Differences in Healthcare Services Utilization Between Urban and Rural Older Persons in Vietnam: Evidence from a National Survey

Abstract

This research compared healthcare services utilization among older persons living in urban and rural Vietnam and determined the key factors contributing to their differences. A nationally representative sample of 3,049 older Vietnamese people in 2019 was used with multivariate logistic regressions and Poisson regressions to identify factors influencing the probability and frequency of service utilization. The standard concentration index (CI) and Erreygers concentration index (EI) were applied to measure the differences. The extension of the Oaxaca-Blinder decomposition was used to examine the rural-urban differences in healthcare services utilization and quantify factors contributing to the differences. CIs and EIs were relatively small and nearly zero, meaning that both inpatient service utilization and outpatient service utilization were equally distributed across all household wealth quintiles, regardless of their residence of living. Oaxaca-Blinder decomposition analysis results revealed statistically significant differences between rural and urban older persons in outpatient service utilization, in terms of its probability and frequency. Rural persons had 4.9% lower in the probability of outpatient services utilization than their urban counterparts. Similarly, rural residents had 3.033 times lower in the frequency of outpatient services utilization than urban residents. The use of health insurance during medical treatments and poor health status were two significant determinants explaining the urban-rural differences in healthcare utilization among older adults. Based on the crucial findings, continual development of the health insurance scheme and investment is recommended to support the rural older population in accessing healthcare. Future research opportunities include exploring a more comprehensive understanding of this field, such as examining the factors influencing the choice of healthcare facilities, reimbursement rates, and out-of-pocket expenses for older individuals in urban and rural areas. Additionally, longitudinal studies are recommended to investigate causal relationships in healthcare service utilization among older Vietnamese.

Keywords

concentration index Erreygers concentration index health services utilization Oaxaca-Blinder socio-economic differences older persons Vietnam

What do we already know about this topic?

Urban-rural gaps have been a critical issue in all socio-economic and health aspects, particularly in low- and middle-income countries (LMICs) facing aging populations. The gap in utilization of healthcare services has been emerged in both academic and policy studies since demand for healthcare and accessibility to healthcare services between urban and rural older people are diverse.

How does your research contribute to the field?

Our study contributed to the existing literature by using a nationally representative data with various approaches to decompose differences to show the case of older persons in Vietnam as a middle-income country with a rapidly aging population.

What are your research’s implications towards theory, practice, or policy?

Our study provided different policy implications for LMICs which are facing rapidly aging populations along with high demand for healthcare of older persons, that is, developing health insurance schemes for the rural older persons is essential to provide equitable benefits, particularly for chronic disease management and preventive care, while minimizing out-of-pocket expenses through subsidies or free services for low-income individuals.

Background

Population aging is one of the common socio-economic concerns where numerous countries have been experiencing profound population transitions. For the international comparability, several population aging terms were provided. An aging society is determined if the percentage of people aged 65 years and above ranges between 7% and 14%, meanwhile an aged society is defined if this percentage varies between 15% and 20%, and a super-aged society is classified if this percentage is 21% and above.¹ By regions, East and Southeast Asia region is leading this demographic transition where Hongkong, South Korea, and Japan were expected to experience the highest proportion of older population (those aged 65 and above) by 2050: 40.6%, 39.4%, and 37.5% of the total population, respectively.^2,3 Following this trend, Vietnam became an aging society and was forecasted to experience a rapid older population growth rate within this region.^2,4 While high-income countries took several decades to transit from aging to aged societies, for instance 26 years for Japan and 33 years for Hongkong, Vietnam is estimated to take only 20 years for this transition (from 2015 to 2035).⁵

As a middle-income country, experiencing a bulging aging population posed an enormous social and economic challenges for Vietnam. In response to the rapid aging population, the Government of Vietnam (GOV) has simultaneously raised awareness to society and made tremendous efforts on enhancing the well-being, ensuring the social protection for this sub-population, and boost the macroeconomics of Vietnam. The Law on the Elderly and the Law on Social Health Insurance have been impactful policies that GOV has promulgated with the aim of improving access to and utilization of healthcare among older adults.^6,7 In detail, these laws emphasized the rights and benefits of older citizens, as well as the responsibilities of families, establishments, and society in supporting older persons. Moreover, these laws designed to support disadvantaged and vulnerable older persons in accessing healthcare and social supports. Combining with the social assistance programs, which were amended by Decree 136/2013 (dated 21 October 2013), Decree 20/2021 (dated 15 March 2021), and the most recently Decree 76/2024 (dated 1 July 2024), older persons are provided with more packages of health and social care services along with better financial protection. For example, Decree 76/2024 regulated that older persons aged 80 and over without pension and other social welfare benefits are eligible to get free health insurance, and those with severe disabilities can receive home-based care where their voluntary care-givers can receive financial support. Also, commune health centers (CHCs) are in charge of managing treatments for older persons with high blood pressure and/or diabetes and all are paid by social health insurance. Previous evidence was revealed the efficacy of these policies on improving the accessibility and reducing economic burden on healthcare among the Vietnamese older adults.^8
-10

As people in their advanced age, the prevalence of comorbidity (mainly non-communicable and chronic diseases) was relatively high among older population, resulting in a growing demand for healthcare utilization in Vietnam.^11
-13 The healthcare system in Vietnam has three levels of health service delivery: (i) primary level, known as “grassroots healthcare system” (i.e. district health facilities and commune health centers); (ii) secondary level (i.e. provincial health facilities); and (iii) tertiary level (tertiary hospitals under the central government of Vietnam). These levels provide multiple healthcare services for older adults, depending on the nature of services and the severity of health conditions. Previous literature reported that the Vietnamese older adults were more likely to use outpatient services than inpatient services. Moreover, older adults were revealed differences in their healthcare seeking behaviors with respect to the choice of healthcare facilities.¹⁴ Accordingly, among those who used healthcare services, only 8.84% chose commune health centers, while approximately 66.63% chose higher level of healthcare providers (i.e. district, provincial and central hospitals). Due to a shortage of trained personnel and limited availability of specialized care services for older patients in commune health centers, older adults with more complex health conditions are referred to higher-level hospitals.^7,15,16 Healthcare-seeking behaviors and utilization among older adults are associated by various factors, such as age, gender, place of residence (urban/rural), employment status, household wealth, ability to pay for healthcare expenses, educational attainment level, health insurance, health-risk behaviors (such as alcohol and tobacco consumption), health programs supports, and health conditions.^{17,9,18,14,19}

Urban-rural differences in accessibility and utilization of healthcare services are a crucial issue that need to be solved to promote a healthy aging society. Urban-rural differences in healthcare utilization among older persons have been widely investigated in countries at different income levels.^20
-24 Interestingly, urban older persons were reported to have better probabilities or higher frequencies of healthcare utilization than their rural counterparts, highlighting that, regardless of wealth status, nations encounter relatively similar urban-rural differences in healthcare access, but might be different level of the differences. Previous studies consistently elaborated that urban people benefit from more developed infrastructure, a higher density of medical professionals, a wider range of specialized services, and easier access to healthcare than those in rural regions.²⁰ In other words, as a result of healthcare system factors, rural older persons might face multiple barriers in utilizing healthcare services when their needs arise. Thus, alleviating these differences within a community is essential for improving population health and avoiding further severe consequences, such as health inequalities or unmet need for healthcare and social care.^25
-27

Although the bulging older population has been observed in a decade, urban-rural differences in healthcare utilization among older persons have received little attention in Vietnam. To the best of our knowledge, few related studies have been conducted in Vietnam, and none of them investigated urban-rural difference aspect.^14,28,29 One of them studied gender differences,²⁸ while the remaining studied differences in choosing healthcare facilities.^14,29 Moreover, these studies explored the issue by using outdated data and applying the logistic regression analysis, which made it inadequate for capturing the nuanced drivers behind the observed differences.

Given the international findings on urban-rural differences on healthcare utilization among older persons, it should be explored urgently in the Vietnam context as older population is substantially growing in this middle-income country. Thus, this study was conducted to address two research objectives: i) determine differences on healthcare service utilization among urban and rural Vietnamese older persons; and ii) examine factors contributing to the differences. This study was conducted using the nationally representative data on older persons of Vietnam, and thus all estimations can be inferential for the Vietnamese older population as a whole, rather than outdated or non-representative groups of older persons as in various previous studies.^14,28,29

Data and Methods

Data

This study utilized secondary data from the Survey on Older Persons and Social Health Insurance in Vietnam in 2019 (in short, OP&SHI 2019). This cross-sectional survey comprised rich information of older participants in terms of demographic characteristics (such as age, gender, marital status, and ethnicity), family life and living conditions (such as living arrangements, housing conditions, and assets), health conditions (such as self-rated health and access to healthcare services), economic activities and sources of income (such as current employment, income and social protection benefits), and social relations (such as participation in community activities).

