What’s Known

Over one billion people around the world, mostly in countries with low or average income, do not have access to healthcare services. Andersen’s behavioral model provides a useful framework for informing the analysis of the contributing factors to health service utilization.

What’s New

Structural-level factors such as residential stability, distance to healthcare delivery centers, travel time to the nearest health center, density of health service providers and health centers, population size of municipalities, and the state-level income play an important role in health service utilization.

Introduction

The underutilization of health services has become an essential concern of public health and policy issues worldwide. ¹ Various countries, especially in the developing world, seek to improve health service utilization (HSU) and equitable access to healthcare. ^{1 - 4} Over one billion people around the world, mostly in countries with low or average income, do not have access to healthcare services, ^{2 , 4} which stems from a complex set of interactive factors. In fact, decision making of individuals to use healthcare services depends on a host of interacting factors relevant to health and self-reported health situation as well as the availability of health services. ⁵ There have been a lot of studies probing into why HSU patterns are different from one individual to another. Concerning HSU, various theoretical models have been developed with the purpose of perceiving and exploring a multitude of factors governing it and the extent they vary based on economic, psychological, behavioral, and epidemiological veiwpoints. ⁶

HSU is defined as obtaining healthcare provided by healthcare services in the form of healthcare contacts. ⁶ In other words, HSU refers to the point in health systems in which the needs of the patients are met on the part of health professionals. In order to explain this process, most studies have used the Behavioral Model of Health Services Utilization (BM), ^{7 - 13} which was developed in 1968 by the American medical sociologist and health services researcher, Ronald M Andersen. The BM is a multilevel model that incorporates both individual and contextual determinants of HSU. ^{6 , 10 , 12 , 14 , 15} Individual characteristics are evaluated at the individual level, whereas contextual characteristics such as families, communities, and national healthcare systems are measured at an aggregate level. ^{7 , 11 , 12 , 14 , 15} The BM provides a useful framework for informing the analysis of contributing factors to HSU, ^{7 , 11 , 16 , 17} and it is built upon three components, which are presumably associated with HSU and could be applied as predictors of utilization, as follows: ^{14 , 15}

• Predisposing factors are comprised of the sociodemographic characteristics that create the condition to increase the probability of HSU. At the individual level, these factors include age, sex, marital status, and ethnicity, along with attitudes, beliefs, values, and knowledge vis-à-vis health and health services. The contextual factors that predispose individuals to HSU encompass the demographic and social composition of communities and their collective and organizational values, as well as cultural norms. ^{9 , 10 , 12 , 14 , 17 , 18}
• Enabling factors are considered those that can hinder or facilitate HSU. At the individual level, these factors include income, wealth, health insurance status, and regular sources of care. At the contextual level, the enabling factors consist of per capita community income; the rate of health insurance coverage; the amount, variety, location, structure, and distribution of health service facilities and personnel; provider-related factors; physician and hospital density; distance from healthcare services; ⁸ the availability of transportation; ^{4 , 10} the quality of healthcare; and health policies. ^{1 , 2 , 4 , 9 , 12}
• Need factors are understood as variables concerning the perception of a change in individuals’ health status. At the individual level, these factors encompass both the perceived need for health services and the evaluated need. At the contextual level, the need factors comprise not only environmental need features, namely occupational and traffic- and crime-based accidents and death rates but also health indices including the epidemiological indicators of mortality, morbidity, and disability. ^{8 , 12 , 19}

The evaluation of HSU patterns is useful for identifying the subgroups of patients who are either under- or overutilizing services. ¹² Service underutilization has consequences for patients. By way of example, patients who are not fully engaged in care are liable to have poorer outcomes. ¹⁸

Furthermore, understanding the factors that facilitate and inhibit HSU is essential for enhancing HSU, which explains why to ensure fair access to healthcare services, policymakers need to identify the factors that influence HSU. ⁴

