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Additional file 1: Figure S1. Flowchart of sampling selection for independent variable BMI. Figure S2. Flowchart of sampling selection for independent variable tobacco consumption. Figure S3. Flowchart of sampling selection for independent variable physical activity. Figure S4. Flowchart of sampling selection for independent variable ageing. Table S1. List of variables for 2014 IFLS analysis. Table S2. Sample characteristics stratified by age groups, before matching. Table S3. Sample characteristics stratified by tobacco consumption groups, before matching. Table S4. Sample characteristics stratified by BMI groups, before matching. Table S5. Sample characteristics stratified by physical activity (PA) groups, before matching. Table S6. Mean biases of covariates after matching using individual t-test (age group 50-59 vs 40-49). Table S7. Mean biases of covariates after matching using individual t-test (age group 60-69 vs 40-49). Table S8. Mean biases of covariates after matching using individual t-test (age group 70+ vs 40-49). Table S9. Mean biases of covariates after matching using individual t-test (former vs never use tobacco). Table S10. Mean biases of covariates after matching using individual t-test (light user vs never use tobacco). Table S11. Mean biases of covariates after matching using individual t-test (moderate user vs never use tobacco). Table S12. Mean biases of covariates after matching using individual t-test (heavy user vs never use tobacco). Table S13. Mean biases of covariates after matching using individual t-test (overweight vs normal BMI). Table S14. Mean biases of covariates after matching using individual t-test (obesity vs normal BMI). Table S15. Mean biases of covariates after matching using individual t-test (underweight vs normal BMI). Table S16. Mean biases of covariates after matching using individual t-test (low vs high physical activity. Table S17. Mean biases of covariates after matching using individual t-test (moderate vs high physical activity). Table S18. The ATT of the number of chronic conditions across different matching algorithms. Table S19. The ATT of multimorbidity presence across different matching algorithms. Table S20. The ATT of the number of outpatient visits across different matching algorithms. Table S21. The ATT of the number of inpatient visits across different matching algorithms. Table S22. The ATT of CHE >25% of total household expenditure across different matching algorithms. Table S23. The ATT of CHE >40% of total non-food expenditure across different matching algorithms. Table S24. The ATT of labour participation across different matching algorithms. Table S25. The ATT of the number of days primary activity missed across different matching algorithms. Table S26. Logistic and ZINB regression for the number of chronic condition and presence of multimorbidity. Table S27. Logistic and ZINB regression for the number of outpatient and inpatient visits. Table S28. Logistic regression for CHE >25% and 40%. Table S29. Logistic and ZINB regression for the productivity loss.

To achieve equitable access of its citizen to health care, since 2014, the Government of Indonesia has implemented the national health insurance scheme, namely JKN. However, equal access to specialistic health care is still challenging. Using a convergent parallel mixed-method approach, and the utilization of cardiovascular services as an example, this study aims to evaluate the good governance and equity aspects of the JKN program in Central Java, a province in Indonesia which records a high poverty rate above the national rate. Based on IHME, Central Java has the highest proportion of mortality associated with cardiovascular diseases among the provinces in Indonesia. This study found that most of the districts in this province are struggling to achieve the 2019 JKN roadmap target in terms of JKN participation coverage. Unequal distribution of health facilities and human resources is recorded affecting access to and utilization of cardiovascular care services. More contribution from the district and provincial government as well participation from private sectors (the employers) to fund the JKN program is essential to ensure the availability of specialistic care services in rural areas to narrow the gaps in cardiovascular care services.

The implementation of National Health Insurance in several regions in Indonesia faces challenges, such as health service providers, health insurance management , and the community. The outcome that occurs may vary depending on the implementation of different aspects of governance, equity or social justice, and aspects of service quality. Therefore, research was conducted by evaluating the implementation of the national health insurance in DKI Jakarta Province. The evaluation is carried out based on the 2019 JKN road map target indicators, grouped into three aspects: including governance, achievement of equal distribution of health services (equity), and quality of service. This research used a mixed-methods design that combined the approach of quantitative and qualitative elements. Quantitative data were collected from secondary data from Health Social Security Agency (BPJS Kesehatan), the National Social Security Council Monitoring and Evaluation System (Sismonev DJSN). The qualitative data comes from in-depth interviews with selected informants using the purposive sampling technique. The result was that DKI Province had achieved the JKN membership target. There are still JKN participants who are inactive / in arrears from participants in the government and private wage-earning segments. Health facilities and human health resources have met the standards, but related to the package of benefits for heart diseases services, the availability of cardiac specialists and cath lab facilities has not been evenly distributed, especially in the Seribu Islands. Patient preferences in choosing the desired health service resulted in implementing a tiered referral system and back-referral still having problems in health facilities.