The OP&SHI 2019 sample was derived from the 2019 Population and Housing Census using the probability proportional to size (PPS) method. This sample was proportionately distributed according to the older population size across six ecological regions of Vietnam, namely Northern Midlands and Mountains, Red River Delta, Central Coast, Central Highlands, Southeast, and Mekong River Delta. The OP&SHI 2019 sample was drawn from both urban and rural residents. A multiple-stage sampling approach was employed to recruit eligible participants. In short, adopting the PPS method, 12 provinces/cities were selected across six ecological regions of Vietnam. From each of these provinces/cities, three districts were chosen, followed by the selection of four communes per district. Subsequently, three villages (clusters) were selected from each communes using a systematic random sampling. Finally, 10 eligible participants per village were chosen using the same systematic random sampling technique. One eligible participant each household were recruited in this study. The OP&SHI 2019 utilized complex household survey data with a large sample size applied using the PPS sampling technique. Since study interviewed one older adult per household, the unit of analysis in this study was individual older adults. As such, in all calculations and estimations, we used the sample weight to make all results representative for the entire Vietnamese older population.

This study applied the definition of older persons as those who aged 60 and above.³⁰ Regarding the inclusion criteria, eligible participants were those who: (i) were Vietnamese residents; (ii) aged 60 and above at the time of conducting interviews; (iii) currently resided and were available in the assigned villages for conducting research. Concerning the exclusion criteria, ineligible participants were those who: (i) dead; (ii) were absent at the time of conducting research (due to several reasons such as permanent changes in residence or short-term stay at different locations for medical treatments). In such cases of older persons who were unable to answer questionnaires, instead, their household representatives participated in the study. However, some questions required to be answered by the older participants (such as self-rated health) were excluded. Given the final response rate of the OP&SHI 2019 was 97.2%, the sample size of the OP&SHI 2019 was 3,049 older persons (those aged 60 and above).

Data was collected through face-to-face interviews. Prior to participating in the interviews, the eligible respondents were informed clearly about the purposes and content of the questionnaire. The interviews were conducted after the eligible respondents signed the informed consents. The ethical approval for the OP&SHI 2019 was issued by the Institutional Review Board of Biomedical Research of the Institute of Social and Medical Studies (ISMS) via Decision No. 01/HDDD-ISMS dated July 12th, 2019. Authors were allowed to use the OP&SHI 2019 data with the permission from the Asian Development Bank (as technical adviser and funder) and ISMS (survey conductor).

Methods

Conceptual framework

Regarding the conceptual framework, this study applied Andersen & Newman’s behavioral model³¹ to investigate determinants of healthcare services utilization. The model has been widely adopted in previous literature.^32,33

The demand for healthcare utilization of an older individual depends on not only ill health conditions but also multiple socioeconomic factors surrounding that individual, for instance ability to pay for healthcare, health belief, age, gender, healthcare infrastructure.^34,35,31 Accordingly, three groups of determinants of healthcare utilization: (i) predisposing factors reflect respondents’ demographic characteristics and health behaviors which have been appeared over a long period of time before that individual experienced ill health conditions, (ii) enabling factors refer to the conditions that facilitate the use of healthcare services, (iii) health need factors reflect health conditions of individuals and refer to the direct factors associated with the use of healthcare services (Figure 1).

Figure 1.

Conceptual framework of study.

Measures of variables

Measurement of dependent variables:

The healthcare services utilization was measured by the number of inpatient admissions and outpatient visits of Vietnamese older persons in the past 12 months. Participants were asked two following questions: “During the last 12 months, how many times you were in inpatient care?” and “During the last 12 months, how many times you were in outpatient care?”. The answer to these questions were “0, 1, 2, 3, 4, etc.”. The respondents were considered to access inpatient admissions or outpatient visits if their responses were at least one time of these two types of services. Persons who answered “0” for inpatient admissions or outpatient visits were considered those who did not use healthcare services.

As such, there were four dependent variables: (i) Frequency of inpatient admissions; (ii) Frequency of outpatient visits; (iii) Probability of inpatient admissions; and (iv) Probability of outpatient visits. The first two variables were count variables, while the last two variables were binary variables.

Measurement of explanatory variables:

Following the Andersen & Newman behavioral model, the explanatory variables were classified into three clusters: (i) predisposing variables; (ii) enabling variables; and (iii) health need variables.

Predisposing factors were as follows. Age was categorized into three groups: 60–69 = 0 (young-old); 70–79 = 1 (middle-old); 80 and above = 2 (the oldest-old). Educational attainment level was divided into four groups: No schooling/Incomplete primary education = 0; complete primary education = 1; complete lower and upper secondary education = 2; and complete college and above = 3. The other predisposing variables included gender (male = 0; female = 1); marital status (never married/divorced/separated/widowed = 0; currently married = 1); ethnicity (Kinh = 0; none-Kinh = 1); religion (Luong/Freethinker = 0; Buddhism = 1; Other = 2); employment status (Currently not employed = 0; Currently employed = 1); currently smoking (or tobacco consumption) (No = 0; Yes= = 1); and currently alcoholic drinking (or alcohol consumption) (No = 0; Yes = 1).

Enabling factors included region of residence (Northern, Central, and Southern); place of residence (rural and urban); household financial supports (No = 0; Yes = 1); living arrangements (living alone with children nearby; live alone without children nearby; living with a spouse; living with at least one child; and living with others), using health insurance during medical treatments (respondents were asked whether they used health insurance when receiving treatment during the last 12 months prior to the interview; No = 0 and Yes = 1), social participation (no social participation = 0, and participate in at least a social or culture activity = 1); and household wealth index, which was computed by applying the principal components analysis (PCA) method³⁶ for various household durable assets (such as televisions, radios, computers, telephones, refrigerators, computers, etc.), hygiene quality (such as water source, type of restroom), and housing quality (such as materials for building roofs and walls), and then five quintiles were constructed, in which the first and the fifth quintile represented the poorest and the richest, respectively.

Health need factors included: Activities of daily living (ADL) which determined whether older persons had difficulties in accomplishing any basic activities (i.e. eating, dressing, bathing, toileting, and getting up when lying down). The ADL variable was categorized into two groups: No ADL (No difficulty) and at least one ADL (difficulty in at least one activity); self-rated health (SRH) was assessed by older persons with five levels (very good; good; neutral; poor; very poor), but we grouped these into three main levels (very good/good; neutral, and poor/very poor).

All the explanatory variables were treated as categorical variables, with a value 0 as reference groups.

Statistical analysis

The descriptive analyses were presented in frequency, mean, and standard deviation to describe the characteristics of the older people for the full sample and the place of residence-disaggregated sample.

We used Pearson’s chi-square test to examine whether differences in the healthcare services utilization between rural and urban areas and other characteristics were statistically significant. This non-parametric statistic test was widely applied to examine differences between two categorical variables.^37
-40 More specifically, Pearson’s chi-square test was employed to analyze differences in dependent variables as well as independent variables among urban and rural older persons.

Moreover, to select the final independent variables for the regression models, the variance inflation factor (VIF) statistic was applied to detect the multicollinearity issues among the explanatory variables, and those with VIF values lower than 4 were included in the following analyses.⁴¹

Regression models

Given two of dependent variables were binary outcomes,⁴² multivariate logistic regressions were applied to estimate the likelihood of using or not using inpatient or outpatient services. Previous studies on this field also employed this regression model.^38,43,44 The multivariate logistic regressions were performed as follows:

\ln (\frac{p}{1 - p}) = β_{0} + β_{i} X_{i},

(1)

In which: p is the probability of an older person using inpatient or outpatient services; $X_{i}$ is a set of variables representing for predisposing factors, enabling factors, and health need factors of an older person; $β_{i}$ is a set of coefficients for these variables; and β₀ is constant.

The Poisson regressions were used to examine factors influencing the frequency of inpatient or outpatient services utilization. The Poisson regressions are applied in the case that the dependent variables are count data (only integer, non-negative values).^45
-47 The Poisson regressions were performed in the following formula:

f (Y | y_{i}) = \Pr (Y = y_{i}) = \frac{e^{λ_{i}} λ_{i} y_{i}}{y_{i}!}, y_{i} = 0, 1, 2, \dots.,

(2)

In which: f(Y|yi) represents the probability that variable Y takes on a non-negative, integer value y_i, and y_i! (y! = y.(y - 1).(y - 2). . . . .2.1, with 0!=1) and λ is the parameter of the Poisson distribution. Note that the Poisson distribution has a single parameter (λ) which is unlike the normal distribution with two parameters (i.e. mean and variance).

The Poisson regression model can be written as follows:

y_{i} = E (y_{i}) + u_{i} = λ_{i} + u_{i},

(3)

In which: y is independently distributed as Poisson random variables with mean λ_i for each individual, expressed as follows:

λ_{i} = E (y_{i} | X_{i}) = \exp [B_{1} + B_{2} X_{2 i} + \dots + B_{k} X_{k i}] = \exp (B X),

(4)

In which: exp(BX) (or e^XB); B are parameters; and X are independent variables in the model.

