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This study investigates disparities in the utilization of primary health care (PHC) between urban and rural populations in Indonesia, focusing on socioeconomic and demographic determinants. Understanding these patterns is essential for promoting equity under the National Health Insurance (Jaminan Kesehatan Nasional, JKN) program.
Methods
Data were obtained from the 2023 National Socioeconomic Survey, which included 334,887 individuals. Binary logistic regression was used to examine the association between individual characteristics and PHC utilization.
Results
Overall utilization rates were similar across urban and rural areas, but significant disparities were observed. Women were more likely to use PHC than men (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10–1.13). The association between higher education and PHC utilization was negative (OR, 0.78; 95% CI, 0.75–0.81), while access to information technology slightly reduced utilization (OR, 0.98; 95% CI, 0.96–0.99). Wealth effects diverged sharply: affluent urban residents were less likely to use PHC (OR, 0.84; 95% CI, 0.81–0.87), whereas wealthier rural residents were more likely to utilize PHC (OR, 1.09; 95% CI, 1.05–1.13). Dual insurance ownership had a strong positive effect in rural areas (OR, 1.56; 95% CI, 1.25–1.94).
Conclusion
These findings highlight structural inequalities in PHC utilization. Policy efforts must prioritize enhancing the quality and attractiveness of PHC in urban areas, improving financial protection and infrastructure in rural areas, and addressing the digital divide. Such targeted measures are essential for achieving equitable and inclusive health coverage under JKN.
Primary health care (PHC) serves as the foundation of health systems globally, providing accessible and equitable services essential for achieving Universal Health Coverage and the Sustainable Development Goals, particularly Goal 3, which focuses on health and well-being [1,2]. The Astana Declaration 2018 reaffirmed PHC as a cornerstone for reducing health disparities and promoting health equity worldwide [3]. Furthermore, the World Health Organization has underscored the role of PHC in promoting health equity and addressing health determinants, with particular attention to marginalized populations who often face barriers in accessing essential health services [4,5].
In Indonesia, PHC is provided through an extensive network of community health centers (Puskesmas) and supplementary facilities such as Posyandu (integrated health service posts), which offer promotive, preventive, curative, and rehabilitative services [6]. Despite significant government investment, inequalities in PHC utilization remain, especially between urban and rural populations [7]. Rural residents face barriers such as restricted geographical access, inadequate infrastructure, and an uneven distribution of healthcare professionals, with the majority located in urban areas [8-10]. These disparities pose a challenge to the equity goals of the National Health Insurance (Jaminan Kesehatan Nasional, JKN) program, which seeks to provide universal access to quality healthcare [11,12].
Although prior studies have examined access to PHC in Indonesia, most have considered urban or rural settings in isolation, resulting in a limited understanding of the comparative dynamics between these two settings [13,14]. Most have not provided a comprehensive analysis of the socioeconomic, demographic, and institutional factors shaping urban-rural disparities in PHC utilization.
This study addresses this research gap by examining how socioeconomic, demographic, and regional factors interact to influence PHC utilization across Indonesia, using nationally representative data. The objective is to generate evidence that can inform equitable health policies and strengthen the implementation of the JKN program.
Methods
Study design and participants
This study employed secondary data from the 2023 National Socioeconomic Survey (SUSENAS) conducted by Statistics Indonesia (Badan Pusat Statistik, BPS). The survey provides nationally representative data encompassing 34 provinces and 514 districts/cities across Indonesia. The 2023 SUSENAS employed a stratified two-stage sampling procedure, with census blocks serving as the primary sampling units and households as the secondary units. A total of 345,000 households representing 334,887 individuals participated in the study.
Dependent variable
PHC utilization was measured using self-reported responses to questions on healthcare usage. Outpatient utilization was assessed by asking whether respondents had accessed outpatient services (doctors or midwife’s practice, joint practice, or Puskesmas) in the past month, while inpatient utilization was assessed for the past 12 months. The varying recall periods follow the established design of SUSENAS, recognizing that outpatient care occurs more frequently and requires a shorter recall period, whereas inpatient care is less frequent and can be reliably recorded over a longer period. Responses were coded as binary outcomes (“yes” for utilization and “no” otherwise). Missing or inconsistent responses were excluded from the analysis.
