Decreased health service use among migrants improves through an internal locus of control,especially in men

Abstract

We explore the role of internal locus of control (LOC), migration status and gender, in healthcare utilization, using the Andersen Model. It addresses the knowledge gap in understanding how these factors influence healthcare access, especially in migrant populations. Utilization was assessed using the 2020 German Socioeconomic Panel with 26,028 adults (6,968 migrants). In this cross-sectional survey study, four outcomes were analyzed via regression models, including predisposing, enabling, and need factors. A migration background reduced the likelihood of doctor visits, while LOC increased it. Among migrants, LOC predicted even higher likelihood, especially in men, mitigating the negative impact of migration status. Migration background and LOC were not linked to hospital visits, and gender differences in doctor visits were found only in men. While individuals with a migration background had a lower chance of visiting doctors, internal control beliefs proved to be a significant resource for health behavior.

Keywords

healthcare immigration status gender differences internal control beliefs Andersen behavioral model of health services use

Introduction

Background

Migrants are more vulnerable to a range of health issues compared to non-migrants, including both infectious and chronic diseases, as well as mental health conditions. These increased health risks are often exacerbated by various social and structural factors that affect their access to healthcare (Sheath et al., 2020; Vonneilich et al., 2021). Understanding the factors influencing healthcare utilization among individuals with a migration background is essential, as disparities in access can deepen existing health inequalities within this group. Addressing these factors is key to improving equity in healthcare access and outcomes for migrants (Lebano et al., 2020). Identifying barriers to healthcare utilization is crucial for developing targeted interventions that can enhance access to care and improve health outcomes. By addressing these barriers, healthcare systems can better support marginalized groups, ultimately reducing disparities and fostering greater health equity (Galanis et al., 2022). Additionally, understanding psychological factors like control beliefs can help design interventions that are more effective in meeting individual needs and preferences.

Theoretical framework

The Behavioral Model of Healthcare Utilization is a conceptual framework that specifies the influencing factors and contextual elements related to the utilization of health services (Andersen, 1968, 1995). Andersen’s model emphasizes that the use of health services is influenced not only by illness but also by individual, social, and structural levels. These include (A) predisposing factors, which encompass individual characteristics such as age, gender, education, health knowledge, and mental resources; (B) need factors, which describe current health problems or quality of life; and (C) enabling factors, which describe resources such as income, insurance, and the infrastructure of health services that can either facilitate or hinder the use of health services.

In the context of individuals with a migration background, all three factors can influence the utilization of health services. In previous studies, Andersen’s model was used to explain ethnicity and gender differences concerning health service utilization for physical health (Lyons and Bhagwandeen, 2023; Shafeek Amin and Driver, 2022; Shao et al., 2018) and mental health (Krzyż et al., 2023). This study focuses on the psychosocial resource of internal control beliefs, which can be viewed as a predisposing factor. The theory of Locus of Control (LOC), according to Rotter (Rotter, 1966), explores whether an individual believes they have control over their life events (internal control beliefs) or believes that their efforts contribute little to their life situation (external control beliefs).

Studies have demonstrated a direct association between internal control beliefs and health measures. Internal control beliefs are directly linked to better physical, mental, and subjective health (Awaworyi Churchill et al., 2020; Kesavayuth et al., 2020), as well as various health-promoting behaviors. Individuals with internal control beliefs tend to engage in health-promoting behaviors such as increased physical activity, better dental hygiene, and more beneficial dietary habits (Conell-Price and Jamison, 2015; Grotz et al., 2011; Kesavayuth et al., 2020; Menec and Chipperfield, 1997; Sangeeta and Rana, 2015; Steptoe and Wardle, 2001). They are also more inclined to expand their enabling resources, such as obtaining private supplementary health insurance (Bonsang and Costa-Font, 2022), which in Germany is associated with shorter waiting times for doctor appointments (Werbeck et al., 2021).