Results were presented as regression coefficients and standard errors. A p-value of .05 or less was regarded as statistically significant.

Measures of differences

This research adopted the standard concentration index (CI) and the Erreygers concentration index (EI) for measuring differences. CI has been widely adopted as a measurement of health gap in the previous literature,^48
-50 and it was employed in this study for difference analysis. The formula of the CI can be written as follows:

C I = \frac{2}{μ} c o v_{w} (y_{i}, r_{i}),

(5)

Where: $y_{i}$ is denoted as the measure of health services utilization; µ is the mean of outcome measure; $r_{i}$ is the relative fractional rank of an individual i in the distribution of household wealth (i = 1 for the poorest, and i = N for the richest).

CI is twice the area between the concentration curve and the line of equality,⁵¹ and its value ranges between −1 and +1 to reflect the degree of difference in healthcare services utilization. When CI equals 0, namely “perfect equality” (or no difference), healthcare services utilization is equally distributed between two groups in comparison. When CI ranges between −1 and 0 (i.e. −1<CI<0), it is indicated that healthcare utilization is more concentrated among the poor. When CI ranges between 0 and 1 (i.e. 0<CI<1), it is indicated that healthcare utilization is more concentrated among the wealthy.

Although the standard CI has numerous properties for measuring health differences, it reveals some limitations that caused biases in the health difference calculations elsewhere.⁵² To solve CI’s drawbacks, the Erreygers concentration index (EI), also known as the corrected CI, was used in this paper and could be computed as follows:

E I = \frac{4 μ}{y_{m a x} - y_{m i n}} x C I,

(6)

Where: $y_{m a x}$ and $y_{m i n}$ are the maximum and minimum values of the health variable; CI is the standard CI computed from the formula (5). The values of EI range between −1 and 1, and the interpretation similarity to that of the standard CI. Moreover, the magnitude of the EI represents the extent of the health inequalities, which can be comparable between different populations.⁵³

Various studies on health differences have been conducted using the application of EI measurement theory.^54
-56 Wagstaff et al.⁵⁷ re-emphasized that the original CI reflected the relationship between the health variable and the rank of the living standard variable, not on the living standard variable itself. This study ranked the household wealth index to construct the difference indices. We calculated both CI and EI for health variables and compared the estimates for urban and rural older persons.

Decomposition analysis of socio-economic differences

We adopted the extension of the Oaxaca-Blinder decomposition analysis to quantify the contribution of individual and social determinants to differences in healthcare services utilization between urban and rural older persons. The Oaxaca-Blinder decomposition analysis has been applied to address similar issues in previous literature.^33,27,58 In particular, we decomposed the logistic regression models for quantifying factors contributing to the urban-rural differences in the probability of healthcare services utilization. Respectively, we decomposed the Poisson regression models for quantifying factors contributing to the urban-rural differences in the frequency of healthcare services utilization. The extension of the Oaxaca-Blinder decomposition formula could be simplified by:

Y = F (X β),

(7)

Where: Y denotes the N x 1 dependent variable vector; X is an N x K matrix of independent variables; and $β$ is a K x 1 vector of coefficients.

The difference in Y between group A (rural – comparison group) and B (urban – reference group) could be decomposed as:

\begin{matrix} {\bar{Y}}_{A} - {\bar{Y}}_{B} = \underset{︸}{\bar{F (X_{A} β_{A})} - \bar{F (X_{B} β_{B})}} \\ = \underset{E}{\underset{︸}{{\bar{F (X_{A} β_{A})} - \bar{F (X_{B} β_{A})}}}} + \underset{C}{\underset{︸}{{\bar{F (X_{B} β_{A})} - \bar{F (X_{B} β_{B})}}}} \end{matrix}

(8)

Where $F (X β)$ is transformed into $\frac{e^{X β}}{1 + e^{X β}}$ for the logistic regression models. For the negative binomial or Poisson models, $F (X β)$ is transformed into $e^{X β}$ . The explained component E refers to the characteristic effect, which measures difference due to the differences between those living in urban and rural in observed characteristics. The unexplained component C measures difference due to the differences in coefficients or effects.

Results

Healthcare Services Utilization Among the Vietnamese Older Persons

The weighted results of respondents’ characteristics for full sample, for urban sample, and rural sample are presented in Table 1. On average, 76.91% and 18.39% of older people had at least one outpatient visit or inpatient admission in the past 12 months prior to the survey, respectively. However, the difference in the healthcare service utilization rates between urban and rural older persons was statistically insignificant. About 88% of older persons had no difficulties in ADLs, and no significant differences were observed between urban and rural groups. More than 50% of older persons self-rated their health status as very poor/poor.

Table 1.

Weighted percentages and means of respondents’ characteristics of the Vietnamese older people, by full sample and residence-disaggregated sample.

Older people’s characteristics	Urban (n = 468)	Rural (n = 2.581)	Total (n = 3.049)	p-value*
Inpatient utilization				.8438
At least one inpatient service in the last 12 months (%)	17.81	18.68	18.39
Frequency of inpatient service in the last 12 months (times, %)	2.95 (4.63)	1.92 (2.18)	2.25 (3.19)
Outpatient utilization				.3157
At least one outpatient service in the last 12 months (%)	80.08	75.36	76.91
Frequency of outpatient service in the last 12 months (times, %)	12.62 (11.38)	9.49 (10.81)	10.49 (11.08)
Predisposing factors
Gender				.158
Male	39.94	47.22	44.82
Female	60.06	52.78	55.18
Age				.453
60-69	62.13	57.32	58.91
70-79	22.1	24.16	23.48
80+	15.76	18.52	17.61
Marital status				.0139
Never married/SingleDivorced/Separated/Widow	36.78	26.82	30.1
Currently married	63.22	73.18	69.9
Ethnicity				.0239
Kinh	98.03	94.75	95.83
None-Kinh	1.973	5.249	4.171
Religion				.0085
Lương/Freethinker	49.68	63.45	58.92
Buddhism	32.65	21.03	24.85
Others	17.67	15.53	16.23
Employment status				.002
Currently not working	67.27	51.75	56.86
Currently working	32.73	48.25	43.14
Tobacco consumption				.1278
No	84.58	76.87	79.41
Yes	15.42	23.13	20.59
Alcohol consumption				.8603
No	63.74	64.64	64.35
Yes	36.26	35.36	35.65
Educational attainment				<.0001
No schooling/Incomplete primary education	20.17	43.43	35.78
Complete primary education	26.24	20.95	22.69
Complete lower and upper secondary education	38.42	32.86	34.69
Complete college and above	15.16	2.76	6.84
Enabling factors
Region				<.0001
Northern	19.06	40.24	33.27
Central	6.438	19.24	15.03
Southern	74.5	40.52	51.70
Household wealth				<.0001
Poorest quintile	4.44	27.63	20.00
Second poorest quintile	7.033	26.37	20.01
Mid quintile	21.06	19.95	20.32
Second wealthiest quintile	26.83	16.52	19.91
Wealthiest quintile	40.63	9.529	19.76
Household financial supports				.0073
No	16.99	7.824	10.84
Yes	83.01	92.18	89.16
Living arrangement				<.0001
Living along, children live nearby	0.7296	2.543	1.946
Living along, children not living nearby	1.877	3.755	3.137
Living with spouse	10.01	23.77	19.24
Living with at least one child	78.81	61.09	66.92
Living with grandchild	3.818	6.852	5.854
Others	4.75	1.991	2.899
Using health insurance during medical treatments				.2735
No	29.58	35.02	33.23
Yes	70.42	64.98	66.77
Social participation				.0502
No social participation	7.842	11.61	10.37
Participating in at least 1 activity	92.16	88.39	89.63
Health need factors
ADL				.3388
No ADL	89.85	87.71	88.42
At least 1 ADL	10.15	12.29	11.58
Self-rated health				<.0001
Very good/good	13.48	8.102	9.87
Neutral	47.82	36.23	40.04
Poor/very poor	38.70	55.67	50.09

Note. *Pearson’s chi-square test; sd: standard deviation. Figures in bold indicate coefficients with statistically significant.

Source. Own calculations, using OP&SHI 2019.

p < .05. **p < .01. ***p < .0001.

Some of highlights in descriptive results as follows. As for the predisposing factors, women dominated men (55.18% vs. 44.82%) and the youngest old (aged 60-69) accounting for the largest share of the study sample (58.91%), followed by the middle-old (aged 70-79) (23.48%). Being married was the most common marital status among older Vietnamese people (about 70% in both areas). Regarding employment status, the number of non-employed people was higher than that of employed counterparts in both areas (56.86% vs. 43.14%, respectively). The majority of older persons did not consume alcohol and tobacco at the time of the survey (64.35% and 79.41%, respectively).

