Risk factor contributions to socioeconomic inequality in cardiovascular risk in the Philippines: a cross-sectional study of nationally representative survey data

Abstract

Background: Primary prevention of cardiovascular diseases (CVD) increasingly relies on monitoring global CVD risk scores. Lack of evidence on socioeconomic inequality in these scores and the contributions that specific risk factors make to this inequality impedes effective targeting of CVD prevention. We aimed to address this evidence gap by measuring and decomposing socioeconomic inequality in CVD risk in the Philippines.

Methods: We used data on 8462 individuals aged 40-74 years from the Philippines National Nutrition Survey and the laboratory-based Globorisk equation to predict 10-year risk of a CVD event from sex, age, systolic blood pressure, total cholesterol, high blood glucose, and smoking. We used a household wealth index to proxy socioeconomic status and measured socioeconomic inequality with a concentration index that we decomposed into contributions of the risk factors used to predict CVD risk. We measured socioeconomic inequalities in these risk factors and decomposed them into contributions of more distal risk factors: body mass index, fat share of energy intake, low physical activity, and drinking alcohol. We stratified by sex.

Results: Wealthier individuals, particularly males, had greater exposure to all risk factors, with the exception of smoking, and had higher CVD risks. Total cholesterol and high blood glucose accounted for 58% and 34%, respectively, of the socioeconomic inequality in CVD risk among males. For females, the respective estimates were 63% and 69%. Systolic blood pressure accounted for 26% of the higher CVD risk of wealthier males but did not contribute to inequality among females. If smoking prevalence had not been higher among poorer individuals, then the inequality in CVD risk would have been 35% higher for males and 75% higher for females. Among distal risk factors, body mass index and fat intake contributed most to inequalities in total cholesterol, high blood sugar, and, for males, systolic blood pressure.

Conclusions: Wealthier Filipinos have higher predicted CVD risks and greater exposure to all risk factors, except smoking. There is need for a nuanced approach to CVD prevention that targets anti-smoking programmes on the poorer population while targeting diet and exercise interventions on the wealthier.

Keywords: Blood glucose; Blood pressure; Cardiovascular disease; Cholesterol; Decomposition; Inequality; Philippines; Risk factors; Smoking; Socioeconomic.

Fig. 1

Mean CVD risk and risk factors by wealth index quintile group and sex. Individuals aged 40-74 years. Females: n=4516. Males: n=3946. Whiskers show 95% confidence intervals. P values from tests of equal means across groups

Fig. 2

Concentration index measure of socioeconomic inequality in CVD risk and risk factors by sex. Females: n = 4516. Males: n = 3946. Whiskers show 95% confidence intervals

Fig. 3

Decomposition of concentration index measure of socioeconomic inequality in CVD risk into contributions of risk factors. Top panel shows absolute contributions. Bottom panel shows these contributions as percentages of the concentration index. Females: n = 4516. Males: n = 3946. Whiskers show 95% confidence intervals obtain from a bootstrap with 1000 replications

Fig. 4

Average marginal effect of an age- and sex-specific standard deviation increase in each risk factor on CVD risk averaged over the sample and within each wealth index quintile group by sex. Females: n = 4516. Males: n = 3946. Whiskers show 95% confidence intervals obtain from a bootstrap with 1000 replications. Q1 = poorest 20%,…, Q5 = richest 20%. All = Average over all individuals

Fig. 5

Concentration index measure of socioeconomic inequality in distal risk factors and age by sex. Females: n = 1801. Males: n = 1099. Whiskers show 95% confidence intervals

Fig. 6

Decomposition of concentration index measure of socioeconomic inequality in predicted SBP, TC, and HBG into relative (%) contributions of distal risk factors. Females: n = 1801. Males: n = 1099