The Potential of Private Health Insurance Ownership Based on the 2018-2020 National Socioeconomic Survey Data

In 2014, the Indonesian Government introduced a social security program in the health sector. However, Indonesia’s out-of-pocket expenses remain high due to a lack of public interest in National Health Insurance services. Financing expensive health services with high out-of-pocket expenses has the potential to cause poverty. Private health insurance is considered a solution to this problem. This study aimed to determine the socioeconomic factors of private health insurance ownership and its potential in Indonesia. This study used secondary data from the 2018, 2019, and 2020 National Socioeconomic Surveys. Logistic regression analysis showed that the variables related to private health insurance ownership were age, sex, education, economic status, employment status, marital status, household status, and location of residence. The most dominant variable in 2018 was per capita expenditure (economic status), while education was the most dominant variable in 2019 and 2020. The result of this study can be used to formulate a strategy for increasing participation in private health insurance. The socioeconomic health sector should use this information to target specific markets for private health insurance.


Introduction
The out-of-pocket (OOP) expenses for health insurance in Indonesia were more than 30% of total health expenditures in 2021. 1 High OOP in health financing can exacerbate the disease burden on individuals due to delayed or missing care, strained personal finances, and an increased likelihood of financial disaster, impoverishment, or deteriorating social determinants of health. The consequences experienced by the community are greater vulnerability to poverty and wider inequality in health. 2 Private health insurance is important in reducing OOP in health financing. 2 Several studies have found that private health insurance as additional insurance has a significant effect on reducing the burden of OOP payments. [4][5][6][7][8] Although the National Health Insurance (NHI) program was introduced in 2014, public interest in NHI services tends to be low because the system is still considered unsatisfactory. 9 People from middle to upper economic statuses prefer OOP rather than using NHI. The small number of private health insurance providers in Indonesia is one of the reasons why private health insurance progress has been extremely slow in Indonesia. 10 Private health insurance companies must develop pro-ducts that people need and know which potential customers to target.
This study aimed to provide a foundation for strengthen ing private health insurance in Indonesia by examining the characteristics of its users and analyzing its determinants so that private companies can know their marketing target. It is hoped that private health insurance companies will be interested in making health insurance services that strengthen private health insurance ownership in Indonesia. In addition, it is expected that the government will consider the results of this study when developing additional health insurance programs for NHI participants.

Method
This study used secondary data from the 2018, 2019, and 2020 National Socioeconomic Surveys (NSS)/Survei Sosial Ekonomi Nasional (SUSENAS). These data used the head-of-household level as the unit of research analysis. Univariate analysis was conducted to determine the characteristics of the head of household, and multivariate analysis was used to determine these characteristics' relationships to private health insurance ownership.
The univariate analysis in this study consisted of descriptive responses to the variables and examined the characteristics of 70,102,253 heads of households. The variables studied consisted of age (in years); sex, divided into two categories (male and female); educational background, divided into five categories (uneducated, elementary school/equivalent, junior high school/equivalent, senior high schools/equivalent, and higher education); and economic status or per capita expenditure (expenses per household per month) in Indonesian Rupiah (IDR). Employment status was divided into occupations (unemployed, informal, and formal) and types of occupations (extractives, manufacturing, and services).
Marital status was divided into single and married. Household status was divided into the number of household members, the number of household children under five were considered, the NHI ownership, private health insurance ownership, and insurance ownership (without Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal). 2022; 17 (4): 279-286 Area of Residence was classified as urban or rural (Table  1). Multivariate analysis was performed after the logistic regression analysis to determine which variables significantly influenced the variable of private health insurance ownership. The logistic regression equation was used to estimate the probability of private health insurance ownership. Variables were selected by binary logistic analysis in advance of the logistic regression analysis, and it was used to select the correlated variable to the dependent variable (private health insurance ownership) with a significance level of 5%. The selected variables were then analyzed using logistic regression analysis. The coefficient in this analysis indicates the magnitude of the probability of a category, and a positive value indicates that the probability of a category is greater than that of the comparison category (the variable defined as a base). However, a negative coefficient means that the probability of the category is smaller than that of the comparison category. The results of the exponential estimated value of the regression coefficient (βi) obtained the value of the odds ratio, with a significance level of 5%.

