Measurement and decomposition of socioeconomic inequality in single and multimorbidity in older adults in China and Ghana: results from the WHO study on global AGEing and adult health (SAGE)

Abstract

Background

Globally people are living longer and enduring non-communicable diseases (NCDs) many of which co-occur as multimorbidity. Demographic and socioeconomic factors are determinants of inequalities and inequities in health. There is a need for country-specific evidence of NCD inequalities in developing countries where populations are ageing rapidly amid economic and social change. The study measures and decomposes socioeconomic inequality in single and multiple NCD morbidity in adults aged 50 and over in China and Ghana.

Methods

The data source is the World Health Organization Study on Global AGEing and Adult Health (SAGE) Wave 1 (2007–2010). Nationally representative cross-sectional data collected from adults in China ( n = 11,814) and Ghana ( n = 4,050) are analysed. Country populations are ranked by a socioeconomic index based on ownership of household assets. The study uses a decomposed concentration index (CI) of single and multiple NCD morbidity (multimorbidity) covering arthritis, diabetes, angina, stroke, asthma, depression, chronic lung disease and hypertension. The CI quantifies the extent of overall inequality on each morbidity measure. The decomposition utilises a regression-based approach to examine individual contributions of demographic and socioeconomic factors, or determinants, to the overall inequality.

Results

In China, the prevalence of single and multiple NCD morbidity was 64.7% and 53.4%, compared with 65.9% and 55.5% respectively in Ghana. Inequalities were significant and more highly concentrated among the poor in China (single morbidity CI = −0.0365: 95% CI = −0.0689,–0.0040; multimorbidity CI = −0.0801: 95% CI = −0.1233,-0.0368;). In Ghana inequalities were significant and more highly concentrated among the rich (single morbidity CI = 0.1182; 95% CI = 0.0697, 0.1668; multimorbidity CI = 0.1453: 95% CI = 0.0794, 0.2083). In China, rural residence contributed most to inequality in single morbidity (36.4%) and the wealth quintiles contributed most to inequality in multimorbidity (39.0%). In Ghana, the wealth quintiles contributed 24.5% to inequality in single morbidity and body mass index contributed 16.2% to the inequality in multimorbidity.

Conclusions

The country comparison reflects different stages of economic development and social change in China and Ghana. More studies of this type are needed to inform policy-makers about the patterning of socioeconomic inequalities in health, particularly in developing countries undergoing rapid epidemiological and demographic transitions.