Regarding the enabling factors, the difference in household wealth between urban and rural groups was statistically significant. The poorest and the near-poor quintiles of household wealth were disproportionately concentrated among rural residents, while the richest and the near-rich quintiles were highly concentrated among urban residents. Most of respondents (about 90%) received financial supports from their families and the weighted mean in household financial supports in the rural group was statistically significantly higher than that in the urban group. Approximately two-thirds of older persons (66.77%) used health insurance during medical treatments. No statistically significant differences in social participation and using health insurance during medical treatments were observed between urban and rural groups.

Regarding health conditions, Figure 2 reported the weighted percentage of older persons receiving medications in the last 12 months prior to the survey. It shows that those taking medicines for their comorbidities (at least two diseases) accounted for the largest share among all age groups. About 20% of those in the youngest and middle-old groups and nearly 10% of the oldest-old had no medical treatments since they did not suffer from any disease in the last 12 months.

Figure 2.

Weighted percentage of the Vietnamese older persons reporting morbidities received medications in the last 12 months by age group.

Factors associated with utilization of healthcare services

Table 2 reports several factors that were associated with the probability and frequency of healthcare services utilization. Interestingly, using health insurance during medical treatments and poor health status had statistically significant association with both probability and frequency of healthcare services utilization.

Table 2.

Factors associated with healthcare services utilization among the Vietnamese older persons.

Older people’s characteristics	Inpatient utilization		Outpatient utilization
Older people’s characteristics	Probability	Frequency	Probability	Frequency
Gender (Males as reference)
Female	−0.94 (0.27)	−0.60 (0.24)*	0.63(0.22)***	−0.09 (0.11)
Age (60–69 as reference)
70–79	−0.53 (0.17)	−0.44 (0.21)**	0.02 (0.22)	−0.12 (0.09)
80+	−0.13 (0.22)	0.18 (0.25)	−0.13 (0.29)	−0.17 (0.11)
Marital status (Never married/Single/Divorced/Separated/Widow as reference)
Currently married	0.12 (0.19)	0.18 (0.22)	0.50 (0.24)	0.33 (0.12)
Ethnicity (Kinh as reference)
Non-Kinh	−0.03 (0.27)	−0.10 (0.28)	−0.37 (0.27)	−0.35 (0.18)
Religion (Lương/Freethinker as reference)
Buddhism	0.35 (0.25)	0.02 (0.24)	0.66 (0.27)*	0.16 (0.13)
Others	−0.31 (0.24)	−0.68 (0.24)***	0.70 (0.28)*	0.07 (0.14)
Residence (urban as reference)
Rural	−0.21 (0.21)	−0.08 (0.26)	0.29 (0.26)	−0.02 (0.12)
Employment status (Currently not working as reference)
Currently working	−0.36 (0.18)*	−0.40 (0.21)	−0.11 (0.23)	−0.18 (0.09)
Tobacco consumption (No as reference)
Yes	−0.41 (0.26)	−0.45 (0.26)	−0.11 (0.24)	−0.24 (0.14)
Alcohol consumption (No as reference)
Yes	0.56 (0.23)	−0.90 (0.25)	−0.06 (0.21)	−0.28 (0.10)
Educational attainment (No schooling/Incomplete primary education as reference)
Complete primary education	0.26 (0.19)	0.23 (0.21)	−0.13 (0.24)	−0.17 (0.12)
Complete lower and upper secondary education	−0.24 (0.18)	−0.29 (0.24)	0.03 (0.26)	−0.11 (0.11)
Complete college and above	−1.43 (0.39)***	−1.37 (0.46)***	0.53 (0.51)	0.11 (0.19)
Region (Northern as reference)
Central	−0.59 (0.25)*	−0.59 (0.25)	−0.67 (0.27)*	−0.21 (0.12)
Southern	−0.48 (0.24)	−0.48 (0.24)	1.01(0.31)***	0.73(0.13)***
Household wealth (Poorest quintile as reference)
Second poorest quintile	0.05 (0.18)	0.16 (0.21)	−0.21 (0.23)	0.08 (0.13)
Mid quintile	0.07 (0.22)	0.02 (0.24)	0.18 (0.24)	−0.25 (0.13)
Second wealthiest quintile	−0.59 (0.22)	−0.42 (0.26)*	0.09 (0.30)	−0.14 (0.18)
Wealthiest quintile	−0.33 (0.29)	−0.13 (0.31)	0.02 (0.34)	−0.35 (0.16)
Household financial supports (No as reference)
Yes	0.32 (0.23)	0.24 (0.28)	0.11 (0.39)	−0.09 (0.23)
Living arrangement (Living along. children live nearby as reference)
Living along. children not living nearby	−0.77 (0.37)	−0.54 (0.43)*	−0.54 (0.46)	−0.02 (0.30)
Living with spouse	−0.76 (0.37)	−0.52 (0.41)*	0.90 (0.45)	−0.29 (0.25)
Living with at least one child	−0.38 (0.32)	−0.33 (0.36)	0.87 (0.42)*	−0.14 (0.24)
Living with grandchild	−0.69 (0.42)	−0.42 (0.48)	−0.34 (0.58)	−0.36 (0.28)
Others	−1.87 (0.61)	1.36 (0.69)	−0.65 (0.66)	−0.21 (0.44)
Using health insurance during medical treatments (No as reference)
Yes	3.23 (0.35)***	3.21 (0.33)***	3.45(0.18)***	2.14(0.12)***
Social participation (No social participation as reference)
Participating in at least 1 activity	−0.42 (0.20)	−0.22 (0.24)*	1.31(0.26)***	−0.14 (0.13)
ADL (No ADL as reference)
At least 1 ADL	0.59 (0.17)***	0.99 (0.21)	0.69 (0.27)*	0.37 (0.14)
SRH (Self-rated health as very good/good as reference)
Self-rated health as normal	−2.75 (0.67)	0.91 (0.49)	0.79 (0.49)	0.76 (0.57)
Self-rated health as poor/very poor	0.24 (0.14)	0.53 (0.31)	0.32 (0.14)*	0.28(0.07)***

Note: Multivariate logistic regressions for the probability of using inpatient/outpatient services. Poisson regressions for the frequency of using inpatient/outpatient services.

Source: Own calculations, using OP&SHI 2019.

p < .05. **p < .01. ***p < .0001.

All independent variables included in the following multivariate logistic regression analyses based on the VIF results which were presented in Table A1.

Differences in Healthcare Services Utilization Among Urban and Rural Older Persons

Tables 3 and 4 respectively present the measure of healthcare services utilization differences among Vietnamese older persons by place of residence samples and the full sample. The magnitude of results was relatively small and nearly 0.

Table 3.

Standard concentration index (CI) and Erreygers concentration index (EI) of inpatient utilization among the Vietnamese older persons.

	CI		EI
	Frequency of inpatient visits	Probability of inpatient visits	Frequency of inpatient visits	Probability of inpatient visits
Urban	0.058	−0.067	0.005	−0.064
Rural	−0.050	−0.029	−0.003	−0.023
Total	0.041	−0.049	0.002	−0.040

Source. Own calculations, using OP&SHI 2019.

Table 4.

Standard concentration index (CI) and Erreygers concentration index (EI) of outpatient utilization among the Vietnamese older persons.

Place of residence	CI		EI
Place of residence	Frequency of outpatient visits	Probability of outpatient visits	Frequency of outpatient visits	Probability of outpatient visits
Urban	−0.143	−0.006	−0.053	−0.039
Rural	−0.093	0.001	−0.024	0.004
Total	−0.053	0.002	−0.017	0.015

Source. Own calculations, using OP&SHI 2019.

When considering the economic differences in the healthcare utilization among older persons, findings indicated that both inpatient service utilization and outpatient service utilization were equally distributed across all household wealth quintiles. This pattern of equitable healthcare utilization strongly consistent, regardless older persons resided in urban or rural areas.

The Extension of Oaxaca-Blinder Decomposition Analysis

Decomposition of rural-urban differences in inpatient services utilization

Table 5 shows that the difference in the mean of inpatient admissions between urban and rural older persons was −0.075 (95% CI = −0.193; 0.043) visits, but it is statistically insignificant. Similarly, the difference in the probability of inpatient admissions among rural and urban older people was 0.8% (95% CI = −0.049; 0.066) points, but it is statistically insignificant.

Table 5.

Oaxaca-Blinder decomposition of rural-urban differences in inpatient services utilization among the Vietnamese older people.