Independent variable
The independent variables included living area (urban/rural), gender, age group, marital status, education, employment status, insurance ownership, access to information technology (IT), and wealth quintile. Age bands were categorized into six groups (0–4, 5–14, 15–24, 25–44, 45–59, and ≥60 years) following Statistics Indonesia’s classification system, which allows comparability with national reporting standards. Education categories (no schooling/not yet, primary school, junior high school, senior high school, and higher education) were aligned with the Indonesian education structure. IT access was measured at the household level and operationalized as a binary variable. A household was classified as having IT access if at least one household member owned a mobile phone, or if the household reported having internet access for any purpose, including communication, education, or daily activities. This definition follows the operational standards of the SUSENAS survey.
Statistical analysis
Descriptive statistics were used to present the population characteristics. Associations between PHC utilization and categorical variables were examined using chi-square tests, whereas continuous variables were analyzed using t-tests. Multicollinearity tests were performed to ensure that no significant correlations existed between the independent variables (Supplement 1). Binary logistic regression was employed to assess the relationship between independent variables and PHC utilization. All independent variables were simultaneously entered into the logistic regression model using the enter method. Logistic regression was chosen as the most appropriate method for a dichotomous outcome variable, offering clear interpretability through odds ratios (ORs). This approach is widely used in health-services research and allows for the simultaneous adjustment of multiple covariates. The analysis was conducted using IBM SPSS ver. 26 (IBM Corp.), with statistical significance set at P-value <0.05. ORs and 95% confidence intervals (CIs) were determined.
Ethical considerations
The National Ethics Committee of the National Research and Innovation Agency classified this study as exempt, as it involved secondary data without personal identifiers. SUSENAS participants provided informed consent as part of BPS survey protocol.
Results
Table 1 shows the distribution of PHC utilization across demographic and socioeconomic characteristics, comparing urban and rural populations. Overall, PHC utilization rates were broadly similar between urban and rural populations, at 25.9% and 26.1%, respectively (P=0.409). However, significant differences emerged when the data were stratified according to socio-demographic and economic characteristics. Women consistently utilized PHC services more than men, and young children (0–4 years) and older adults (≥60 years) showed the highest use compared with other age groups. Individuals without formal education or who were unemployed also reported higher utilization rates than their more educated or employed colleagues.
The results of the multivariate analysis indicate that insurance ownership strongly influenced utilization (Table 2). Respondents with government insurance demonstrated higher odds of PHC use compared with the uninsured, while the most striking effect was observed among those with dual insurance, particularly in rural areas (OR, 1.56; 95% CI, 1.25–1.94). This underscores the role of financial protection in overcoming barriers to rural healthcare.
Patterns in education and wealth revealed clear contrasts between urban and rural settings. Higher educational attainment was negatively correlated with the utilization of primary healthcare, with this effect being more significant in urban areas, indicating a greater reliance on specialist or private care among the highly educated. Wealth effects also diverged: in urban areas, the wealthiest individuals were less likely to use PHC (OR, 0.84; 95% CI, 0.81–0.87), whereas in rural areas, wealthier respondents demonstrated higher utilization (OR, 1.09; 95% CI, 1.05–1.13). Access to IT showed a modest but significant negative association with PHC utilization overall (OR, 0.98; 95% CI, 0.96–0.99), particularly in urban areas, which may reflect increased reliance on digital health alternatives.
Table 2 is complemented by a forest plot that visually summarizes the adjusted ORs and highlights the magnitude of the associations between urban and rural populations (Figure 1).
Discussion
This study reveals a nuanced landscape of PHC utilization in Indonesia. While overall utilization rates are comparable between urban and rural settings, significant disparities emerge when examining socioeconomic, demographic, and technological factors. The analysis highlights three core policy insights. First, higher educational attainment is associated with reduced PHC use, suggesting a tendency to bypass primary care in favor of other services [15]. Second, access to IT correlates with lower PHC utilization, potentially reflecting a shift toward self-care and digital health resources [16,17]. Third, and most strikingly, wealth exerts a divergent influence: wealthier individuals in urban areas use PHC less, whereas their counterparts in rural areas use it more frequently [18-21]. These findings underscore that disparities are not merely geographical but are deeply embedded within broader structural inequalities.
The inverse relationship between education and PHC utilization suggests that more educated individuals may prioritize specialized care or possess the health literacy to engage in preventive behaviors, thereby reducing their need for routine primary care visits [15,22]. This trend is more pronounced in urban areas where alternative healthcare options are more accessible. The negative association between IT access and PHC use indicates that digital resources may facilitate health information-seeking and self-management, particularly among urban populations with higher digital literacy [16,17]. Although potentially efficient, this shift may worsen inequities, if digital health solutions remain inaccessible to all populations.
