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Abstract

Health-seeking behavior is a sequence of actions taken to promote health and prevent disease. This study aims to investigate health-seeking behavior among the communities in Hosanna town, Central Ethiopia Region. We used a cross-sectional study among (n = 443) communities in Hosanna town. The health-seeking behavior of study participants was assessed using the mean score of each dimension (health promotion and disease prevention activities) as a cut-off value. Having a score below the mean on each target dimension was equated with having a low level of health-seeking behavior. Eighty-five percent (85.4%) of participants had a low level of health-seeking behavior. Males (AOR: 1.8; CI 1.03–3.42), widowed participants (AOR: 4.8; CI 2.1–17.1), and those who had not attended formal education were more likely to have low health-seeking behavior (AOR: 4.5; CI 1.16–17.8). The study revealed low health-seeking behavior among study participants, and the finding implies the need for urgent intervention.

Keywords

disease prevention health policy health promotion health-seeking behavior leisure activities substance use

Introduction

Health is a comprehensive concept that encompasses all social and biological aspects of life. The term “health-seeking behavior” refers to a sequence of actions taken to promote health and prevent disease (Nutbeam and Kickbusch, 1998; World Health Organization [WHO], 2021). The health policies and strategies entail knowledge about health-seeking behavior for health promotion, disease prevention, and improved quality of life (Nutbeam and Kickbusch, 1998). Better health-seeking behavior has the potential to reduce the occurrence of disease, disability, and death (World Health Organization [WHO], 2021). Healthcare utilization, which is an immediate outcome of health-seeking behavior, is also found to be important to get health counseling (family planning, antenatal care, growth monitoring), screening for chronic diseases, and adherence to effective treatment (U.S. Agency for International Development, 2010; Clewley et al., 2018). However, pieces of evidence claim that there is a growing burden of low healthcare utilization and increased disease prevalence (Kevany et al., 2012; Gabrani et al., 2021; World Health Organization Regional Office for Africa, 2012). Previous studies showed that Africa accounts for the largest portion of the global disease burden (WHO, 2012). Similarly, despite improved access to physical health facilities, the burden of diseases is not decreasing, and the health service coverage index is still low (39%) in Ethiopia (Assefa et al., 2020). A systematic review of the global burden of disease analysis findings estimated age-standardized death rates of 800 per 100,000 population for non-communicable diseases in Ethiopia (Misganaw et al., 2014).

Regarding service utilization, the Ethiopian demographic and health survey findings of 2011 reported a low level of health service utilization. More than four in five mothers did not receive antenatal care, and only 11.7% were delivered by skilled delivery attendants, and 9.7% of women had a postnatal health checkup (Tarekegn et al., 2014). To the best of our understanding, the observed evidence does not reflect nature as more deserving for high-income countries than low-income countries; rather, the difference is because of the level of health-seeking behavior in high- and low-income countries (Tarekegn et al., 2014; Tey and Lai, 2013).

While the type and extent of health-seeking behavior differ, it remains essential for individuals in both developed and low-income countries (Hamzah et al., 2024; Poortaghi et al., 2015; Wu et al., 2023). Individuals in low-income countries experience diseases associated with low socioeconomic status, including communicable diseases (Hamzah et al., 2024). Whereas lifestyle-related diseases, such as chronic non-communicable diseases (NCDs), are prevalent in developed countries (Hamzah et al., 2024; Poortaghi et al., 2015; Wu et al., 2023). As a result, health-seeking behavior is crucial worldwide.

Ethiopia is currently experiencing an incidence of newly emerging and reemerging health problems and is in a state of transition that requires comprehensive healthcare policies and programs (Mackey et al., 2014). Similarly, unlike the traditional assumptions, chronic non-communicable illnesses are not confined to people in developed countries (Misganaw et al., 2014; Yosef, 2020).

