice recommendations Level of evidence Description A Clear evidence from well-conducted, generalizable RCTs that are adequately powered, including: • Evidence from a well-conducted multicenter trial• Evidence from a meta-analysis that incorporated quality ratings in the analysisCompelling nonexperimental evidence, i.e., “all or none” rule developed by the Centre for Evidence-Based Medicine at the University of OxfordSupportive evidence from well-conducted RCTs that are adequately powered, including:• Evidence from a well-conducted trial at one or more institutions• Evidence from a meta-analysis that incorporated quality ratings in the analysis B Supportive evidence from well-conducted cohort studies• Evidence from a well-conducted prospective cohort study or registry• Evidence from a well-conducted meta-analysis of cohort studiesSupportive evidence from a well-conducted case-control study C Supportive evidence from poorly controlled or uncontrolled studies• Evidence from randomized clinical trials with one or more major or three or more minor methodological flaws that could invalidate the results• Evidence from observational studies with high potential for bias (such as case series with comparison with historical controls)• Evidence from case series or case reportsConflicting evidence with the weight of evidence supporting the recommendation E Expert consensus or clinical experience These standards of care are revised annually by the ADA’s multidisciplinary Professional Practice Committee, incorporating new evidence. For the current revision, committee members systematically searched Medline for human studies related to each subsection and published since 1 January 2011. Recommendations (bulleted at the beginning of each subsection and also listed in the “Executive Summary: Standards of Medical Care in Diabetes—2013”) were revised based on new evidence or, in some cases, to clarify the prior recommendation or match the strength of the wording to the strength of the evidence. A table linking the changes in recommendations to new evidence can be reviewed at http://professional.diabetes.org/CPR. As is the case for all position statements, these standards of care were reviewed and approved by the Executive Committee of ADA’s Board of Directors, which includes health care professionals, scientists, and lay people. Feedback from the larger clinical community was valuable for the 2013 revision of the standards. Readers who wish to comment on the “Standards of Medical Care in Diabetes—2013” are invited to do so at http://professional.diabetes.org/CPR. Members of the Professional Practice Committee disclose all potential financial conflicts of interest with industry. These disclosures were discussed at the onset of the standards revision meeting. Members of the committee, their employer, and their disclosed conflicts of interest are listed in the “Professional Practice Committee for the 2013 Clinical Practice Recommendations” table (see p. S109). The ADA funds development of the standards and all its position statements out of its general revenues and does not use industry support for these purposes. I. CLASSIFICATION AND DIAGNOSIS A. Classification The classification of diabetes includes four clinical classes: Type 1 diabetes (results from β-cell destruction, usually leading to absolute insulin deficiency) Type 2 diabetes (results from a progressive insulin secretory defect on the background of insulin resistance) Other specific types of diabetes due to other causes, e.g., genetic defects in β-cell function, genetic defects in insulin action, diseases of the exocrine pancreas (such as cystic fibrosis), and drug- or chemical-induced (such as in the treatment of HIV/AIDS or after organ transplantation) Gestational diabetes mellitus (GDM) (diabetes diagnosed during pregnancy that is not clearly overt diabetes) Some patients cannot be clearly classified as type 1 or type 2 diabetic. Clinical presentation and disease progression vary considerably in both types of diabetes. Occasionally, patients who otherwise have type 2 diabetes may present with ketoacidosis. Similarly, patients with type 1 diabetes may have a late onset and slow (but relentless) progression of disease despite having features of autoimmune disease. Such difficulties in diagnosis may occur in children, adolescents, and adults. The true diagnosis may become more obvious over time. B. Diagnosis of diabetes For decades, the diagnosis of diabetes was based on plasma glucose criteria, either the fasting plasma glucose (FPG) or the 2-h value in the 75-g oral glucose tolerance test (OGTT) (5). In 2009, an International Expert Committee that included representatives of the ADA, the International Diabetes Federation (IDF), and the European Association for the Study of Diabetes (EASD) recommended the use of the A1C test to diagnose diabetes, with a threshold of ≥6.5% (6), and the ADA adopted this criterion in 2010 (5). The diagnostic test should be performed using a method that is certified by the NGSP and standardized or traceable to the Diabetes Control and Complications Trial (DCCT) reference assay. Although point-of-care (POC) A1C assays may be NGSP certified, proficiency testing is not mandated for performing the test, so use of these assays for diagnostic purposes could be problematic. Epidemiological datasets show a similar relationship for A1C to the risk of retinopathy as has been shown for the corresponding FPG and 2-h PG thresholds. The A1C has several advantages to the FPG and OGTT, including greater convenience (since fasting is not required), evidence to suggest greater preanalytical stability, and less day-to-day perturbations during periods of stress and illness. These advantages must be balanced by greater cost, the limited availability of A1C testing in certain regions of the developing world, and the incomplete correlation between A1C and average glucose in certain individuals. In addition, HbA1c levels may vary with patients’ race/ethnicity (7,8). Some have posited that glycation rates differ by race (with, for example, African Americans having higher rates of glycation), but this is controversial. A recent epidemiological study found that, when matched for FPG, African Americans (with and without diabetes) indeed had higher A1C than whites, but also had higher levels of fructosamine and glycated albumin and lower levels of 1,5 anhydroglucitol, suggesting that their glycemic burden (particularly postprandially) may be higher (9). Epidemiological studies forming the framework for recommending use of the A1C to diagnose diabetes have all been in adult populations. Whether the cut point would be the same to diagnose children or adolescents with type 2 diabetes is an area of uncertainty (3,10). A1C inaccurately reflects glycemia with certain anemias and hemoglobinopathies. For patients with an abnormal hemoglobin but normal red cell turnover, such as sickle cell trait, an A1C assay without interference from abnormal hemoglobins should be used (an updated list is available at www.ngsp.org/interf.asp). For conditions with abnormal red cell turnover, such as pregnancy, recent blood loss or transfusion, or some anemias, the diagnosis of diabetes must employ glucose criteria exclusively. The established glucose criteria for the diagnosis of diabetes (FPG and 2-h PG) remain valid as well (Table 2). Just as there is less than 100% concordance between the FPG and 2-h PG tests, there is no perfect concordance between A1C and either glucose-based test. Analyses of the National Health and Nutrition Examination Survey (NHANES) data indicate that, assuming universal screening of the undiagnosed, the A1C cut point of ≥6.5% identifies one-third fewer cases of undiagnosed diabetes than a fasting glucose cut point of ≥126 mg/dL (7.0 mmol/L) (11), and numerous studies have confirmed that at these cut points the 2-h OGTT value diagnoses more screened people with diabetes (12). However, in practice, a large portion of the diabetic population remains unaware of its condition. Thus, the lower sensitivity of A1C at the designated cut point may well be offset by the test’s greater practicality, and wider application of a more convenient test (A1C) may actually increase the number of diagnoses made. Table 2 Criteria for the diagnosis of diabetes As with most diagnostic tests, a test result diagnostic of diabetes should be repeated to rule out laboratory error, unless the diagnosis is clear on clinical grounds, such as a patient with a hyperglycemic crisis or classic symptoms of hyperglycemia and a random plasma glucose ≥200 mg/dL. It is preferable that the same test be repeated for confirmation, since there will be a greater likelihood of concurrence in this case. For example, if the A1C is 7.0% and a repeat result is 6.8%, the diagnosis of diabetes is confirmed. However, if two different tests (such as A1C and FPG) are both above the diagnostic thresholds, the diagnosis of diabetes is also confirmed. On the other hand, if two different tests are available in an individual and the results are discordant, the test whose result is above the diagnostic cut point should be repeated, and the diagnosis is made based on the confirmed test. That is, if a patient meets the diabetes criterion of the A1C (two results ≥6.5%) but not the FPG ( 9 lb or were diagnosed with GDM• hypertension (≥140/90 mmHg or on therapy for hypertension)• HDL cholesterol level 250 mg/dL (2.82 mmol/L)• women with polycystic ovary syndrome• A1C ≥5.7%, IGT, or IFG on previous testing• other clinical conditions associated with insulin resistance (e.g., severe obesity, acanthosis nigricans)• history of CVD 2. In the absence of the above criteria, testing for diabetes should begin at age 45 years. 3. If results are normal, testing should be repeated at least at 3-year intervals, with consideration of more frequent testing depending on initial results (e.g., those with prediabetes should be tested yearly) and risk status. * At-risk BMI may be lower in some ethnic groups. For many illnesses, there is a major distinction between screening and diagnostic testing. However, for diabetes, the same tests would be used for “screening” as for diagnosis. Diabetes may be identified anywhere along a spectrum of clinical scenarios ranging from a seemingly low-risk individual who happens to have glucose testing, to a higher-risk individual whom the provider tests because of high suspicion of diabetes, to the symptomatic patient. The discussion herein is primarily framed as testing for diabetes in those without symptoms. The same assays used for testing for diabetes will also detect individuals with prediabetes. A. Testing for type 2 diabetes and risk of future diabetes in adults Prediabetes and diabetes meet established criteria for conditions in which early detection is appropriate. Both conditions are common, increasing in prevalence, and impose significant public health burdens. There is a long presymptomatic phase before the diagnosis of type 2 diabetes is usually made. Relatively simple tests are available to detect preclinical disease. Additionally, the duration of glycemic burden is a strong predictor of adverse outcomes, and effective interventions exist to prevent progression of prediabetes to diabetes (see Section IV) and to reduce risk of complications of diabetes (see Section VI). Type 2 diabetes is frequently not diagnosed until complications appear, and approximately one-fourth of all people with diabetes in the U.S. may be undiagnosed. The effectiveness of early identification of prediabetes and diabetes through mass testing of asymptomatic individuals has not been proven definitively, and rigorous trials to provide such proof are unlikely to occur. In a large randomized controlled trial (RCT) in Europe, general practice patients between the ages of 40–69 years were screened for diabetes and then randomly assigned by practice to routine care of diabetes or intensive treatment of multiple risk factors. After 5.3 years of follow-up, CVD risk factors were modestly but significantly more improved with intensive treatment. Incidence of first CVD event and mortality rates were not significantly different between groups (18). This study would seem to add support