BackgroundChronic diseases contribute a large share of disease burden in low- and middle-income countries (LMICs). Chronic diseases have a tendency to occur simultaneously and where there are two or more such conditions, this is termed as ‘multimorbidity’. Multimorbidity is associated with adverse health outcomes, but limited research has been undertaken in LMICs. Therefore, this study examines the prevalence and correlates of multimorbidity as well as the associations between multimorbidity and self-rated health, activities of daily living (ADLs), quality of life, and depression across six LMICs.MethodsData was obtained from the WHO’s Study on global AGEing and adult health (SAGE) Wave-1 (2007/10). This was a cross-sectional population based survey performed in LMICs, namely China, Ghana, India, Mexico, Russia, and South Africa, including 42,236 adults aged 18 years and older. Multimorbidity was measured as the simultaneous presence of two or more of eight chronic conditions including angina pectoris, arthritis, asthma, chronic lung disease, diabetes mellitus, hypertension, stroke, and vision impairment. Associations with four health outcomes were examined, namely ADL limitation, self-rated health, depression, and a quality of life index. Random-intercept multilevel regression models were used on pooled data from the six countries.ResultsThe prevalence of morbidity and multimorbidity was 54.2 % and 21.9 %, respectively, in the pooled sample of six countries. Russia had the highest prevalence of multimorbidity (34.7 %) whereas China had the lowest (20.3 %). The likelihood of multimorbidity was higher in older age groups and was lower in those with higher socioeconomic status. In the pooled sample, the prevalence of 1+ ADL limitation was 14 %, depression 5.7 %, self-rated poor health 11.6 %, and mean quality of life score was 54.4. Substantial cross-country variations were seen in the four health outcome measures. The prevalence of 1+ ADL limitation, poor self-rated health, and depression increased whereas quality of life declined markedly with an increase in number of diseases.ConclusionsFindings highlight the challenge of multimorbidity in LMICs, particularly among the lower socioeconomic groups, and the pressing need for reorientation of health care resources considering the distribution of multimorbidity and its adverse effect on health outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0402-8) contains supplementary material, which is available to authorized users.

This article describes the prevalence of multi-morbidity and its association with self-rated and functional health using Longitudinal Aging Study in India (LASI), Pilot survey, 2010 data, on 1,683 older adults aged 45+. The prevalence of multimorbidity is assessed as count of self-reported chronic diseases for an older adult. Limitations in activities of daily living (ADL) are used as a measure of functional health. Zero-inflated Poisson regression is estimated to examine the covariates of multi-morbidity. Moreover, logit models are used to assess the association of multi-morbidity with functional health and self-rated health. Results depict a large prevalence of multi-morbidity, limitations in ADL and poor self-rated health with pronounced state variations. Prevalence of multimorbidity was higher at higher level of education, wealth, and caste. However, educational status of older adults is seen to be negatively associated with prevalence of ADL limitations and poor self-rated health. Household wealth and caste showed no clear association with limitations in ADL and poor self-rated health. Multimorbid older adults were found with substantially high risk of ADL limitations and poor self-rated health.

As India rapidly urbanizes, within urban areas socioeconomic disparities are rising and health inequality among urban children is an emerging challenge. This paper assesses the relative contribution of socioeconomic factors to child health inequalities between the less developed Empowered Action Group (EAG) states and more developed South Indian states in urban India using data from the 2005-06 National Family Health Survey. Focusing on urban health from varying regional and developmental contexts, socioeconomic inequalities in child health are examined first using Concentration Indices (CIs) and then the contributions of socioeconomic factors to the CIs of health variables are derived. The results reveal, in order of importance, pronounced contributions of household economic status, parent's illiteracy and caste to urban child health inequalities in the South Indian states. In contrast, parent's illiteracy, poor economic status, being Muslim and child birth order 3 or more are major contributors to health inequalities among urban children in the EAG states. The results suggest the need to adopt different health policy interventions in accordance with the pattern of varying contributions of socioeconomic factors to child health inequalities between the more developed South Indian states and less developed EAG states.

This study quantified and decomposed health inequalities among the older population in India and analyzes how health status varies for populations between 60 to 69 years and 70 years and above. Data from the 60th round of the National Sample Survey (NSS) was used for the analyses. Socioeconomic inequalities in health status were measured by using Concentration Index (CI) and further decomposed to find critical determinants and their relative contributions to total health inequality. Overall, CI estimates were negative for the older population as a whole (CI = -0.1156), as well as for two disaggregated groups, 60 to 69 years (CI = -0.0943) and 70 years and above (CI = -0.08198). This suggests that poor health status is more concentrated among the socioeconomically disadvantaged older population. Decomposition analyses revealed that poor economic status (54 %) is the dominant contributor to total health inequalities in the older population, followed by illiteracy (24 %) and rural place of residence (20 %). Other indicators, such as religion, gender and marital status were positive, while Caste was negatively associated with health inequality in the older populations. Finally, a comparative assessment of decomposition results suggest that critical contributors for health inequality vary for the older population of 60 to 69 years and 70 years and above. These findings provide important insights on health inequalities among the older population in India. Implications are advanced.

Child undernutrition remains a major child health and developmental issue in low- and middle-income countries. The concentration (clustering) of underweight children among siblings at the family level is known to exist in India. This study examined the extent and covariates of clustering of underweight children at the sibling and family level in Uttar Pradesh, the largest state of northern India. Clustering of underweight (low weight-for-age) children was assessed using data on 7533 under-five children from the National Family Health Survey (NFHS) conducted in 2005-06, analysed using binary logistic and binomial regression models. Related bio-demographic, socioeconomic and health care variables were used as covariates in the models. The odds of being underweight for the index child were about two times higher (OR=2.34, p<0.001) if any of the siblings within the household was malnourished or underweight. A longer birth interval increased the odds of a child being underweight. The odds of underweight were significantly lower (OR=0.69, p<0.001) for children born to normal-weight mothers compared with those born to underweight mothers. Similarly, the odds of underweight were significantly lower (OR=0.49, p=0.01) for children born to educated mothers (high school and above) compared with those born to illiterate mothers. The results of the binomial regression model suggested that the deviations between observed and expected number of children were positive (3.09, 3.78 and 2.71) for 1, 2 and 2+ underweight children within the households of underweight women, indicating the concentration of underweight children among underweight/malnourished mothers. Underweight children were found to be clustered among underweight mothers with multiple underweight siblings. The findings suggest that policy interventions need to focus on underweight mothers with multiple underweight children.