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      Socioeconomic inequalities in risk factors for non communicable diseases in low-income and middle-income countries: results from the World Health Survey

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          Abstract

          Background

          Monitoring inequalities in non communicable disease risk factor prevalence can help to inform and target effective interventions. The prevalence of current daily smoking, low fruit and vegetable consumption, physical inactivity, and heavy episodic alcohol drinking were quantified and compared across wealth and education levels in low- and middle-income country groups.

          Methods

          This study included self-reported data from 232,056 adult participants in 48 countries, derived from the 2002–2004 World Health Survey. Data were stratified by sex and low- or middle-income country status. The main outcome measurements were risk factor prevalence rates reported by wealth quintile and five levels of educational attainment. Socioeconomic inequalities were measured using the slope index of inequality, reflecting differences in prevalence rates, and the relative index of inequality, reflecting the prevalence ratio between the two extremes of wealth or education accounting for the entire distribution. Data were adjusted for confounding factors: sex, age, marital status, area of residence, and country of residence.

          Results

          Smoking and low fruit and vegetable consumption were significantly higher among lower socioeconomic groups. The highest wealth-related absolute inequality was seen in smoking among men of low- income country group (slope index of inequality 23.0 percentage points; 95% confidence interval 19.6, 26.4). The slope index of inequality for low fruit and vegetable consumption across the entire distribution of education was around 8 percentage points in both sexes and both country income groups. Physical inactivity was less prevalent in populations of low socioeconomic status, especially in low-income countries (relative index of inequality: (men) 0.46, 95% confidence interval 0.33, 0.64; (women) 0.52, 95% confidence interval 0.42, 0.65). Mixed patterns were found for heavy drinking.

          Conclusions

          Disaggregated analysis of the prevalence of non-communicable disease risk factors demonstrated different patterns and varying degrees of socioeconomic inequalities across low- and middle-income settings. Interventions should aim to reach and achieve sustained benefits for high-risk populations.

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          Most cited references27

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          Measuring the magnitude of socio-economic inequalities in health: an overview of available measures illustrated with two examples from Europe.

          In this paper we review the available summary measures for the magnitude of socio-economic inequalities in health. Measures which have been used differ in a number of important respects, including (1) the measurement of "relative" or "absolute" differences; (2) the measurement of an "effect" of lower socio-economic status, or of the "total impact" of socio-economic inequalities in health upon the health status of the population; (3) simple versus sophisticated measurement techniques. Based on this analysis of summary measures which have previously been applied, eight different classes of summary measures can be distinguished. Because measures of "total impact" can be further subdivided on the basis of their underlying assumptions, we finally arrive at 12 types of summary measure. Each of these has its merits, and choice of a particular type of summary measure will depend partly on technical considerations, partly on one's perspective on socio-economic inequalities in health. In practice, it will often be useful to compare the results of several summary measures. These principles are illustrated with two examples: one on trends in the magnitude of inequalities in mortality by occupational class in Finland, and one on trends in the magnitude of inequalities in self-reported morbidity by level of education in the Netherlands.
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            Non-communicable diseases in low- and middle-income countries: context, determinants and health policy.

            The rise of non-communicable diseases and their impact in low- and middle-income countries has gained increased attention in recent years. However, the explanation for this rise is mostly an extrapolation from the history of high-income countries whose experience differed from the development processes affecting today's low- and middle-income countries. This review appraises these differences in context to gain a better understanding of the epidemic of non-communicable diseases in low- and middle-income countries. Theories of developmental and degenerative determinants of non-communicable diseases are discussed to provide strong evidence for a causally informed approach to prevention. Health policies for non-communicable diseases are considered in terms of interventions to reduce population risk and individual susceptibility and the research needs for low- and middle-income countries are discussed. Finally, the need for health system reform to strengthen primary care is highlighted as a major policy to reduce the toll of this rising epidemic.
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              Weight of nations: a socioeconomic analysis of women in low- to middle-income countries.

              The increasing trend in body mass index (BMI) and overweight in rapidly developing economies is well recognized. We assessed the association between socioeconomic status and BMI and overweight in low- to middle-income countries. We conducted a cross-sectional analysis of nationally representative samples of 538,140 women aged 15-49 y drawn from 54 Demographic and Health Surveys conducted between 1994 and 2008. BMI, calculated as weight in kilograms divided by height squared in meters, was specified as the outcome, and a BMI (in kg/m(2)) of ≥25 was additionally specified to model the likelihood of being overweight. Household wealth and education were included as markers of individual socioeconomic status, and per capita Gross Domestic Product (pcGDP) was included as a marker of country-level economic development. Globally, a one-quartile increase in wealth was associated with a 0.54 increase in BMI (95% CI: 0.50, 0.64) and a 33% increase in overweight (95% CI: 26%, 41%) in adjusted models. Although the strength of this association varied across countries, the association between wealth and BMI and overweight was positive in 96% (52 of 54) of the countries. Similar patterns were observed in urban and rural areas, although SES gradients tended to be greater in urban areas. There was a positive association between pcGDP and BMI or overweight, with only weak evidence of an interaction between pcGDP and wealth. Higher BMI and overweight remain concentrated in higher socioeconomic groups, even though increasing BMI and overweight prevalence are important global public concerns.
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                Author and article information

                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central
                1471-2458
                2012
                28 October 2012
                : 12
                : 912
                Affiliations
                [1 ]Department of Health Statistics and Information Systems, World Health Organization, 20, Avenue Appia, Geneva, CH-1211, Switzerland
                [2 ]Department of Public Health, AMC, University of Amsterdam, Amsterdam, Netherlands
                [3 ]Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada
                [4 ]Department of Chronic Diseases and Health Promotion, World Health Organization, Geneva, Switzerland
                [5 ]Department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland
                [6 ]Tobacco Free Initiative, World Health Organization, Geneva, Switzerland
                Article
                1471-2458-12-912
                10.1186/1471-2458-12-912
                3507902
                23102008
                889c2e61-7dcb-49ca-b7ed-b06e69d5009d
                Copyright ©2012 Hosseinpoor et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 March 2012
                : 22 October 2012
                Categories
                Research Article

                Public health
                risk factors,socioeconomic factors,chronic disease,developing countries
                Public health
                risk factors, socioeconomic factors, chronic disease, developing countries

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