A pivotal finding of this study is the contrasting effect of wealth on PHC utilization across settings. In urban areas, wealthier individuals are likely to prefer private healthcare providers because they perceive them as offering higher-quality, more convenient, and specialized services [19]. Conversely, in rural areas, where private options are scarce, wealth becomes a key enabler for accessing the available public PHC services, covering costs related to transportation, lost wages, and other out-of-pocket expenses [18,20,21]. This urban-rural wealth paradox is a critical consideration for equity-focused policies and underscores the significance of context-specific interventions.
The consistently higher PHC utilization among women aligns with global patterns, often attributed to gender-based health-seeking behaviors and women’s roles in managing family health, including care for children and the elderly [23-25]. While this serves as a positive indicator for maternal and child health program [26,27]; however, it also reflects the need to understand and address the barriers that deter men from engaging in preventive and primary care services.
These findings must be interpreted with regard to the specific limitations. Reliance on self-reported data may introduce recall or social desirability bias. Furthermore, although data capture is utilized, it does not reflect the perceived quality of care [19]. This factor is a significant determinant of health-seeking behavior and may explain why some groups, particularly the educated and urban wealthy, choose to bypass PHC facilities.
The insights are directly relevant to the JKN scheme that seeks to achieve universal health coverage [12]. The program’s success depends on both financial coverage and the ability to maintain the quality and attractiveness of PHC services under the JKN for all population segments [11,28,29]. The tendency of educated and wealthy urbanites to opt out of PHC highlights the need to enhance service quality, reduce wait times, and integrate more comprehensive care within the PHC framework to make it a compelling choice [30]. In rural areas, the JKN must continue to mitigate financial barriers while concurrently addressing the infrastructural and workforce deficiencies that limit physical access [8-10].
Achieving equitable health coverage in Indonesia requires moving beyond a simple urban-rural dichotomy. Policy interventions must be tailored to address the specific barriers faced by different socioeconomic groups in each context. Strengthening the quality, availability, and responsiveness of PHC services is essential to ensure that they serve as an effective and preferred first point of contact for the entire population, in line with the equity goals of the JKN program and the broader social determinants of the health framework [4,5].
In conclusion, this study elucidates the complex socioeconomic and demographic determinants influencing PHC utilization in Indonesia, revealing that significant disparities persist within rather than merely between urban and rural areas. The findings indicate that while the JKN program provides a foundational layer of financial protection, achieving equitable health access requires targeted, multi-faceted strategies that address the specific barriers faced by different population groups.
Therefore, future policy interventions must be forward-looking and innovative. First, the inverse relationship between IT access and PHC utilization presents an opportunity. Public health initiatives should actively leverage digital tools, such as telehealth platforms and mobile health applications, to enhance the accessibility of PHC services, provide reliable health information, and facilitate appointment systems, especially in remote and underserved communities.
Second, the strategic reform of the JKN program is paramount. Our findings underscore its positive role in enabling access; however, its potential to further narrow inequities can be unlocked through policy refinement. This includes enhancing the benefit package for outpatient care at PHC facilities, improving provider reimbursement rates to ensure service quality, and implementing performance-based incentives to attract and retain a skilled health workforce in rural areas. The JKN must transition from serving merely as a passive payer to becoming an active purchaser of high-quality, equitable primary care.
Finally, the outreach and service delivery models must be tailored to local contexts. In urban areas, efforts should focus on making PHC a more attractive option for educated and wealthier populations through improved service quality, shorter waiting times, and integrated care. Conversely, in rural areas, interventions must address the fundamental barriers of geography and infrastructure, potentially through mobile clinics, community health worker programs, and transportation subsidies, to ensure that economic advancement continues to translate into better access to healthcare.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
Thank you to Statistics Indonesia for allowing the author to study the 2023 National Socioeconomic Survey for this research.
Funding
None.
Data availability
The data supporting this study’s findings are available from the Indonesian Statistics. Still, restrictions apply to the availability of these data, which were used under license for the current research and are not publicly available. The data is available to researchers who need it. They can submit a request to Indonesian Statistics via https://silastik.bps.go.id/v3/index.php/site/login/.