for early treatment of screen-detected diabetes, as risk factor control was excellent even in the routine treatment arm and both groups had lower event rates than predicted. The absence of a control unscreened arm limits the ability to definitely prove that screening impacts outcomes. Mathematical modeling studies suggest that screening independent of risk factors beginning at age 30 years or age 45 years is highly cost-effective ( 85th percentile for age and sex, weight for height >85th percentile, or weight >120% of ideal for height) Plus any two of the following risk factors: • Family history of type 2 diabetes in first- or second-degree relative • Race/ethnicity (Native American, African American, Latino, Asian American, Pacific Islander) • Signs of insulin resistance or conditions associated with insulin resistance (acanthosis nigricans, hypertension, dyslipidemia, polycystic ovary syndrome, or small-for-gestational-age birth weight) • Maternal history of diabetes or GDM during the child’s gestation Age of initiation: age 10 years or at onset of puberty, if puberty occurs at a younger age Frequency: every 3 years * Persons aged 18 years and younger. The incidence of type 2 diabetes in adolescents has increased dramatically in the last decade, especially in minority populations (31), although the disease remains rare in the general pediatric population (32). Consistent with recommendations for adults, children and youth at increased risk for the presence or the development of type 2 diabetes should be tested within the health care setting (33). The recommendations of the ADA consensus statement “Type 2 Diabetes in Children and Adolescents,” with some modifications, are summarized in Table 5. C. Screening for type 1 diabetes Recommendations Consider referring relatives of those with type 1 diabetes for antibody testing for risk assessment in the setting of a clinical research study. (E) Generally, people with type 1 diabetes present with acute symptoms of diabetes and markedly elevated blood glucose levels, and some cases are diagnosed with life-threatening ketoacidosis. Evidence from several studies suggests that measurement of islet autoantibodies in relatives of those with type 1 diabetes identifies individuals who are at risk for developing type 1 diabetes. Such testing, coupled with education about symptoms of diabetes and follow-up in an observational clinical study, may allow earlier identification of onset of type 1 diabetes and lessen presentation with ketoacidosis at time of diagnosis. This testing may be appropriate in those who have relatives with type 1 diabetes, in the context of clinical research studies (see, for example, http://www.diabetestrialnet.org). However, widespread clinical testing of asymptomatic low-risk individuals cannot currently be recommended, as it would identify very few individuals in the general population who are at risk. Individuals who screen positive should be counseled about their risk of developing diabetes and symptoms of diabetes, followed closely to prevent development of diabetic ketoacidosis, and informed about clinical trials. Clinical studies are being conducted to test various methods of preventing type 1 diabetes in those with evidence of autoimmunity. Some interventions have demonstrated modest efficacy in slowing β-cell loss early in type 1 diabetes (34,35), and further research is needed to determine whether they may be effective in preventing type 1 diabetes. III. DETECTION AND DIAGNOSIS OF GDM Recommendations Screen for undiagnosed type 2 diabetes at the first prenatal visit in those with risk factors, using standard diagnostic criteria. (B) In pregnant women not previously known to have diabetes, screen for GDM at 24–28 weeks of gestation, using a 75-g 2-h OGTT and the diagnostic cut points in Table 6. (B) Screen women with GDM for persistent diabetes at 6–12 weeks postpartum, using the OGTT and nonpregnancy diagnostic criteria. (E) Women with a history of GDM should have lifelong screening for the development of diabetes or prediabetes at least every 3 years. (B) Women with a history of GDM found to have prediabetes should receive lifestyle interventions or metformin to prevent diabetes. (A) Table 6 Screening for and diagnosis of GDM Perform a 75-g OGTT, with plasma glucose measurement fasting and at 1 and 2 h, at 24–28 weeks of gestation in women not previously diagnosed with overt diabetes. The OGTT should be performed in the morning after an overnight fast of at least 8 h. The diagnosis of GDM is made when any of the following plasma glucose values are exceeded:• Fasting: ≥92 mg/dL (5.1 mmol/L)• 1 h: ≥180 mg/dL (10.0 mmol/L)• 2 h: ≥153 mg/dL (8.5 mmol/L) For many years, GDM was defined as any degree of glucose intolerance with onset or first recognition during pregnancy (13), whether or not the condition persisted after pregnancy, and not excluding the possibility that unrecognized glucose intolerance may have antedated or begun concomitantly with the pregnancy. This definition facilitated a uniform strategy for detection and classification of GDM, but its limitations were recognized for many years. As the ongoing epidemic of obesity and diabetes has led to more type 2 diabetes in women of childbearing age, the number of pregnant women with undiagnosed type 2 diabetes has increased (36). Because of this, it is reasonable to screen women with risk factors for type 2 diabetes (Table 4) for diabetes at their initial prenatal visit, using standard diagnostic criteria (Table 2). Women with diabetes found at this visit should receive a diagnosis of overt, not gestational, diabetes. GDM carries risks for the mother and neonate. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study (37), a large-scale (∼25,000 pregnant women) multinational epidemiological study, demonstrated that risk of adverse maternal, fetal, and neonatal outcomes continuously increased as a function of maternal glycemia at 24–28 weeks, even within ranges previously considered normal for pregnancy. For most complications, there was no threshold for risk. These results have led to careful reconsideration of the diagnostic criteria for GDM. After deliberations in 2008–2009, the International Association of Diabetes and Pregnancy Study Groups (IADPSG), an international consensus group with representatives from multiple obstetrical and diabetes organizations, including ADA, developed revised recommendations for diagnosing GDM. The group recommended that all women not known to have prior diabetes undergo a 75-g OGTT at 24–28 weeks of gestation. Additionally, the group developed diagnostic cut points for the fasting, 1-h, and 2-h plasma glucose measurements that conveyed an odds ratio for adverse outcomes of at least 1.75 compared with women with the mean glucose levels in the HAPO study. Current screening and diagnostic strategies, based on the IADPSG statement (38), are outlined in Table 6. These new criteria will significantly increase the prevalence of GDM, primarily because only one abnormal value, not two, is sufficient to make the diagnosis. The ADA recognizes the anticipated significant increase in the incidence of GDM diagnosed by these criteria and is sensitive to concerns about the “medicalization” of pregnancies previously categorized as normal. These diagnostic criteria changes are being made in the context of worrisome worldwide increases in obesity and diabetes rates, with the intent of optimizing gestational outcomes for women and their babies. Admittedly, there are few data from randomized clinical trials regarding therapeutic interventions in women who will now be diagnosed with GDM based on only one blood glucose value above the specified cut points (in contrast to the older criteria that stipulated at least two abnormal values). However, there is emerging observational and retrospective evidence that women diagnosed with the new criteria (even if they would not have been diagnosed with older criteria) have increased rates of poor pregnancy outcomes similar to those of women with GDM by prior criteria (39,40). Expected benefits to these pregnancies and offspring are inferred from intervention trials that focused on women with more mild hyperglycemia than identified using older GDM diagnostic criteria and that found modest benefits (41,42). The frequency of follow-up and blood glucose monitoring for these women is not yet clear, but likely to be less intensive than for women diagnosed by the older criteria. It is important to note that 80–90% of women in both of the mild GDM studies (whose glucose values overlapped with the thresholds recommended herein) could be managed with lifestyle therapy alone. The American College of Obstetricians and Gynecologists announced in 2011 that they continue to recommend use of prior diagnostic criteria for GDM (43). Several other countries have adopted the new criteria, and a report from the WHO on this topic is pending at the time of publication of these standards. The National Institutes of Health is planning to hold a consensus development conference on this topic in 2013. Because some cases of GDM may represent pre-existing undiagnosed type 2 diabetes, women with a history of GDM should be screened for diabetes 6–12 weeks postpartum, using nonpregnant OGTT criteria. Because of their prepartum treatment for hyperglycemia, use of the A1C for diagnosis of persistent diabetes at the postpartum visit is not recommended (44). Women with a history of GDM have a greatly increased subsequent risk for diabetes (45) and should be followed up with subsequent screening for the development of diabetes or prediabetes, as outlined in Section II. Lifestyle interventions or metformin should be offered to women with a history of GDM who develop prediabetes, as discussed in Section IV. In the prospective Nurses’ Health Study II, risk of subsequent diabetes after a history of GDM was significantly lower in women who followed healthy eating patterns. Adjusting for BMI moderately, but not completely, attenuated this association (46). IV. PREVENTION/DELAY OF TYPE 2 DIABETES Recommendations Patients with IGT (A), IFG (E), or an A1C of 5.7–6.4% (E) should be referred to an effective ongoing support program targeting weight loss of 7% of body weight and increasing physical activity to at least 150 min/week of moderate activity such as walking. Follow-up counseling appears to be important for success. (B) Based on the cost-effectiveness of diabetes prevention, such programs should be covered by third-party payers. (B) Metformin therapy for prevention of type 2 diabetes may be considered in those with IGT (A), IFG (E), or an A1C of 5.7–6.4% (E), especially for those with BMI >35 kg/m2, aged 64 years of age previously immunized when they were 5 years ago. Other indications for repeat vaccination include nephrotic syndrome, chronic renal disease, and other immunocompromised states, such as after transplantation. (C) Administer hepatitis B vaccination to unvaccinated adults with diabetes who are aged 19 through 59 years. (C) Consider administering hepatitis B vaccination to unvaccinated adults with diabetes who are aged ≥60 years. (C) Influenza and pneumonia are common, preventable infectious diseases associated with high mortality and morbidity in the elderly and in people with chronic diseases. Though there are limited studies reporting the morbidity and mortality of influenza and pneumococcal pneumonia specifically in people with diabetes, observational studies of patients with a variety of chronic illnesses, including diabetes, show that these conditions are associated with an increase in hospitalizations for influenza and its complications. People with diabetes may be at increased risk of the bacteremic form of pneumococcal infection and have been reported to have a high risk of nosocomial bacteremia, which has a mortality rate as high as 50% (243). Safe and effective vaccines are available that can greatly reduce the risk of serious complications from these diseases (244,245). In a case-control series, influenza vaccine was shown to reduce diabetes-related hospital admission by as much as 79% during flu epidemics (244). There is sufficient evidence to support that people with diabetes have appropriate