Disparity in the Hospitals Utilization among Regions in Indonesia

doi:10.21203/rs.3.rs-18070/v1

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Disparity in the Hospitals Utilization among Regions in Indonesia

https://doi.org/10.21203/rs.3.rs-18070/v1

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Background. The utilization of hospital services is a benchmark for the success of a health care referral system. Indonesia as the largest archipelago in the world encounters challenging conditions along with lack of infrastructure posed economic and health disparity among its regions. Disparities as a result of this development also have an impact on the accessibility of health care facilities.

Methods. Chi Square test was used to test dichotomy variables and t-tests were performed for analyzing the difference among continuous variables. These tests were employed to assess the hypothesis that there was significant regional difference in the access of health care in Indonesia. Estimation using multinomial logistic regression test was used to study the disparity between regions in hospital utilization.

Results. The results of this study indicate that there were disparities between regions in Indonesia. In the inpatient category all regions have better utilization than the Papua region, except the Sumatra region. The highest disparity occurs between the Nusa Tenggara region and the Papua region. Possibility of utilizing hospital vs. inpatient facilities not using the hospital 1,439 times in adults in the Nusa Tenggara region compared to the Papua region (OR = 1,439; 95% CI = 1,271 - 1,629). In the category of outpatient utilization as well as hospitalization in hospitals, the Papua region has better hospital utilization compared to other regions. The greatest disparity with the Sumatra region (OR = 0.484; 95% CI = 0.392 - 0.597).

Conclusion. In conclusion, there were disparities between regions in Indonesia even though the odds ratio for mortality between regions decreased compared to the previous period.

Ethical Clearance. The 2013 RISKESDAS survey had ethical clearance that was approved by the national ethical committee in the NIHRD (ethic number: 01.1206.207). Informed consent was used during data collection, which considered aspects of data collection procedure, voluntary, and confidentiality.

Health Policy

Health Economics & Outcomes Research

disparities

hospital utilization

Indonesia

inpatient

outpatient

Indonesia is the fourth most populous country in the world with over 260 thousand citizens. Its territory stretches over 1,000 miles from north to south and over more than 3,100 miles from east to west, making it the largest archipelago in the world. These challenging conditions along with lack of infrastructure posed economic and health disparity among its regions. Disparities as a result of this development also have an impact on the accessibility of health care facilities. Regions with good economic movements tend to have good accessibility to health care facilities (1). For this reason, reducing disparity to achieve universal health services is the goal of health planners and policy makers (2).

Several studies on the disparity in the use of health services take the focus of urban-rural. The results found significant differences between urban and rural (2–8). Based on the results of these studies it was assumed that if one region has many urban areas, then it has better utilization of health services compared to areas dominated by rural areas.

The characteristics of Indonesian regions are unique. This is due to the division of regions that refers to the main islands. This condition is motivated by Indonesia's geographical conditions in the form of islands. Indonesia consists of at least 16,056 islands that have been verified by the United Nations Group of Experts on Geographical Names (UNGEGN) from United Nation. This number is part of the total of 17,504 islands recognizedby Indonesia.

From the consumer perspective, hospital utilization is an indicator to assess the quality of health services in a particular area (9,10). Although this still has to be combined with regulations regarding the technical standards of a hospital. On the other hand, from the perspective of basic health services, the utilization of hospital services is also a benchmark for the success of a health care referral system (11). The purpose of this article is to report on the disparity in hospital utilization between regions in Indonesia.

Data source for this study was the Basic Health Survey (Riskesdas) in 2013 (12). Riskesdas was a national scale survey carried out by the Indonesian Ministry of Health's Research and Development Agency. Hospital utilization includes government and private hospitals. The region grouping was based on geographical location.

The unit of analysis in this study was Indonesian population aged 15 years and over. At that age, it was assumed that the respondent has grown up and able to their own decision to access hospital services or not. The sample size analyzed in this paper was 722,329 respondents.