Author contribution
Conceptualization: AK, WPN, ADL. Methodology: AK, RM. Software: RM. Validation: LL, TSW. Formal analysis: AK, ADL, LL. Investigation: TR, TSW. Resources: WPN. Data curation: RM. Project administration: IA, DY. Visualization: AK, DY. Supervision: WPN, TR. Writing–original draft: AK, RM, LL. Writing–review & editing: AK, RM, LL, IA. Final approval of the manuscript: all authors.
Values are presented as odds ratio (confidence interval).
Ref, reference; IT, information technology.
**P<0.01,
***P<0.001 (Statistical significance).
References
1. United Nations. Transforming our world: the 2030 agenda for sustainable development [Internet]. United Nations; 2015 [cited 2025 Mar 13]. Available from: https://docs.un.org/en/A/RES/70/1
2. World Health Organization. Primary health care: report of the international conference on primary health care, Alma-Ata, USSR, 6-12 September 1978 [Internet]. World Health Organization; 1978 [cited 2025 Mar 13]. Available from: https://www.who.int/publications/i/item/9241800011
3. World Health Organization. Declaration of Astana: report of global conference on primary health care: Astana, Kazakhstan, 25 and 26 October 2018 [Internet]. World Health Organization; 2018 [cited 2025 Mar 13]. Available from: https://www.who.int/publications/i/item/WHO-HIS-SDS-2018.61
4. Arredondo A, Recaman AL, Castrejon B. Universal health coverage in the framework of the 2030 global agenda for sustainable development: agreements and challenges. J Glob Health 2020;10:010316.
7. Munira L, Liamputtong P, Viwattanakulvanid P. Barriers and facilitators to access mental health services among people with mental disorders in Indonesia: a qualitative study. Belitung Nurs J 2023;9:110-7.
8. Nwankwo ON, Ugwu CI, Nwankwo GI, Akpoke MA, Anyigor C, Obi-Nwankwo U, et al. A qualitative inquiry of rural-urban inequalities in the distribution and retention of healthcare workers in southern Nigeria. PLoS One 2022;17:e0266159.
9. Wenang S, Schaefers J, Afdal A, Gufron A, Geyer S, Dewanto I, et al. Availability and accessibility of primary care for the remote, rural, and poor population of Indonesia. Front Public Health 2021;9:721886.
10. Nugraheni WP, Rachmawati T, Susianti N, Yulianti A, Kusnali A, Nuraini S, et al. A decade of telehealth implementation for promotive and preventive care in Indonesia: a scoping review. J Soc Heal Behav 2024;7:123-33.
11. Laksono AD, Nugraheni WP, Ipa M, Rohmah N, Wulandari RD. The role of government-run insurance in primary health care utilization: a cross-sectional study in Papua region, Indonesia, in 2018. Int J Health Serv 2022 Sep 25 [Epub]. https://doi.org/10.1177/00207314221129055
12. Nugraheni WP, Mubasyiroh R, Hartono RK. The influence of Jaminan Kesehatan Nasional (JKN) on the cost of delivery services in Indonesia. PLoS One 2020;15:e0235176.
13. Saud M, Margono H, Asfaq A, Ahmad A. The role of community health centres (CHCs) in promoting the provision of primary health care services in rural areas. Fam Med Prim Care Rev 2023;25:62-7.
16. Xiao N, Sharman R, Rao HR, Upadhyaya S. Factors influencing online health information search: an empirical analysis of a national cancer-related survey. Decis Support Syst 2014;57:417-27.
17. Purnami CT, Prasetjo AB. Satisfaction analysis of the use of the COVID-19 vaccination Pcare system in health officers in public health center. Media Publikasi Promosi Kesehatan Indones 2022;5:421-7.
18. Gao Q, Prina AM, Ma Y, Aceituno D, Mayston R. Inequalities in older age and primary health care utilization in low- and middle-income countries: a systematic review. Int J Health Serv 2022;52:99-114.
19. Conlan C, Cunningham T, Watson S, Madan J, Sfyridis A, Sartori J, et al. Perceived quality of care and choice of healthcare provider in informal settlements. PLOS Glob Public Health 2023;3:e0001281.
20. Zhou Z, Gao J, Fox A, Rao K, Xu K, Xu L, et al. Measuring the equity of inpatient utilization in Chinese rural areas. BMC Health Serv Res 2011;11:201.
21. Sharma J, Pavlova M, Groot W. Rural-urban inequalities in health care utilization in Bhutan: a decomposition analysis. Int J Equity Health 2024;23:69.