serological and clinical responses to these vaccinations. The Centers for Disease Control and Prevention (CDC) Advisory Committee on Immunization Practices recommends influenza and pneumococcal vaccines for all individuals with diabetes (http://www.cdc.gov/vaccines/recs/). Late in 2012, the Advisory Committee on Immunization Practices of the CDC recommended that all previously unvaccinated adults with diabetes aged 19 through 59 years be vaccinated against hepatitis B virus (HBV) as soon as possible after a diagnosis of diabetes is made and that vaccination be considered for those aged ≥60 years, after assessing risk and likelihood of an adequate immune response (246). At least 29 outbreaks of HBV in long-term care facilities and hospitals have been reported to the CDC, with the majority involving adults with diabetes receiving “assisted blood glucose monitoring,” in which such monitoring is done by a health care professional with responsibility for more than one patient. HBV is highly transmissible and stable for long periods of time on surfaces such as lancing devices and blood glucose meters, even when no blood is visible. Blood sufficient to transmit the virus has also been found in the reservoirs of insulin pens, resulting in warnings against sharing such devices between patients. The CDC analyses suggest that, excluding persons with HBV-related risk behaviors, acute HBV infection is about twice as high among adults with diabetes aged ≥23 years compared with adults without diabetes. Seroprevalence of antibody to HBV core antigen, suggesting past or current infection, is 60% higher among adults with diabetes than those without, and there is some evidence that diabetes imparts a higher HBV case fatality rate. The age differentiation in the recommendations stems from CDC economic models suggesting that vaccination of adults with diabetes who were aged 20–59 years would cost an estimated \$75,000 per quality-adjusted life-year saved, while cost per quality-adjusted life-year saved increased significantly at higher ages. In addition to competing causes of mortality in older adults, the immune response to the vaccine declines with age (246). These new recommendations regarding HBV vaccinations serve as a reminder to clinicians that children and adults with diabetes need a number of vaccinations, both those specifically indicated because of diabetes as well as those recommended for the general population (http://www.cdc.gov/vaccines/recs/). VI. PREVENTION AND MANAGEMENT OF DIABETES COMPLICATIONS A. CVD CVD is the major cause of morbidity and mortality for individuals with diabetes and the largest contributor to the direct and indirect costs of diabetes. The common conditions coexisting with type 2 diabetes (e.g., hypertension and dyslipidemia) are clear risk factors for CVD, and diabetes itself confers independent risk. Numerous studies have shown the efficacy of controlling individual cardiovascular risk factors in preventing or slowing CVD in people with diabetes. Large benefits are seen when multiple risk factors are addressed globally (247,248). There is evidence that measures of 10-year coronary heart disease (CHD) risk among U.S. adults with diabetes have improved significantly over the past decade (249). 1. Hypertension/blood pressure control Recommendations Screening and diagnosis Blood pressure should be measured at every routine visit. Patients found to have elevated blood pressure should have blood pressure confirmed on a separate day. (B) Goals People with diabetes and hypertension should be treated to a systolic blood pressure goal of 120/80 mmHg should be advised on lifestyle changes to reduce blood pressure. (B) Patients with confirmed blood pressure ≥140/80 mmHg should, in addition to lifestyle therapy, have prompt initiation and timely subsequent titration of pharmacological therapy to achieve blood pressure goals. (B) Lifestyle therapy for elevated blood pressure consists of weight loss, if overweight; Dietary Approaches to Stop Hypertension (DASH)-style dietary pattern including reducing sodium and increasing potassium intake; moderation of alcohol intake; and increased physical activity. (B) Pharmacological therapy for patients with diabetes and hypertension should be with a regimen that includes either an ACE inhibitor or an angiotensin receptor blocker (ARB). If one class is not tolerated, the other should be substituted. (C) Multiple-drug therapy (two or more agents at maximal doses) is generally required to achieve blood pressure targets. (B) Administer one or more antihypertensive medications at bedtime. (A) If ACE inhibitors, ARBs, or diuretics are used, serum creatinine/estimated glomerular filtration rate (eGFR) and serum potassium levels should be monitored. (E) In pregnant patients with diabetes and chronic hypertension, blood pressure target goals of 110–129/65–79 mmHg are suggested in the interest of long-term maternal health and minimizing impaired fetal growth. ACE inhibitors and ARBs are contraindicated during pregnancy. (E) Hypertension is a common comorbidity of diabetes, affecting the majority of patients, with prevalence depending on type of diabetes, age, obesity, and ethnicity. Hypertension is a major risk factor for both CVD and microvascular complications. In type 1 diabetes, hypertension is often the result of underlying nephropathy, while in type 2 diabetes it usually coexists with other cardiometabolic risk factors. Screening and diagnosis Measurement of blood pressure in the office should be done by a trained individual and follow the guidelines established for nondiabetic individuals: measurement in the seated position, with feet on the floor and arm supported at heart level, after 5 min of rest. Cuff size should be appropriate for the upper arm circumference. Elevated values should be confirmed on a separate day. Home blood pressure self-monitoring and 24-h ambulatory blood pressure monitoring may provide additional evidence of “white coat” and masked hypertension and other discrepancies between office and “true” blood pressure. Studies in nondiabetic populations found that home measurements may better correlate with CVD risk than office measurements (250,251). However, the preponderance of the evidence of benefits of treatment of hypertension in people with diabetes is based on office measurements. Treatment goals Epidemiological analyses show that blood pressure >115/75 mmHg is associated with increased cardiovascular event rates and mortality in individuals with diabetes (252–254) and that systolic blood pressure above 120 mmHg predicts long-term end-stage renal disease (ESRD). Randomized clinical trials have demonstrated the benefit (reduction of CHD events, stroke, and nephropathy) of lowering blood pressure to 120 mmHg or diastolic blood pressure >80 mmHg). If the blood pressure is confirmed to be ≥140 mmHg systolic and/or ≥80 mmHg diastolic, pharmacological therapy should be initiated along with nonpharmacological therapy (252). Lowering of blood pressure with regimens based on a variety of antihypertensive drugs, including ACE inhibitors, ARBs, β-blockers, diuretics, and calcium channel blockers, has been shown to be effective in reducing cardiovascular events. Several studies suggested that ACE inhibitors may be superior to dihydropyridine calcium channel blockers in reducing cardiovascular events (265–267). However, a variety of other studies have shown no specific advantage to ACE inhibitors as initial treatment of hypertension in the general hypertensive population, but rather an advantage on cardiovascular outcomes of initial therapy with low-dose thiazide diuretics (252,268,269). In people with diabetes, inhibitors of the renin-angiotensin system (RAS) may have unique advantages for initial or early therapy of hypertension. In a nonhypertension trial of high-risk individuals, including a large subset with diabetes, an ACE inhibitor reduced CVD outcomes (270). In patients with congestive heart failure (CHF), including diabetic subgroups, ARBs have been shown to reduce major CVD outcomes (271–274), and in type 2 diabetic patients with significant nephropathy, ARBs were superior to calcium channel blockers for reducing heart failure (275). Though evidence for distinct advantages of RAS inhibitors on CVD outcomes in diabetes remains conflicting (255,269), the high CVD risks associated with diabetes, and the high prevalence of undiagnosed CVD, may still favor recommendations for their use as first-line hypertension therapy in people with diabetes (252). Recently, the blood pressure arm of the ADVANCE trial demonstrated that routine administration of a fixed combination of the ACE inhibitor perindopril and the diuretic indapamide significantly reduced combined microvascular and macrovascular outcomes, as well as CVD and total mortality. The improved outcomes could also have been due to lower achieved blood pressure in the perindopril-indapamide arm (259). Another trial showed a decrease in morbidity and mortality in those receiving benazepril and amlodipine compared with benazepril and hydrochlorothiazide (HCTZ). The compelling benefits of RAS inhibitors in diabetic patients with albuminuria or renal insufficiency provide additional rationale for use of these agents (see Section VI.B). If needed to achieve blood pressure targets, amlodipine, HCTZ, or chlorthalidone can be added. If eGFR is 50 mg/dL, and triglycerides 40 mg/dL (1.0 mmol/L) in men and >50 mg/dL (1.3 mmol/L) in women are desirable (C). However, LDL cholesterol–targeted statin therapy remains the preferred strategy. (A) Combination therapy has been shown not to provide additional cardiovascular benefit above statin therapy alone and is not generally recommended. (A) Statin therapy is contraindicated in pregnancy. (B) Evidence for benefits of lipid-lowering therapy Patients with type 2 diabetes have an increased prevalence of lipid abnormalities, contributing to their high risk of CVD. Multiple clinical trials demonstrated significant effects of pharmacological (primarily statin) therapy on CVD outcomes in subjects with CHD and for primary CVD prevention (279,280). Subanalyses of diabetic subgroups of larger trials (281–285) and trials specifically in subjects with diabetes (286,287) showed significant primary and secondary prevention of CVD events +/− CHD deaths in diabetic populations. Meta-analyses including data from over 18,000 patients with diabetes from 14 randomized trials of statin therapy, followed for a mean of 4.3 years, demonstrate a 9% proportional reduction in all-cause mortality and 13% reduction in vascular mortality, for each mmol/L reduction in LDL cholesterol (288). As is the case in nondiabetic individuals, absolute reductions in “hard” CVD outcomes (CHD death and nonfatal MI) are greatest in people with high baseline CVD risk (known CVD and/or very high LDL cholesterol levels), but overall the benefits of statin therapy in people with diabetes at moderate or high risk for CVD are convincing. There is an increased risk of incident diabetes with statin use (289,290), which may be limited to those with risk factors for diabetes. These patients may benefit additionally from diabetes screening when on statin therapy. In an analysis of one of the initial studies suggesting that statins are linked to risk of diabetes, the cardiovascular event rate reduction with statins outweighed the risk of incident diabetes even for patients at highest risk for diabetes. The absolute risk increase was small (over 5 years of follow-up, 1.2% of participants on placebo developed diabetes and 1.5% on rosuvastatin) (291). The relative risk-benefit ratio favoring statins is further supported by meta-analysis of individual data of over 170,000 persons from 27 randomized trials. This demonstrated that individuals at low risk of vascular disease, including those undergoing primary prevention, received benefits from statins that included reductions in major vascular events and vascular death without increase in incidence of cancer or deaths from other causes (280). Low levels of HDL cholesterol, often associated with elevated triglyceride levels, are the most prevalent pattern of dyslipidemia in persons with type 2 diabetes. However, the