Hospital utilization was defined as public access to the hospital, whether it was outpatient or inpatient care in the hospital. Outpatient care was services in the hospital that did not require staying overnight in the hospital or hospital service that was used intermittently in the past month of the survey. While hospitalization or inpatients care was hospital service that required staying overnight in the hospital in the past year. The decision to use this time limit for recall was based on the assumption that the respondent believed to be able to remember accessing the outpatient and inpatient care in the hospital very well.

Chi Square test was used to test dichotomy variables and t-tests was performed for analysing the difference among continuous variables. These tests were employed to assess the hypothesis that there was significant regional difference in the access of health care in Indonesia. Estimation using multinomial logistic regression test was used to study the disparity between regions in hospital utilization.

Descriptive Results

Table 1 showed that there were significant differences between adults to access healthcare in each region. Table 1 showed that the average adult in Indonesia who uses the hospital is around 3%. The highest proportion is in Papua (3.9%), while the lowest proportion is in Sumatra (2.2%).

The average age of the respondents was also different across regions (Table 1). Papua region has the youngest average age (36.64), while Java-Bali region has the oldest average age (41.90).

The composition of the population in each region is dominated by those who live in rural areas, except in the Java-Bali region. Based on gender, all regions are dominated by women, except in the Papua region.

In general, table 1 shows the dominance of married people compared to single and divorce in all regions. The level of adult education in Indonesia is still dominated by those with elementary school education and below, with the highest percentage in the Nusa Tenggara region (56.8%). Based on the type of work is dominated by those who do not work, with the highest proportion in the Sulawesi region (47.0%). Socioeconomically some regions show composition with wide gaps. In the Papua region, for example, quintile 1 (very poor) was 55.4%, while those in quintile 5 were very rich at only 8.1%. Unlike the Sumatra region, the population of quintile 1 is only 15.1%, while quintile 5 has 27.8%. In the average insurance ownership variable that does not have insurance is below 50%. The lowest is in the region of Papua, which only has 28.5% of the population of uninsured adults.

Multivariate Regression Analyses

We performed a multinomial logistic regression test to detect disparity in hospital utilization between regions in Indonesia (Table 2). As a reference, the category "no utilization" was chosen. Table 2 shows clear disparities between regions in Indonesia. The Papua Region was chosen as a reference as it showed the best utilization of hospitals in the outpatient category. The closest region is Java-Bali, which has the possibility of utilizing hospital vs. outpatient facilities. no hospital use 0.401 times occurs in adults in the Java-Bali region compared to the Papua region (OR = 0.401; 95% CI = 0.368 - 0.439).

In the inpatient category all regions have better utilization than the Papua region, except the Sumatra region. While the Kalimantan region although has a smaller OR is not meaningful (insignificant). The highest disparity occurs between the Nusa Tenggara region and the Papua region. Possibility of utilizing hospital vs. inpatient facilities not using the hospital 1,439 times in adults in the Nusa Tenggara region compared to the Papua region (OR = 1,439; 95% CI = 1,271 - 1,629). In the category of outpatient utilization as well as hospitalization in hospitals, the Papua region has better hospital utilization compared to other regions. The greatest disparity with the Sumatra region (OR = 0.484; 95% CI = 0.392 - 0.597). Table 2 also shows the disparities that occur in other categories. In all categories of hospital utilization, urban areas have better utilization than rural as a reference. Women have better utilization in the inpatient category than men, but in the category of outpatient utilization as well as hospitalization occur otherwise.

In the marital status category, those who were married were slightly better than those who were divorced in the outpatient utilization in the hospital (OR = 1.092; 95% CI = 1.006 - 1.185). In the category of inpatient utilization those who were not married had lower utilization compared to those who received treatment (OR = 0.757; 95% CI = 0.688 - 0.834). While in the category of outpatient utilization as well as hospitalization in their hospitals, the marriage has the possibility of using 1,230 times compared to the divorce.

Table 2 also shows that better education (college) has better hospital utilization in all categories. In the type of work category those who do not work have better utilization than those who work in other categories in all categories of hospital utilization. Meanwhile in the socioeconomic status category, table 2 shows information that the better the socioekonomic status, the better the utilization of hospital services.