People experiencing racial discrimination report lower self-rated and mental health (Cormack et al., 2018), resulting in a higher risk for needing medical care (Hubbard et al., 2009). At the same time, individuals with a migration background tend to have more external control beliefs (Grotz et al., 2011). For example, they are more likely to believe that they cannot control infections such as SARS-CoV-2 (Neugebauer et al., 2022).

Based on this previous research, we expected that (1) individuals with a migration background were less likely to use health services, (2) internal control beliefs would be positively related to the likelihood of using health services, and (3) internal control beliefs would mitigate the negative association between migration background and health services utilization. This was expected to particularly affect the utilization of outpatient medical services, as they rely more on individual decisions. In contrast, inpatient care, which relies less on individual choices, was expected to have a weaker association with both migration status and internal control beliefs.

Additionally, there are likely to be gender differences (Mauvais-Jarvis et al., 2020). Men exhibit more internal control beliefs than women (Specht et al., 2013), while women tend to visit doctors more frequently than men (Simons et al., 2023). Therefore, intersectional differences regarding the interplay of migration background and gender were also considered in this study.

Methods

Participants and data collection

The Socio-Economic Panel (SOEP) is a nationwide annual panel survey in Germany that longitudinally collects data on socioeconomic and psychological factors such as employment, income, education, health, and well-being (Goebel et al., 2023). Since not all variables of interest for this study are covered every year, a cross-sectional approach was chosen. A total of 26,028 adult individuals participated in the 2020 wave (SOEP Version 38) and filled out the questionnaire for LOC. All participants provided informed consent; the Institutional Review Board of the German Institute for Economic Research approved the study.

Measures

Outcome measures

Four healthcare utilization outcomes were assessed. Firstly, the utilization of outpatient physicians in the last 3 months, represented as a dummy variable. Secondly, the number of doctor visits within that timeframe. Thirdly, hospital stays in the previous calendar year, also represented as a dummy variable. Lastly, the total number of nights spent in the hospital. The outcomes concerning the number of doctor visits and the number of nights spent in the hospital have a minimum value of 1 and are set to missing for individuals without any reported doctor or hospital contacts. These values were set to missing to avoid bias due to those with no reported health services utilization.

Predisposing factors

Internal control beliefs were assessed with a 10-item scale developed by Nolte (Nolte et al., 1997). The items were: “How my life turns out depends on myself”; “Compared to others, I have not achieved what I have earned” (reversed); “What one achieves in life is primarily a question of fate or luck” (reversed); “If one is socially or politically active, one can influence social conditions”; “I often experience that others determine my life” (reversed); “Success is something you have to work hard for”; “When I encounter difficulties in life, I often doubt my abilities” (reversed); “The opportunities I have in life are determined by social circumstances” (reversed); “More important than any effort are the skills one brings to the table” (reversed); “I have little control over the things that happen in my life” (reversed). Respondents indicated their agreement on a scale ranging from 1 (“do not agree at all”) to 7 (“completely agree”). Reversed items were recoded and a sum score (10–70) was calculated, with higher levels indicating stronger internal control beliefs.

Migration background

The study sample was stratified into two distinct groups: individuals with either direct migration experience to Germany or those with a parental (indirect) migration background, versus individuals without any migration background. Gender was mostly indicated by the interviewer and coded as 1 = women, 0 = men. Age was indicated in years. Urban residency was identified based on postal code. The SOEP research data center classified the participants as living in an urban or a rural area, coded as 1 = urban, 0 = rural. Psychosocial resources were measured using the life satisfaction scale (Glatzer and Zapf, 1984), ranging from 0 (“completely dissatisfied”) to 10 (“completely satisfied”). At the survey methodology level, the month of the interview was included as a control variable, as there were distributional differences over the year between individuals with and without a migration background. Since the question about doctor visits pertains to the last 3 months before the survey, distortions between the two sub-samples would have been expected otherwise.

Enabling factors

Type of health insurance was coded as either public or private health insurance. Educational level, according to ISCED-11 (OECD, Eurostat, and UNESCO Institute for Statistics, 2015) was classified into low (0–2), medium (3–4) and high (5–8) levels of education.