Previous studies have examined the role of different factors in determining HSU, including age, ^{4 , 12 , 20 - 22} gender, ^{8 , 9} the education level, ^{8 , 21} socioeconomic status, ^{10 , 11 , 13} race/ethnicity, ^{8 , 13 , 14} employment status, ^{1 , 9 , 11 , 21} marital status, ^{4 , 23 , 24} income, ^{1 , 3 , 4 , 12 , 21 , 25} and health insurance, ^{22 , 26 - 30} along with cultural beliefs and perceptions. ^{8 , 10} Moreover, other studies have focused on factors such as family size, ^{4 , 13 , 31} the cost/price of health services, ¹² perceived need and self-assessed health status, ^{1 , 14 , 17} the urban/rural regions of residence, ^{1 , 2 , 9 , 10 , 22 , 23} the characteristics of the healthcare delivery system, ^{8 , 14} and accessibility of healthcare services. ^{1 , 2 , 8 , 14}

Although some systematic reviews are available on specific populations ^{15 , 32 - 34} or specific types of HSU, ^{18 , 19 , 35} to the best of our knowledge, no comprehensive review has so far been undertaken regarding the factors associated with HSU in the general adult population. Furthermore, all the numerous quantitative studies on the factors influencing HSU in recent years have addressed only one or some factors in preliminary investigations unsystematically and ambiguously. Additionally, there is a gap in the relevant literature regarding the overall association and direction of the relationship between the determinants and HSU. Accordingly, the current study is aimed to review all studies on outpatient HSU both in order to identify factors contributing to HSU among the general population based on Andersen’s BM of HSU in observational, population-based studies and in order to provide a comprehensive and up-to-date overview of these determinants.

Materials and Methods

Study Design

This systematic scoping review reviewed all available studies examined the factors of HSU in the general population. The study was approved by University of Social Welfare and Rehabilitation Sciences (IR.USWR.REC.1397.029).

Search Strategy

This review was conducted between January 2008 and July 2018 and followed the PRISMA guidelines to identify published articles on factors related to HSU. Quantitative studies were searched from the most comprehensive related databases of Web of Science, MEDLINE (PubMed), and Scopus. Similarly, additional records were identified through a manual search of the reference lists of the included studies. In addition, two key concepts, namely determinants (factors) and HSU were combined using the keywords and titles in the respective databases. The search terms related to HSU (i.e. “Health service utilization” OR “Health care utilization” OR “Health service use” OR “Health care use” OR “service utilization” OR “service use” OR “Health care utili*”, OR “Health service utili*”) were combined with those terms for factors (i.e. “Determinant” OR “factor”, OR “predictor”). In order to have a more comprehensive search, we drew upon the entree of SCOPUS and the medical subject headings (MeSH) including the entry terms of PubMed as well.

Inclusion Criteria

Only quantitative, observational, cross-sectional, and secondary analysis studies, along with longitudinal surveys that predicted HSU by the adult population, were included in the study. Further, the outcome measure of this study was outpatient HSU such as any contact with formal HSU including private, public, and general practitioners, together with specialist physicians, for health need reasons by adults aged 15 and older. The study specifically focused on the use of services as a binary outcome (i.e. any use vs. no use). To be eligible for inclusion, the selected study must have assessed the association between HSU and any other factors (determinants). This study encompassed only original peer-reviewed research published in scientific journals in 2008 or afterwards. This cut-off point was chosen for reviewing more recent studies that were published in the last decade, with no restrictions on the geographic area of publication. Only papers published in the English language were included in the present review.

Exclusion Criteria

Studies examining the use of informal health services (e.g. friends, family, and religious support) or complementary/alternative treatments (i.e. those provided outside the formal health sector or traditional medicine) were excluded from this review. Considering that the interest population of this study was the general adult population, the studies that only focused on specific subpopulations such as children, elderly, veterans, military forces, prisoners, immigrants, and those which involved participants not living in community settings (e.g. prisoners, inpatients, and the residents of elderly care homes) or were defined by their occupation (e.g. doctors, police officers, military forces, and students) were excluded from this review. Moreover, studies of HSU in special diseases whose participants’ experiences represented no wider population as well as those in which participants received specific types of HSU (e.g. maternal, mental HSU, or inpatient HSU) were not included in the current investigation. Additionally, these, reviews, letters to the editors, non-English articles, interventional or theoretical studies, irrelevant studies in terms of design and subject, studies with insufficient information or results, studies with data similar to or overlapping with those in other articles, studies with results that did not address outpatient HSU, and studies with retrospective data extracted from medical service center records were removed from the study.