This condition applies to all categories of hospital utilization. Table 2 also shows a good relationship between insurance ownership status and hospital utilization. In all categories of utilization, those who have insurance managed by the government (Askes, Jamkesmas, Jamkesda, Jamsostek) are better utilized. While those who have insurance managed privately (others) as a reference, have better hospital utilization.

The results of this study indicate that there are disparities between regions in Indonesia. This condition is in line with several other studies on the disparity of the region. A study conducted to identify the existence of disparity in outcomes in patients with peripheral artery disease in the United States found that variations in regions contribute to differences in outcomes over the long term (13). Another study conducted to identify disparities between regions in the level of cesarean section also produced the same conclusions. Studies conducted in 28 countries found significant regional disparities. Disparities between regions are estimated due to a combination of inadequate access to emergency obstetric services. Another reason is likely because of the high rate of caesarean section without medical indications in the richest subgroups (14).

Studies conducted to look at regional disparities in inter-hospital referrals in mechanically ventilated patients with sepsis found significant differences in geographical location. The study also found racial differences as other disparities. The researcher explained that this study did not explain the medical diagnosis or the severity of the patient's disease (11). The study of other disparities was carried out in America to see home care services for geriatrics in America. The study found that residents in regions that are economically disadvantaged experience inequalities in accessing nursing homes with higher star ratings. These areas may lack sufficient resources for adequate facility staff and provide treatments that meet industry quality standards (15). The same research results were found in Slovakia. This study was conducted to test the localization parameters selected in the distribution and use of medical equipment. The results are needed to verify the potential for regional differences. The study found significant regional disparities (16).

While studies in Sweden found no disparity between regions. This study was conducted to assess differences in gallstone surgery regarding antibiotic use in Sweden. Significant disparities were found only between hospitals and surgeons (17). Health service utilization is influenced not only because of the distribution of health service facilities in the region, but also influenced by human distribution and supporting environment, which influence decision making and behavior in relation to service utilization (18).

Understanding the etiology of regional disparity in health services is very important to improve services for all people. Analysis of disparities between regions can be used by each region to improve the quality of their services (13,16). In Korea, health service disparities are recognized in facilities, equipment and neonatal health outcomes. Although the number of high-risk neonates has increased in Korea, hospitals are reluctant to open Neonatal Intensive Care Units (NICU) because of low medical costs. Finally, the Korean Government is investing in deficiencies whose information is generated from previous regional disparity studies.

The results show the difference in odds ratio for mortality between regions decreased compared to the previous period. There is an increase in regional differences in neonatal care and mortality of premature infants with government investment in Korea (19). Another way to increase the coverage of health service utilization is in a proactive manner. Officers can make home visits for health care, especially in elderly patients (20).

In conclusion, there were disparities between regions in Indonesia even though the odds ratio for mortality between regions decreased compared to the previous period.

The authors declare that there were no conflicts of interest for this study

Ethics approval and consent to participate

The 2013 RISKESDAS survey had ethical clearance that was approved by the national ethical committee in the NIHRD (ethic number: 01.1206.207). Informed consent was used during data collection, which considered aspects of data collection procedure, voluntary, and confidentiality.

Consent for publication

“Not applicable”

Dissemination, availability of data and material

Dissemination towards the academia and researcher will be done through scientific publication to a peer-reviewed journal. We do not wish to share the data, because of possible misuse or misinterpretation of the data. Furthermore, the Ministry of Health of Indonesia holds the right to data access.

Competing interests

We have no conflict of interest to report for this study entitled “DISPARITY IN THE HOSPITALS UTILIZATION AMONG REGIONS IN INDONESIA”

Funding

There was no funding support for this publication or the study. The data used in the study was obtained by permission from the Ministry of Health of Republic of Indonesia.

Authors’ contributions

TM was responsible for overall and/or sectional scientific management, formulating research question, making concept and design of the study, preparation of draft manuscript, doing revisions. He also gave final approval of the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. ADL was responsible for substantial contributions in design and conception of the study, data analysis, and was involved in manuscript preparation, providing critique, revision of the manuscript, and supervises the study. BB provided critics and revision of the manuscript, interpreting the data analysis. All authors have given approval of the final manuscript.

