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

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.

Figure 1

Wealth-related relative inequality in noncommunicable disease risk factors among adults aged 18 or higher in 48 low- and middle-income countries, World Health Survey 2002-04. The relative index of inequality shows wealth-related inequality in prevalence of current daily smoking, low fruit and vegetable consumption, physical inactivity, and heavy episodic alcohol drinking, among men and women aged 18 or higher, living in 48 low- and middle-income countries that participated in the 2002–04 World Health Survey. Individuals were cumulatively ranked by descending wealth quintiles, and prevalence ratios compared risk factor prevalence in the poorest to risk factor prevalence in the richest. Brackets indicate 95% confidence intervals. Model 1 data are adjusted for country of residence and age; Model 2 data are adjusted for country of residence, age, marital status, urban/rural area and education.

Figure 2

Education-related relative inequality in non communicable disease risk factors among adults aged 18 or higher in 48 low- and middle-income countries, World Health Survey 2002-04. The relative index of inequality shows education-related inequality in prevalence of current daily smoking, low fruit and vegetable consumption, physical inactivity, and heavy episodic alcohol drinking, among men and women aged 18 or higher, living in 48 low- and middle-income countries that participated in the 2002–04 World Health Survey. Individuals were cumulatively ranked by descending education level, and prevalence ratios compared risk factor prevalence in the least educated group to risk factor prevalence in the most educated group. Brackets indicate 95% confidence intervals. Model 1 data are adjusted for country of residence and age; Model 2 data are adjusted for country of residence, age, marital status, urban/rural area and wealth.