Need factors

Health status was controlled for using the physical and mental component scores of the Short Form 12 Health Survey (SF-12; Ware et al., 1996), which are provided by the SOEP research data center. The eight subscales of the SF-12 were shown to load on two factors: a physical dimension (physical fitness, general health, bodily pain, and role physical) and a mental dimension (mental health, role emotional, social functioning, and vitality). For the computation of the physical and mental component score, the values of the two factors were z-transformed (Andersen et al., 2007). Subjective health was indicated on a scale of 1 (“very good”) to 5 (“bad”) and the presence of chronic diseases was included as dummy variable.

Analysis

The utilization of a doctor in the last 3 months and the occurrence of hospital stays in the last calendar year were stratified by gender, migration background, and internal control beliefs (see Figure 1) for a descriptive analysis. For this purpose, the internal LOC was divided into data-driven categories of low (sum score ≤ 28), medium (sum score between 29 and 49), and high values (sum score ≥ 50).

A logistic regression was employed to analyze differences based on migration background and internal control beliefs. The number of doctor visits in the last 3 months and the number of nights spent in the hospital were analyzed using linear regression. Three regression models were used for each outcome variable. In the first model, the predisposing, enabling, and need factors were included as independent variables. Additionally, the interaction variable of the two predisposing factors, migration background and internal control beliefs, was included. The second model for each outcome variable was calculated only for women, and the third model was utilized only for men. Missing values on any of the variables were deleted listwise, resulting in a reduction of 2.95% to N = 25,260 participants. Data preparation and analysis was completed with R 4.3.1. The STROBE guidelines were followed in conducting this study (von Elm et al., 2008).

Results

Descriptive analyses

Table 1 presents the distribution of predictor, outcome, and control variables in the sample. Concerning predictor variables, among the 6,968 individuals with a migration background, there was a lower proportion of women compared to the 18,292 respondents without a migration background. On average, individuals with a migration background exhibited lower internal control beliefs.

Table 1.

Sample description.

		Participants with migration background		Participants without migration background
		N = 6968		N = 18,292
Outcome measures
Doctor visit (dummy, last 3 months)	N (%)	3,993	(57.3)	12,741	(69.7)
Number of doctor visits (last 3 months, min = 1)	Mean (SD)	2.9	(3.7)	3.1	(3.6)
Hospital stay (dummy, previous calendar year)	N (%)	733	(10.5)	2,450	(13.4)
Number of nights spent in hospital (previous calendar year, min = 1)	Mean (SD)	8.6	(17.0)	9.5	(14.9)
Predisposing factors
Internal control beliefs (sum score)	Mean (SD)	45.7	(7.8)	47.2	(7.0)
Women	N (%)	3,586	(51.5)	9,611	(52.5)
Age	Mean (SD)	40.4	(14.5)	51.9	(17.6)
Urban region	N (%)	5,329	(76.5)	11,450	(62.6)
Life satisfaction	Mean (SD)	7.7	(1.7)	7.6	(1.6)
Enabling factors
Statutory health insurance	N (%)	6,649	(95.4)	15,150	(82.8)
ISCED-2011 low	N (%)	1,753	(25.2)	1,851	(10.1)
ISCED-2011 medium	N (%)	3,088	(44.3)	9,896	(54.1)
ISCED-2011 high	N (%)	1,994	(28.6)	6,385	(34.9)
Need factors
SF-12 physical component score	Mean (SD)	51.8	(9.4)	49.5	(10.0)
SF-12 mental component score	Mean (SD)	49.7	(9.9)	50.6	(10.0)
Subjective health	Mean (SD)	3.7	(1.0)	3.4	(0.9)
Chronic diseases	N (%)	1,982	(28.4)	7,948	(43.5)
Control variables
Interview month: January	N (%)	1,071	15.4	7	0
Interview month: February	N (%)	910	13.1	5,050	27.6
Interview month: March	N (%)	1,115	16	5,103	27.9
Interview month: April	N (%)	1,428	20.5	3,334	18.2
Interview month: May	N (%)	787	11.3	1,854	10.1
Interview month: June	N (%)	249	3.6	568	3.1
Interview month: July	N (%)	737	10.6	684	3.7
Interview month: August	N (%)	732	10.5	705	3.9
Interview month: September	N (%)	1,360	19.5	675	3.7
Interview month: October	N (%)	1,103	15.8	539	2.9
Interview month: November	N (%)	1,009	14.5	290	1.6
Interview month: December	N (%)	1,144	16.4	16	0.1