evidence base for drugs that target these lipid fractions is significantly less robust than that for statin therapy (292). Nicotinic acid has been shown to reduce CVD outcomes (293), although the study was done in a nondiabetic cohort. Gemfibrozil has been shown to decrease rates of CVD events in subjects without diabetes (294,295) and in the diabetic subgroup of one of the larger trials (294). However, in a large trial specific to diabetic patients, fenofibrate failed to reduce overall cardiovascular outcomes (296). Combination therapy, with a statin and a fibrate or statin and niacin, may be efficacious for treatment for all three lipid fractions, but this combination is associated with an increased risk for abnormal transaminase levels, myositis, or rhabdomyolysis. The risk of rhabdomyolysis is higher with higher doses of statins and with renal insufficiency and seems to be lower when statins are combined with fenofibrate than gemfibrozil (297). In the ACCORD study, the combination of fenofibrate and simvastatin did not reduce the rate of fatal cardiovascular events, nonfatal MI, or nonfatal stroke, as compared with simvastatin alone, in patients with type 2 diabetes who were at high risk for CVD. Prespecified subgroup analyses suggested heterogeneity in treatment effects according to sex, with a benefit of combination therapy for men and possible harm for women, and a possible benefit for patients with both triglyceride level ≥204 mg/dL and HDL cholesterol level ≤34 mg/dL (298). The AIM-HIGH trial randomized over 3,000 patients (about one-third with diabetes) with established CVD, low levels of HDL cholesterol, and triglyceride levels of 150–400 mg/dL to statin therapy plus extended release niacin or matching placebo. The trial was halted early due to lack of efficacy on the primary CVD outcome and a possible increase in ischemic stroke in those on combination therapy (299). Hence, combination lipid-lowering therapy cannot be broadly recommended. Dyslipidemia treatment and target lipid levels For most patients with diabetes, the first priority of dyslipidemia therapy (unless severe hypertriglyceridemia with risk of pancreatitis is the immediate issue) is to lower LDL cholesterol to a target goal of 1,000 mg/dL) may warrant immediate pharmacological therapy (fibric acid derivative, niacin, or fish oil) to reduce the risk of acute pancreatitis. In the absence of severe hypertriglyceridemia, therapy targeting HDL cholesterol or triglycerides lacks the strong evidence base of statin therapy. If the HDL cholesterol is 10%). This includes most men aged >50 years or women aged >60 years who have at least one additional major risk factor (family history of CVD, hypertension, smoking, dyslipidemia, or albuminuria). (C) Aspirin should not be recommended for CVD prevention for adults with diabetes at low CVD risk (10-year CVD risk 87% sensitivity in detecting DPN. Loss of 10-g monofilament perception and reduced vibration perception predict foot ulcers (380). Importantly, in patients with neuropathy, particularly when severe, causes other than diabetes should always be considered, such as neurotoxic medications, heavy metal poisoning, alcohol abuse, vitamin B12 deficiency (especially in those taking metformin for prolonged periods (381), renal disease, chronic inflammatory demyelinating neuropathy, inherited neuropathies, and vasculitis (382). Diabetic autonomic neuropathy The symptoms and signs of autonomic dysfunction should be elicited carefully during the history and physical examination. Major clinical manifestations of diabetic autonomic neuropathy include resting tachycardia, exercise intolerance, orthostatic hypotension, constipation, gastroparesis, erectile dysfunction, sudomotor dysfunction, impaired neurovascular function, and, potentially, autonomic failure in response to hypoglycemia (383). CAN, a CVD risk factor (93), is the most studied and clinically important form of diabetic autonomic neuropathy. CAN may be indicated by resting tachycardia (>100 bpm), orthostasis (a fall in systolic blood pressure >20 mmHg upon standing without an appropriate heart rate response); it is also associated with increased cardiac event rates. Although some societies have developed guidelines for screening for CAN, the benefits of sophisticated testing beyond risk stratification are not clear (384). Gastrointestinal neuropathies (e.g., esophageal enteropathy, gastroparesis, constipation, diarrhea, fecal incontinence) are common, and any section of the gastrointestinal tract may be affected. Gastroparesis should be suspected in individuals with erratic glucose control or with upper gastrointestinal symptoms without other identified cause. Evaluation of solid-phase gastric emptying using double-isotope scintigraphy may be done if symptoms are suggestive, but test results often correlate poorly with symptoms. Constipation is the most common lower-gastrointestinal symptom but can alternate with episodes of diarrhea. Diabetic autonomic neuropathy is also associated with genitourinary tract disturbances. In men, diabetic autonomic neuropathy may cause erectile dysfunction and/or retrograde ejaculation. Evaluation of bladder dysfunction should be performed for individuals with diabetes who have recurrent urinary tract infections, pyelonephritis, incontinence, or a palpable bladder. Symptomatic treatments DPN The first step in management of patients with DPN should be to aim for stable and optimal glycemic control. Although controlled trial evidence is lacking, several observational studies suggest that neuropathic symptoms improve not only with optimization of control, but also with the avoidance of extreme blood glucose fluctuations. Patients with painful DPN may benefit from pharmacological treatment of their symptoms: many agents have confirmed or probable efficacy confirmed in systematic reviews of RCTs (379), with several U.S. Food and Drug Administration (FDA)-approved for the management of painful DPN. Treatment of autonomic neuropathy Gastroparesis symptoms may improve with dietary changes and prokinetic agents such as metoclopramide or erythromycin. Treatments for erectile dysfunction may include phosphodiesterase type 5 inhibitors, intracorporeal or intraurethral prostaglandins, vacuum devices, or penile prostheses. Interventions for other manifestations of autonomic neuropathy are described in the ADA statement on neuropathy (380). As with DPN treatments, these interventions do not change the underlying pathology and natural history of the disease process, but may have a positive impact on the quality of life of the patient. E. Foot care Recommendations For all patients with diabetes, perform an annual comprehensive foot examination to identify risk factors predictive of ulcers and amputations. The foot examination should include inspection, assessment of foot pulses, and testing for loss of protective sensation (LOPS) (10-g monofilament plus testing any one of the following: vibration using 128-Hz tuning fork, pinprick sensation, ankle reflexes, or vibration perception threshold). (B) Provide general foot self-care education to all patients with diabetes. (B) A multidisciplinary approach is recommended for individuals with foot ulcers and high-risk feet, especially those with a history of prior ulcer or amputation. (B) Refer patients who smoke, have LOPS and structural abnormalities, or have history of prior lower-extremity complications to foot care specialists for ongoing preventive care and lifelong surveillance. (C) Initial screening for peripheral arterial disease (PAD) should include a history for claudication and an assessment of the pedal pulses. Consider obtaining an ankle-brachial index (ABI), as many patients with PAD are asymptomatic. (C) Refer patients with significant claudication or a positive ABI for further vascular assessment and consider exercise, medications, and surgical options. (C) Amputation and foot ulceration, consequences of diabetic neuropathy and/or PAD, are common and major causes of morbidity and disability in people with diabetes. Early recognition and management of risk factors can prevent or delay adverse outcomes. The risk of ulcers or amputations is increased in people who have the following risk factors: Previous amputation Past foot ulcer history Peripheral neuropathy Foot deformity Peripheral vascular disease Visual impairment Diabetic nephropathy (especially patients on dialysis) Poor glycemic control Cigarette smoking Many studies have been published proposing a range of tests that might usefully identify patients at risk for foot ulceration, creating confusion among practitioners as to which screening tests should be adopted in clinical practice. An ADA task force was therefore assembled in 2008 to concisely summarize recent literature in this area and then recommend what should be included in the comprehensive foot exam for adult patients with diabetes. Their recommendations are summarized below, but clinicians should refer to the task force report (385) for further details and practical descriptions of how to perform components of the comprehensive foot examination. At least annually, all adults with diabetes should undergo a comprehensive foot examination to identify high-risk conditions. Clinicians should ask about history of previous foot ulceration or amputation, neuropathic or peripheral vascular symptoms, impaired vision, tobacco use, and foot care practices. A general inspection of skin integrity and musculoskeletal deformities should be done in a well-lit room. Vascular assessment would include inspection and assessment of pedal pulses. The neurologic exam recommended is designed to identify LOPS rather than early neuropathy. The clinical examination to identify LOPS is simple and requires no expensive equipment. Five simple clinical tests (use of a 10-g monofilament, vibration testing using a 128-Hz tuning fork, tests of pinprick sensation, ankle reflex assessment, and testing vibration perception threshold with a biothesiometer), each with evidence from well-conducted prospective clinical cohort studies, are considered useful in the diagnosis of LOPS in the diabetic foot. The task force agrees that any of the five tests listed could be used by clinicians to identify LOPS, although ideally two of these should be regularly performed during the screening exam—normally the 10-g monofilament and one other test. One or more abnormal tests would suggest LOPS, while at least two normal tests (and no abnormal test) would rule out LOPS. The last test listed, vibration assessment using a biothesiometer or similar instrument, is widely used in the U.S.; however, identification of the patient with LOPS can easily be carried out without this or other expensive equipment. Initial screening for PAD should include a history for claudication and an assessment of the pedal pulses. A diagnostic ABI should be performed in any patient with symptoms of PAD. Due to the high estimated prevalence of PAD in patients with diabetes and the fact that many patients with PAD are asymptomatic, an ADA consensus statement on PAD (386) suggested that a screening ABI be performed in patients over 50 years of age and be considered in patients under 50 years of age who have other PAD risk factors (e.g., smoking, hypertension, hyperlipidemia, or duration of diabetes >10 years). Refer patients with significant symptoms or a positive ABI for further vascular assessment and consider exercise, medications, and surgical options (386). Patients with diabetes and high-risk foot conditions should be educated regarding their risk factors and appropriate management. Patients at risk should understand the implications of the loss of protective sensation, the importance of foot monitoring on a daily basis, the proper care of the foot, including nail and skin care, and the selection of appropriate footwear. Patients with LOPS should be educated on ways to substitute other sensory modalities (hand palpation, visual inspection) for surveillance of early foot problems. The patients’ understanding of these issues and their physical ability to conduct proper foot surveillance and care should be assessed. Patients with visual difficulties, physical constraints