UNMET NEEDS FOR CARDIOVASCULAR CARE IN INDONESIA Asri Maharani, MD, PhD 1 1 Cathie Marsh Institute for Social Change, University of Manchester, UK.

Cardiovascular diseases (CVDs) remain the biggest cause of deaths worldwide 2 Source: Institute for Health Metrics and Evaluation (2016) CVDs caused 31% of total deaths globally (2013) The burden of CVDs has begun to shift from developed to developing countries

How do developed countries reduce the burden of CVDs? 3 Source: Report on The Epidemiology of Cardiovascular Disease by the University of Ottawa, Canada. The importance of CVDs prevention.

Cardiovascular diseases (CVDs) in Indonesia 4 Source: Institute for Health Metrics and Evaluation (2016) CVDs caused 34% of total deaths in Indonesia (2013) Deaths due to CVDs increased 1.92% annually from 1990 to 2013

The unequal distribution of health facilities in Indonesia 5 Source: Calculated based on Podes 2011 Spatial distribution of hospitals in Indonesia 2011 The bulk of hospitals serve the population in Java, leaving those in other islands with few facilities.

6 Source: Calculated based on Podes 2011 The unequal distribution of health professionals in Indonesia Spatial distribution of physicians in Indonesia 2011 Physicians tend to work disproportionately in Java, the country’s most developed island.

Measuring CVD risk using Reynold Risk Score 7 For male: – B = x natural logarithm (age) x natural logarithm (systolic blood pressure) x natural logarithm (high-sensitivity C-reactive protein) x natural logarithm (total cholesterol) –0.772 x natural logarithm (high- density lipoprotein cholesterol) (if current smoker) (if family history premature myocardial infarction) For female: – B = (age) x natural logarithm (systolic blood pressure) natural logarithm (high-sensitivity C-reactive protein) x natural logarithm (total cholesterol) – x natural logarithm (high-density lipoprotein cholesterol) x haemoglobin A1c (if diabetic) (if current smoker) (if family history premature myocardial infarction).

Do people at risk get the CVD care they need? 8 Note: CVD risk is calculated using Reynold Risk score. Source: Calculated based on IFLS4 Two out of three people in Indonesia have moderate and high CVD risk. However, only one in three people with moderate and high CVD risk got the healthcare.

9 Who are they? Percentages of men and women at risk Men are at higher risk than women. Percentages of unmet needs among men and women However, the proportion of men with unmet needs is higher than that of women. Source: Calculated based on IFLS4

10 Where do they live? Percentages of people at risk in rural and urban areas People aged years old living in urban areas have higher CVD risk than those in rural areas. Percentages of unmet needs in rural and urban The proportion of people living in urban areas with unmet needs is lower than that of people living in rural areas. Source: Calculated based on IFLS4

11 Source: Calculated based on IFLS4 Mapping the unmet needs of CVD care in Indonesia Less than 20% of people with moderate and high CVD risk got CVD care in one in three districts included in IFLS4. The other 323 districts not included in IFLS4 were assumed to have lower rates of met needs.

Determinants of met needs: A multilevel analysis 12 Source: Calculated based on IFLS4 Higher income, having health insurance and living in urban areas increase the probability of met needs.

Social gradients in health: who can access the healthcare? Rich people have higher probability to get healthcare regardless the area of residence and the ownership of health insurance. 13 Source: Calculated based on IFLS4 Predicted probability of met needs for residential location and per capita expenditure Predicted probability of met needs for health insurance and per capita expenditure

Can adding the number of health facilities solve the problem? Greater presence of health facilities only marginally improves the probability of met needs for CVD care. 14 Source: Calculated based on IFLS4 Predicted probability of met needs for residential location and density of health facilities Predicted probability of met needs for health insurance and density of health facilities

THANK YOU 15