One indicator to see the quality of health system performance was to look at the disparity in the utilization of healthcare facilities. The research objective was to analyze the disparity between regions in the utilization of health centers in rural areas in Indonesia. The results of the 2013 Basic Health Survey (Riskesdas) were used as analysis material. The 2013 Riskesdas was designed a cross-sectional survey. Respondents obtained 388,598 using the multi-stage cluster random sampling method. Binary Logistic Regression Test was used to analyze data. Data is obtained through a structured questionnaire. The results showed that there were statistically significant disparities between regions. All regions showed better utilization than the Papua region as a reference. The best utilization was in the Sumatra region, which was 3.781 times more utilizing health centers than the Papua region (OR = 3.781; 95% CI = 3.580-3.993). The utilization of health centres that approached the Papua region was the Nusa Tenggara region (OR = 1.582; 95% CI = 1.490-1.679) and the Maluku region (OR = 2.175; 95% 1.999–2.366). All three regions are all in the Eastern part of Indonesia. The research concluded there was a disparity in health center utilization between regions in rural Indonesia. Regions in the western part of Indonesia tend to have better health center utilization in rural areas. Research results could be used as a reference for making policies that focus on equality of services to reduce existing disparities.

Introduction: The contraceptive prevalence rate in Indonesia has not experienced much improvement, which has led to an increase in the number of pregnancies. This study aimed to analyse the barriers to contraception use among women of childbearing age in rural Indonesia. Methods: This study used a cross-sectional design with data from the Indonesian Demographic and Health Survey (IDHS) of 2017. The independent variables were age, employment status, education, marital status, wealth status, health insurance and parity. The dependent variable was the use of contraception. The statistical significance was set at p <0.05 using bivariate analysis and binary logistic regression. Results: The study showed that the age group of 45–49 years (OR 0.199; 95% CI 0.149–0.266), secondary education (OR 2.227; 95% CI 2.060–2.514), women married/living with their partner (OR 43.752; 95% CI: 35.484–53.946), wealth status: middle (OR 1.492; 95% CI 1.400–1.589) and multipara (OR 2.524; 95% CI: 2.328–2.737) exhibited the increased use of contraception among women of childbearing age in rural Indonesia. Conclusion: The variables proven to represent obstacles to contraceptive use among women of childbearing age in rural Indonesia include old age, no education, no husband/partner, poverty and already having one child.

Introduction: Previous studies have reported that low birth weight (LBW) correlates with neonatal death and 15 - 20% of all births worldwide are LBW. This research aimed to analyse the factors related to LBW in Indonesia. Methods: The authors collated secondary data from the 2017 Indonesian Demographic and Health Survey (IDHS). The sample consisted of 17,443 respondents. Besides LBW as the dependent variable, the independent variables consisted of maternal age, residence, wealth, education, employment, marital status, health insurance, antenatal care (ANC) visits, smoking behaviour, and gender of the baby. The final stage employed binary logistic regression. Results: Women aged 35-39 years were 0.688 times less likely than women aged 15-19 years to give birth to LBW babies. The wealthiest women were 0.712 times less likely than the poorest women to give birth to LBW babies. Women with higher education levels were 0.670 times less likely to have a LBW baby than women with no education level. Women who attended ≥4 ANC visits were 0.829 times less likely to have LBW babies than women who attended <4 ANC visits. Baby girls were 1.161 times more likely than baby boys to be born with LBW. Conclusion: The study concluded that the factors related to LBW in Indonesia were maternal age, wealth, education, ANC, and gender of the baby.