preventing movement, or cognitive problems that impair their ability to assess the condition of the foot and to institute appropriate responses will need other people, such as family members, to assist in their care. People with neuropathy or evidence of increased plantar pressure (e.g., erythema, warmth, callus, or measured pressure) may be adequately managed with well-fitted walking shoes or athletic shoes that cushion the feet and redistribute pressure. Callus can be debrided with a scalpel by a foot care specialist or other health professional with experience and training in foot care. People with bony deformities (e.g., hammertoes, prominent metatarsal heads, bunions) may need extra-wide or -depth shoes. People with extreme bony deformities (e.g., Charcot foot) who cannot be accommodated with commercial therapeutic footwear may need custom-molded shoes. Foot ulcers and wound care may require care by a podiatrist, orthopedic or vascular surgeon, or rehabilitation specialist experienced in the management of individuals with diabetes. Guidelines for treatment of diabetic foot ulcers have recently been updated (387). VII. ASSESSMENT OF COMMON COMORBID CONDITIONS Recommendations For patients with risk factors, signs or symptoms, consider assessment and treatment for common diabetes-associated conditions (see Table 14). (B) Table 14 Common comorbidities for which increased risk is associated with diabetes Hearing impairment Obstructive sleep apnea Fatty liver disease Low testosterone in men Periodontal disease Certain cancers Fractures Cognitive impairment Depression In addition to the commonly appreciated comorbidities of obesity, hypertension, and dyslipidemia, diabetes is also associated with other diseases or conditions at rates higher than those of age-matched people without diabetes. A few of the more common comorbidities are described herein and listed in Table 14. Hearing impairment Hearing impairment, both high frequency and low/mid frequency, is more common in people with diabetes, perhaps due to neuropathy and/or vascular disease. In an NHANES analysis, hearing impairment was about twice as great in people with diabetes compared with those without, after adjusting for age and other risk factors for hearing impairment (388). Controlling for age, race, and other demographic factors, high frequency loss in those with diabetes was significantly associated with history of CHD and with peripheral neuropathy, while low/mid frequency loss was associated with low HDL cholesterol and with poor reported health status (389). Obstructive sleep apnea Age-adjusted rates of obstructive sleep apnea, a risk factor for CVD, are significantly higher (4- to 10-fold) with obesity, especially with central obesity, in men and women (390). The prevalence in general populations with type 2 diabetes may be up to 23% (391), and in obese participants enrolled in the Look AHEAD trial exceeded 80% (392). Treatment of sleep apnea significantly improves quality of life and blood pressure control. The evidence for a treatment effect on glycemic control is mixed (393). Fatty liver disease Unexplained elevation of hepatic transaminase concentrations is significantly associated with higher BMI, waist circumference, triglycerides, and fasting insulin, and with lower HDL cholesterol. Type 2 diabetes and hypertension are independently associated with transaminase elevations in women (394). In a prospective analysis, diabetes was significantly associated with incident nonalcoholic chronic liver disease and with hepatocellular carcinoma (395). Interventions that improve metabolic abnormalities in patients with diabetes (weight loss, glycemic control, treatment with specific drugs for hyperglycemia or dyslipidemia) are also beneficial for fatty liver disease (396). Low testosterone in men Mean levels of testosterone are lower in men with diabetes compared with age-matched men without diabetes, but obesity is a major confounder (397). The issue of treatment in asymptomatic men is controversial. The evidence for effects of testosterone replacement on outcomes is mixed, and recent guidelines suggest that screening and treatment of men without symptoms are not recommended (398). Periodontal disease Periodontal disease is more severe, but not necessarily more prevalent, in patients with diabetes than those without (399). Numerous studies have suggested associations with poor glycemic control, nephropathy, and CVD, but most studies are highly confounded. A comprehensive assessment, and treatment of identified disease, is indicated in patients with diabetes, but the evidence that periodontal disease treatment improves glycemic control is mixed. A meta-analysis reported a significant 0.47% improvement in A1C, but noted multiple problems with the quality of the published studies included in the analysis (400). Several high-quality RCTs have not shown a significant effect (401). Cancer Diabetes (possibly only type 2 diabetes) is associated with increased risk of cancers of the liver, pancreas, endometrium, colon/rectum, breast, and bladder (402). The association may result from shared risk factors between type 2 diabetes and cancer (obesity, age, and physical inactivity) but may also be due to hyperinsulinemia or hyperglycemia (401,403). Patients with diabetes should be encouraged to undergo recommended age- and sex-appropriate cancer screenings and to reduce their modifiable cancer risk factors (obesity, smoking, and physical inactivity). Fractures Age-matched hip fracture risk is significantly increased in both type 1 (summary RR 6.3) and type 2 diabetes (summary RR 1.7) in both sexes (404). Type 1 diabetes is associated with osteoporosis, but in type 2 diabetes an increased risk of hip fracture is seen despite higher bone mineral density (BMD) (405). One study showed that prevalent vertebral fractures were significantly more common in men and women with type 2 diabetes, but were not associated with BMD (406). In three large observational studies of older adults, femoral neck BMD T-score and the WHO fracture risk algorithm (FRAX) score were associated with hip and nonspine fracture, although fracture risk was higher in diabetic participants compared with participants without diabetes for a given T-score and age or for a given FRAX score risk (407). It is appropriate to assess fracture history and risk factors in older patients with diabetes and recommend BMD testing if appropriate for the patient’s age and sex. For at-risk patients, it is reasonable to consider standard primary or secondary prevention strategies (reduce risk factors for falls, ensure adequate calcium and vitamin D intake, avoid use of medications that lower BMD, such as glucocorticoids), and to consider pharmacotherapy for high-risk patients. For patients with type 2 diabetes with fracture risk factors, avoiding use of thiazolidinediones is warranted. Cognitive impairment Diabetes is associated with significantly increased risk of cognitive decline, a greater rate of cognitive decline, and increased risk of dementia (408,409). In a 15-year prospective study of a community-dwelling people over the age of 60 years, the presence of diabetes at baseline significantly increased the age- and sex-adjusted incidence of all-cause dementia, Alzheimer disease, and vascular dementia compared with rates in those with normal glucose tolerance (410). In a substudy of the ACCORD study, there were no differences in cognitive outcomes between intensive and standard glycemic control, although there was significantly less of a decrement in total brain volume by magnetic resonance imaging in participants in the intensive arm (411). The effects of hyperglycemia and insulin on the brain are areas of intense research interest. Depression As discussed in Section V.H, depression is highly prevalent in people with diabetes and is associated with worse outcomes. VIII. DIABETES CARE IN SPECIFIC POPULATIONS A. Children and adolescents Recommendations As is the case for all children, children with diabetes or prediabetes should be encouraged to engage in at least 60 min of physical activity each day. (B) 1. Type 1 diabetes Three-quarters of all cases of type 1 diabetes are diagnosed in individuals 130/80 mmHg, if 95% exceeds that value) should be considered as soon as the diagnosis is confirmed. (E) ACE inhibitors should be considered for the initial treatment of hypertension, following appropriate reproductive counseling due to its potential teratogenic effects. (E) The goal of treatment is a blood pressure consistently 2 years of age soon after diagnosis (after glucose control has been established). If family history is not of concern, then consider the first lipid screening at puberty (≥10 years of age). For children diagnosed with diabetes at or after puberty, consider obtaining a fasting lipid profile soon after the diagnosis (after glucose control has been established). (E) For both age-groups, if lipids are abnormal, annual monitoring is reasonable. If LDL cholesterol values are within the accepted risk levels ( 160 mg/dL (4.1 mmol/L), or LDL cholesterol >130 mg/dL (3.4 mmol/L) and one or more CVD risk factors, is reasonable. (E) The goal of therapy is an LDL cholesterol value 1% above the normal range for a nondiabetic pregnant woman. Preconception care of diabetes appears to reduce the risk of congenital malformations. Five nonrandomized studies compared rates of major malformations in infants between women who participated in preconception diabetes care programs and women who initiated intensive diabetes management after they were already pregnant. The preconception care programs were multidisciplinary and designed to train patients in diabetes self-management with diet, intensified insulin therapy, and SMBG. Goals were set to achieve normal blood glucose concentrations, and >80% of subjects achieved normal A1C concentrations before they became pregnant. In all five studies, the incidence of major congenital malformations in women who participated in preconception care (range 1.0–1.7% of infants) was much lower than the incidence in women who did not participate (range 1.4–10.9% of infants) (106). One limitation of these studies is that participation in preconception care was self-selected rather than randomized. Thus, it is impossible to be certain that the lower malformation rates resulted fully from improved diabetes care. Nonetheless, the evidence supports the concept that malformations can be reduced or prevented by careful management of diabetes before pregnancy. Planned pregnancies greatly facilitate preconception diabetes care. Unfortunately, nearly two-thirds of pregnancies in women with diabetes are unplanned, leading to a persistent excess of malformations in infants of diabetic mothers. To minimize the occurrence of these devastating malformations, standard care for all women with diabetes who have childbearing potential, beginning at the onset of puberty or at diagnosis, should include 1) education about the risk of malformations associated with unplanned pregnancies and poor metabolic control and 2) use of effective contraception at all times, unless the patient has good metabolic control and is actively trying to conceive. Women contemplating pregnancy need to be seen frequently by a multidisciplinary team experienced in the management of diabetes before and during pregnancy. The goals of preconception care are to 1) involve and empower the patient in the management of her diabetes, 2) achieve the lowest A1C test results possible without excessive hypoglycemia, 3) assure effective contraception until stable and acceptable glycemia is achieved, and 4) identify, evaluate, and treat long-term diabetes complications such as retinopathy, nephropathy, neuropathy, hypertension, and CHD (106). Among the drugs commonly used in the treatment of patients with diabetes, a number may be relatively or absolutely contraindicated during pregnancy. Statins are category X (contraindicated for use in pregnancy) and should be discontinued before conception, as should ACE inhibitors (450). ARBs are category C (risk cannot be ruled out) in the first trimester but category D (positive evidence of risk) in later pregnancy and should generally be discontinued