Study Selection

Through the database search, we found 2530 papers. Further, we could identify another set of studies (i.e. 13) by carrying out a manual search in the relevant reference lists. After removing the duplicates, we reached 1813 articles, leading us to the title- and abstract-screening stage. The selection process was carried out in two phases. Following database search, the first (NS) and second (SEK) authors screened the identical 1813 titles/abstracts independently. Afterwards, they developed and adapted an eligibility assessment with reference to the initially specified features (refer to eligibility criteria). Later, the abstracts which remained were split into two groups and screened separately by each author. In cases of discord on the scope of the inclusion criteria, the researchers consulted the senior author (SHMK) to reach consensus. Following this procedure, 1667 studies were excluded at this phase of data selection. Moreover, from the remaining 146 studies, NS and SHMK assessed full-text papers independently to arrive at the final set of the most eligible studies. Meanwhile, they continued their discussions to resolve possible cases of disagreement under the supervision of the senior author (ASF). On the whole, 109 studies failed to satisfy the defined eligibility criteria, and thus they were excluded from further investigation. Therefore, merely 37 studies were found to be eligible for inclusion. The full procedure for study selection was conducted in line with PRISMA guidelines (figure 1).

Figure 1. PRISMA flowchart shows the selection of studies.

The findings based on Anderson’s behavioral model are described below.

Quality Assessment

The quality of the selected studies was evaluated by using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. In other words, the quality assessment scale for cross-sectional studies was applied to assess the risk of bias in the included studies. ^{36 , 37} Qualitative assessments were independently conducted by two reviewers (NS and SK), and in the case of disagreement, the samples were referred to a third reviewer (SHMK). For simplicity, the study was considered to be of good or satisfactory quality if it achieved a score higher than 16 on the STROBE statement. No study turned out to be poor in terms of quality. Expressed differently, eight papers were classified as fair (11≤score≤16), and the remaining 29 papers were considered good (score>16).

Data Extraction

A predefined Excel spreadsheet was applied to extract data from the included studies. The extracted data pertained to date, type, design, and the context of the study, as well as the date of publication, sample size, participants, data source, data-collection times, HSU outcomes, and factors associated with HSU.

Data Analysis

Owing to the special type of this scoping review examining the factors of HSU, a narrative synthesis was deemed the most appropriate method of data analysis.

Results

Study Characteristics

Overall, this review included 37 articles with samples from the general population. The total number of participants ranged from 200 to 327076. Most frequently, it included routine National HSU data or household survey databases.

Sixteen (43.2%), six (16.2%), two (5.4%), four (10.8%), three (8.1%), three (8.1%), and two (5.4%) studies reported data from Asia, Europe, North America, South America, Africa, and the United States of America, along with international data from across world regions, respectively. Furthermore, one (2.7%) study reported data from Australasia and another (2.7%) from Central America.

In general, the participants comprised both men and women aged 15 and over. Most studies (n=34) used retrospective data obtained from self-report measures. Furthermore, the articles included 34 cross-sectional and three longitudinal studies that recruited participants from the general population. Moreover, most studies defined HSU as a dichotomous dependent variable. Thus, 30 days or 12 months were used as HSU indices prior to data collection (the interview) whether there was a self-reported need for outpatient care services, including primary or secondary health services, and whether the respondents had contacts with (visit) healthcare professionals (e.g. general practitioners and specialists) and received medication in the preceding two weeks. A summary of the results of the included studies is presented in table 1.

