11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Variation in health system performance for managing diabetes among states in India: a cross-sectional study of individuals aged 15 to 49 years

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Understanding where adults with diabetes in India are lost in the diabetes care cascade is essential for the design of targeted health interventions and to monitor progress in health system performance for managing diabetes over time. This study aimed to determine (i) the proportion of adults with diabetes in India who have reached each step of the care cascade and (ii) the variation of these cascade indicators among states and socio-demographic groups.

          Methods

          We used data from a population-based household survey carried out in 2015 and 2016 among women and men aged 15–49 years in all states of India. Diabetes was defined as a random blood glucose (RBG) ≥ 200 mg/dL or reporting to have diabetes. The care cascade—constructed among those with diabetes—consisted of the proportion who (i) reported having diabetes (“aware”), (ii) had sought treatment (“treated”), and (iii) had sought treatment and had a RBG < 200 mg/dL (“controlled”). The care cascade was disaggregated by state, rural-urban location, age, sex, household wealth quintile, education, and marital status.

          Results

          This analysis included 729,829 participants. Among those with diabetes (19,453 participants), 52.5% (95% CI, 50.6–54.4%) were “aware”, 40.5% (95% CI, 38.6–42.3%) “treated”, and 24.8% (95% CI, 23.1–26.4%) “controlled”. Living in a rural area, male sex, less household wealth, and lower education were associated with worse care cascade indicators. Adults with untreated diabetes constituted the highest percentage of the adult population (irrespective of diabetes status) aged 15 to 49 years in Goa (4.2%; 95% CI, 3.2–5.2%) and Tamil Nadu (3.8%; 95% CI, 3.4–4.1%). The highest absolute number of adults with untreated diabetes lived in Tamil Nadu (1,670,035; 95% CI, 1,519,130–1,812,278) and Uttar Pradesh (1,506,638; 95% CI, 1,419,466–1,589,832).

          Conclusions

          There are large losses to diabetes care at each step of the care cascade in India, with the greatest loss occurring at the awareness stage. While health system performance for managing diabetes varies greatly among India’s states, improvements are particularly needed for rural areas, those with less household wealth and education, and men. Although such improvements will likely have the greatest benefits for population health in Goa and Tamil Nadu, large states with a low diabetes prevalence but a high absolute number of adults with untreated diabetes, such as Uttar Pradesh, should not be neglected.

          Electronic supplementary material

          The online version of this article (10.1186/s12916-019-1325-6) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

          Summary Background 18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016. Methods Using all available data sources, the India State-level Disease Burden Initiative estimated burden (metrics were deaths, disability-adjusted life-years [DALYs], prevalence, incidence, and life expectancy) from 333 disease conditions and injuries and 84 risk factors for each state of India from 1990 to 2016 as part of GBD 2016. We divided the states of India into four epidemiological transition level (ETL) groups on the basis of the ratio of DALYs from communicable, maternal, neonatal, and nutritional diseases (CMNNDs) to those from non-communicable diseases (NCDs) and injuries combined in 2016. We assessed variations in the burden of diseases and risk factors between ETL state groups and between states to inform a more specific health-system response in the states and for India as a whole. Findings DALYs due to NCDs and injuries exceeded those due to CMNNDs in 2003 for India, but this transition had a range of 24 years for the four ETL state groups. The age-standardised DALY rate dropped by 36·2% in India from 1990 to 2016. The numbers of DALYs and DALY rates dropped substantially for most CMNNDs between 1990 and 2016 across all ETL groups, but rates of reduction for CMNNDs were slowest in the low ETL state group. By contrast, numbers of DALYs increased substantially for NCDs in all ETL state groups, and increased significantly for injuries in all ETL state groups except the highest. The all-age prevalence of most leading NCDs increased substantially in India from 1990 to 2016, and a modest decrease was recorded in the age-standardised NCD DALY rates. The major risk factors for NCDs, including high systolic blood pressure, high fasting plasma glucose, high total cholesterol, and high body-mass index, increased from 1990 to 2016, with generally higher levels in higher ETL states; ambient air pollution also increased and was highest in the low ETL group. The incidence rate of the leading causes of injuries also increased from 1990 to 2016. The five leading individual causes of DALYs in India in 2016 were ischaemic heart disease, chronic obstructive pulmonary disease, diarrhoeal diseases, lower respiratory infections, and cerebrovascular disease; and the five leading risk factors for DALYs in 2016 were child and maternal malnutrition, air pollution, dietary risks, high systolic blood pressure, and high fasting plasma glucose. Behind these broad trends many variations existed between the ETL state groups and between states within the ETL groups. Of the ten leading causes of disease burden in India in 2016, five causes had at least a five-times difference between the highest and lowest state-specific DALY rates for individual causes. Interpretation Per capita disease burden measured as DALY rate has dropped by about a third in India over the past 26 years. However, the magnitude and causes of disease burden and the risk factors vary greatly between the states. The change to dominance of NCDs and injuries over CMNNDs occurred about a quarter century apart in the four ETL state groups. Nevertheless, the burden of some of the leading CMNNDs continues to be very high, especially in the lowest ETL states. This comprehensive mapping of inequalities in disease burden and its causes across the states of India can be a crucial input for more specific health planning for each state as is envisioned by the Government of India's premier think tank, the National Institution for Transforming India, and the National Health Policy 2017. Funding Bill & Melinda Gates Foundation; Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India; and World Bank
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Household catastrophic health expenditure: a multicountry analysis.