Regarding outcome variables, individuals with a migration background had a lower likelihood of visiting a doctor or being hospitalized. Accordingly, they reported fewer doctor visits and hospital stays overall. Furthermore, in terms of sociodemographic control variables, individuals with a migration background were, on average, younger, more likely to have statutory health insurance (as opposed to private), predominantly residing in urban areas, and possessed lower levels of educational attainment.

Regarding health-related control variables, individuals with a migration background exhibited slightly higher scores in the SF-12 physical component, subjective health, and life satisfaction (potentially associated with their younger age). However, no difference was visible regarding the SF-12 mental component score.

In Figure 1, descriptive differences in healthcare utilization between individuals with and without a migration background vary according to gender and internal control beliefs. Particularly concerning visiting a doctor, the gaps between migrants and non-migrants and individuals with low, medium, or high internal LOC are smaller in women than in men. In men, individuals with a low internal LOC seem to be less likely to visit a doctor. No such clear differences can be observed regarding hospital stays.

Regression analyses

All regression analyses were initially assessed for multicollinearity, and no issues were observed in the variance inflation factors. When adjusted for sociodemographic and health-related factors, as well as the month of the interview, individuals with a migration background were less likely to have visited a doctor in the 3 months preceding the survey. Furthermore, exhibiting internal control beliefs was associated with an increased likelihood of having visited a doctor. Moreover, stronger internal control beliefs moderated the negative impact of migration background: the higher the internal control beliefs, the greater the probability that individuals with a migration background would seek ambulatory medical care. Additionally, female respondents exhibited a higher probability of having visited a doctor. However, among those respondents who did visit a doctor at least once, the frequency of doctor visits did not significantly differ based on migration background, internal control beliefs, or gender.

There was no discernible difference observed in regards to migration background, internal control beliefs, or gender concerning hospital stays in the past calendar year. The same applies to the number of nights spent in the hospital. All significant results are presented in Table 2.

Table 2.

Results for all genders.

	Doctor visit		Hospital stay
	Yes/No	Number of visits	Yes/No	Number of nights
	Odds ratio	Estimate	Odds ratio	Estimate
Predisposing factors
Migration background	0.52**	0.01	0.87	0.12
Internal control beliefs	1.01**	0.01	1.00	0.00
Migration × Internal control beliefs	1.01*	−0.02	1.00	−0.13
Women	1.38***	0.00	1.03	0.00
Age	1.01***	−0.10***	1.00	0.06**
Urban region	1.11***	0.04***	1.00	0.02
Life satisfaction	1.04***	0.01	1.02	−0.06**
Enabling factors
Statutory health insurance	0.95	−0.05***	0.91	0.01
ISCED-2011 low	0.83***	0.01	1.15*	0.04
ISCED-2011 medium	0.86***	0.00	1.08	0.01
Need factors
SF-12 physical component score	0.96***	−0.25***	0.96***	−0.05
SF-12 mental component score	0.98***	−0.11***	0.99***	−0.12***
Subjective health	0.82***	−0.11***	0.86***	−0.08*
Chronic diseases	2.56***	0.05***	1.43***	0.05*
Control variables
Interview month February	1.62***	0.02	1.17	−0.01
Interview month March	1.39**	0.01	1.05	−0.01
Interview month April	1.12	−0.02	0.97	0.00
Interview month May	0.84	−0.02	1.05	−0.01
Interview month June	0.86	0.00	1.06	−0.02
Interview month July	0.93	0.00	0.97	0.03
Interview month August	0.89	0.02	0.92	−0.03
Interview month September	0.99	−0.01	0.79	−0.02
Interview month October	0.97	−0.01	0.87	0.00
Interview month November	1.13	0.01	1.02	−0.01
Interview month December	1.56**	0.00	0.81	0.02
R ² (Log.Reg.: Nagelkerke’s R ²	0.21	0.13	0.10	0.07
Number of observations	25,143	16,713	25,170	3,179

p < 0.05. **p < 0.01. ***p < 0.001. Two-tailed tests.