Although there are controversies and inconsistencies regarding the findings related to factors associated with health-seeking behavior, few empirical studies have highlighted that literacy, socio-economic status, cultural issues, and health service quality are among the factors affecting the health-seeking behavior of the community (Latunji and Akinyemi, 2018; Schooley et al., 2009; Webair and Bin-Gouth, 2013). Regarding the previous studies, most of them are limited to the selected segments of the community, civil servants working in public organizations (Latunji and Akinyemi, 2018), children (Webair and Bin-Gouth, 2013), and women (Schooley et al., 2009). In Ethiopia as a whole, the burden of disease is high, and health service use is low. Healthcare-seeking behavior is closely linked with health service utilization and has a multifaceted effect on reducing the disease burden of the nation. Despite this, the extent of health-seeking behavior is not well-researched in the study area. Thus, in this study, we assessed the level of health-seeking behaviors and associated factors in urban households.

Materials and methods

Study design

This was a community-based descriptive cross-sectional study that was carried out among urban residents in Ethiopia in August 2017.

Study area and period

The study was conducted in Hosanna town, the capital of the Hadiya administrative zone, located at a distance of 232 km southwest of Addis Ababa, the capital of Ethiopia. There were 69,995 (male 35,523, female 34,472) people living in Hosanna town (Central Statistical Agency (CSA), (2012). The town as a whole has five administrative units namely; Sech Duna (male 14,295, female 13,668, total population 27,963, households 6909), Adis Ketema (male 13, 543, female 13,512, total population 27,055, households 6814), Gofer Meda (male 7297, female 7222, total population 14,519, households 3211), Bobcho Kalehiwot Gospel (male 96, female 46, total population 142, households 26) and prison (male 292, female 24, total population 316, households 2; Central Statistical Agency (CSA), (2012).

Participants

The sample size for this study was estimated using a single population proportion formula by considering a two-sided confidence level of 95%, the proportion of health-seeking behavior at 50%, and a margin of error of 5%(Charan and Biswas, 2013; Pourhoseingholi et al., 2013). Consequently, a total sample of 443 participants was included after considering a 15% non-response rate. The final sample size was proportionally allocated to sub-administrative units (Sech Duna, n1 = 180), Adis Ketema (n2 = 177), Gofer Meda (n3 = 83), Bobcho Kalehiwot Gospel (n4 = 2), and Prison (n5 = 1). The sampling frame was created for households from the family folder file documented in the offices of each sub-administrative unit. Meanwhile, we used computer-generated numbers to select the households. From each of the selected households, the head (prior decision-maker) of the household was selected and included in the study.

Data collection instruments and procedures

The WHO STEP-wise approach to surveillance (STEPS) instrument and global physical activity questionnaire (GPAQ; Cleland et al., 2014; Riley et al., 2016) were modified and used for data collection. The modified questionnaire was validated and translated into the local language, Amharic. Before the data collection, the instrument was pretested on 5% of the sample in Butajira town 2 weeks before the final data collection to make sure that the questions were clear and could be understood by the respondents. Based on the findings of the pretest, the questionnaire was further modified and used for the final data collection. The internal consistency of the 36 items in the questionnaire was measured by Cronbach’s alpha (0.83), exceeding the acceptable threshold of 0.7 (Taber, 2018). Furthermore, the correlation between the items of the health-seeking behavior questionnaire was tested, and the finding shows Pearson correlation coefficients (r), ranging from 0.51 to 0.67, which is deemed acceptable, indicating the questionnaire’s validity (Schober et al., 2018).

Data were collected through face–to–face interviews using a structured questionnaire. Following informed oral consent procedures, the head of the household was interviewed at the home level in a quiet corner away from the presence of other people. Each took approximately 50 minutes. Probes and clarifications were sought as deemed necessary.

A 2-day training session was provided for data collectors and supervisors on research ethics, data collection procedures, and the instrument’s contents to enhance the quality of our data. The supervisors carried out supportive supervision daily during the data collection period. The completed questionnaire was checked daily for completeness and consistency. Four health extension workers and two Master of Public Health graduates gathered data and supervised the fieldwork, respectively.

Variables and their measurement

The dependent variable was health-seeking behavior. It is a composite variable measured using a model construct. The overall health-seeking behavior of study participants was assessed using the mean score of each of the following dimensions. Actions taken when one gets ill, general health screening, regular exercise, health-oriented leisure activities, and reducing risks or engaging in preventative measures were the dimensions used to measure health-seeking behavior in this study.