before pregnancy. Since many pregnancies are unplanned, health care professionals caring for any woman of childbearing potential should consider the potential risks and benefits of medications that are contraindicated in pregnancy. Women using medications such as statins or ACE inhibitors need ongoing family planning counseling. Among the oral antidiabetic agents, metformin and acarbose are classified as category B (no evidence of risk in humans) and all others as category C. Potential risks and benefits of oral antidiabetic agents in the preconception period must be carefully weighed, recognizing that data are insufficient to establish the safety of these agents in pregnancy. For further discussion of preconception care, see the ADA’s consensus statement on pre-existing diabetes and pregnancy (106) and the position statement (451) on this subject. C. Older adults Recommendations Older adults who are functional, cognitively intact, and have significant life expectancy should receive diabetes care with goals similar to those developed for younger adults. (E) Glycemic goals for some older adults might reasonably be relaxed, using individual criteria, but hyperglycemia leading to symptoms or risk of acute hyperglycemic complications should be avoided in all patients. (E) Other cardiovascular risk factors should be treated in older adults with consideration of the time frame of benefit and the individual patient. Treatment of hypertension is indicated in virtually all older adults, and lipid and aspirin therapy may benefit those with life expectancy at least equal to the time frame of primary or secondary prevention trials. (E) Screening for diabetes complications should be individualized in older adults, but particular attention should be paid to complications that would lead to functional impairment. (E) Diabetes is an important health condition for the aging population; at least 20% of patients over the age of 65 years have diabetes, and this number can be expected to grow rapidly in the coming decades. Older individuals with diabetes have higher rates of premature death, functional disability, and coexisting illnesses such as hypertension, CHD, and stroke than those without diabetes. Older adults with diabetes are also at greater risk than other older adults for several common geriatric syndromes, such as polypharmacy, depression, cognitive impairment, urinary incontinence, injurious falls, and persistent pain. A consensus report on diabetes and older adults (452) influenced the following discussion and recommendations. The care of older adults with diabetes is complicated by their clinical and functional heterogeneity. Some older individuals developed diabetes years earlier and may have significant complications; others who are newly diagnosed may have had years of undiagnosed diabetes with resultant complications or may have truly recent-onset disease and few or no complications. Some older adults with diabetes are frail and have other underlying chronic conditions, substantial diabetes-related comorbidity, or limited physical or cognitive functioning. Other older individuals with diabetes have little comorbidity and are active. Life expectancies are highly variable for this population, but often longer than clinicians realize. Providers caring for older adults with diabetes must take this heterogeneity into consideration when setting and prioritizing treatment goals. There are few long-term studies in older adults demonstrating the benefits of intensive glycemic, blood pressure, and lipid control. Patients who can be expected to live long enough to reap the benefits of long-term intensive diabetes management, who have good cognitive and functional function, and who choose to do so via shared decision making may be treated using therapeutic interventions and goals similar to those for younger adults with diabetes. As with all patients, DSME and ongoing DSMS are vital components of diabetes care for older adults and their caregivers. For patients with advanced diabetes complications, life-limiting comorbid illness, or substantial cognitive or functional impairment, it is reasonable to set less intensive glycemic target goals. These patients are less likely to benefit from reducing the risk of microvascular complications and more likely to suffer serious adverse effects from hypoglycemia. However, patients with poorly controlled diabetes may be subject to acute complications of diabetes, including dehydration, poor wound healing, and hyperglycemic hyperosmolar coma. Glycemic goals at a minimum should avoid these consequences. Although control of hyperglycemia may be important in older individuals with diabetes, greater reductions in morbidity and mortality may result from control of other cardiovascular risk factors rather than from tight glycemic control alone. There is strong evidence from clinical trials of the value of treating hypertension in the elderly (453,454). There is less evidence for lipid-lowering and aspirin therapy, although the benefits of these interventions for primary and secondary prevention are likely to apply to older adults whose life expectancies equal or exceed the time frames seen in clinical trials. Special care is required in prescribing and monitoring pharmacological therapy in older adults. Costs may be a significant factor, especially since older adults tend to be on many medications. Metformin may be contraindicated because of renal insufficiency or significant heart failure. Thiazolidinediones, if used at all, should be used very cautiously in those with, or at risk for, CHF and have also been associated with fractures. Sulfonylureas, other insulin secretagogues, and insulin can cause hypoglycemia. Insulin use requires that patients or caregivers have good visual and motor skills and cognitive ability. Dipeptidyl peptidase 4 (DPP-4) inhibitors have few side effects, but their costs may be a barrier to some older patients; the latter is also the case for GLP-1 agonists. Screening for diabetes complications in older adults also should be individualized. Particular attention should be paid to complications that can develop over short periods of time and/or that would significantly impair functional status, such as visual and lower-extremity complications. D. Cystic fibrosis–related diabetes Recommendations Annual screening for cystic fibrosis–related diabetes (CFRD) with OGTT should begin by age 10 years in all patients with cystic fibrosis who do not have CFRD (B). Use of A1C as a screening test for CFRD is not recommended. (B) During a period of stable health, the diagnosis of CFRD can be made in cystic fibrosis patients according to usual glucose criteria. (E) Patients with CFRD should be treated with insulin to attain individualized glycemic goals. (A) Annual monitoring for complications of diabetes is recommended, beginning 5 years after the diagnosis of CFRD. (E) CFRD is the most common comorbidity in persons with cystic fibrosis, occurring in about 20% of adolescents and 40–50% of adults. The additional diagnosis of diabetes in this population is associated with worse nutritional status, more severe inflammatory lung disease, and greater mortality from respiratory failure. Insulin insufficiency related to partial fibrotic destruction of the islet mass is the primary defect in CFRD. Genetically determined function of the remaining β-cells and insulin resistance associated with infection and inflammation may also play a role. Encouraging new data suggest that early detection and aggressive insulin therapy have narrowed the gap in mortality between cystic fibrosis patients with and without diabetes and have eliminated the sex difference in mortality (455). Recommendations for the clinical management of CFRD can be found in the recent ADA position statement on this topic (456). IX. DIABETES CARE IN SPECIFIC SETTINGS A. Diabetes care in the hospital Recommendations All patients with diabetes admitted to the hospital should have their diabetes clearly identified in the medical record. (E) All patients with diabetes should have an order for blood glucose monitoring, with results available to all members of the health care team. (E) Goals for blood glucose levels: Critically ill patients: Insulin therapy should be initiated for treatment of persistent hyperglycemia starting at a threshold of no greater than 180 mg/dL (10 mmol/L). Once insulin therapy is started, a glucose range of 140–180 mg/dL (7.8–10 mmol/L) is recommended for the majority of critically ill patients. (A) More stringent goals, such as 110–140 mg/dL (6.1–7.8 mmol/L) may be appropriate for selected patients, as long as this can be achieved without significant hypoglycemia. (C) Critically ill patients require an intravenous insulin protocol that has demonstrated efficacy and safety in achieving the desired glucose range without increasing risk for severe hypoglycemia. (E) Non–critically ill patients: There is no clear evidence for specific blood glucose goals. If treated with insulin, the premeal blood glucose targets generally 140 mg/dL (7.8 mmol/L). Levels that are significantly and persistently above this may require treatment in hospitalized patients. A1C values >6.5% suggest, in undiagnosed patients, that diabetes preceded hospitalization (466). Hypoglycemia has been defined as any blood glucose <70 mg/dL (3.9 mmol/L). This is the standard definition in outpatients and correlates with the initial threshold for the release of counter-regulatory hormones. Severe hypoglycemia in hospitalized patients has been defined by many as <40 mg/dL (2.2 mmol/L), although this is lower than the ∼50 mg/dL (2.8 mmol/L) level at which cognitive impairment begins in normal individuals (467). As with hyperglycemia, hypoglycemia among inpatients is also associated with adverse short- and long-term outcomes. Early recognition and treatment of mild to moderate hypoglycemia (40–69 mg/dL [2.2–3.8 mmol/L]) can prevent deterioration to a more severe episode with potential adverse sequelae (468). Critically ill patients Based on the weight of the available evidence, for the majority of critically ill patients in the ICU setting, insulin infusion should be used to control hyperglycemia, with a starting threshold of no higher than 180 mg/dL (10.0 mmol/L). Once intravenous insulin is started, the glucose level should be maintained between 140 and 180 mg/dL (7.8 and 10.0 mmol/L). Greater benefit maybe realized at the lower end of this range. Although strong evidence is lacking, somewhat lower glucose targets may be appropriate in selected patients. One small study suggested that medical intensive care unit (MICU) patients treated to targets of 120–140 mg/dL had less negative nitrogen balance than those treated to higher targets (469). However, targets <110 mg/dL (6.1 mmol/L) are not recommended. Use of insulin infusion protocols with demonstrated safety and efficacy, resulting in low rates of hypoglycemia, are highly recommended (468). Non–critically ill patients With no prospective RCT data to inform specific glycemic targets in non–critically ill patients, recommendations are based on clinical experience and judgment (470). For the majority of non–critically ill patients treated with insulin, premeal glucose targets should generally be <140 mg/dL (7.8 mmol/L) with random blood glucose <180 mg/dL (10.0 mmol/L), as long as these targets can be safely achieved. To avoid hypoglycemia, consideration should be given to reassessing the insulin regimen if blood glucose levels fall below 100 mg/dL (5.6 mmol/L). Modification of the regimen is required when blood glucose values are <70 mg/dL (3.9 mmol/L), unless the event is easily explained by other factors (such as a missed meal). There is some evidence that systematic attention to hyperglycemia in the emergency room leads to better glycemic control in the hospital for those subsequently admitted (471). Occasional patients with a prior history of successful tight glycemic control in the outpatient setting who are clinically stable may be maintained with a glucose range below the above cut points. Conversely, higher glucose ranges may be acceptable in terminally ill patients or in patients with severe comorbidities, as well as in those in patient care settings where frequent glucose monitoring or close nursing supervision is not feasible. Clinical judgment, combined with ongoing assessment of the patient’s clinical status, including changes in the trajectory of glucose measures, the severity of illness, nutritional status, or concurrent use of medications that might affect glucose levels (e.g., steroids, octreotide), must be incorporated into the day-to-day decisions regarding insulin dosing (468). 