In Table 3, the results for female respondents are shown. For women, migration background was not related to the likelihood of having visited a doctor. However, higher internal control beliefs were positively correlated with having visited a doctor. There was no interaction effect between migration background and internal control beliefs. Additionally, no difference was observed regarding migration background and locus of control concerning the number of doctor visits, the likelihood of having stayed in a hospital, and the number of nights spent in a hospital, respectively.

Table 3.

Results for women.

	Doctor visit		Hospital stay
	Yes/No	Number of visits	Yes/No	Number of nights
	Odds ratio	Estimate	Odds ratio	Estimate
Predisposing factors
Migration background	0.78	0.01	0.72	0.19
Internal control beliefs	1.01***	0.01	1.00	0.04
Migration × Internal control beliefs	1.00	−0.02	1.01	−0.19
Age	1.00	−0.11***	0.99***	0.06
Urban region	1.11*	0.04***	0.94	0.07**
Life satisfaction	1.03	−0.01	1.04	−0.08*
Enabling factors
Statutory health insurance	0.92	−0.04***	0.95	0.02
ISCED-2011 low	0.82**	0.01	1.17	0.05
ISCED-2011 medium	0.84***	0.00	1.10	0.02
Need factors
SF-12 physical component score	0.95***	−0.24***	0.96***	0.01
SF-12 mental component score	0.98***	−0.10***	0.98***	−0.11***
Subjective health	0.87***	−0.11***	0.92	−0.11*
Chronic diseases	2.61***	0.05***	1.45***	0.05
Control variables
Interview month February	1.61**	0.01	1.30	0.00
Interview month March	1.36	−0.01	1.11	0.01
Interview month April	1.05	−0.03	1.04	0.03
Interview month May	0.78	−0.03	1.10	0.02
Interview month June	0.82	0.00	1.16	−0.02
Interview month July	0.92	0.00	0.97	0.04
Interview month August	0.82	0.02	1.02	−0.03
Interview month September	0.93	−0.02	0.78	0.00
Interview month October	0.92	−0.02	0.75	0.02
Interview month November	1.04	0.01	0.92	0.00
Interview month December	1.73*	0.00	0.79	0.02
R ² (Log.Reg.: Nagelkerke’s R ²	0.19	0.12	0.08	0.06
Number of observations	13,134	9,295	13,149	1,760

p < 0.05. **p < 0.01. ***p < 0.001. Two-tailed tests.

Table 4 reveals a reversal in results for men: while migration background was associated with a lower likelihood of visiting a doctor, internal locus of control did not exhibit a main effect. Nonetheless, a stronger internal locus of control statistically counteracted the negative association of migration background with the likelihood of seeking ambulatory medical attention. Similar to the overall models and the models for women, no effects of migration background or locus of control were observed on the frequency of doctor visits, the likelihood of hospital stays, or the number of nights spent in inpatient care.

Table 4.

Results for men.