Quality of Studies

The quality of the included studies was assessed using the STROBE checklist. The outcomes of the quality ratings in the assessment checklist are pre-defined as “good” (score>16), “fair” (11≤score≤16), or “poor” (score<11). After assessing all the studies (n=37) with the 22-item checklist, 8 studies ^{3 , 40 , 49 , 50 , 55 , 60 , 63 , 67} achieved the fair scores (11≤score≤16), while the remaining 29 studies received a “good” rating, ^{1 , 2 , 4 , 21 , 27 , 31 , 38 , 39 , 41 - 48 , 51 - 54 , 56 - 59 , 61 , 62 , 64 - 66} and no studies were excluded because of poor quality rating. Expressed differently, the study quality was rated as “good” (78.37%) for more than half of the studies, “fair” for 21.62% of the studies, and “poor” for no study.

The aforementioned scoring procedure was not meant to assess the quality of the studies considering their own primary aims. As a matter of fact, this rating system was intended to assess the quality of the evidence pertinent to this review. As the majority of the scores for the quality assessment were ranked as “good” and a few with “fair” quality, all 37 studies were included in the results section, and the results were extracted. The results of the quality rating of the studies can be seen in table 1.

Factors Associated with Outpatient Health Service Utilization

Anderson’s behavioral model of HSU was employed as a framework to classify the findings (Table 2) of this review into predisposing, enabling, and need factors. ^{7 , 12 , 17}

Predisposing Factors

Almost all of the 37 included articles reported HSU rates by the predisposing factors such as gender, ^{2 - 4 , 21 , 31 , 38 , 39 , 41 , 44 , 46 , 47 , 53 , 54 , 56 , 57 , 60 - 62 , 64 , 65 , 67} age, ^{1 - 3 , 38 , 40 , 41 , 45 , 47 , 51 , 52 , 56 , 57 , 60 , 61 , 65} marital status, ^{3 , 4 , 21 , 38 , 41 , 44 , 60} residency/immigration status, and ethnicity. ^{1 , 2 , 41 , 45 , 63}

Age Group

Significant associations were found between age and HSU in the majority of the studies. ^{1 - 4 , 38 , 40 , 41 , 45 , 47 , 51 , 56 , 57 , 60 , 61 , 65} Most of the studies indicated that middle-aged and older respondents were most likely to use health services. However, Kim and Lee found that older individuals were less likely to use outpatient health services. ⁶⁰ Other researchers such as Sozmen and others, in addition to Morera Salas and others, reported that the probability of using outpatient health services was not different across age groups. ^{62 , 43}

Gender

Most of the reviewed studies demonstrated associations between gender and HSU. ^{2 - 4 , 21 , 31 , 38 , 39 , 41 , 44 , 46 , 47 , 53 , 54 , 56 , 57 , 60 - 62 , 64 , 65 , 67} Additionally, females were frequently found with more tendency toward increased HSU. ^{2 - 4 , 31 , 38 , 39 , 46 , 53 , 54 , 56 , 57 , 60 - 62 , 64 , 65 , 67} In contrast, the results of four studies demonstrated that the rate of HSU was greater in men than women. ^{21 , 41 , 44 , 47}

Marital Status

In most studies, marriage was positively associated with increased HSU. More precisely, the results indicated greater use of services by married individuals. ^{3 , 4 , 21 , 38 , 41 , 44 , 60}

Citizenship Status

Only two of the 37 included studies investigated the relationship between HSU and citizenship. The results unveiled that citizenship increased the probability of HSU. ^{2 , 63}

Ethnicity

Differences in HSU between different ethnic groups were reported in five studies. ^{1 , 2 , 41 , 45 , 63} Ethnic majorities ⁶³ had lower rates of HSU than ethnic minorities. ⁶³ In addition, mestizos had higher rates of HSU than those of indigenous descent. ⁴¹ Skin color also determined HSU, and the likelihood of HSU increased among white people compared with Africans. ²

Enabling Factors

Education

A good number of studies in this review have found that higher education levels are correlated with HSU, ^{38 , 39 , 41 , 43 , 45 , 46 , 48 , 51 , 53 , 55 , 57} although this was not found across all the studies. Three studies concluded that a lower level of education was correlated with a higher likelihood of HSU. ^{4 , 27 , 31} Otherwise stated, the outcomes of three studies revealed that individuals with a lower education level had a higher probability of visiting a general physician (GP), ^{53 , 54 , 62} while those with high education levels probably further utilized specialist care services. ⁶²

