Community Health Center: A METHODS 1. Community health center (puskesmas) point data. 2. Community health center (puskesmas) basic information. Place Where Poor People Can Source: Ministry of Health Source: Indonesia Ministry of Health Access Health Care WebGIS http://gis.depkes.go.id/ Data Fikriyah Winata

University of Illinois at Urbana-Champaign 3. Socioeconomic data: 1) Population; 2) # of unemployment per village; 3) # of consists of 5 cities and 1 This study excludes 1 district in official poverty letters issued by village 4. Jakarta district, sub-district and BACKGROUND district (Thousand Islands) or 44 the island that consists of 2 sub- office; 4) # of people with low-income village administrative boundaries. ▪ In 1969, under the Indonesian Ministry of Health, Indonesia established community health sub-districts, 268 villages. district (6 villages) occupations. Source: Indonesia Statistics (BPS) center (CHC or Puskesmas) across the country to provide the basic health care services for Source: Indonesia Statistics (BPS) the poor. (Frederick & Worden, 1993; Heywood & Harahap, 2009). Study area ▪ Puskesmas is a government-based community health center administratively located in sub- 1. Kernel Density Estimation 2. Weighted KDE district and village levels to be closer to the low-income communities. (Heywood & Harahap, for Puskesmas points for Health Workers in Puskesmas (General 2009). Total population 10,075,310 (Indonesia Statistics, 2014) ArcGIS Default bandwidth Practitioner/GP, Midwives, and Nurses) There are 327 puskesmas in 262 ▪ Need and demand for CHC’s services far exceed service supply. The impacts of this mismatch 2 villages. Population density 15,211/Km include: long waiting time, crowding, uncomfortable circumstances, and limited opening (BPS, 2014), 14,464/ Km2 (World Spatial Analysis hours. Population Review, 2017) Methods

RESULTS 3. Local Indicator Spatial 4. Spatial Regression: Spatial Lag Autocorrelation (LISA) and Spatial Error Result 1: Kernel Density Estimation of Puskesmas in Jakarta

Result 2: Weighted Kernel Density Estimation for Health Professions Kernel map shows the density of CHC in Subdistrict, CHC in Subdistrict, West Jakarta CHC in Subdistrict, puskesmas concentrated in the middle of the study area where also as the densely populated OBJECTIVE areas and slightly lower density To examine spatial and socioeconomic inequalities in availability of CHCs in the edge or suburb of Jakarta. (Puskesmas) in the Jakarta region. Possible issues: 1. This is the first study assessing whether - Issue with the edge, puskesmas are effective in providing health puskesmas located in the accessibility to the low-income communities in edge of the study area. Jakarta-- from a spatial perspective. - This study excludes the Why this study is puskesmas that are not in Weighted KDE of General important? Jakarta. Weighted KDE of Midwives Weighted KDE of Nurses 2. This study offers important evidence for Jakarta Practitioners (GPs) Department of Health in determining where new Weighted KDE of 3 health professions shows the uneven availability of different puskesmas should be built, to improve access for type of services. The density of 2 health professions (GPs and Midwives) low-income people. Result 4: Spatial Regression (Spatial Lag Model) concentrated in the middle (central), north, and south parts of study area. However, in the north and west parts of Jakarta, density of nurses is not as high as GPs and RESEARCH QUESTION Midwives.

Does community health center (or Puskesmas) in Jakarta, Indonesia provide a health Result 3: LISA Clusters of Unemployment, Letter of Poverty, and Low-Income accessibility to the low-Income community? Occupations REFERENCES CONCLUSION ▪ Frederick., WH & Worden., RL. editors (1993) ▪ Puskesmas, and the health professions within Indonesia: A Country Study. Washington: GPO for them are unevenly distributed within Jakarta. the Library of Congress. The regression results show that puskesmas ▪ Heywood, P., & Harahap, N. P. (2009). Health are concentrated in the areas with high number facilities at the district level in Indonesia. Australia of letter of poverty and number of LISA clusters of LISA clusters of the LISA clusters of the and New Zealand Health Policy, 6, 13. letter of low-income Spatial Lag Model Independent variables: Positive coefficients of 3 independent unemployment letter of poverty occupations http://doi.org/10.1186/1743-8462-6-13 unemployment, but lower number of low- - # of poverty letters variables suggested that the low-income ▪ Anselin, L. (1995). Local indicators of spatial income occupations. Dependent variable: - # of unemployment areas in Jakarta have higher density of autocorrelation: LISA. Geographical Analysis, 27 # of puskesmas per village - # of low-income ▪ In term of the number of the facilities and occupations Puskesmas. (2), 93–115. health professions, puskesmas in Jakarta do ▪ Anselin, L., Syabri, I. and Kho, Y. (2006), GeoDa: provide healthcare accessibility to the low- LISA clusters of (unemployment, letter of poverty, and low-income occupations) reveal the An Introduction to Spatial Data Analysis. income community. However, some low- FUTURE DIRECTION similar pattern at the H-H clusters that located in the north and west parts of Jakarta. Geographical Analysis, 38: 5–22. One direction for future research is to run the spatial regression models for each of the consistently shows the L-L clusters both unemployment and low-income doi:10.1111/j.0016-7363.2005.00671. income areas lack of puskesmas, especially 3 type of health professions. occupations. While letter of poverty cluster is L-L in South and . ▪ Picture sources: 1) Wartakota, 2017; 2) Media areas of northeast and northwest Jakarta. Indonesia, 2017; 3) BPJS-kesehatan.co.id, 2017 Puskesmas, should be added in these areas. ACKNOWLEDGEMENT: I want to thank to Dr. Sara McLafferty for her invaluable supervisions, advices, guidelines as I conducted this study. For any comment and inquiry, please contact me at [email protected]