Ethnic Differences in Participation in Medical Check-ups Among the Elderly: Evidence from Malaysia

BACKGROUND: There were ethnic differences in the prevalence of non-communicable diseases among the elderly in Malaysia.

OBJECTIVE: To examine ethnic differences in participation in medical check-ups among the elderly.

DESIGN: A nationally representative data set was employed. Multiple logistic regressions were utilised to examine the relationship between ethnicity and the likelihood of undergoing medical check-ups. The regressions were stratified by age, income, marital status, gender, household location, insurance access and health status. These variables were also controlled for in the regressions (including stratified regressions).

PARTICIPANTS: The respondents were required to be residents of Malaysia and not be institutionalised. Overall, 30,806 individuals were selected to be interviewed, but only 28,650 were actually interviewed, equivalent to a 93% response rate. Of those, only 2248 were used in the analyses, because 26,402 were others or below aged 60.

MAIN MEASURES: The dependent variable was participation in a medical check-up. The main independent variables were the three major ethnic groups in Malaysia (Malay, Chinese, Indian).

KEY RESULTS: Among the elderly aged 70-79 years, Chinese (aOR 1.89; 95% CI 1.28, 2.81) and Indians (aOR 2.39; 95% CI 1.20, 4.74) were more likely to undergo medical check-ups than Malays. Among the elderly with monthly incomes of ≤ RM999, Chinese (aOR 1.44; 95% CI 1.12, 1.85) and Indians (aOR 1.50; 95% CI 0.99, 2.28) were more likely to undergo medical check-ups than Malays. Indian males were more likely to undergo medical check-ups than Malay males (aOR 2.32; 95% CI 1.15, 4.67). Chinese with hypercholesterolaemia (aOR 1.45; 95% CI 1.07, 1.98) and hypertension (aOR 1.32; 95% CI 1.02, 1.72) were more likely to undergo medical check-ups than Malays.

CONCLUSIONS: There were ethnic differences in participation in medical check-ups among the elderly. These ethnic differences varied across age, income, marital status, gender, household location, insurance access and health status.