	Doctor visit		Hospital stay
	Yes/No	Number of visits	Yes/No	Number of nights
	Odds ratio	Estimate	Odds ratio	Estimate
Predisposing factors
Migration background	0.34***	−0.01	1.14	0.01
Internal control beliefs	1.00	0.02	1.00	−0.05
Migration × Internal control beliefs	1.02**	−0.01	0.99	−0.05
Age	1.01***	−0.08***	1.01***	0.06
Urban region	1.11*	0.03**	1.08	−0.04
Life satisfaction	1.05**	0.04**	1.01	−0.05
Enabling factors
Statutory health insurance	1.00	−0.06***	0.90	−0.01
ISCED-2011 low	0.88	0.03*	1.28*	0.03
ISCED-2011 medium	0.89**	0.01	1.09	−0.01
Need factors
SF-12 physical component score	0.97***	−0.27***	0.96***	−0.13**
SF-12 mental component score	0.99***	−0.11***	0.99*	−0.12***
Subjective health	0.78***	−0.11***	0.80***	−0.06
Chronic diseases	2.51***	0.04***	1.40***	0.05
Control variables
Interview month February	1.60**	0.03	0.94	−0.05
Interview month March	1.39*	0.04	0.90	−0.04
Interview month April	1.17	−0.01	0.83	−0.06
Interview month May	0.90	0.00	0.91	−0.05
Interview month June	0.89	0.00	0.88	−0.03
Interview month July	0.93	0.00	0.91	0.01
Interview month August	0.94	0.02	0.74	−0.02
Interview month September	1.03	0.00	0.76	−0.07
Interview month October	1.01	−0.01	1.03	−0.01
Interview month November	1.22	0.01	1.10	−0.03
Interview month December	1.47	0.01	0.83	0.04
R ² (Log.Reg.: Nagelkerke’s R ²	0.22	0.13	0.14	0.10
Number of observations	12,009	7,418	12,021	1,419

p < 0.05. **p < 0.01. ***p < 0.001. Two-tailed tests.

Discussion

This study aimed to investigate differences in the utilization of health services based on migration background and internal locus of control among women and men.

Likelihood of doctor visits: Migration background

The results of our study shed light on internal control beliefs as a key factor in the context of healthcare utilization among individuals with and without a migration background. In the overall group, migration background was detrimental to the odds of visiting a doctor. This is in line with previous studies (Klein and von Dem Knesebeck, 2018; Lebano et al., 2020; Rechel et al., 2013).

Likelihood of doctor visits: Internal control beliefs

Our study highlights the significant role of control beliefs in ambulatory healthcare utilization. While some studies align with our findings (De Jesus and Xiao, 2014; Slopieck and Chrapek, 2019), others show contradictory results, such as no link between age and education with doctor visits (Hajek and König, 2017) or reduced utilization in those with an internal locus of control (Kesavayuth et al., 2020; Musich et al., 2020). These inconsistencies may stem from differences in measurement methods or the failure to account for subjective health factors, which could explain lower utilization in individuals with stronger internal control beliefs.

Likelihood of doctor visits: Interaction of migration background and internal control beliefs

In our model, internal control beliefs mitigated the negative effect of migration background on ambulatory healthcare utilization, highlighting the importance of psychosocial resources like social support when other resources are limited (Dang et al., 2018). The decision to seek care is crucial and comparable to enabling factors like accessibility or perceived care quality (Nabieva and Souares, 2019). In refugee populations, control beliefs exhibit relationships with mental and somatic symptoms (Schlechter et al., 2023).

Studies indicate cultural differences in control beliefs, with migrants’ health-related control beliefs often shaped by their country of origin and migration experience (Kirkcaldy et al., 2007; Milz et al., 2016), but also by duration of stay (Adedeji et al., 2022). Additionally, internal control beliefs can help reduce ethnic disparities in depressive symptoms and are influenced by factors like migration reasons, with individuals migrating for career reasons displaying stronger internal control beliefs (Jain, 2017; van Dijk et al., 2013).

Doctor visits: Gender differences

Women were overall more likely to go to the doctor, a trend supported by research (Kirzinger et al., 2011; Simons et al., 2023), partly attributed to traditional masculine values discouraging help-seeking (Vargas et al., 2023). For women, migration background did not affect ambulatory healthcare utilization, but a stronger internal LOC increased doctor visits. In men, migration background reduced doctor visits, though this effect was lessened by a stronger internal LOC.