Characteristics	Frequency		Probability
Characteristics	Coef. (S.E)	%	Coef. (S.E)	%
Difference in mean of inpatient services utilization	−0.075 (0.060)		0.008 (0.029)
Difference explained due to characteristics	0.078 (0.031)*	−104.13	0.039 (0.014)**	464.08
Difference explained due to coefficients	−0.153 (0.067)*	204.13	−0.031 (0.033)	−364.08
Explained contribution (due to characteristics effects)
Age (70−79)	−0.002 (0.001)	2.23	−0.002 (0.001)	−13.52
Age (80+)	−0.002 (0.002)	2.85	−0.002 (0.001)	−13.52
Female	0.010 (0.005)*	−13.49	0.004 (0.002)*	50.94
Complete primary education	−0.001 (0.003)	0.25	0.01 (0.007)	38.15
Complete lower and upper secondary education	0.006 (0.004)	−7.43	0.01 (0.007)	38.15
Complete college and above	0.023 (0.024)	−30.45	0.01 (0.007)	38.15
Central	−0.015 (0.009)	19.74	0.019 (0.008)*	113.92
Southern	0.067 (0.023)**	−88.97	0.019 (0.008)*	113.92
Currently married	0.001 (0.007)	−0.34	0.001 (0.001)	4.85
Non−Kinh	−0.001 (0.002)	1.62	0.000 (0.001)	1.17
Currently working	−0.02 (0.011)	26.34	−0.005 (0.004)	−56.49
Participating in at least 1 activity	0.003 (0.003)	−4.53	0.001 (0.001)	11.34
Second poorest quintile	0.008 (0.011)	−10.74	0.023 (0.011)*	67.41
Mid quintile	0.000 (0.001)	−0.05	0.023 (0.011)*	67.41
Second wealthiest quintile	0.013 (0.008)	−17.25	0.023 (0.011)*	67.41
Wealthiest quintile	0.025 (0.028)	−33.54	0.023 (0.011)*	67.41
Received financial support	−0.004 (0.009)	5.92	0.002 (0.004)	29.55
Using health insurance during medical treatments	−0.058 (0.020)**	77.92	−0.023 (0.005)***	−276.53
Living along, children not living nearby	−0.005 (0.003)	6.72	−0.001 (0.003)	−1.47
Living with spouse	−0.02 (0.02)	26.25	−0.001 (0.003)	−1.47
Living with at least one child	0.02 (0.023)	−26.01	−0.001 (0.003)	−1.47
Living with grandchild	−0.006 (0.005)	8.56	−0.001 (0.003)	−1.47
Others	0.011 (0.006)	−15.07	−0.001 (0.003)	−1.47
At least 1 ADL	0.004 (0.002)*	−5.92	0.002 (0.001)**	23.87
Self−rated health at neutral	−0.010 (0.015)	13.98	0.008 (0.004)	46.60
Self−rated health at poor/very poor	0.033 (0.023)	−44	0.008 (0.004)	46.60
Unexplained contribution (due to coefficients effects)
Age (70−79)	−0.011 (0.012)	14.43	0.014 (0.035)	79.89
Age (80+)	0.008 (0.008)	−11.32	0.014 (0.035)	79.89
Female	−0.047 (0.047)	63.32	−0.039 (0.073)	−456.54
Complete primary education	0.013 (0.015)	−17.07	0.120 (0.221)	−164.80
Complete lower and upper secondary education	0.001 (0.018)	−1.09	0.120 (0.221)	−164.80
Complete college and above	−0.01 (0.014)	12.92	0.120 (0.221)	−164.80
Central	−0.001 (0.007)	0.45	0.120 (0.221)	711.60
Southern	0.062 (0.075)	−82.28	0.120 (0.221)	711.60
Currently married	0.031 (0.033)	−40.86	0.025 (0.055)	301.17
Non-Kinh	−0.001(0.002)	1.05	−0.106 (0.193)	−1256.40
Currently working	−0.001 (0.016)	1.23	−0.022 (0.044)	−265.00
Participate at least 1 social activity	−0.042 (0.052)	56.51	−0.03 (0.064)	−360.25
Second poorest quintile	−0.002 (0.006)	2.68	0.07 (0.128)	205.36
Mid quintile	0.018 (0.020)	−24.26	0.07 (0.128)	205.36
Second wealthiest quintile	0.002 (0.020)	−3.06	0.07 (0.128)	205.36
Wealthiest quintile	0.04 (0.044)	−53.28	0.07 (0.128)	205.36
Received financial support	0.076 (0.08)	−101.79	0.063 (0.150)	747.84
Using health insurance during medical treatments	0.062 (0.075)	−82.85	0.066 (0.108)	783.63
Living along, children not living nearby	−0.001 (0.002)	1.36	−0.04 (0.076)	−94.09
Living with spouse	−0.023 (0.018)	31.08	−0.04 (0.076)	−94.09
Living with at least one child	−0.082 (0.078)	109.28	−0.04 (0.076)	−94.09
Living with grandchild	−0.001 (0.004)	1.85	−0.04 (0.076)	−94.09
Others	−0.119 (0.069)	158.58	−0.04 (0.076)	−94.09
At least 1 ADL	−0.001 (0.005)	1.67	0.005 (0.009)	58.15
Self-rated health at neutral	0.015 (0.038)	−19.8	−0.006 (0.033)	−34.79
Self-rated health at poor/very poor	0.025 (0.035)	−33.54	−0.006 (0.033)	−34.79

Note. Numbers in bold indicate that coefficients are statistically significant.

p < .05. **p < .01. ***p < .0001; Coef indicated Coefficient; S.E: denoted standard error.

Source: Own calculations, using OP&SHI 2019.

Although there was statistical insignificance in overall urban-rural differences, some determinants were reported to contribute to the explained part of the differences. More particularly, gender, region, using health insurance during medical treatments, household wealth, and ADLs were reported to be the statistically significant factors contributing to differences in both frequency and probability of inpatient services utilization. More specifically, using health insurance during medical treatments was the only factor reducing the difference, while being female, living in Central and Southern regions, living in households at 2nd, 3rd and 4th wealth quintile, and suffering from at least one ADL were factors that worsen the difference.

When the rural regression coefficients were substituted for those of the urban regression, no significant contributors to the difference in the “unexplained” component were found.

Decomposition of rural-urban differences in outpatient services utilization

Regarding the frequency of outpatient services, Table 6 shows that older persons living in rural areas had a lower frequency of outpatient visits than those living in urban areas. More particularly, the statistically significant difference in the mean of visits between older persons living in urban and rural areas were −3.033 visits (95% CI = −4.282; −1.783), of which −3.201 visits (95% CI = −4.567; −1.833) (or 105.53% of total differences) were explained by differences between rural and urban in the distribution of social determinants. The remaining 0.168 visits (95% CI = −1.809; 2.144) (or −5.53% of total differences) were explained by unobserved factors.

Table 6.

Oaxaca-Blinder decomposition of rural-urban differences in outpatient services utilization among Vietnamese older people.