Actions taken when one gets ill

Actions taken when one gets ill evaluate the immediate responses individuals have when they experience symptoms of disease. The health-seeking behavior of participants for this dimension was obtained from the following questions: “During your last illness, did you seek treatment?” This question had the following response categories: “Yes, if seek care from health facilities and or traditional healers,” “No, did not seek treatment.”

Screening for general health

The general health screening dimension measures how often individuals participate in regular health check-ups and screenings for disease and health services usage. This dimension was assessed using the following questions with “Yes -coded as 1” or “No -coded as 0” responses; “Have you ever checked your blood pressure to know the level of your blood pressure?” “Did you ever check your blood sugar level to know the level of your blood sugar?,” “Have you ever tested for human immune deficiency virus (HIV) infection for early care and treatment?,” “Did you vaccinate children and any family member who is eligible?,” “Did you or a member of your family monitor the growth of the recent child in the family?,” “Did you or member of your family followed antenatal care for the resent pregnancy?” In this dimension, participants having a score above the mean were equated with having high health-seeking behavior in screening for general health and were coded as “Yes 1,” and otherwise coded as “No 0,” indicating that they had low health-seeking behavior in screening for general health.

Health-oriented leisure activities

Exercising health-oriented leisure activities measures how involved participants are in activities that improve well-being, such as physical exercise and social engagements. The health-seeking behavior of participants in health-oriented leisure activities was measured as high “Yes 1” -if scored above the mean for questions of aerobic physical activities (walking, running, swimming, and bicycling) and health-oriented leisure activities (playing tennis, jumping rope, lifting weights) or low level of health-seeking for health-oriented leisure activities “No 0”-if scores below the mean.

Risk exposure

Reducing risks of exposure examines the measures individuals take to minimize their exposure to potential health hazards, such as avoiding health-risky behaviors like harmful alcohol use and smoking tobacco products. Health-seeking behavior of participants of risk exposure was measured using the question, “Did you take alcohol?,” “Did you smoke tobacco products?” and “Did you chew Khat?” with “yes” or “no” responses in both. Participants who responded “Yes” to at least one of these questions were coded as “0,” indicating that they had high-risk exposure and low health-seeking behavior. Overall, having a score above the mean on each of the target dimensions was equated with having a high level of health-seeking behavior. The exposure variables included age, sex, education, occupation, marital status, family income, and distance from a health facility.

Operational definitions

High health-seeking behavior

Participants having a score above (⩾) the mean on each of the target dimensions were equated with having a high level of health-seeking behavior.

Low health-seeking behavior

Participants having a score below the mean on each of the target dimensions were equated with having a high level of health-seeking behavior.

Data analysis techniques

The collected data were cleaned and entered into Epi-Data version 3.2 and exported to the software package Stata Corporation, College Station, Texas, 77845, USA (STATA 14) for analysis. We described the data using mean, median, frequencies, and percentages. We used logistic regression analysis to assess whether there was a significant association between the associated factors and the health-seeking behavior. Before fitting and reporting the final model, we performed the correlation and goodness-of-fit tests. The variance inflation factors (VIFs) test was used to assess correlation between the independent variables, and those that showed no multicollinearity were fitted to the multivariable logistic regression model. The variables with a p-value of <0.2 in the bivariable analysis were considered for multivariable logistic regression analysis. The Hosmer-Lemeshow goodness-of-fit statistic was used to check the model’s fitness. We used the adjusted odds ratio with a 95% confidence interval (CI) to examine the strength and direction of the association. A p-value of less than 0.05 was used to define statistical significance. Finally, the findings were presented in the form of tables and text. STATA 14 software package (Stata Corporation, College Station, Texas, 77845, USA) and IBM SPSS 27.0 were used for data analysis.