Employment Status

Some studies indicated a positive association between HSU and unemployment or being out of the labor force. ^{2 , 38 , 39 , 53} For example, it was reported that being a retiree or a housewife increased the likelihood of using specialist health services. ^{39 , 53} Contrary to the results of the above-mentioned studies, the occupational category of the household head (i.e. belonging to the regular-income group) and the ratio of earning members in the household were reported to increase the utilization of outpatient health services. ³

Income Level/Socioeconomic Status

Economic features were addressed in almost all the included studies that mainly focused on the income level, socioeconomic status, or household wealth. ^{4 , 21 , 31 , 38 - 40 , 44 , 45 , 48 , 51 , 52 , 54 , 56 , 61 - 64} Based on the findings of two studies from Ethiopia, individuals with annual household incomes greater than the poverty line were more likely to use health services. ^{21 , 64} High-income individuals used more private medical specialist services, as well as curative and hospital outpatient services, than low-income individuals. ^{45 , 48 , 51 , 52}

Contrarily, two studies reported that lower-income individuals and the poor probably visited physicians more frequently (used health services), and one study revealed that the determinant for the use of specialist care was the lowest household expenditure index quintile. ^{4 , 40}

The results of an investigation in a Turkish context disclosed a significant difference between the type of health service and the income level. Individuals belonging to the lowest income quintile were more likely to visit a GP, whereas those with high-income levels had a higher probability of specialist service utilization. ⁶² Dependency to the highest asset and consumption quintile categories (i.e. higher-income earners) increased the likelihood of using preventive health services. ^{41 , 61}

Insurance Status

Most of the included studies confirmed the association between HSU and health insurance status. In other words, insurance significantly increased the likelihood of HSU, while uninsured individuals represented fewer probabilities for using health services. ^{2 , 4 , 31 , 41 , 44 , 52 , 56 - 58 , 67} The findings revealed significant differences between public and private insurance in terms of HSU. ^{39 , 46 , 50} Moreover, service use varied by the type of health insurance. ^{27 , 39 , 46 , 50 , 58} More precisely, having private (supplemental insurance) and health insurance continuity ²⁷ increased the probability of HSU. The type of insurance (scheme) and the continuity of health insurance also influenced the type of health services. For instance, individuals with gaps in health insurance had 29% more emergency room visits than those with continuous insurance. ²⁷ Similarly, the possibility of using specialist health services was higher among individuals with doubled coverage insurance in comparison with those who were publicly insured, ³⁹ and the likelihood of visiting a GP rose among those who had no private health insurance. ³⁹

Region of Residence (Urban/Rural)

Several studies identified an inconsistent association between the regions of residence and HSU. In some studies, living in rural areas significantly increased the likelihood of HSU, ⁴² consultations with a private doctor, referring to clinics, ⁴⁶ or visiting a GP. ⁶² In contrast, three studies revealed that living in urban ^{41 , 62} or metropolitan ²⁷ areas increased the probability of using GP care. ^{27 , 41 , 62}

Trust in the Health System

Only one study explicitly indicated an association between trust and HSU. Additionally, higher amounts of distrust in clinics are positively correlated with a remarkably higher possibility of visiting hospitals even for common cold and headache symptoms. ⁵⁹

Regular Source of Care/Family Doctors

Having a usual source of care was reported only in one study, indicating that the presence of a family doctor increases the likelihood of HSU. ⁴⁶

Household/Family Size

Only three studies investigated the association between household size and HSU. The results of two of these studies indicated that smaller household size increased the likelihood of using health services. ^{3 , 57} Nonetheless, the other study reported that individuals from families with more than five members were more likely to use health services. ⁴

Other Enabling Factors

Owning a private property, ⁵⁷ living in single-person households as a household composition, and living arrangements increase the probability of HSU. ⁴⁷

Health Literacy

There was only one published investigation available on the association between HSU and health literacy, indicating the greater likelihood of HSU with lower levels of health literacy. ⁵⁵

Family Network/Group Support Activities

Some of the included studies examined enabling factors such as family networks, group support activities, and social support. According to the results of three different studies and given social motivation, respondents who participated in group support activities, ⁴ those who belonged to the Scheduled Caste (an officially designated group of people in India), ³ and those who had a smaller family network ⁶¹ visited physicians more frequently.