Results
The univariate analysis in this study consisted of descriptive responses to the variables. Household characteristics are shown in Table 2. Based on sex, the 2018-2020 NSS was dominated by males. Based on the head of the household's type of work was dominated by work in the informal sector. The head of the household's occupation category was dominated by the service sector. The mari-tal status of the head of the household was dominated by married status. Urban areas dominated the location of the household residences.
An average of four members dominated the number of household members. The distribution of the ownership of the NHI was dominated by members of the NHI. Private health insurance ownership distribution was dominated by households without it. The status of insurance ownership distribution was dominated by only NHI ownership.
The logistic regression equation estimated the opportunity for private health insurance ownership for NHI members with specific characteristics according to the abovementioned variables. The coefficient sign indicates the magnitude of the probability of a category; a positive sign indicates that the probability of a category is greater than the comparison category, while a negative coefficient sign means that the probability of the category is smaller than the comparison category. The base variable was used as the comparison variable.
The equation in Table 3 showed that the intercept value = -25.4885 when all independent variables are 0, including the ownership of additional private health insurance for NHI members, women living in a village, were uneducated, did not work, had never been married, family members less than four, and no children under five. The accuracy of the logistic regression model in predicting empirical data was seen in the classification table output, which was shown in the overall percentage value of 18%, meaning that the variation in the rate of additional private health insurance ownership among NHI members was only 18%, as determined by the overall predictor. It means that 82% of the additional private health  insurance ownership rate in NHI member households was determined by factors other than the analyzed predictors. From Table 3, the odds ratio value was indicated by the magnitude of the Exp(B) named coefficient value, which can be explained as follows:

Age
Older heads of households tended to have private health insurance coverage 0.145 times less. Health quality declines with age.

Sex
The male head of household participants tended to have private health insurance coverage 2.3 times greater than female participants in 2018, 1.1 times greater than females in 2019, and 2.6 times greater than females in 2020. Therefore, male heads of households tended to have insurance coverage. Overall, male private health insurance ownership was 2.03 times greater than that of females.

Education
In 2018, compared to uneducated heads of households, the participating heads of households with an elementary school education tended to have private health insurance coverage 4.2 times greater, and heads of households with a junior high school education tended to have private health insurance coverage 3.8 times greater. Heads of households with a senior high school education tended to have private health insurance coverage 9.5 ti-  In 2019, compared to uneducated heads of households, participating heads of households with an elementary school education tended to have private health insurance coverage 6.9 times greater, and heads of households with a junior high school education tended to have private health insurance coverage 7.9 times greater. Heads of households with a senior high school education tended to have private health insurance coverage 1.3 times greater. Overall, participating heads of households with higher education tended to have private health insurance coverage 1.6 times greater than the uneducated heads of households.
In 2020, compared to uneducated heads of households, participating heads of households with an elementary school education tended to have private health insurance coverage 1.3 times greater, and heads of households with a junior high school education tended to have private health insurance coverage 4.4 times greater.
Heads of households with a senior high school education tended to have private health insurance coverage 0.8647 times greater. Overall, heads of households with higher education tended to have private health insurance coverage 1.4 times greater than uneducated heads of households.
Over all three years, in comparison to uneducated heads of households, participating heads of households with an elementary school education tended to have private health insurance coverage 1.3 times greater, heads of households with a junior high school education tended to have private health insurance coverage 4.4 times greater, and heads of households with a senior high school education tended to have private health insurance coverage 8.6 times greater. Overall, heads of households with higher education tended to have private health insurance coverage 1.4 times greater than uneducated heads of households.

Economic Status
In 2018, concerning per capita expenditure, participant heads of households tended to have private health

Employment Status
In 2018, in comparison to unemployed heads of house holds, participating heads of households in the agriculture/mining sector tended to have private health insurance coverage 4.8 times greater, heads of households in the manufacturing sector tended to have private health insurance coverage 1.05 times greater, and heads of households in the service sector tended to have private health insurance coverage 0.0040 times less. In 2019, heads of households in the agriculture/mining sector tended to have private health insurance coverage 4.5 times greater than those who were unemployed, while heads of households in the manufacturing sector tended to have private health insurance coverage 9.9 times greater, and heads of households in the service sector tended to have private health insurance coverage 0.7 times less.
In 2020, heads of households in the agriculture/mining sector tended to have private health insurance coverage 8.2 times greater than those who were unemployed, while heads of households in the manufacturing sector tended to have private health insurance coverage 1.3 times greater, and heads of households in the service sector tended to have private health insurance coverage 2.8 times greater. Overall, from 2018-2020, household heads in the agriculture/mining sector tended to have private health insurance coverage 5.7 times greater than those who were unemployed, while heads of households in the manufacturing sector tended to have private health insurance coverage 1.1 times greater, and heads of households in the service sector tended to have private health insurance coverage 1.05 times greater. Hence, the results of this study indicated that working status greatly affected private health insurance coverage compared to unemployed people.