Characteristics	Frequency		Probability
Characteristics	Coef (S.E)	%	Coef (S.E)	%
Difference in mean of outpatient services utilization	−3.033 (0.638)***		−0.049 (0.018)**
Difference explained due to characteristics	-3.201 (0.698)***	105.53	−0.085 (0.024)***	175.84
Difference explained due to coefficients	0.168 (1.009)	−5.53	0.037 (0.024)	−75.84
Explained contribution (due to characteristics effects)
Age (70-79)	−0.018 (0.013)	0.60	0.000 (0.001)	−0.09
Age (80+)	−0.032 (0.020)	1.05	−0.001 (0.001)	1.67
Female	0.052 (0.049)	−1.71	0.006 (0.002)**	−12.51
Complete primary education	0.004 (0.036)	−0.14	−0.001 (0.002)	1.05
Complete lower and upper secondary education	−0.006 (0.043)	0.19	0.001 (0.002)	−2.89
Complete college and above	−0.199 (0.167)	6.549	−0.006 (0.007)	11.78
Central	−0.189 (0.086)*	6.23	−0.011 (0.004)**	23.09
Southern	−2.226 (0.345)***	73.39	−0.030 (0.010)**	62.24
Currently married	0.140 (0.069)	−4.61	0.008 (0.003)	−16.15
Non-Kinh	−0.025 (0.032)	0.81	−0.001 (0.001)	2.21
Currently working	0.206 (0.099)*	−6.79	−0.002 (0.004)	4.25
Participate at least 1 social activity	0.001 (0.033)	−0.03	−0.005 (0.001)***	9.84
Second poorest quintile	−0.035 (0.137)	1.15	−0.002 (0.005)	4.84
Mid quintile	0.003 (0.009)	−0.1	−0.001 (0.001)	0.82
Second wealthiest quintile	0.106 (0.094)	−3.49	−0.004 (0.004)	9.25
Wealthiest quintile	−0.120 (0.345)	3.96	−0.014 (0.013)	29.32
Received financial support	0.222 (0.106)*	−7.32	0.001 (0.003)	−1.91
Using health insurance during medical treatments	−0.721 (0.098)***	23.76	−0.021 (0.002)***	42.45
Living along, children not living nearby	−0.004 (0.023)	0.13	−0.001 (0.001)	2.69
Living with spouse	−0.151 (0.166)*	4.98	−0.017 (0.008)*	35.85
Living with at least one child	0.138 (0.194)	−4.55	0.022 (0.009)	−46.02
Living with grandchild	−0.116 (0.046)*	3.83	−0.003 (0.002)	7.02
Others	−0.094 (0.079)	3.09	0.006 (0.002)	−11.50
At least 1 ADL	0.023 (0.017)	−0.752	0.002 (0.001)**	−3.78
Self-rated health at neutral	0.047 (0.133)	−1.53	−0.001 (0.005)	0.23
Self-rated health at poor/very poor	0.352 (0.178)*	−11.62	0.009 (0.007)	−17.95
Unexplained contribution (due to coefficients effects)
Age (70-79)	0.06 (0.317)	−1.97	−0.001 (0.005)	0.47
Age (80+)	0.053 (0.287)	−1.75	0.005 (0.007)	−10.42
Female	0.183 (1.001)	−6.02	-0.023 (0.034)	46.79
Complete primary education	0.034 (0.206)	−1.11	-0.005 (0.01)	10.96
Complete lower and upper secondary education	0.122 (0.680)	−4.02	0.018 (0.023)	−36.82
Complete college and above	0.053 (0.298)	−1.74	0.002 (0.008)	−3.56
Central	−0.063 (0.338)	2.06	0.004 (0.005)	−8.52
Southern	0.111 (0.666)	−3.64	0.003 (0.027)	−6.19
Currently married	0.159 (0.876)	−5.23	−0.035 (0.047)	72.84
Non-Kinh	0.016 (0.089)	−0.53	0.001 (0.002)	−0.62
Currently working	-0.094 (0.506)	3.09	0.007 (0.012)	−15.22
Participating in at least 1 activity	0.226 (1.217)	−7.45	0.087 (0.089)	−178.75
Second poorest quintile	-0.046 (0.253)	1.53	0.003 (0.004)	−6.39
Mid quintile	−0.042 (0.234)	1.40	0.012 (0.013)	−24.83
Second wealthiest quintile	−0.142 (0.773)	4.69	0.018 (0.018)	−37.03
Wealthiest quintile	−0.017 (0.155)	0.56	0.026 (0.026)	−52.78
Received financial support	0.533 (2.897)	−17.56	−0.029 (0.039)	58.85
Using health insurance during medical treatments	−0.466 (2.536)	15.37	0.078 (0.077)	−161.39
Living along, children not living nearby	0.003 (0.016)	-0.09	−0.001 (0.001)	2.46
Living with spouse	−0.019 (0.109)	0.61	−0.004 (0.006)	8.23
Living with at least one child	−0.324 (1.766)	10.68	−0.036 (0.054)	75.22
Living with grandchild	−0.036 (0.198)	1.19	0.003 (0.004)	−5.17
Others	0.053 (0.297)	−1.76	0.003 (0.005)	−7.18
At least 1 ADL	0.054 (0.298)	−1.80	−0.003 (0.005)	7.18
Self-rated health at neutral	0.012 (0.139)	−0.41	0.005 (0.017)	−10.71
Self-rated health at poor/very poor	−0.040 (0.264)	1.32	0.008 (0.015)	−16.85

Note. Numbers in bold indicate coefficients with statistically significant.

p < .05. **p < .01. ***p < .0001; Coef indicated Coefficient; S.E: denoted standard error.

Source: Own calculations, using OP&SHI 2019.

Similarly, as for the probability of utilization, older persons living in rural areas had a lower probability of outpatient visits than those living in urban areas. More specifically, the statistically significant difference in the probability of visits between older persons living in urban and rural areas were 4.9% (95% CI = −0.084; −0.013) was statistically significant. This difference was explained by characteristic effects by −8.5% (95% CI = −0.117; −0.054) points (or 175.84% of total differences), which was statistically significant. The remaining 3.7% (95% CI = −0.009; 0.083) points (or −75.84% of total differences) were explained by unobserved factors but were not statistically significant.

Gender, region, employment status, social participation, financial support, living arrangements, using health insurance during medical treatments, ADL and SRH contributed to the difference in the frequency and probability of outpatient services utilization among older persons. More specifically, living in Central and Southern areas and using health insurance during medical treatments were two main factors contributing to reduce urban-rural differences, while the other factors worsened the differences.

When the coefficients in regression for rural older persons were substituted into those of regression for urban counterparts, there were no significant contributors to urban-rural differences in the “unexplained” component.

Discussion

To our best knowledge, this study was the first attempt to bridge the knowledge gap concerning urban-rural differences in healthcare services utilization among Vietnamese older population. This study is significantly essential, given Vietnam is a middle-income country experiencing a rapidly aging population. By considering inpatient and outpatient healthcare service utilization of older persons, this study revealed some key findings that need to be informed.

Generally, most of Vietnamese older persons reported using outpatient services (76.91% of older persons, average of 10.49 visits). However, the significantly lower percentage of Vietnamese older persons using inpatient services (18.39% of older persons, average of 2.25 admissions). In this study, the healthcare utilization information was self-reported by older persons when they have to visit medical professionals for diagnoses and treatments. Hence, the scope of this study merely focused on the healthcare utilization raised due to the presence of diseases. Apparently, the higher prevalence of comorbidity, particularly chronic diseases and non-communicable diseases, was observed among Vietnamese older persons in previous studies.^33,59 Frequent medical diagnoses and medications for following up and managing the disease severity were explained the findings on healthcare utilization of older persons in this study.⁶⁰

When considering the economic differences in the healthcare utilization among older persons, findings indicated that both inpatient and outpatient services utilizations were mostly equally distributed across all household wealth quintiles. This pattern of equitable healthcare utilization strongly consistent, regardless older persons resided in urban or rural areas. The findings implied that the distribution healthcare service utilization was not significantly associated with their household wealth status. One thing to note that the EI and CI analyses were conducted in entire population and by place of residence separately. Differences in healthcare utilization between urban and rural older residents failed to capture within these analyses. Thus, while the inpatient and outpatient service utilization appeared to be mostly evenly distributed, further analyses should explore whether other factors exist which may cause rural-urban differences in healthcare utilization.

In further analyses, the Oaxaca-Blinder decomposition analyses revealed that there were significant rural-urban differences in outpatient services utilization among Vietnamese older persons. Particularly, older adults residing in rural regions had 4.9% lower in the probability of outpatient services utilization than those in urban regions. Similarly, rural residents had 3.033 times lower in the frequency of outpatient services utilization than urban residents. These urban-rural differences were consistent with some previous findings, particularly in China and India.^20,61,62,24

The urban-rural differences could be attributable to health system and individual factors. Given the scope of this study focusing on older individuals, it revealed several interesting factors influencing the urban-rural differences, including poor health status and the use of health insurance during medical treatments. Firstly, findings indicated that while poor health status (suffering at least one ADL limitation and very poor/poor SRH) was a health need factor worsening the rural-urban differences, using health insurance during medical treatments was an enabling factor improving urban-rural differences. In other words, very poor/poor SRH or suffering at least one ADL limitation were contributed to widen urban-rural differences in healthcare utilization. In contrast, those using health insurance during medical treatments were found to reduce the urban-rural differences in healthcare utilization.

As people get older, their health needs become more complex, and they may require more medical attention to manage their health conditions. Older persons having at least one difficulty in ADL and very poor/poor SRH were found to worse urban-rural differences. These findings were consistent with some previous literature.^32,63
-65 Possible reasons could be: (i) rural older persons tend to face more economic difficulties than their urban counterparts, and as such, rural people find financial barriers in receiving healthcare services; (ii) rural older persons might face with difficulties in travelling to the healthcare facilities due to poor infrastructure and limited transportation modes.

Secondly, the use of health insurance during medical treatments was a crucial factor in reducing urban-rural differences in healthcare services utilization among the older Vietnamese. Given the coverage rate of social health insurance for older persons was about 90.85% in 2020⁶⁶ that 66.77% of respondents used health insurance when receiving medical treatments as found in this study apparently implied that, for various reasons, there was a particular proportion of insured older persons not using health insurance for treatments. Previous studies illustrated the importance of health insurance as a third-payer party in improving access to healthcare utilization among older adults.^32,61 Older adults were more likely to face financial constraints due to limited income sources from pensions or social security and insufficient retirement savings. Together, the urban-rural gap in employment and income in Vietnam has been investigated elsewhere,⁶⁷ and as a result, the healthcare utilization behaviors between urban and rural older adults might be different and the use of health insurance may help to improve the access to healthcare, especially for rural residents. Apparently, the important role of health insurance on mitigating the urban-rural differences in healthcare services utilization was emphasized in previous investigations, especially in rural areas.^68,18,69

In Vietnam, health insurance was considered to be the best solution for reducing the financial burden of healthcare utilization among older persons, especially when their income mainly depends on pension fund and family supports.⁷⁰ GOV has shifted its concerns to aging field since aging population was alarming in the early 2010s. More particularly, the Health Insurance Law and the Social Insurance Law have been amended in various aspects relating to the older population. For instance, older persons aged 80 and over who do not have monthly pension or monthly social assistance are eligible for receiving social protection benefits and a free-of-charge social health insurance card. The amendment has helped change older persons’ behaviors toward using health insurance in medical examination and treatment services.