Acknowledgements

“ADL and TM thank the Ministry of Health of the Republic of Indonesia for allowing the use of RISKESDAS data for this study.”

Authors’ information

TM is Associate Professor in the Department of Nutrition – Faculty of Public Health, Universitas Airlangga, INDONESIA also the Director of the Center for Health and Nutrition Education, Counseling and Empowerment (CHeNECE). ADL is currently a doctoral student in the Doctoral Program in Public Health, Universitas Airlangga and also researcher at the Indonesia Agency for Health Research and Development, Ministry of Health, Republic of Indonesia, INDONESIA. BB is faculty member and Professor at the Australian Centre for Precision Health, School of Health Sciences, University of South Australia Cancer Research Institute, Adelaide, AUSTRALIA.

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Table 1. Descriptive Statistics

Characteristic	Region n (%)							All	P
Characteristic	Sumatera	Java-Bali	Nusa Tenggara	Kalimantan	Sulawesi	Maluku Islands	Papua
Hospital Utility									<0.001*
● Outpatient	1.867 (0.9%)	3.262 (1.3%)	374 (0.9%)	575 (0.8%)	777 (0.8%)	218 (1.0%)	699 (2.3%)	7.772 (1.1%)
● Inpatient	2.470 (1.1%)	4.794 (1.9%)	806 (1.9%)	975 (1.4%)	2.037 (2.2%)	325 (1.5%)	375 (1.2%)	11.782 (1.6%)
● Outpatient + inpatient	530 (0.2%)	1.065 (0.4%)	104 (0.2%)	165 (0.2%)	253 (0.3%)	48 (0.2%)	111 (0.4%)	2.276 (0.3%)
● No utilization	212.293 (97.8%)	237.135 (96.3%)	41.839 (97.0%)	67.328 (97.5%)	91.029 (96.7%)	21.440 (97.3%)	29.435 (96.1%)	700.499 (97.0%)
Age (mean)	217160 (38.59)	246256 (41.90)	43123 (39.60)	69043 (38.65)	94096 (40.22)	22031 (38.75)	30620 (36.64)	722329 (39.92)	<0.001*
Area									<0.001*
● Urban	91.263 (42.0%)	145.984 (59.3%)	15.238 (35.3%)	30.048 (43.5%)	35.304 (37.5%)	8.383 (38.1%)	7.511 (24.5%)	333.731 (46.2%)
● Rural (Ref.)	125.897 (58.0%)	100.272 (40.7%)	27.885 (64.7%)	38.995 (56.5%)	58.792 (62.5%)	13.648 (61.9%)	23.109 (75.5%)	388.598 (53.8%)
Gender									<0.001*
● Male	106.349 (49.0%)	117.024 (47.5%)	20.343 (47.2%)	33.900 (49.1%)	44.295 (47.1%)	10.355 (47.0%)	15.557 (50.8%)	347.823 (48.2%)
● Female (Ref.)	110.811 (51.0%)	129.232 (52.5%)	22.780 (52.8%)	35.143 (50.9%)	49.801 (52.9%)	11.676 (53.0%)	15.063 (49.2%)	374.506 (51.8%)
Marital status									<0.001*
● Single	57.209 (26.3%)	50.616 (20.6%)	11.134 (25.8%)	14.872 (21.5%)	22.035 (23.4%)	5.201 (23.6%)	5.668 (18.5%)	166.735 (23.1%)
● Married	144.956 (66.8%)	172.258 (70.0%)	29.257 (67.8%)	49.280 (71.4%)	64.120 (68.1%)	15.499 (70.4%)	23.392 (76.4%)	498.762 (69.0%)
● Divorce (Ref.)	14.995 (6.9%)	23.382 (9.5%)	2.732 (6.3%)	4.891 (7.1%)	7.941 (8.4%)	1.331 (6.0%)	1.560 (5.1%)	56.832 (7.9%)