Additional file 1: Appendix 1. Sample flowchart. Appendix 2. List of variables for 2014 IFLS-5 analysis. Appendix 3. Sample characteristics of respondents with multimorbidity. Appendix 4. The incremental outpatient visits by population quantile. Appendix 5. The incremental inpatient visits by population quantile. Appendix 6. The incremental out-of-pocket expenditure by population quantile. Appendix 7. Predictive four-weekly OOPE by number of NCDs. Appendix 8. Average number of outpatient visit, inpatient visits, and four-weekly OOPE by population quantile.

Background: Maternal and child mortality rate in Indonesia is still high. Indonesia is also expected not to reach the target of MDG 4 and 5 in 2015. In an effort to improve the health status of mothers and children in the context of decentralization, planning and budgeting at the district health is an important element of the health system. Evidence-Based Budgeting Planning Approach (EBP/PPBB), is a planning approach models that use data and academic health evidence in the framework of program decision-making as a references. Objectives: Describe the impact results of the PPBB KIA implementation in order to analyze, the impact of BAPPEDA involvement in maternal and child health planning advocacy through coordination across sectors.Research Methods: This study is a descriptive-correlative study that analyze the impact of BAPPEDA involvement in cross- sectoral planning for MCH programs, that are carried out in accordance with bottleneck analysis of the existing health system, using 66 interventions based continuum of care.Results: Cross-sectoral involvement in the planning and budgeting for MNCH brings a positive impact on the district health work plan. Bappeda involvement is crucial to coordinate cross-sectoral program for MNCH.Conclusions: Capacity of district planning staff is limited and should be improved, lack of evidence in planning is due to lack of health data, and the cross sector advocacy for health budgeting is insufficient. PPBB approach can improve capacity on planning on evidence-based and integrated planning for MNCH across-sectoral. Key Word: Planning, Maternal and Child Health, Cross Sectoral ABSTRAKLatar Belakang: Angka kematian ibu dan anak di Indonesia masih tinggi. Indonesia juga diprediksi tidak dapat mencapai target MDG 4 dan 5 pada tahun 2015. Dalam upaya peningkatan status kesehatan ibu dan anak dalam konteks desentralisasi, perencanaan dan penganggaran kesehatan di kabupaten merupakan elemen sistem kesehatan yang penting. Pendekatan Perencanaan Penganggaran Berbasis Bukti (PPBB) KIA, merupakan bentuk model perencanaan yang menggunakan bukti data dan referensi akademis dalam rangka pengambilan keputusan program kesehatan.Tujuan: memaparkan hasil implementasi PPBB-KIA dalam rangka melihat hubungan keterlibatan Bappeda dalam advokasi perencanaan perencanaan kesehatan ibu dan anak melalui koordinasi lintas sektor.Metode Penelitian: Kajian ini secara deskriptif-korelatif keterlibatan Bappeda dalam perencanaan lintas sector untuk progam KIA yang dilakukan sesuai dengan sumbatan sistem kesehatan yang ada dan berdasarkan 66 intervensi berbasis continuum of care.Hasil: Keterlibatan lintas sektor dalam perencanaan dan penganggaran KIA membawa dampak positif terhadap rencana kerja. Peran Bappeda semakin kuat dan memberikan kontribusi positif dalam kegiatan ini. Keterlibatan Bappeda terlihat berpengaruh dalam koordinasi program KIA untuk lintas sektor. Kesimpulan: Peningkatan kapasitas staf perencanaan kabupaten yang masih terbatas, kurangnya data kesehatan, peran advokasi lintas sektor ke Pemda yang belum kuat untuk meningkatkan pembiayaan kesehatan. Pendekatan PPBB dapat meningkatkan kapasitas perencanaan KIA secara berbasis bukti nyata terpadu dan terintegrasi dan lintas sektor di daerah. Kata Kunci: Perencanaan, Kesehatan Ibu dan Anak, Lintas Sektor

IntroductionUsing nationally representative survey data from China and India, this study examined (1) the distribution and patterns of multimorbidity in relation to socioeconomic status and (2) association between multimorbidity and out-of-pocket expenditure (OOPE) for medicines by socioeconomic groups.MethodsSecondary data analysis of adult population aged 45 years and older from WHO Study on Global Ageing and Adult Health (SAGE) India 2015 (n=7397) and China Health and Retirement Longitudinal Study (CHARLS) 2015 (n=11 570). Log-linear, two-parts, zero-inflated and quantile regression models were performed to assess the association between multimorbidity and OOPE for medicines in both countries. Quantile regression was adopted to assess the observed relationship across OOPE distributions.ResultsBased on 14 (11 self-reported) and 9 (8 self-reported) long-term conditions in the CHARLS and SAGE datasets, respectively, the prevalence of multimorbidity in the adult population aged 45 and older was found to be 63.4% in China and 42.2% in India. Of those with any long-term health condition, 38.6% in China and 20.9% in India had complex multimorbidity. Multimorbidity was significantly associated with higher OOPE for medicines in both countries (pConclusionMultimorbidity was associated with substantially higher OOPE for medicines in China and India compared with those without multimorbidity. Our findings provide supporting evidence of the need to improve financial protection for populations with an increased burden of chronic diseases in low-income and middle-income countries.