22. Barrio-Cortes J, Mateos-Carchenilla MP, Martinez-Cuevas M, Beca-Martinez MT, Herrera-Sancho E, Lopez-Rodriguez MC, et al. Comorbidities and use of health services in people with diabetes mellitus according to risk levels by adjusted morbidity groups. BMC Endocr Disord 2024;24:115.
23. Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in the utilization of health care services. J Fam Pract 2000;49:147-52.
24. Memirie ST, Verguet S, Norheim OF, Levin C, Johansson KA. Inequalities in utilization of maternal and child health services in Ethiopia: the role of primary health care. BMC Health Serv Res 2016;16:51.
25. Santos TM, Cata-Preta BO, Mengistu T, Victora CG, Hogan DR, Barros AJ. Assessing the overlap between immunisation and other essential health interventions in 92 low- and middle-income countries using household surveys: opportunities for expanding immunisation and primary health care. EClinicalMedicine 2021;42:101196.
26. Harsono AA, Bond CL, Enah C, Ngong MG, Kyeng RM, Wallace E, et al. Structural barriers to maternity care in Cameroon: a qualitative study. Reprod Health 2024;21:108.
27. Terefe B, Workneh BS, Zeleke GA, Mekonen EG, Zegeye AF, Aemro A, et al. Uncovering women’s healthcare access challenges in low- and middle-income countries using mixed effects modelling approach: insights for achieving the Sustainable Development Goals. PLoS One 2025;20:e0314309.
28. Li K, Lorgelly P, Jasim S, Morris T, Gomes M. Does a working day keep the doctor away?: a critical review of the impact of unemployment and job insecurity on health and social care utilisation. Eur J Health Econ 2023;24:179-86.
29. Nugraheni WP, Laksono AD, Kusnali A, Ardani I, Latifah L, et al. National health insurance membership in Indonesia: do socio-economic elements matter? PLoS One 2025;20:e0329781.
30. Sorensen TH, Olsen KR, Vedsted P. Association between general practice referral rates and patients’ socioeconomic status and access to specialised health care a population-based nationwide study. Health Policy 2009;92:180-6.
Urban-rural disparities in primary health care utilization in Indonesia: a cross-sectional study
Figure. 1. Forest plot of adjusted odds ratios for primary health care utilization: (A) overall, (B) urban, and (C) rural models. IT, information technology.
Graphical abstract
Figure. 1.
Graphical abstract
Urban-rural disparities in primary health care utilization in Indonesia: a cross-sectional study
Characteristic
All (n=334,887)
Urban (n=142,975)
Rural (n=191,912)
No (n=247,782)
Yes (n=87,105)
P-value
No (n=105,891)
Yes (n=37,084)
P-value
No (n=141,891)
Yes (n=50,021)
P-value
Living area
0.409
-
-
Urban
74.1
25.9
-
-
-
-
Rural
73.9
26.1
-
-
-
-
Sex
<0.001
<0.001
<0.001
Male
75.2
24.8
75.1
24.9
75.3
24.7
Female
73.0
27.0
73.2
26.8
72.8
27.2
Age group (y)
<0.001
<0.001
<0.001
0–4
65.1
34.9
64.8
35.2
65.3
34.7
5–14
71.3
28.7
69.9
30.1
72.4
27.6
15–24
78.9
21.1
78.7
21.3
79.0
21.0
25–44
78.5
21.5
79.6
20.4
77.7
22.3
45–59
75.1
24.9
75.5
24.5
74.7
25.3
≥60
71.7
28.3
72.0
28.0
71.4
28.6
Marital status
<0.001
<0.001
<0.001
Never married
72.3
27.7
71.8
28.2
72.7
27.3
Married
75.8
24.2
76.5
23.5
75.3
24.7
Divorced/widowed
71.7
28.3
71.8
28.2
71.5
28.5
Education level
<0.001
<0.001
<0.001
No schooling/not yet