Ethical statement

This study has been reviewed and approved by the authorized institutional research review board in Ethiopia. Additionally, participants were informed that their involvement in the study could provide valuable information to help improve health-seeking behavior. They were also told that participation was voluntary and that the information they shared would be used solely for research purposes. A brief explanation of the research purpose and confidentiality was provided to potential participants. To protect confidentiality, data collection took place in a comfortable setting, such as the participant’s home, and personal identifiers were removed from the data collection tools. Informed verbal consent was obtained from all study participants before data collection, after explaining the objectives of the research. In this research, we obtained informed verbal consent from the research participants because all the data sought was associated purely with information rather than human samples or did not put participants in the experiment, which requires national ethical approval in our context. We obtained ethical clearance for the research to be conducted in this way. This is the reason why we obtained more informed verbal consent than written consent.

Results

Characteristics of study participants

A total of 443 questionnaires were received, but only 424 questionnaires were valid and included in the analysis. Of the questionnaires deemed not valid, 4 respondents refused, and the rest did not complete the questionnaire. The mean age of the study participants was 33.8 ± 8.06 standard deviation (SD) years. More than 80% (81.5%) of the participants were in the age group 20–50, and about 8.5% were over 50. Regarding the family size, on average, five people live in one house. Of the 424 participants, 51.3% were females and 61.6% were currently married. More than one in three participants were government employees (Table 1). Regarding the role of participants in their households, 43.5% were mothers and 34% were fathers. The majority (34%), (27.6%) of study participants were government employees and merchants, respectively.

Table 1.

Sociodemographic characteristics of study participants.

Variables		Frequency	Percent
Sex	Male	206	48.6
Sex	Female	218	51.4
Marital status	Married	262	61.8
	Single	84	19.8
	Divorced	45	10.6
	Widowed	33	7.8
Educational status	No formal education	55	13
	Primary education	162	38.2
	Secondary education	109	25.7
	Higher education	98	23.1
The house family lives in	Own	282	30.4
	Rent	129	66.5
	Other	13	3.1
Monthly income	<18.5 USD	75	17.7
	18.5–36.5 USD	177	41.7
	36.6–72.5 USD	120	28.3
	>72.6 USD	52	12.3
Total		424	100.00

Health-seeking behaviors

Table 2 shows the health-seeking behavior of study participants. Accordingly, 397 (93.6%) took action when they got ill. Of those who took action when they got ill, 345 (86.9%) visited medical institutions, and 24 (6.0%) used only traditional home remedies. Of the participants who visited medical institutions, 139 (40.2%) preferred private clinics as their first choice, 106 (30.4%) sought health care from a hospital, 82 (23.7%) sought health care from a pharmacy, and only 18 (5.4%) sought health care from public health centers. The study participants were also asked to rate their perceived health status, and the self-rated perceived health status showed that 113 (26.7%) participants felt very well, 110 (25.9%) felt excellent, and 17 (4.0%) were unable to express their health status.

Table 2.

Health-seeking behavior of study participants in Hosanna.

Characteristics		Number	Percent
Perceived health status of participants	Excellent	110	25.6
	Very good	113	26.7
	Good	100	23.6
	Fair	51	12.0
	Bad	33	7.8
	Difficult to express	17	4.0
Take action when one gets ill	Yes	397	93.6
Take action when one gets ill	No	27	6.4
Reported actions are taken when one gets ill	Visiting medical institutions	345	86.9
	Use traditional medicine	24	6.0
	Combination of both	28	7.1
Medical institution of the first choice	Private clinics	139	40.2
	Hospital	106	30.7
	Health centers	18	5.4
	Pharmacy	82	23.7
Screened for general health	Yes	72	17.0
Screened for general health	No	352	83.0
Health-seeking behavior	Low	362	85.4
Health-seeking behavior	High	62	14.6
Total		424	100

Only 72 (17.0%) participants were screened for general health status, and 352 (83.0%) study participants were not screened. Similarly, more than eighty percent (81.6%) of participants did not undertake health-oriented leisure activities. The reported reasons not to take health-oriented leisure activities were lack of knowledge (45.5%), lack of access (25.1%), and lack of time. Overall, 85.4% of the study participants had low levels of health-seeking behaviors.

Factors associated with health-seeking behaviors

The goodness of fit statistic test of the final model was (chi-square = 2.13, p-value = 0.62), which was greater than the conventional threshold (p-value 0.05), indicating that the model fits well (Archer and Lemeshow, 2006; Dey et al., 2025). A VIF test finding was (0.92–1.03), closer to 1 for a given range of independent variables, indicating the absence of multicollinearity among the predictors in the model (Akinkunmi and Etebefia, 2019).