Marital Status
In 2018, heads of households who were single tended to have private health insurance coverage 6.4 times greater than their married counterparts. In 2019, heads of households tended to have private health insurance coverage 4.6 times greater than those who were single. In 2020, heads of households who were married tended to have private health insurance coverage 7.6 times greater than those who were single. Overall, from 2018-2020, heads of households who were married tended to have private health insurance coverage 6.7 times greater than those who had never been married.

Household Status
In 2018, households with more than four family members tended to have private health insurance coverage 2.6 times greater than households with fewer than four family members. In 2019, households with more than four family members tended to have private health insurance coverage 1.9 times greater than households with fewer than four family members. In 2020, households with more than four family members tended to have private health insurance coverage 3.08 times greater than households with fewer than four family members. Overall, from 2018-2020, households with more than four family members tended to have private health insurance coverage 2.8 times greater than households with fewer than four. Hence, the results of this study indicated that households with fewer than four family members were less likely to have private health insurance.
In 2018, households with one child under five tended to have private health insurance coverage 2.6 times greater than households without children under five. In 2019, households with one child under five tended to have private health insurance coverage 2.6 times greater than households without a child under five. In 2020, households with one child under five tended to have private health insurance coverage 0.6 times less than households without children under five. Overall, from 2018-2020, households with one child under five tended to have private health insurance coverage 1.2 times greater than those households without children under five.

Area of Residence
In 2018, households in urban area tended to have private health insurance coverage 5.4 times greater than households in rural area. In 2019, households in urban area tended to have private health insurance coverage 6.8 times greater than those in rural area. In 2020, households in urban area tended to have private health insurance coverage 4.5 times greater than those in rural area. Overall, from 2018-2020, households in urban area tend ed to have private health insurance 5.6 times greater than households in rural area.

Discussion
This study implied that older individuals tended to have health insurance more than younger ones. This result was in line with a study by Shao, et al., 11 stated that the older someone is, the more they will be aware of health insurance. 11,12 People in the 40-44 and 45-49 age groups had an 11% and 8% higher likelihood of health insurance, respectively. 11 Heads of households with higher education tended to  13 Education also helped individuals make informed choices about health issues, including purchasing health insurance to avoid huge health expenses when they were ill. People with higher education had a higher view of the need for health insurance to deal with unexpected health problems. In contrast, people with low education were unaware of the threat caused by unforeseen health problems. 11,14 The higher economic status (seen from the level of expenditure) of the household's head in this study tended to have private health insurance coverage 1.3 times greater. Accordingly, the results of this study indicated that the tendency of per capita spending was highly influential in having private health insurance guarantees. Income is an important determinant of both the demand for health services and the decision to have health insurance. 8 Some studies have stated that the most critical factor affecting general insurance was income. 11,14,15 Regarding occupation, the employed status significantly affected private health insurance guarantees compared to those un employed. Participation in the formal sector's health insurance was dominated by workers in the public sector (civil servants and armed forces), while health insurance participation in the informal sector was dominated by farm ers, fishermen, and the like. 16,17 In terms of marital status, people who were single were less likely to have private health insurance ownership. The status of living together is likely to be greater than that of those who are divorced/dead. 16 Married women were more likely to have private coverage than the singles in almost all income groups. 11,12 Households with one child under five tended to have private health insurance coverage 1.2 times greater than households without one. Thus, the results of this study indicated that households without a child under five were less likely to have private health insurance. A study in Bangladesh found that parents might have less capacity to pay premium health insurance than other family members. 16 Health insurance schemes sometimes view women as wives or mothers, rather than as individuals or workers, even though each individual's right to social insurance is fundamental. If women's access to social or health insurance comes through their husbands, this can protect the family, not women's autonomy. 8 In this study, households in urban area were likelier to have private health insurance coverage than those in rural ones. Hence, an individual residing in a village was less likely to have private health insurance. The reason of that informal sector workers in rural areas had a lower chance of having health insurance compared to those li-ving in urban areas was that public health insurance companies were mostly found in urban areas, and these companies adjusted their health insurance products to meet the needs of urban people. 17 The difficulty of access and the high cost of transportation also made health insurance less valuable because it was difficult to use; thus, informal sector workers in villages did not feel the need to have health insurance. 18 In brief, rural people did not consider health insurance a need because the product design did not match them.

Conclusion
The results of this study show a relationship between age, sex, education, economic status, employment status, marital status, household status, and location of residence with private health insurance ownership. Particularly, most households in this study do not enroll in private health insurance. The government should understand this situation and find the best solution to strengthen the health insurance ecosystem in Indonesia. These results can be used to formulate a strategy for strengthening private health insurance ownership. The health economic sector should use this information to expand the target market for private health insurance.