Education level									<0.001*
● Under primary school	91.540 (42.2%)	126.767 (51.5%)	24.486 (56.8%)	34.399 (49.8%)	45.350 (48.2%)	9.520 (43.2%)	16.691 (54.5%)	348.753 (48.3%)
● Junior high school	49.179 (22.6%)	47.670 (19.4%)	7.323 (17.0%)	14.136 (20.5%)	19.067 (20.3%)	4.832 (21.9%)	5.449 (17.8%)	147.656 (20.4%)
● Senior high school	61.002 (28.1%)	55.569 (22.6%)	8.563 (19.9%)	15.866 (23.0%)	22.079 (23.5%)	5.881 (26.7%)	6.389 (20.9%)	175.349 (24.3%)
● College (Ref.)	15.439 (7.1%)	16.250 (6.6%)	2.751 (6.4%)	4.642 (6.7%)	7.600 (8.1%)	1.798 (8.2%)	2.091 (6.8%)	50.571 (7.0%)
Work type									<0.001*
● No work	87.676 (40.4%)	96.555 (39.2%)	15.413 (35.7%)	26.934 (39.0%)	44.266 (47.0%)	9.957 (45.2%)	11.178 (36.5%)	291.979 (40.4%)
● Public servant/army/ police	10.281 (4.7%)	7.256 (2.9%)	2.024 (4.7%)	3.842 (5.6%)	5.233 (5.6%)	1.631 (7.4%)	2.263 (7.4%)	32.530 (4.5%)
● Employee	11.206 (5.2%)	26.428 (10.7%)	1.547 (3.6%)	5.932 (8.6%)	3.249 (3.5%)	618 (2.8%)	1.101 (3.6%)	50.081 (6.9%)
● Entrepreneur	29.243 (13.5%)	35.663 (14.5%)	3.677 (8.5%)	10.384 (15.0%)	10.141 (10.8%)	1.802 (8.2%)	2.220 (7.3%)	93.130 (12.9%)
● Farmer/Fisherman /Labor	70.593 (32.5%)	71.739 (29.1%)	18.167 (42.1%)	18.956 (27.5%)	25.575 (27.2%)	6.921 (31.4%)	12.693 (41.5%)	224.644 (31.1%)
● Others (Ref.)	8.161 (3.8%)	8.615 (3.5%)	2.295 (5.3%)	2.995 (4.3%)	5.632 (6.0%)	1.102 (5.0%)	1.165 (3.8%)	29.965 (4.1%)
Socioeconomic status									<0.001*
● Quintile 1	32.815 (15.1%)	27.421 (11.1%)	17.534 (40.7%)	11.971 (17.3%)	19.136 (20.3%)	6.901 (31.1%)	16.969 (55.4%)	132.747 (18.4%)
● Quintile 2	38.790 (17.9%)	47.131 (19.1%)	10.812 (25.1%)	12.497 (18.1%)	19.611 (20.8%)	5.786 (26.3%)	5.026 (16.4%)	139.653 (19.3%)
● Quintile 3	40.274 (18.5%)	61.534 (25.0%)	6.610 (15.3%)	13.493 (19.5%)	17.604 (18.7%)	4.170 (18.9%)	3.089 (10.1%)	146.774 (20.3%)
● Quintile 4	44.952 (20.7%)	61.877 (25.1%)	5.277 (12.2%)	13.625 (19.7%)	18.787 (20.0%)	3.812 (17.3%)	3.062 (10.0%)	151.392 (21.0%)
● Quintile 5 (Ref.)	60.329 (27.8%)	48.293 (19.6%)	2.890 (6.7%)	17.457 (25.3%)	18.958 (20.1%)	1.362 (6.2%)	2.474 (8.1%)	151.763 (21.0%)
Insurance									<0.001*
● No insurance	95.517 (44.0%)	121.895 (49.5%)	14.985 (34.7%)	33.162 (48.0%)	28.826 (30.6%)	9.425 (42.8%)	8.736 (28.5%)	312.546 (43.3%)
● Askes, Jamkesmas, Jamkesda, Jamsostek	116.897 (53.8%)	116.426 (47.3%)	27.882 (64.7%)	34.028 (49.3%)	64.412 (68.5%)	12.488 (56.7%)	21.444 (70.0%)	393.577 (54.5%)
● Others (Ref.)	4.746 (2.2%)	7.935 (3.2%)	256 (0.6%)	1.853 (2.7%)	858 (0.9%)	118 (0.5%)	440 (1.4%)	16.206 2.2%)

Note: Chi Square test was used for dichotomous variables, and T test for continuous variables. *Significant at alpha <0.001.