In bivariable analysis, sex (X² = 4.9, p-0.02), marital status (X² = 8.9, p-0.03), and level of education (X² = 2.14, p-0.049) have shown an association with health-seeking behavior. Table 3 presents the findings of the logistic regression model fitted to assess factors associated with health-seeking behavior. Accordingly, marital status, sex, and level of education were independently associated with health-seeking behavior. The odds of low health-seeking behavior among widowed participants were 4.8 times higher than single participants (AOR = 4.8, CI: 2.1, 17.1). As shown in the adjusted model, the likelihood of low health-seeking behavior was significantly higher in males than in females (AOR = 1.8, CI: 1.04, 3.42). Similarly, in this study, the association between level of education and health-seeking behavior was statistically significant (p < 0.05), indicating that participants who had no formal education had low health-seeking behavior compared to those having higher education (AOR = 4.5, CI: 1.16, 17.8).

Table 3.

Variables associated with health-seeking behavior.

		Health-seeking behavior
Variables		Low (%)	High (%)	AOR^a (95%CI)	p-value
Sex	Male	184 (89.3)	22 (10.7)	1.8 (1.04,3.42)	0.02^c
Sex	Female	178 (79.8)	40 (18.3)	1
Marital status	Married	218 (83.2)	44 (16.8)	1
	Single^b	69 (82.1)	15 (17.9)	2.1 (0.9,12.5)	0.21
	Divorced	43 (95.6)	2 (3.0)	4.2 (1.1,21.1)	0.03^c
	Widowed	32 (97.0)	1 (3.0)	4.8 (2.1,17.1)	0.02^c
Educational status	No formal education	47 (85.5)	8 (14.5)	4.5 (1.16,17.8)	0.02^c
	Primary	139 (85.8)	23 (14.2)	1.4 (0.50,4.20)	0.48
	Secondary	89 (81.7)	20 (18.3)	2.1 (0.8,5.7)	0.12
	Higher education	87 (88.8)	11 (11.2)	1

1: reference category.

Adjusted odds ratio.

Not ever married.

p-value < 0.05.

Discussion

Many pieces of evidence suggest that addressing health-seeking behavior paves the way for appropriate utilization of health care services (Bourne, 2009). This study tried to measure health-seeking behavior in multidimensional approaches to improve specific health behavior changes to prevent disease and promote health. Based on our measure, the study showed that the majority (85.4%) of participants had a low level of health-seeking behavior.

This finding is consistent with findings reported for mothers’ health care-seeking behavior for child health illness in Dera district, North Shewa zone in Oromiya regional state, and Anded district in Northwest Ethiopia (Simieneh et al., 2019; Tsion et al., 2014). However, the extent of health-seeking behavior in the current study was remarkably low when compared to reports from Southwest Ethiopia concluded that 58.4% of the households sought care from modern healthcare facilities (Begashaw et al., 2016).

About 93.6% of participants in our study took action and sought medical help when they got ill, which is more than reported in South Africa (Otwombe et al., 2015). Our data also showed that 40.3% of participants primarily chose private clinics when they sought medical help. A similar study is not available to compare the findings of the present study. Although the reasons why participants prefer private clinics warrant further study, longer waiting times and dissatisfaction with the healthcare providers in public health sectors could more likely explain the observed differences.

Maternal and child health care utilization was also used as an indicator of health-seeking behavior. The results of the present study indicated that the health-seeking behavior for antenatal care (ANC) was 94.6%, while the rate for child vaccination was 92.7%.

This is incomparably higher than the findings of the demographic and health survey (DHS) of 2016 in Ethiopia (Central Statistical Agency (CSA) (2016). This could be because our sample consisted of participants entirely from the urban setting, unlike DHS, which encompasses both urban and rural regions throughout the country.

