Background
Achieving universal access to healthcare services is a key element to the Sustainable Development Goal 3 of “ensur[
ing] healthy lives and promot[
ing] well-being for all at all ages” [
1]. Specifically, target 3.8 sets to “achieve universal health coverage (UHC), including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all” (ibid.). The standard metrics for measuring progress towards the financial risk protection aspect of UHC are catastrophic health expenditures (CHE) [
2], identifying whether out-of-pocket (OOP) health expenditures represent a “catastrophic” share of the overall household expenditures, usually set at 40% [
2‐
4], or impoverishing health expenditures, which document whether the household’s falling below the poverty line is attributable to health expenditures [
5]. These metrics can be easily computed from widely available household surveys.
A recent comprehensive assessment of UHC progress combined CHE prevalence with a measure of service coverage capturing both prevention and treatment indicators at the country level [
6]. Service coverage is meant to document the aspects of access which are part of UHC and might be at odds with CHE, especially in the context of low- and middle-income countries (LMICs) where lower OOP might reflect the lower quality of health services [
7,
8], unmet health needs [
9], or even a younger, healthier population [
10]. Indeed, Wagstaff and colleagues found an association between low incidence of CHE and low service coverage in LMICs.
At the population level, access to quality health services is usually measured through health services utilization [
11], often within specific populations exhibiting health needs, e.g., children’s immunization records, women with a recent pregnancy, or individuals having experienced a recent or chronic illness. It involves heavy data collection processes and long interviews focusing on specific events in a given timeframe (e.g., two years for recent pregnancy and birth, 12 months for inpatient visits, etc.).
In LMICs, the literature has specifically investigated women’s self-reported barriers to seeking medical care [
12,
13], which are collected as part of the Demographic and Health Surveys (DHS) [
14]. These questions record perceptions on both the financial (possession of, or perceived ability to obtain monetary resources) and the geographic accessibility (distance and transportation means) as well as barriers pertaining to cultural and social norms (i.e., concerns about obtaining permission and going alone)—thereby covering a wide range of elements which have been identified as determinants to healthcare seeking and health services utilization [
15‐
19].
Existing studies have documented an association between reporting at least one significant barrier and lower maternal and prenatal health services utilization [
20‐
22]. A 2012 study combined socioeconomic, geographical, and psychosocial barriers from the 2003 DHS in Burkina Faso to create a tri-dimensional score of women’s perceived ability to overcome barriers to healthcare seeking [
23] and validated the score in relation to a select number of socio-demographic variables (specifically age, education level, poverty status and rural versus urban living) without investigating associations with the utilization of maternal or child services. In addition, all these studies solely focused on women.
This study seeks to elaborate, and validate a synthetic measure of perceived access to healthcare in both men and women, in the context of LMICs. We investigate whether items on perceived barriers to medical care can be combined into a score, and assess both the score’s construct validity (with respect to documented determinants of healthcare seeking), and predictive validity (with respect to primary care utilization).
Discussion
As in the 2012 study on women from Burkina Faso [
23], we found that obstacles were higher in under-educated, poorer individuals and those living in rural areas (i.e., in our sample, participants living further away from semi-urban—health—facilities). In contrast, in both samples used in the present study, the EFA yielded a one-dimensional factor score, whereas Nikiema and colleagues built a second-stage score combining all six items over three dimensions (specifically, psychosocial, socioeconomic, and geographic barriers). However, the Burkina Faso data was from 2005, only among women, a sizeable share of whom was living in urban areas. This suggests that the structure of the score might need to be validated when computed in very different settings or samples.
In line with the literature, we found that perceived barriers were strongly associated with the utilization of prenatal and maternal health services [
21,
38]. In our study, the PBMC score’s prediction of health services utilization was robust to the type of primary care utilization, health need, and population: specifically, the score can be employed to predict the probability of foregoing medical consultation or expenses at the household level, of medical consultation and non-utilization (self-medication) in individuals with a recent episode of illness, and of maternal health services utilization in women who had a live birth the past two years (documented through delivery in a health facility and the number of prenatal consultations).
Value-added of the PBMC score and policy implications
The main implications for public health practitioners are two-fold. First, our results highlight the importance to pay attention to perceived obstacles, both in terms of number and intensity—as they predict primary care utilization in rural sub-Saharan Africa. Second, through the PBMC score, we offer a valid, synthetic, simple, and sensitive measure to be used in future studies.
Unlike measures of access focusing on individuals that experienced an event prompting health services utilization (e.g., individuals with a recent episode of illness or women with a recent pregnancy or birth), the PBMC score can be documented in the general population through simple, and relatively light data collection and data analysis processes.