Table 2. Multinomial Logistic Regression of Indonesia Adults Hospital Utilization using RKD 2013 data

Predictor	Outpatient			Inpatient			Outpatient + Inpatient
Predictor	OR	Upper Bound	Lower Bound	OR	Upper Bound	Lower Bound	OR	Upper Bound	Lower Bound
Age	1,029*	1,027	1,031	1,015*	1,013	1,016	1,032*	1,029	1,036
Region: Sumatera	0,273*	0,249	0,300	0,717*	0,641	0,801	0,484*	0,392	0,597
Region: Java-Bali	0,401*	0,368	0,439	1,178*	1,056	1,313	0,821	0,670	1,005
Region: Nusa Tenggara	0,354*	0,312	0,403	1,439*	1,271	1,629	0,622*	0,475	0,814
Region: Kalimantan	0,277*	0,247	0,311	0,907	0,803	1,025	0,499*	0,391	0,638
Region: Sulawesi	0,249*	0,224	0,277	1,304*	1,165	1,459	0,485*	0,386	0,608
Region: Maluku	0,374*	0,321	0,437	1,052	0,905	1,223	0,513*	0,365	0,722
Area: Urban	1,410*	1,335	1,489	1,181*	1,132	1,232	1,259*	1,142	1,389
Gender: Male	0,964	0,917	1,015	0,935*	0,896	0,975	1,210*	1,100	1,330
Marital Status: single	1,023	0,908	1,153	0,757*	0,688	0,834	0,899	0,717	1,127
Marital Status: married	1,092*	1,006	1,185	1,014	0,949	1,084	1,230*	1,062	1,425
Education: under primary school	0,556*	0,507	0,609	0,788*	0,726	0,854	0,672*	0,565	0,800
Education: junior high school	0,743*	0,676	0,816	0,839*	0,772	0,912	0,908	0,760	1,084
Education: senior high school	0,849*	0,784	0,920	0,884*	0,820	0,952	0,935	0,800	1,093
Work: No work	1,238*	1,101	1,392	1,215*	1,108	1,331	1,416*	1,148	1,745
Work: Public servant/army/ police	1,128	0,982	1,296	0,932	0,827	1,051	0,824	0,633	1,072
Work: Employee	1,073*	0,937	1,228	0,927	0,829	1,036	0,766*	0,594	0,988
Work: Entrepreneur	0,838*	0,736	0,954	0,850*	0,768	0,941	0,734*	0,580	0,928
Work: Farmer/fisherman/labor	0,769*	0,677	0,872	0,760*	0,689	0,838	0,610*	0,486	0,767
Socioeconomic: quintile 1	0,494*	0,448	0,545	0,507*	0,468	0,548	0,420*	0,349	0,506
Socioeconomic: quintile 2	0,549*	0,504	0,598	0,700*	0,655	0,747	0,581*	0,500	0,676
Socioeconomic: quintile 3	0,697*	0,649	0,749	0,824*	0,777	0,873	0,681*	0,597	0,777
Socioeconomic: quintile 4	0,852*	0,801	0,907	0,946*	0,898	0,997	0,885*	0,791	0,991
Insurance: No insurance	0,290*	0,262	0,321	0,466*	0,423	0,514	0,252*	0,208	0,305
Insurance: Askes, Jamkesmas, Jamkesda, Jamsostek	0,617*	0,561	0,679	0,729*	0,662	0,802	0,620*	0,518	0,740

Note: The reference category is “No Utilization”; 95% Confidence Interval for OR; *Significant at level 95%.

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Disparity in the Hospitals Utilization among Regions in Indonesia

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