The socio-demographic characteristics of study participants were tested for association. The results illustrated that sex, marital status, and level of education showed an association with health-seeking behavior. Males had a low score for health-seeking behavior compared to females. This finding was in line with the study reported in southwest Ethiopia (Girma et al., 2011). Socially, women are more responsible for their families, often stay longer in the home, and take time to identify their health problems. Observations claimed that women are sensitive to their health. Conversely, men stay longer out of the door and are busy with social matters, representing their families in Ethiopia.

Regarding the influence of marital status on health-seeking behavior, divorced and widowed participants had low health-seeking behavior in our study. This finding was consistent with the study reported for Jamaica in 2009 (Bourne, 2009). Perhaps in most cases, divorced and widowed individuals experience social, psychological, and mental health problems more than married adults. These reactions could impair the value people give to their health and reduce health-seeking behavior. Participants who reported a lower level of education had low health-seeking behavior. Most of the reports from Ethiopia and other countries supported this finding (Begashaw et al., 2016; Bourne, 2009; Tsion et al., 2014).

Limitations of the study

One of the limitations of this study is related to the cross-sectional study design, in which the temporal relationships between the outcome and predictor variables cannot be established. Moreover, the sample was limited to the urban population, which can limit the generalizability of the results. In addition, most of the references we used for comparison were not from Ethiopia; this may be considered a limitation of the study. We recommend an exhaustive exploration of health-seeking behavior and factors associated with it in a large sample in an urban and rural population in Ethiopia.

Conclusions

The overall health-seeking behavior of households was low in Ethiopia. Especially, taking health-oriented leisure activities and screening for general health were incredibly low in the community. This implies the need to work on the promotion of healthcare on the health-seeking behavior of the population in the country. The majority of the population takes action when getting ill and underestimates the value of screening for general health and health-oriented leisure activities. Further consideration should also be given to the risk factors, including sex, marital status, and level of education.

In line with the findings of this study, the available evidence suggests that obtaining something desirable requires a conscious desire or search for a particular object or experience. In other words, without a need, there’s no motivation to acquire it. Hence, health-seeking behavior is a motivating factor to prevent diseases and promote well-being (Bourne, 2009; El-Khawaga et al., 2025).

This study has several key implications. Low health service utilization, including maternal health services such as family planning, antenatal care, institutional delivery, postnatal care, and vaccination, indicates a limited health-seeking behavior within the community. Proper utilization of health care services is critical for preventing illness and premature death. Enhancing health-seeking behavior leads to better utilization of health services, which in turn helps prevent morbidity and mortality. The findings of this study highlight the necessity for actions aimed at improving the health-seeking behavior of the community.

These interventions should target examining and removing barriers that affect health-seeking behavior while also enhancing health literacy. Health literacy packages considering the identified differences may be designed to enhance awareness of the community about the need for health-seeking behavior.

Footnotes

Acknowledgements

The authors would like to thank Hosanna Health Sciences College’s research and community service directorate for its consorted technical and financial support. We are also grateful to Hosanna town residents, data collectors, and the Hosanna Town health office for their cooperation during the entire process of data collection.

ORCID iD

Likawunt Samuel Asfaw

Ethical considerations

The institutional review committee of Hosanna College of Health Sciences approved this study. Additionally, participants were informed that their involvement in the study could provide valuable information to help improve health-seeking behavior. They were also told that participation was voluntary and that the information they shared would be used solely for research purposes. A brief explanation of the research purpose and confidentiality was provided to potential participants. To protect confidentiality, data collection took place in a comfortable setting, such as the participant’s home, and personal identifiers were removed from the data collection tools.

Consent to participate

Informed oral consent was obtained from all study participants before data collection after explaining the research objectives. In this research, we obtained informed oral consent from the research participants because all the data sought was associated purely with information rather than human samples and did not put participants in an experiment, which requires national ethical approval in our context. We obtained ethical clearance for the research to be conducted in this way. This is the reason why we received more informed verbal consent than written.

Consent for publication

Consent for publication is not applicable to this article as it does not contain any identifiable data

Author contributions

LSA: Conceived and designed the study idea, developed a proposal, organized the data collection tool, created a data entry template, interpreted findings, and wrote the manuscript. SYA: Reviewed the proposal and the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Self and Hosanna Health Sciences College provided technical and material support

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

Data will be available from the corresponding author upon reasonable request.

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