Social Support

Only one Australian study reported associations between social support and HSU. This study demonstrated that individuals who received lower social support were more likely to use healthcare services. ⁴⁷

Need Factors

Need factors were most consistently associated with HSU. These factors were comprised of self-assessed health status or healthcare needs, ^{1 , 4 , 21 , 39 , 40 , 43 - 45 , 48 , 51 , 52 , 58 , 61 , 62 , 64} the duration and type of illness, ^{1 , 3 , 4 , 39 , 40 , 43 - 45 , 55 , 57 , 58 , 61 , 62 , 64 , 67} the number of health problems, ^{41 , 55 , 63 , 64} disability or functional impairment, ^{1 , 21 , 51 , 58} and disease severity. ^{53 , 64}

Perceived Need and Self-assessed Health Status

Several studies reported significant associations between self-assessed general health status and HSU. ^{1 , 4 , 21 , 39 , 40 , 43 - 45 , 48 , 51 , 52 , 58 , 61 , 62 , 64} Poorer physical and mental health status was a significant predictor of increased utilization in nearly all the studies. ⁴⁵ Respondents with poor self-assessed health status, ^{4 , 44 , 52 , 58} and poor perception of health status ⁶⁴ manifested a higher probability of utilizing health services.

Disability or Functional Impairment

Individuals who experienced any limitation of daily activities ⁵⁸ and functional impairment, ⁵¹ as well as those who had a disability (gynecological problems and psychological symptoms) ¹ or disabling health problems,21 were significantly more likely to avail themselves of hospital and physician services.

Type of Illness (Chronic or Acute)/Duration

Several studies concluded that the duration of illness, self-assessed long-standing illnesses, or the presence of chronic illnesses as need factors increased the likelihood of using health services. ^{1 , 3 , 4 , 39 , 40 , 43 - 45 , 55 , 57 , 58 , 61 , 62 , 64 , 67} Otherwise speaking, those with a history of heart disease, hypertension, diabetes, or high blood cholesterol had significantly more physician visits than other individuals. ⁴⁰

Severity of Health Problems

Two studies established that the probability of outpatient HSU tended toward a rise as the measure of the severity of the illness represented an increase. ^{53 , 64} Individuals with disease severity ⁵³ and high (severe) perceived severity of illness ⁶⁴ were reported to use healthcare services 96% more often than other individuals. However, another study reported a decrease in outpatient utilization by those with the most severe health problems. ⁶³

Number of Health Problems

The likelihood of using health services was higher in persons with more health conditions and more frequent physical symptoms. ⁵⁵ A rise in the number of health problems was in tandem with an increase in the probability of using health services. ^{41 , 63 , 64} Contrarily, individuals who reported no health problems during the previous 30 days at the time of the survey used more preventive services. ⁴¹

Other Need Factors

Nonsmoking individuals with frequent health checkups and periodic physical and dental examinations who were satisfied with health services and were not hospitalized during the preceding three years were more likely to use health services than their counterparts. ⁴ A similar pattern was observed among individuals who did self-treatment over the preceding 12 months, had proper documentation, and were willing to receive treatment abroad; they were neither worried nor willing to obtain information about food and nutrition.

Contextual Level Factors

Overall, no outstanding number of studies, except for a few cases, associated contextual level factors with HSU.

Residential Stability

There was limited published evidence available on residential stability as a contextual factor. The results of one study indicated that individuals who lived in a place for longer than four years were more likely to use health services. ⁴

Distance to the Healthcare Delivery Centers

Contextual factors such as distance to the nearest healthcare delivery centers (i.e. shorter distance to health facilities) demonstrated a significant positive association with HSU. ^{21 , 49 , 58 , 64} On the other hand, long distances (>2 km) to healthcare facilities (OR=3.6, 95% CI: 1.5 to 8.3) decreased the likelihood of HSU. ⁶³

Travel Time to the Nearest Health Center

According to some studies, ^{41 , 47 , 63} the likelihood of HSU rose with a shorter travel time to the nearest facility (<500 meters).