The factor-based score also has the advantage of being expressed in the same scale as the original items, with values that can be easily interpreted: a 0 score corresponds to having declared “not a problem” to all items, a 2 score indicates that all items were reported as “a big problem”, and values in between reflect increasing levels in barriers. In contrast to studies documenting ‘any’ perceived barrier [
12,
21] or focusing on a specific barrier such as distance [
20], the PBMC score, therefore, provides a much more precise and sensitive measure of both the intensity and the width of barriers to medical care. Additionally, with a factor-based score, only the structure of the score (i.e. the selection of the set of items used to build the score) may be sample-dependent.
As illustrated by the absence of association with CHE, the PBMC score captures something other than financial risk protection. Indeed, our results suggest that people who perceive high financial barriers in accessing healthcare are less likely to afford or incur significant healthcare expenses. The score is therefore valuable in providing information on additional deficits in, and progress towards UHC attainment. There is a wide range of possible uses for the score. For instance, the identification of individual and structural characteristics associated with the intensity of the score can help characterize populations and areas that should be targeted by specific interventions or policies aiming at improving UHC. The score can also be used to evaluate such interventions through the comparison of changes in individual score levels over time (before/after intervention or longitudinal studies)—to name just a few potential applications.
Limits
Our study has limitations. The main concern is that it relies on self-reported measures, which can be subject to heterogeneity in reporting associated with psycho-social and socio-economic variables—such biases have been extensively documented in the literature on self-assessed health [
39‐
44]. In addition, our results reveal an association between the PBMC score and psychosocial variables (specifically risk aversion, generalized trust, and perceived quality of the healthcare system), which ought to be accounted for, both in potentially future multivariate regressions and in policy design. However, we provide ample evidence that our score is significantly associated with objective measures and determinants of healthcare-seeking (distance to the health facility, sex, formal education, several measures of wealth and poverty, etc.).
A second limitation is that, though multidimensional, the PBMC score only provides a partial view of access. In particular, it does not include supply-side information on the availability or quality of healthcare services, professionals, equipment, or medications in the area of interest—i.e., the health system’s side of Levesque’s comprehensive framework of patient-centered access to healthcare [
45]. Items used to build the PBMC score encompasses the “ability to seek”, “ability to reach” and “ability to pay” of populations defined in this framework, but its scope falls short of abilities to perceive and engage that are instrumental in the populations’ access to healthcare.
A final, and related, limitation is that, by using DHS-based items in a top-down process, the PBMC score may overlook context-specific barriers that are relevant to accessing healthcare goods and services in rural Senegal. Bottom-up approaches to tailoring items to the specific context would gain in internal validity though potentially at the expense of external validity
3. Indeed, the PBMC score has the ambition of being used in other settings, e.g. through DHS surveys, though data availability is limiting—especially in men
4.
Conclusion
We used DHS-based items on perceived barriers to medical care to build a one-dimensional score in both men and women living in rural Senegal. This PBMC score is internally consistent and confirmed in the Niakhar HDSS using a different dataset representative of adult individuals living in the same area. The score is significantly associated with a wide range of determinants of healthcare-seeking (including, but not limited to, sex, education, marital status, poverty, and distance to the health facility). Additionally, the score can predict non-utilization of primary care at the household level, utilization and non-utilization of primary care following an individual’s episode of illness, and utilization of primary care during pregnancy and birth. The score was confirmed using CFA in the general adult population living in the Niakhar HDSS, though further investigation is warranted to confirm its validity in other settings.
As a valid, sensitive, and easily documented individual-level indicator, the PBMC score can be a complement to regional or national level health services coverage to measure health services access and utilization. At the individual or household level, the PBMC score can also be combined with conventional metrics of financial risk protection such as CHE to comprehensively document deficits in, and progress towards UHC.
Acknowledgements
We are grateful to all study participants, the staff at the Niakhar Health and Demographic Surveillance System, and the members of the UNISSAHEL program. A complete list of members of the UNISSAHEL Study Group is given in Appendix A1 in the Supplementary Material. We also thank Bruno Ventelou, Sylvie Boyer, and Mohammad Abu-Zaineh, who co-led the UNISSAHEL economic research program. Our thanks also go to Andrainolo Ravalihasy, Richard Lalou, Jean-Yves Le Hesran, Bruno Boidin, Jérôme Wittwer, Fatoumata Hane and participants to the AMSE PhD seminar, the UCL Global Health Brown Bag seminar, and the 2022 European Health Economics conference for their questions and suggestions. We are also thankful to two anonymous reviewers for their valuable comments.
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