The universal health insurance coverage has been implementing with the aim of increasing the health equity, but several issues have been raised. Although the health insurance coverage rate reached about 90.85% of the entire population,⁶⁶ numerous medications and medical supplies failed to be covered by health insurance. In addition, the out-of-pocket payment was relatively high (approximately 40% of total health spending)⁷¹, incurring potentially catastrophic burdens, especially for the older poor. Thus, in the scope of the aging field, health insurance policies related to older persons should be designed more specifically to reduce their out-of-pocket payments when using healthcare services. Some policy recommendations are: (i) achieving 100% of health insurance coverage rate for older persons; (ii) expanding the scope of healthcare services covered by insurance (such as expanding the list of medications and medical supplies frequently used by older persons); (iii) increasing the reimbursement rate for inpatient and/or outpatient services. Expanding the range of health services provided was reported to have a significant impact on reducing inequality in the healthcare services utilization among Korean older people.⁷² Addressing the above challenges that older individuals usually face in accessing healthcare services can improve the quality of life of this vulnerable population and promote healthy aging. Maintaining a good health status for older people and thus improving their capacity to work will also be beneficial to other policies such as Social Insurance Law aiming at higher retirement ages for both men and women to actuarially balance the pension fund.⁷⁰

This study had some limitations that need to be stated for further improvements. First, the study analyzed evidence based on the total sample population and sub-populations (urban and rural residents), but it is insufficient to observe the comprehensive picture of socioeconomic differences by analyzing only subgroup of residence, meaning that more dimensions in subgroups need to be analyzed. Second, although the database is reliable and representative, it was a cross-sectional data in 2019 so that it was impossible to establish causal relationships between the dependent and independent variables. Third, this study examined the status of possessing social health insurance but failed to report the use of social health insurance while using healthcare utilization. Hence, the results might not reflect the comprehensive picture of social health insurance on mitigating urban-rural differences.

Our findings revealed several policy implications and future research opportunities for addressing the urban-rural differences in healthcare utilization among older adults. Regarding policy implications, developing health insurance schemes supporting the rural older population in accessing healthcare is essential to ensure that they receive benefits equivalent to those of their urban counterparts, particularly for chronic disease management and preventive care. Moreover, GOV should implement subsidies or free-of-charge for low-income rural older persons to minimize out-of-pocket expenses.

This study focused on some individual factors but failed to examine health system-related factors. Hence, for future research directions, studies investigating the choice of healthcare facilities of older persons when they use healthcare utilization are crucial to exploring deeper nuances. It is expected to reveal interesting findings that provide explicit information about the current health system in Vietnam. Regarding the health insurance factor, future information on the reimbursement rates and out-of-pocket payment should be investigated since the capacity to pay for healthcare is an important determinant for understanding the health-seeking behaviors and healthcare utilization among older adults. In terms of study design, this cross-sectional study failed to investigate any causal relationships. Hence, in subsequent studies, available longitudinal data will help address socioeconomic issues in further detail and explore causal relationships in the use of healthcare services among older Vietnamese.

Concluding Remarks

Using the nationally representative data for older Vietnamese persons, this study confirmed the differences in healthcare services utilization among urban and rural older persons. Several important factors were reported to contribute to urban-rural differences. While the poor health status widen the rural-urban differences, the health insurance contributes to reduce the rural-urban differences. Findings are crucial as evidence for continually developing the health insurance scheme and investment to support the rural older population in accessing healthcare. Future research opportunities are proposed to explore deeper comprehensive understanding on this field, including investigations on the choice of healthcare facilities, the reimbursement rates and out-of-pocket payment of older persons when using healthcare services among urban and rural older residents. Also, longitudinal studies are proposed to explore further causal relationships in the use of healthcare services among older Vietnamese.

Footnotes

Appendix

Table A1.

VIF test results of multicollinearity.

Variables	VIF	1/VIF
Gender	1.81	0.553984
Age	1.59	0.627937
Marital status	1.45	0.690448
Ethnicity	1.16	0.864711
Religion	1.34	0.747667
Working status	1.39	0.719715
Tobacco consumption	1.29	0.773538
Alcohol consumption	1.43	0.698799
Educational Attainment	1.57	0.637208
Region	1.71	0.586105
Residence	1.37	0.731051
Household wealth	1.50	0.668805
Household financial support	1.03	0.969227
Living arrangement	1.08	0.922027
Using health insurance during medical treatments	1.03	0.967574
Social participation	1.35	0.740582
ADL	1.25	0.800584

Source: Own calculations, using OP&SHI 2019.

Acknowledgements

We would like to thank the Asian Development Bank (ADB) and the Institute of Social and Medical Studies (ISMS) for permitting us to use the data.

ORCID iD

Long Thanh Giang

Ethical Considerations

Our study did not require an ethical board approval because we used the secondary data, which was ethically approved.

Author Contributions

The study’s concept and design were performed by Long Thanh Giang and Tien Thuy Ngoc Doan. Material preparation was performed by Tien Thuy Ngoc Doan and verified by Long Thanh Giang. Both authors worked on data analyses and the drafts of the research. The final manuscript and its revised versions were read and approved by all authors.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

United Nations. World Population Prospects 2024: Methodology of the United Nations Population Estimates and Projections. 2024. https://population.un.org/wpp/Publications/Files/WPP2024_Methodology.pdf

Chomik

Piggott

Population Ageing and Social Security in Asia. Asian Economic Policy Review. 2015;10(2):199-222.

United Nations Department of Economic and Social Affairs. World Population Prospects 2022: Summary of Results. 2022. United Nations.

GSO. The Population and Housing Census 2019: Population ageing and Older persons in Vietnam. 2021. Youth Publishing House.

World Bank. Population ages 65 and above (% of total population). Accessed October 26th, 2024. https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Quốc hội Việt Nam. Luật người cao tuổi. 39/2009/QH12. Việt Nam2009.

The Vietnam National Assembly. Law on Health Insurance. 25/2008/QH12. Vietnam; 2008.

Giang

Pham

THT

Phi

Nguyen

NT.

Healthcare services utilisation and financial burden among Vietnamese older people and their households. Int J Environ Res Public Health. 2023;20(12):6097.

Huu

Bui

The healthcare support program and healthcare utilization of older adults in ethnic minority areas in Vietnam. Social Science & Medicine. 2024;361:117357.

10.

World Health Organization. Health Service Utilization and the Financial Burden on Households in Vietnam: The Impact of Social Health Insurance. 2006. World Health Organization.

11.

Vietnam Women’s Union. Khảo sát Người cao tuổi Việt Nam 2011: Một số kết quả chủ yếu. 2012. The Women’s Publishing House.

12.

HTT

Nguyen

, et al. Prevalence of frailty and its associated factors in older hospitalised patients in Vietnam. BMC Geriatr. 2017;17(1):216. doi:10.1186/s12877-017-0609-y

13.

Cheng

Gao

, et al. Factors associated with oral health service utilization among adults and older adults in China, 2015-2016. Community Dent Oral Epidemiol. 2020;48(1):32-41. doi:10.1111/cdoe.12497

14.

Nguyen

Giang

LT.

Factors influencing the Vietnamese older persons in choosing healthcare facilities. Health Serv Insights. 2021;14:11786329211017426.

15.

Van Minh

Bautista

MAC

Tuan Anh

Describing the primary care system capacity for the prevention and management of non-communicable diseases in rural Vietnam. Int J Health Plann Manag. 2014;29(2):e159-e173.

16.

World Health Organization. Health systems governance in Viet Nam. Accessed October 27th, 2024. https://www.who.int/vietnam/health-topics/health-systems-governance

17.

Đoàn Ngọc Thuỷ Tiên, Hà Quỳnh Hoa, Giang Thanh Long. Các nhân tố tác động đến việc sử dụng dịch vụ khám, chữa bệnh của người cao tuổi Việt Nam. Tạp chí Kinh tế&Phát triển. 2023;316(10/2023):13-23. doi:10.33301/JED.VI.1367

18.

Long

Tham

PTH

Phong

Nam

NT.

Healthcare services utilisation and financial burden among Vietnamese older people and their households. Int J Environ Res Public Health. 2023;20(12):6097. doi:10.3390/ijerph20126097

19.

Thuong

NTT

Huy

Tai

Kien

. Impact of health insurance on health care utilisation and out-of-pocket health expenditure in Vietnam. BioMed Res Int. 2020;2020:5287. doi:10.1155/2020/9065287

20.

Banerjee

Determinants of rural-urban differential in healthcare utilization among the elderly population in India. BMC Public Health. 2021;21(1):1-18.

21.

Kovacs

Piko

Juhasz

, et al. Comparative analysis of health status and health service utilization patterns among rural and urban elderly populations in Hungary: a study on the challenges of unhealthy aging. GeroScience. 2024;46(2):2017-2031.