This could be due to more male refugees arriving without families, reducing social support, which is crucial for healthcare-seeking behavior (Dang et al., 2018). Furthermore, refugees tend to have a higher share of men, and unemployment or lower education may weaken internal LOC, further explaining these gender differences in healthcare utilization. (Kesavayuth et al., 2020).

Number of doctor visits

Even though individuals with a migration background face higher barriers to seeking a doctor, once that barrier is overcome, the differences between individuals with and without a migration background diminished in the results presented here: There was no disparity in the number of doctor visits associated with migration background, locus of control, or gender, once the initial barrier of visiting a doctor was overcome. Similar results were found in a study in Switzerland, where compared to non-migrants, immigrants were overall less likely to having visited a doctor, but no significant differences were found in the number of doctor visits (Tzogiou et al., 2021).

Likelihood of hospitalization and number of nights spent at a hospital

Our analysis found no association between migration background, internal LOC, or gender. Previous research suggests a negative link between internal LOC and inpatient services utilization (Mautner et al., 2017), but health-related control variables were not considered there. Thus, lower healthcare utilization may reflect better health status in those with a stronger internal LOC. Seeking care is an active health behavior, while hospital stays are typically necessary.

The relationship between LOC and healthcare use is complex, especially for migrants, with factors like social support (Vassilev et al., 2011) and physician density (Léonard et al., 2009) playing a role. Policies targeting refugees, such as digital health initiatives or integrating healthcare into language programs, could improve access (Jervelund et al., 2018; Mohammadi et al., 2021). Additionally, an increasing number of immigrant doctors (Lipovsek et al., 2024) and medical interpreters could help overcome language and cultural barriers (Pines et al., 2020).

Limitations

Possible limitations include the lack of clarity on how respondents define terms like “seeking a doctor” or “hospital stay,” as these could vary in scope. Our cross-sectional data limits causal inferences. The study was conducted during the 2020 COVID-19 pandemic, which may have influenced healthcare-seeking behaviors and therefore limit generalizability, though we accounted for temporal variations. Moreover, the migration background variable oversimplifies the complexities of migrant groups, not considering ethnic (Sundquist et al., 2000) or linguistic differences that could affect healthcare utilization (Nowak and Hornberg, 2023). Additionally, variations in countries of origin where not accounted for (Grochtdreis et al., 2025) and we were unable to stratify our analyses by ethnic or cultural group, which may mask important differences in health-seeking behaviors and should be examined in future studies. Due to incomplete data on year of migration, we were unable to assess the impact of length of residence on migrants’ internal locus of control and healthcare utilization without substantially reducing our sample or overfitting the model. Future studies with complete timing information should examine how time in the host country, season of migration, and age at arrival jointly influence migrants’ healthcare-seeking behaviors and sense of control.

New contribution to the literature

This study bridges a significant gap in the literature by demonstrating the interplay of psychological, social, and demographic factors in shaping healthcare utilization patterns in migrants. By integrating internal locus of control as a predisposing factor into the Andersen model, this study provides new insights into how psychological resources influence healthcare utilization in marginalized groups, while the gendered perspective contributes to a deeper understanding of disparities in health behavior.

Footnotes

ORCID iD

Doreen Müller

Ethical considerations

The Institutional Review Board of the German Institute for Economic Research approved the study.

Consent to participate

All participants of the SOEP provided oral or written informed consent depending on the module.

Consent for publication

Not applicable.

Author contributions

DM: Conceptualization, data curation, formal analysis, methodology, visualization, writing – original draft. JOS: conceptualization, supervision, writing – review & editing. PG: supervision, writing – review & editing. ME: conceptualization, methodology, supervision, writing – review & editing.

Funding

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

Declaration of conflicting interests

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

Data availability statement

This study is based on data from the German Socio-Economic Panel (SOEP), which is collected by the German Institute for Economic Research (DIW Berlin). The data are subject to strict data protection regulations and are available to researchers upon request. Access to the SOEP data requires the signing of a data use agreement with DIW Berlin. Further information on data access procedures can be found at .

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