            Health policy makers have long been concerned with protecting people from the possibility that ill health will lead to catastrophic financial payments and subsequent impoverishment. Yet catastrophic expenditure is not rare. We investigated the extent of catastrophic health expenditure as a first step to developing appropriate policy responses. We used a cross-country analysis design. Data from household surveys in 59 countries were used to explore, by regression analysis, variables associated with catastrophic health expenditure. We defined expenditure as being catastrophic if a household's financial contributions to the health system exceed 40% of income remaining after subsistence needs have been met. The proportion of households facing catastrophic payments from out-of-pocket health expenses varied widely between countries. Catastrophic spending rates were highest in some countries in transition, and in certain Latin American countries. Three key preconditions for catastrophic payments were identified: the availability of health services requiring payment, low capacity to pay, and the lack of prepayment or health insurance. People, particularly in poor households, can be protected from catastrophic health expenditures by reducing a health system's reliance on out-of-pocket payments and providing more financial risk protection. Increase in the availability of health services is critical to improving health in poor countries, but this approach could raise the proportion of households facing catastrophic expenditure; risk protection policies would be especially important in this situation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB population-based cross-sectional study

              Previous studies have not adequately captured the heterogeneous nature of the diabetes epidemic in India. The aim of the ongoing national Indian Council of Medical Research-INdia DIABetes study is to estimate the national prevalence of diabetes and prediabetes in India by estimating the prevalence by state.
                Bookmark

                Author and article information

                Contributors
                +49 15735928966 , jonas.prenissl@gmail.com
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                13 May 2019
                13 May 2019
                2019
                : 17
                : 92
                Affiliations
                [1 ]ISNI 0000 0001 2190 4373, GRID grid.7700.0, Heidelberg Institute of Global Health, , Heidelberg University, ; Im Neuenheimer Feld 130/3, 69120 Heidelberg, Germany
                [2 ]ISNI 0000 0001 2190 4373, GRID grid.7700.0, Medical Faculty Mannheim, , Heidelberg University, ; Mannheim, Germany
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Global Health and Population, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [4 ]ISNI 0000 0004 1761 0198, GRID grid.415361.4, Public Health Foundation of India, ; New Delhi, Delhi NCR India
                [5 ]ISNI 0000 0004 1794 3718, GRID grid.429336.9, Madras Diabetes Research Foundation & Dr. Mohan’s Diabetes Specialities Centre, ; Chennai, Tamil Nadu India
                [6 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Division of Infectious Diseases, , Massachusetts General Hospital, Harvard Medical School, ; Boston, MA USA
                [7 ]ISNI 0000 0004 1937 1135, GRID grid.11951.3d, MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Education Campus, , University of Witwatersrand, ; Johannesburg, Gauteng South Africa
                [8 ]ISNI 0000 0004 1936 7486, GRID grid.6572.6, Institute of Applied Health Research, , University of Birmingham, ; Birmingham, UK
                [9 ]GRID grid.501262.2, Indian Institute of Public Health, ; Gandhinagar, Gujarat India
                [10 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, Harvard University, ; Boston, MA USA
                [11 ]GRID grid.488675.0, Africa Health Research Institute, ; Mtubatuba, KwaZulu-Natal South Africa
                [12 ]ISNI 0000 0001 2364 4210, GRID grid.7450.6, Department of Economics and Centre for Modern Indian Studies, , University of Goettingen, ; Göttingen, Germany
                Author information
                http://orcid.org/0000-0003-4906-5043
                Article
                1325
                10.1186/s12916-019-1325-6
                6515628
                31084606
                c97a6675-6200-4859-857d-f27f6193cda6
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 November 2018
                : 15 April 2019
                Funding
                Funded by: Harvard McLennan Fund
                Award ID: None
                Award ID: None
                Award ID: None
                Award ID: None
                Award Recipient :
                Funded by: Department of Science and Technology, Government of India, New Delhi, INSPIRE Faculty program
                Award ID: None
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Medicine
                diabetes,india,care cascade,health system performance
                Medicine
                diabetes, india, care cascade, health system performance

                Comments

                Comment on this article