69.7
30.3
68.1
31.9
70.7
29.3
Primary school
75.2
24.8
74.3
25.7
75.6
24.4
Junior high school
76.8
23.2
76.8
23.2
76.8
23.2
Senior high school
77.6
22.4
78.4
21.6
76.7
23.3
Higher education
79.2
20.8
80.6
19.4
76.9
23.1
Employment status
<0.001
<0.001
<0.001
Unemployed
72.1
27.9
72.1
27.9
72.1
27.9
Employed
76.8
23.2
77.3
22.7
76.5
23.5
Insurance ownership
<0.001
<0.001
<0.001
No insurance
75.3
24.7
74.9
25.1
75.6
24.4
Government insurance
73.5
26.5
73.7
26.3
73.3
26.7
Non-government insurance
78.1
21.9
78.0
22.0
78.4
21.6
Double insurance
73.2
26.8
74.8
25.2
68.7
31.3
Access to IT
<0.001
<0.001
<0.001
No access
71.0
29.0
69.3
30.7
71.9
28.1
Has access
75.6
24.4
75.8
24.2
75.4
24.6
Wealth quintile
<0.001
<0.001
<0.001
Lowest
73.7
26.3
72.5
27.5
74.3
25.7
Low
73.6
26.4
72.2
27.8
74.5
25.5
Middle
73.1
26.9
72.4
27.6
73.5
26.5
High
73.8
26.2
74.4
25.6
73.3
26.7
Highest
76.1
23.9
77.7
22.3
73.7
26.3
Characteristic
All
Urban
Rural
Living area
Urban
Ref
Ref
Ref
Rural
0.990 (0.974–1.007)
-
-
Sex
Male
Ref
Ref
Ref
Female
1.115*** (1.096–1.134)
1.111*** (1.082–1.140)
1.117*** (1.092–1.142)
Age group (y)
0–4
Ref
Ref
Ref
5–14
0.747*** (0.725–0.769)
0.813*** (0.777–0.850)
0.700*** (0.673–0.729)
15–24
0.526*** (0.503–0.549)
0.602*** (0.562–0.644)
0.478*** (0.451–0.506)
25–44
0.450*** (0.427–0.475)
0.479*** (0.440–0.522)
0.433*** (0.404–0.465)
45–59
0.518*** (0.490–0.548)
0.566*** (0.519–0.617)
0.495*** (0.461–0.533)
≥60
0.579*** (0.548–0.611)
0.619*** (0.569–0.674)
0.566*** (0.527–0.608)
Marital status
Never married
Ref
Ref
Ref
Married
1.312*** (1.257–1.369)
1.281*** (1.200–1.367)
1.331*** (1.258–1.409)
Divorced/widowed
1.385*** (1.317–1.457)
1.386*** (1.284–1.497)
1.373*** (1.285–1.468)
Education level
No schooling/not yet
Ref
Ref
Ref
Primary school
0.893*** (0.871–0.915)
0.855*** (0.820–0.892)
0.911*** (0.883–0.939)
Junior high school
0.902*** (0.874–0.931)
0.835*** (0.793–0.880)
0.946*** (0.909–0.985)
Senior high school
0.859*** (0.833–0.886)
0.775*** (0.738–0.814)
0.949** (0.911–0.988)
Higher education
0.781*** (0.749–0.814)
0.705*** (0.663–0.749)
0.901*** (0.848–0.956)
Employment status
Unemployed
Ref
Ref
Ref
Employed
0.951*** (0.931–0.971)
0.995 (0.963–1.027)
0.913*** (0.889–0.939)
Insurance ownership
No insurance
Ref
Ref
Ref
Government insurance
1.203*** (1.180–1.225)
1.178*** (1.143–1.215)
1.224*** (1.195–1.253)
Non-government insurance
0.958 (0.892–1.030)
0.990 (0.906–1.083)
0.903 (0.795–1.027)
Double insurance
1.299*** (1.155–1.461)
1.263*** (1.096–1.454)
1.560*** (1.254–1.939)
Access to IT
No access
Ref
Ref
Ref
Has access
0.979** (0.960–0.997)
0.950*** (0.921–0.980)
0.992 (0.969–1.017)
Wealth quintile
Lowest
Ref
Ref
Ref
Low
1.027** (1.003–1.051)
1.045** (1.004–1.087)
1.005 (0.976–1.035)
Middle
1.073*** (1.048–1.099)
1.045** (1.004–1.087)
1.078*** (1.046–1.111)
High
1.047*** (1.021–1.073)
0.970 (0.932–1.010)
1.096*** (1.061–1.131)
Highest
0.946*** (0.921–0.972)
0.839*** (0.805–0.874)
1.091*** (1.050–1.133)
Table 1. Distribution of the population by primary health care utilization
Values are presented as %.
IT, information technology.
Table 2. Multivariate analysis of primary health care utilization (n=334,887)
Values are presented as odds ratio (confidence interval).
, Wahyu Pudji Nugraheni