Density of Health Service Providers and Health Centers

A higher density of healthcare facilities (OR: 1.03, 95% CI: 1.01 to 1.04; P<0.001) was significantly associated with higher HSU. ⁶⁵ Otherwise stated, the density of various sectors such as public practice health personnel, service providers, health services per 10,000 inhabitants, ⁴² the number of health centers within 30 minutes of walking distance of one’s residence, ⁶⁵ and private practice physicians was substantially correlated with higher utilization. ^{42 , 65}

Population Size of Municipalities

There is a positive association between the population size of urban area and the likelihood of utilizing GP care and private specialist services. Regardless of small-town residents’ use of outpatient care services, as reported in one study, ³ the inhabitants of the largest municipalities represented higher probabilities of visiting a GP or a private medical specialist. ⁵¹

State-level Income and Other Contextual Factors

Only two studies reported that living in areas with high median income levels and low violence levels augmented the probability of using health services. ^{3 , 56} Table 2 presents the main variables that were examined by the reviewed studies according to Andersen’s behavioral model of HSU.

Discussion

In this review, we investigated 37 quantitative studies addressing the factors of HSU in the general population. We sought to identify the factors (determinants) of outpatient HSU. In line with several previous systematic reviews, ^{12 , 15 , 19 , 32 - 35} we applied Anderson’s behavioral model for general populations, as an organizational framework, to present and discuss the findings. ^{7 , 8 , 10 , 16 , 17} In accordance with Andersen’s theoretical framework, predisposing, enabling, and need factors were most consistently found to be associated with HSU. ^{8 - 12 , 14 , 17}

The included studies scrutinized the association between HSU and predisposing (e.g. age, gender, marital status, and ethnicity), enabling (e.g. the education level, employment status, income, socioeconomic status, health insurance, access to a usual source of care, and the region of residence), and need (e.g. self-reported health status, the type of illness and the presence of chronic illnesses, the severity of the disease, and the duration and number of health problems) factors.

Based on the current review, it appears that several factors may increase the likelihood of HSU among people. For instance, females, ^{2 - 4 , 31 , 38 , 39 , 46 , 53 , 54 , 56 , 57 , 60 - 62 , 64 , 65 , 67} married individuals, ^{3 , 4 , 21 , 38 , 41 , 44 , 60} and those of older age ^{1 - 3 , 38 , 40 , 41 , 45 , 47 , 51 , 61 , 65} used more health services. In addition to the aforementioned factors, having insurance, ^{2 , 4 , 31 , 41 , 44 , 52 , 56 - 58 , 67} high-income and socioeconomic status, ^{45 , 48 , 51 , 52} poor perceived need, ^{4 , 44 , 52 , 58} and severe health problems ^{53 , 64} contributed significantly to the likelihood of HSU. Evidence suggested that the severity of the disease, ^{53 , 64} the duration, ^{3 , 4 , 49} and presence of chronic illnesses, ^{39 , 40 , 43 , 45 , 57 , 61 , 67} were related to higher HSU. ¹⁸ However, the question whether individuals afflicted with more severe diseases are more probable to utilize health services still remains unanswered as the findings of the present study were mostly based on studies with retrospective cross-sectional designs. Thus, individuals using health services probably perceive their conditions to be more severe than those not using HSU.

Likewise, the findings revealed that age was markedly associated with the increased use of outpatient services. Further evidence was also provided as regards the increased utilization rates ^{3 , 49} among those characterized by white/Hispanic ethnicity, ^{13 , 45} private insurance, ^{27 , 30 , 50} and urban residence.