2. Antihyperglycemic agents in hospitalized patients In the hospital setting, insulin therapy is the preferred method of glycemic control in majority of clinical situations (468). In the ICU, intravenous infusion is the preferred route of insulin administration. When the patient is transitioned off intravenous insulin to subcutaneous therapy, precautions should be taken to prevent hyperglycemia escape (472,473). Outside of critical care units, scheduled subcutaneous insulin that delivers basal, nutritional, and correction (supplemental) components is preferred. Typical dosing schemes are based on body weight, with some evidence that patients with renal insufficiency should be treated with lower doses (474). Prolonged therapy with sliding-scale insulin (SSI) as the sole regimen is ineffective in the majority of patients, increases risk of both hypoglycemia and hyperglycemia, and has recently been shown in a randomized trial to be associated with adverse outcomes in general surgery patients with type 2 diabetes (475). SSI is potentially dangerous in type 1 diabetes (468). The reader is referred to several recent publications and reviews that describe currently available insulin preparations and protocols and provide guidance in use of insulin therapy in specific clinical settings including parenteral nutrition (476), enteral tube feedings and with high dose glucocorticoid therapy (468). There are no data on the safety and efficacy of oral agents and injectable noninsulin therapies such as GLP-1 analogs and pramlintide in the hospital. They are generally considered to have a limited role in the management of hyperglycemia in conjunction with acute illness. Continuation of these agents may be appropriate in selected stable patients who are expected to consume meals at regular intervals, and they may be initiated or resumed in anticipation of discharge once the patient is clinically stable. Specific caution is required with metformin, due to the possibility that a contraindication may develop during the hospitalization, such as renal insufficiency, unstable hemodynamic status, or need for an imaging study that requires a radio-contrast dye. 3. Preventing hypoglycemia In the hospital, multiple risk factors for hypoglycemia are present. Patients with or without diabetes may experience hypoglycemia in the hospital in association with altered nutritional state, heart failure, renal or liver disease, malignancy, infection, or sepsis. Additional triggering events leading to iatrogenic hypoglycemia include sudden reduction of corticosteroid dose, altered ability of the patient to report symptoms, reduction of oral intake, emesis, new NPO status, inappropriate timing of short- or rapid-acting insulin in relation to meals, reduction of rate of administration of intravenous dextrose, and unexpected interruption of enteral feedings or parenteral nutrition. Despite the preventable nature of many inpatient episodes of hypoglycemia, institutions are more likely to have nursing protocols for the treatment of hypoglycemia than for its prevention. Tracking such episodes and analyzing their causes are important quality-improvement activities (468). 4. Diabetes care providers in the hospital Inpatient diabetes management may be effectively championed and/or provided by primary care physicians, endocrinologists, intensivists, or hospitalists. Involvement of appropriately trained specialists or specialty teams may reduce length of stay, improve glycemic control, and improve outcomes (468). In the care of diabetes, implementation of standardized order sets for scheduled and correction-dose insulin may reduce reliance on sliding-scale management. As hospitals move to comply with “meaningful use” regulations for electronic health records, as mandated by the Health Information Technology Act, efforts should be made to assure that all components of structured insulin order sets are incorporated into electronic insulin order sets (477,478). A team approach is needed to establish hospital pathways. To achieve glycemic targets associated with improved hospital outcomes, hospitals will need multidisciplinary support to develop insulin management protocols that effectively and safely enable achievement of glycemic targets (479). 5. Self-management in the hospital Self-management of diabetes in the hospital may be appropriate for competent adult patients who have a stable level of consciousness, have reasonably stable daily insulin requirements, successfully conduct self-management of diabetes at home, have physical skills needed to successfully self-administer insulin and perform SMBG, have adequate oral intake, and are proficient in carbohydrate counting, use of multiple daily insulin injections or insulin pump therapy, and sick-day management. The patient and physician, in consultation with nursing staff, must agree that patient self-management is appropriate under the conditions of hospitalization. Patients who use CSII pump therapy in the outpatient setting can be candidates for diabetes self-management in the hospital, provided that they have the mental and physical capacity to do so (468). A hospital policy and procedures delineating inpatient guidelines for CSII therapy are advisable, and availability of hospital personnel with expertise in CSII therapy is essential. It is important that nursing personnel document basal rates and bolus doses taken on a regular basis (at least daily). 6. MNT in the hospital The goals of MNT are to optimize glycemic control, to provide adequate calories to meet metabolic demands, and to create a discharge plan for follow-up care (457,480). The ADA does not endorse any single meal plan or specified percentages of macronutrients, and the term “ADA diet” should no longer be used. Current nutrition recommendations advise individualization based on treatment goals, physiological parameters, and medication usage. Consistent carbohydrate meal plans are preferred by many hospitals because they facilitate matching the prandial insulin dose to the amount of carbohydrate consumed (481). Because of the complexity of nutrition issues in the hospital, a registered dietitian, knowledgeable and skilled in MNT, should serve as an inpatient team member. The dietitian is responsible for integrating information about the patient’s clinical condition, eating, and lifestyle habits and for establishing treatment goals in order to determine a realistic plan for nutrition therapy (482,483). 7. Bedside blood glucose monitoring POC blood glucose monitoring performed at the bedside is used to guide insulin dosing. In the patient who is receiving nutrition, the timing of glucose monitoring should match carbohydrate exposure. In the patient who is not receiving nutrition, glucose monitoring is performed every 4 to 6 h (484,485). More frequent blood glucose testing ranging from every 30 min to every 2 h is required for patients on intravenous insulin infusions. Safety standards should be established for blood glucose monitoring prohibiting sharing of finger-stick lancing devices, lancets, needles, and meters to reduce the risk of transmission of blood borne diseases. Shared lancing devices carry essentially the same risk as is conferred from sharing of syringes and needles (486). Accuracy of blood glucose measurements using POC meters has limitations that must be considered. Although the FDA allows a +/− 20% error for blood glucose meters, questions about the appropriateness of these criteria have been raised (388). Glucose measures differ significantly between plasma and whole blood, terms that are often used interchangeably and can lead to misinterpretation. Most commercially available capillary blood glucose meters introduce a correction factor of ∼1.12 to report a “plasma-adjusted” value (487). Significant discrepancies between capillary, venous, and arterial plasma samples have been observed in patients with low or high hemoglobin concentrations, hypoperfusion, and the presence of interfering substances particularly maltose, as contained in immunoglobulins (488). Analytical variability has been described with several POC meters (489). Increasingly newer generation POC blood glucose meters correct for variation in hematocrit and for interfering substances. Any glucose result that does not correlate with the patient’s status should be confirmed through conventional laboratory sampling of plasma glucose. The FDA has become increasingly concerned about the use of POC blood glucose meters in the hospital and is presently reviewing matters related to their use. 8. Discharge planning and DSME Transition from the acute care setting is a high-risk time for all patients, not just those with diabetes or new hyperglycemia. Although there is an extensive literature concerning safe transition within and from the hospital, little of it is specific to diabetes (490). It is important to remember that diabetes discharge planning is not a separate entity, but is part of an overall discharge plan. As such, discharge planning begins at admission to the hospital and is updated as projected patient needs change. Inpatients may be discharged to varied settings, including home (with or without visiting nurse services), assisted living, rehabilitation, or skilled nursing facilities. The latter two sites are generally staffed by health professionals, so diabetes discharge planning will be limited to communication of medication and diet orders. For the patient who is discharged to assisted living or to home, the optimal program will need to consider the type and severity of diabetes, the effects of the patient’s illness on blood glucose levels, and the capacities and desires of the patient. Smooth transition to outpatient care should be ensured. The Agency for Healthcare Research and Quality (AHRQ) recommends that at a minimum, discharge plans include the following: Medication reconciliation: The patient’s medications must be cross-checked to ensure that no chronic medications were stopped and to ensure the safety of new prescriptions. Whenever possible, prescriptions for new or changed medication should be filled and reviewed with the patient and family at or before discharge. Structured discharge communication: Information on medication changes, pending tests and studies, and follow-up needs must be accurately and promptly communicated to outpatient physicians. Discharge summaries should be transmitted to the primary physician as soon as possible after discharge. Appointment keeping behavior is enhanced when the inpatient team schedules outpatient medical follow-up prior to discharge. Ideally the inpatient care providers or case managers/discharge planners will schedule follow-up visit(s) with the appropriate professionals, including the primary care provider, endocrinologist, and diabetes educator (491). Teaching diabetes self-management to patients in hospitals is a challenging task. Patients are ill, under increased stress related to their hospitalization and diagnosis, and in an environment not conducive to learning. Ideally, people with diabetes should be taught at a time and place conducive to learning: as an outpatient in a recognized program of diabetes education. For the hospitalized patient, diabetes “survival skills” education is generally a feasible approach to provide sufficient information and training to enable safe care at home. Patients hospitalized because of a crisis related to diabetes management or poor care at home need education to prevent subsequent episodes of hospitalization. An assessment of the need for a home health referral or referral to an outpatient diabetes education program should be part of discharge planning for all patients. DSME cannot wait until discharge, especially in those new to insulin therapy or in