Explorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka.
A sample of 1300 participants, (aged 35–64 years) randomly selected using a stratified multi-stage cluster sampling method, were studied employing a cross sectional descriptive design. The socioeconomic indicators (SEIs) of the individual were education level and occupational category, and at the household level, the household income, social status level and area deprivation level. DM was diagnosed if the fasting plasma glucose was ≥126 and a body mass index (BMI) of > 27.5 kg/m2 was considered high. Asian cut-off values were used for high waist circumference (WC). Validated tools were used to assess the diet and level of physical activity. The slope index of inequality (SII), relative index of inequality (RII) and concentration index (CI) were used to assess inequalities.
The prevalence of DM and its risk factors (at individual or household level) showed no consistent relationship with the three measures of inequality (SII, RII and CI) of the different indices of socio economic status (education, occupation, household income, social status index or area unsatisfactory basic needs index).
The prevalence of diabetes showed a more consistent pro-rich distribution in females compared to males. Of the risk factors in males and females, the most consistent and significant pro-rich relationship was for high BMI and WC. In males, the significant positive relationship with high BMI for SII ranged from 0.18 to 0.35, and RII from 1.56 to 2.25. For high WC, the values were: SII from 0.13 to 0.27 and RII from 1.9 to 3.97. In females the significant positive relationship with high BMI in SII ranged from 0.13 to 0.29, and RII from 2.3 to 4.98. For high WC the values were: SII from 028 to 0.4 and RII 1.99 to 2.39.
Of the other risk factors, inadequate fruit intake showed a consistent significant pro-poor distribution only in males using SII (− 0.25 to − 0.36) and in both sexes using CI. Smoking also showed a pro-poor distribution in males especially using individual measures of socio-economic status (i.e. education and occupation).
The results show a variable relationship between socioeconomic status and prevalence of diabetes and its risk factors. The inequalities in the prevalence of diabetes and risk factors vary depending on gender and the measures used. The study suggests that measures to prevent diabetes should focus on targeting specific factors based on sex and socioeconomic status. The priority target areas for interventions should include prevention of obesity (BMI and central obesity) specifically in more affluent females. Males who have a low level of education and in non-skilled occupations should be especially targeted to reduce smoking and increase fruit intake.