The majority of the studies scrutinized reports that being female increased the likelihood of HSU, while three studies showed that the amount of HSU was higher among men. ^{21 , 47 , 65} This is because women may be more distressed and better at self-monitoring their health than men; they are, consequently, more likely to share their health problems with physicians. With regard to education, most of the included studies confirmed that having a higher education level increased the use of health services, particularly specialist care services, ^{38 , 41 , 43 , 45 , 46 , 48 , 54 , 56} whereas several other studies presented counter-evidence and suggested that a lower education level augmented the likelihood of using GP or emergency services. ^{31 , 53 , 57 , 62} The significant association between racial/ethnic background and service use should be interpreted cautiously given that the race/ethnicity of the samples was not reported routinely.

The results of our review showed that living in an urban area was associated with higher HSU. ^{27 , 41 , 62} This is probably due to the availability of more healthcare centers, more access to healthcare centers, and a shorter distance to these centers in urban areas in comparison with rural areas. Such findings are consistent with other studies such as those conducted by Dotse-Gborgbortsi and others, ⁶⁸ who reported that an increase in the distance from the medical service centers was associated with a lower probability of childbirth in health facilities.

In the current review, we encountered a notable literature on some factors as indicated in Andersen’s model. These factors, which comprise psychological factors (e.g. cultural norms, beliefs, and attitudes), were classified as “predisposing factors” and “health system factors” (e.g. the availability and accessibility of services).

Based on the findings from the reviewed studies, the effects of some predisposing factors such as ethnicity and some enabling factors such as social support, health literacy, and access to a usual source of care/family doctors on HSU remain unclear. Nonetheless, the initial findings may imply that social support, health literacy, and access to a usual source of care/family doctors are associated with HSU. ^{4 , 38 , 46 , 47 , 55} Almost, all the identified factors were more related to individual level compared to contextual level factors. In fact, Anderson’s behavioral model could account for all the identified factors.

The current review has several limitations. First, the search was restricted only to three databases and, merely to studies published in the English language. Non-peer reviewed literature, articles published as abstracts, and dissertations/theses were not included in this review. In addition, the generalizability of the results is limited due to the small number of included studies for some variables (e.g. social support, family network, residential stability, health literacy, household composition, and living arrangement, access to a usual source of care, and trust in medical organizations). Furthermore, important contextual variables such as the population size of municipalities, town size, or the state-level income could not be examined due to the limited available data. In addition, it seems almost improbable to detect factors, which exert the greatest influence on HSU. As far as age, which is apparently a simple indicator of service utilization is concerned, the findings revealed incongruities in the strength and direction of this relationship. Further, it was not nearly feasible to make a comparison across the studies, and this constrained the scope of the findings of the present review.

This review identified several determinant factors regarding the use of HSU, which should be taken into account in national health policy-making and planning for future modeling and/or cost-effectiveness studies. We believe that an increased knowledge of the factors governing this process seems essential not only for the identification of the population groups less likely to utilize sufficient professional health services but also for the improvement of their access to proper health services.

Accordingly, we propose that health professionals consider the specific demands, preferences, and needs of individuals, especially those from potentially vulnerable subgroups when providing care. Finally, although it was beyond the aim of this review, there was some evidence to suggest that the factors associated with HSU might vary between GPs and specialists. Of course, this issue warrants further investigation.

Conclusion

In general, the present review identified the importance of predisposing, enabling, and need factors, which affect outpatient HSU. These factors should be considered by policy-makers when developing future model designing. Moreover, it is quite necessary to anticipate prospective demands in order to plan health services well and ascertain that the existing resources are adequately provided and allocated with the purpose of offering the most efficient and effective health services. As discussed in previous sections, most of the reviewed articles were in the form of secondary data analyses, indicating that the authors were obliged to select out of the variables addressed in the original primary studies. Therefore, future longitudinal research is proposed to expound any causal relationship between HSU and predisposing, enabling, need factors (e.g. stigma, living arrangements, attitudes, beliefs, and social support) as well as organizational or health system factors. Finally, a systematic review and meta-analysis is recommended to conclusively assess the causal effect of only one determinant such as income as an enabling factor, gender as a predisposing factor, or the number of health problems as a need factor on HSU.

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