whom the diabetes regimen has been substantially altered during the hospitalization. It is recommended that the following areas of knowledge be reviewed and addressed prior to hospital discharge: Identification of health care provider who will provide diabetes care after discharge Level of understanding related to the diagnosis of diabetes, SMBG, and explanation of home blood glucose goals Definition, recognition, treatment, and prevention of hyperglycemia and hypoglycemia Information on consistent eating patterns When and how to take blood glucose–lowering medications including insulin administration (if going home on insulin) Sick-day management Proper use and disposal of needles and syringes It is important that patients be provided with appropriate durable medical equipment, medication, supplies, and prescriptions at the time of discharge in order to avoid a potentially dangerous hiatus in care. These supplies/prescriptions should include the following: Insulin (vials or pens) if needed Syringes or pen needles (if needed) Oral medications (if needed) Blood glucose meter and strips Lancets and lancing device Urine ketone strips (type 1) Glucagon emergency kit (insulin-treated) Medical alert application/charm More expanded diabetes education can be arranged in the community. An outpatient follow-up visit with the primary care provider, endocrinologist, or diabetes educator within 1 month of discharge is advised for all patients having hyperglycemia in the hospital. Clear communication with outpatient providers either directly or via hospital discharge summaries facilitates safe transitions to outpatient care. Providing information regarding the cause or the plan for determining the cause of hyperglycemia, related complications and comorbidities, and recommended treatments can assist outpatient providers as they assume ongoing care. B. Diabetes and employment Any person with diabetes, whether insulin treated or noninsulin treated, should be eligible for any employment for which he/she is otherwise qualified. Employment decisions should never be based on generalizations or stereotypes regarding the effects of diabetes. When questions arise about the medical fitness of a person with diabetes for a particular job, a health care professional with expertise in treating diabetes should perform an individualized assessment. See the ADA position statement on diabetes and employment (492). C. Diabetes and driving A large percentage of people with diabetes in the U.S. and elsewhere seek a license to drive, either for personal or employment purposes. There has been considerable debate whether, and the extent to which, diabetes may be a relevant factor in determining the driver ability and eligibility for a license. People with diabetes are subject to a great variety of licensing requirements applied by both state and federal jurisdictions, which may lead to loss of employment or significant restrictions on a person’s license. Presence of a medical condition that can lead to significantly impaired consciousness or cognition may lead to drivers being evaluated for fitness to drive. For diabetes, this typically arises when the person has had a hypoglycemic episode behind the wheel, even if this did not lead to a motor vehicle accident. Epidemiological and simulator data suggest that people with insulin-treated diabetes have a small increase in risk of motor vehicle accidents, primarily due to hypoglycemia and decreased awareness of hypoglycemia. This increase (RR 1.12–1.19) is much smaller than the risks associated with teenage male drivers (RR 42), driving at night (RR 142), driving on rural roads compared with urban roads (RR 9.2), and obstructive sleep apnea (RR 2.4), all of which are accepted for unrestricted licensure. The ADA position statement on diabetes and driving (493) recommends against blanket restrictions based on the diagnosis of diabetes and urges individual assessment by a health care professional knowledgeable in diabetes if restrictions on licensure are being considered. Patients should be evaluated for decreased awareness of hypoglycemia, hypoglycemia episodes while driving, or severe hypoglycemia. Patients with retinopathy or peripheral neuropathy require assessment to determine if those complications interfere with operation of a motor vehicle. Health care professionals should be cognizant of the potential risk of driving with diabetes and counsel their patients about detecting and avoiding hypoglycemia while driving. D. Diabetes management in correctional institutions People with diabetes in correctional facilities should receive care that meets national standards. Because it is estimated that nearly 80,000 inmates have diabetes, correctional institutions should have written policies and procedures for the management of diabetes and for training of medical and correctional staff in diabetes care practices. See the ADA position statement on diabetes management in correctional institutions (494) for further discussion. X. STRATEGIES FOR IMPROVING DIABETES CARE Recommendations Care should be aligned with components of the Chronic Care Model (CCM) to ensure productive interactions between a prepared proactive practice team and an informed activated patient. (A) When feasible, care systems should support team-based care, community involvement, patient registries, and embedded decision support tools to meet patient needs. (B) Treatment decisions should be timely and based on evidence-based guidelines that are tailored to individual patient preferences, prognoses, and comorbidities. (B) A patient-centered communication style should be employed that incorporates patient preferences, assesses literacy and numeracy, and addresses cultural barriers to care. (B) There has been steady improvement in the proportion of diabetic patients achieving recommended levels of A1C, blood pressure, and LDL cholesterol in the last 10 years, both in primary care settings and in endocrinology practices. Mean A1C nationally has declined from 7.82% in 1999–2000 to 7.18% in 2004 based on NHANES data (495). This has been accompanied by improvements in lipids and blood pressure control and led to substantial reductions in end-stage microvascular complications in those with diabetes. Nevertheless in some studies only 57.1% of adults with diagnosed diabetes achieved an A1C of <7%, only 45.5% had a blood pressure <130/80 mmHg, and just 46.5% had a total cholesterol <200 mg/dL, with only 12.2% of people with diabetes achieving all three treatment goals (496). Evidence also suggests that progress in risk factor control may be slowing (497). Certain patient groups, such as patients with complex comorbidities, financial or other social hardships, and/or limited English proficiency, may present particular challenges to goal-based care (498,499). Persistent variation in quality of diabetes care across providers and across practice settings even after adjusting for patient factors indicates that there remains potential for substantial further improvements in diabetes care. Although numerous interventions to improve adherence to the recommended standards have been implemented, a major barrier to optimal care is a delivery system that too often is fragmented, lacks clinical information capabilities, often duplicates services, and is poorly designed for the coordinated delivery of chronic care. The CCM has been shown in numerous studies to be an effective framework for improving the quality of diabetes care (500). The CCM includes six core elements for the provision of optimal care of patients with chronic disease: 1) delivery system design (moving from a reactive to a proactive care delivery system where planned visits are coordinated through a team based approach), 2) self-management support, 3) decision support (basing care on evidence-based, effective care guidelines), 4) clinical information systems (using registries that can provide patient-specific and population-based support to the care team), 5) community resources and policies (identifying or developing resources to support healthy lifestyles), and 6) health systems (to create a quality-oriented culture). Redefinition of the roles of the clinic staff and promoting self-management on the part of the patient are fundamental to the successful implementation of the CCM (501). Collaborative, multidisciplinary teams are best suited to provide such care for people with chronic conditions such as diabetes and to facilitate patients’ performance of appropriate self-management (163,165,220,502). NDEP maintains an online resource (www.betterdiabetescare.nih.gov) to help health care professionals design and implement more effective health care delivery systems for those with diabetes. Three specific objectives, with references to literature that outlines practical strategies to achieve each, are outlined below. Objective 1: Optimize provider and team behavior The care team should prioritize timely and appropriate intensification of lifestyle and/or pharmaceutical therapy of patients who have not achieved beneficial levels of blood pressure, lipid, or glucose control (503). Strategies such as explicit goal setting with patients (504); identifying and addressing language, numeracy, or cultural barriers to care (505–508); integrating evidence-based guidelines and clinical information tools into the process of care (509–511); and incorporating care management teams including nurses, pharmacists, and other providers (512–515) have each been shown to optimize provider and team behavior and thereby catalyze reduction in A1C, blood pressure, and LDL cholesterol. Objective 2: Support patient behavior change Successful diabetes care requires a systematic approach to supporting patients’ behavior change efforts, including a) healthy lifestyle changes (physical activity, healthy eating, nonuse of tobacco, weight management, effective coping), b) disease self-management (medication taking and management; self-monitoring of glucose and blood pressure when clinically appropriate), and c) prevention of diabetes complications (self-monitoring of foot health; active participation in screening for eye, foot, and renal complications; immunizations). High-quality DSME has been shown to improve patient self-management, satisfaction, and glucose control (184,516), as has delivery of ongoing DSMS so that gains achieved during DSME are sustained (134,135,152). National DSME standards call for an integrated approach that includes clinical content and skills, behavioral strategies (goal-setting, problem solving), and addressing emotional concerns in each needed curriculum content area. Objective 3: Change the system of care The most successful practices have an institutional priority for providing high quality of care (517). Changes that have been shown to increase quality of diabetes care include basing care on evidence-based guidelines (518), expanding the role of teams and staff (501,519), redesigning the processes of care (520), implementing electronic health record tools (521,522), activating and educating patients (523,524), and identifying and/or developing and engaging community resources and public policy that support healthy lifestyles (525). Recent initiatives such as the Patient-Centered Medical Home show promise to improve outcomes through coordinated primary care and offer new opportunities for team-based chronic disease care (526). Alterations in reimbursement that reward the provision of appropriate and high-quality care rather than visit-based billing (527) and that can accommodate the need to personalize care goals may provide additional incentives to improve diabetes care (528). It is clear that optimal diabetes management requires an organized, systematic approach and involvement of a coordinated team of dedicated health care professionals working in an environment where patient-centered high-quality care is a priority.
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### Author and article information

###### Journal
International Journal of Gynecology & Obstetrics
International Journal of Gynecology & Obstetrics
Elsevier BV
00207292
October 2015
October 2015
February 26 2015
: 131
: S16-S18
###### Article
10.1016/j.ijgo.2015.02.010
© 2015