22.

Liao

Chang

Yang

FA.

Does the universal health insurance program affect urban-rural differences in health service utilization among the elderly? Evidence from a longitudinal study in Taiwan. J Rural Health. 2012;28(1):84-91.

23.

Wulandari

Laksono

AD.

Urban-Rural Disparities in The Utilization of Primary Health Care Center Among Elderly in East Java, Indonesia. 2019. Universitas Airlangga.

24.

Ying

Wang

Bai

Rural-urban differences in health outcomes, healthcare use, and expenditures among older adults under universal health insurance in China. PLoS One. 2020;15(10):e0240194.

25.

Phong

Long

Tham

PTH

. Unmet need of care for activities of daily living among older persons with functional disability in Vietnam. Front Public Health. 2023;11(1)

26.

Sibley

Glazier

RH.

Reasons for self-reported unmet healthcare needs in Canada: a population-based provincial comparison. Healthcare Policy. 2009;5(1):87.

27.

Zhang

Shi

Wang

Rural-urban disparities in the unmet need for home visiting services among oldest-old in china: changes over time and decomposition analysis. Arch Gerontol Geriatrics. 2023;108:104919.

28.

Pham

Nguyen

NTT

ChieuTo

, et al. Gender differences in quality of life and health services utilization among elderly people in rural Vietnam. Int J Environ Res Public Health. 2019;16(1):69.

29.

Tuyet

Long

GT.

Factors associated with regional disparity in utilization of healthcare services among the Vietnamese older people. J Pop Soc Stud. 2021 2021;29:15-31.

30.

Vietnam National Essembly. The Law on the Elderly, Viet Nam (in Vietnamese). 2009.

31.

Andersen

Newman

JF.

Societal and individual determinants of medical care utilization in the United States. Milbank Q. 2005;83(4):00428. doi:10.1111/j.1468-0009.2005.00428.x

32.

Sun

, et al. Influencing factors of inequity in health services utilization among the elderly in China. Int J Equity Health. 2018;17:1-10.

33.

Leon-Gonzalez

Giang

LT.

Decomposing gender inequality in functional disability among older people in Vietnam. Arch of Gerontology and Geriatrics. 2020;87:103989. doi:10.1016/j.archger.2019.103989

34.

Andersen

A behavioral model of families’ use of health services. a behavioral model of families’ use of health services. 1968.

35.

Andersen

Aday

A framework for the study of access to medical care. Health Serv Res. 1974;9:208-20.

36.

Vyas

Kumaranayake

Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459-468. doi:10.1093/heapol/czl029

37.

Cameron

Song

Manheim

Dunlop

DD.

Gender disparities in health and healthcare use among older adults. J Women’s Health. 2010;19(9):1643-1650.

38.

Gajović

Janićijević

Andrić

Đurović

Radević

Gender differences in health care utilization among the elderly. Serb J Exp Clin Res. 2021;22(3):195-203.

39.

McHugh

ML.

The chi-square test of independence. Biochemia medica. 2013;23(2):143-149.

40.

Carlson

Liu

Greenlund

KJ.

Urban–rural differences in health care utilization for inflammatory bowel disease in the USA, 2017. Digestive Dis Sci. 2022:1-11.

41.

O’brien

RM.

A Caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41(5):673-690. doi:10.1007/s11135-006-9018-6

42.

Maalouf

Logistic regression in data analysis: an overview. Int J Data Anal Techniques and Strategies. 2011;3(3):281-299.

43.

Gerst-Emerson

Jayawardhana

Loneliness as a public health issue: the impact of loneliness on health care utilization among older adults. Am J Public Health. 2015;105(5):1013-1019.

44.

Kim

Y-S

Lee

Moon

, et al. Unmet healthcare needs of elderly people in Korea. BMC Geriatr. 2018;18:1-9.

45.

Coxe

West

Aiken

LS.

The analysis of count data: A gentle introduction to Poisson regression and its alternatives. J Personal Assess. 2009;91(2):121-136.

46.

Zhang

The impacts of health insurance on health care utilization among the older people in China. Soc Sci Med. 2013;85:59-65.

47.

Vásquez

Paraje

Estay

Income-related inequality in health and health care utilization in Chile, 2000-2009. Revista Panamericana de Salud Pública. 2013;33(2):98-106.

48.

Kou

You

, et al. Measurement and decomposition of income-related inequality in self-rated health among the elderly in China. Int J Equity Health. 2019;18:1-11.

49.

Kim

H-K

Lee

Factors associated with health services utilization between the years 2010 and 2012 in Korea: using Andersen’s Behavioral model. Osong Public Health Res Perspect. 2016;7(1):18-25. doi:10.1016/j.phrp.2015.11.007

50.

Sözmen

Ünal

Explaining inequalities in health care utilization among Turkish adults: findings from health survey 2008. Health Policy. 2016;120(1):100-110.

51.

Wagstaff

Van Doorslaer

Watanabe

On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam. J Econ. 2003;112(1):207-223.

52.

Wagstaff

The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health Econ. 2005;14(4):429-432.

53.

Erreygers

Correcting the concentration index. J Health Econ. 2009;28(2):504-515.

54.

Ataguba

Akazili

McIntyre

Socioeconomic-related health inequality in South Africa: evidence from General Household Surveys. Int J Equity Health. 2011;10:1-10.

55.

Gonzalo-Almorox

Urbanos-Garrido

RM.

Decomposing socio-economic inequalities in leisure-time physical inactivity: the case of Spanish children. Int J Equity Health. 2016;15:1-10.

56.

Leon-Gonzalez

Giang

Nguyen

AT.

Socio-economic-related health inequality in non-communicable diseases among older people in Viet Nam. Ageing Soc. 2021;41(6):1421-1448.

57.

Wagstaff

O'Donnell

Van Doorslaer

Lindelow

Analyzing health equity using household survey data. Guide Techn Implement. 2007;3:25.

58.

Zhang

Liu

Changes in perceived accessibility to healthcare from the elderly between 2005 and 2014 in china: an Oaxaca–Blinder Decomposition analysis. Int J Environ Res Public Health. 2019;16(20):3824.

59.

Nguyen

Tran

Bui

Nguyen

TN.

Frailty in older patients with acute coronary syndrome in Vietnam. Clin Interv Aging. 2019;14:2213-2222. doi:10.2147/CIA.S234597

60.

Mwangi

Kulane

Van Hoi

Chronic diseases among the elderly in a rural Vietnam: prevalence, associated socio-demographic factors and healthcare expenditures. Int J Equity Health. 2015/11/17/ 2015;14:134. doi:10.1186/s12939-015-0266-8

61.

Shi

Liang

Ding

Urban-rural disparities in health care utilization among Chinese adults from 1993 to 2011. BMC Health Serv Res. 2018;18:1-9.

62.

Zhou

Jiang

, et al. Comparison of access to health services among urban-to-urban and rural-to-urban older migrants, and urban and rural older permanent residents in Zhejiang Province, China: a cross-sectional survey. BMC Geriatr. 2018;18:1-10.

63.

Dou

Wang

Jing

Yin

Horizontal inequity in health care utilization among the middle-aged and elderly in China. Int J Environ Res Public Health. 2017;14(8):842.

64.

Yang

Liu

Jiang

Comparing income-related inequality on health service utilisation between older rural-to-urban migrant workers and older rural residents in China: a cross-sectional study. BMJ Open. 2023;13(2):e060581.

65.

Penning

Zheng

Income inequities in health care utilization among adults aged 50 and older. Can J Aging/La Revue Canadienne du Vieillissement. 2016;35(1):55-69.

66.

Cai

A robust health system to achieve universal health coverage in Vietnam. Lancet Reg Health–Western Pacific. 2023;37:3.

67.

NP.

Education, employment, and income: an overview of rural-urban Gaps in Vietnam. Vietnam J Agricult Sci. 2020;3(2):647-661.

68.

Gong

Kendig

Factors predicting health services use among older people in China: an analysis of the China health and retirement longitudinal study 2013. BMC Health Serv Res. 2016;16:63. doi:10.1186/s12913-016-1307-8

69.

Van Minh

Giang

, et al. Health service utilization among people with noncommunicable diseases in rural Vietnam. J Public Health Manag Pract. 2018;24:S60-S66.

70.

Giang

Nguyen

VC.

The aging population and sustainability of the pension scheme: simulations of policy options for Vietnam. J Econ Develop. 2017;19 (3):40-51.

71.

World Bank. Out-of-pocket expenditure (% of current health expenditure) data Vietnam. Accessed October 26th, 2024 https://donnees.banquemondiale.org/indicator/SH.XPD.OOPC.CH.ZS?locations=VN

72.

Kim

Kwon

The effect of extension of benefit coverage for cancer patients on health care utilization across different income groups in South Korea. Int J Health Care Finance Econ. 2014;14:161-177.