Morbidity burden and predictors of hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana.

Morbidity burden and predictors of hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice PLOS Glob Public Health . 2026 Apr 17;6(4):e0006316. doi: 10.1371/journal.pgph.0006316 Search in PMC Search in PubMed View in NLM Catalog Add to search Morbidity burden and predictors of hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana Ebenezer Asare Aboagye Ebenezer Asare Aboagye 1 Department of Planning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 2 Ghana Communication Technology University, Tesano, Accra-North, Ghana Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing Find articles by Ebenezer Asare Aboagye 1, 2, * , Dina Adei Dina Adei 1 Department of Planning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Conceptualization, Supervision, Writing – review & editing Find articles by Dina Adei 1 , Williams Agyemang-Duah Williams Agyemang-Duah 3 Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada Formal analysis, Methodology, Writing – review & editing Find articles by Williams Agyemang-Duah 3 Editor: Ryan Essex 4 Author information Article notes Copyright and License information 1 Department of Planning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana 2 Ghana Communication Technology University, Tesano, Accra-North, Ghana 3 Department of Public Health Sciences, Queen’s University, Kingston, Ontario, Canada 4 University of Greenwich, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND The authors have declared that no competing interests exist. ✉ * E-mail: [email protected] Roles Ebenezer Asare Aboagye : Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing Dina Adei : Conceptualization, Supervision, Writing – review & editing Williams Agyemang-Duah : Formal analysis, Methodology, Writing – review & editing Ryan Essex : Editor Received 2025 Oct 15; Accepted 2026 Mar 24; Collection date 2026. © 2026 Aboagye et al This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice PMCID: PMC13089720  PMID: 41996346 Abstract A proportion of global migration involves individuals migrating without parental or legal guardianship and those who face varied barriers to citizenship. Yet empirical evidence on their morbidity burdens and hospitalization particularly in Ghana and similar context remains scarce. This limits capacity for informed policy, planning, and response strategies aligned with global health targets. This study examined the burden of communicable and non-communicable diseases (NCDs), and predictors of hospitalization among these vulnerable groups. A cross-sectional survey was conducted from March 2024 to May 2024 among 481 purposively selected unaccompanied migrants and persons prone to statelessness in the Greater Kumasi Metropolitan Area and the Awutu Senya East Municipal Area. Data were analyzed using descriptive statistics (frequency, and percentages) and complementary log-log regression in Stata 14.2. Statistical significance was set at p ≤ 0.05. The analyses revealed a higher prevalence of communicable diseases (23.3%) than NCDs (8.1%). Malaria (90%), flu/cold (30%), typhoid (27%), diabetes (33%) and asthma (21%) emerged as common health conditions with limited and condition-specific subgroup differences. Overall, 8.7% of respondents reported ever being hospitalized. Across models, frequent illness (Model 1: OR = 4.097, 95% CI: 2.056–8.163; Model 2: OR = 3.724, 95% CI: 1.830–7.576; Model 3: OR = 4.224, 95% CI: 2.002–8.913; all p < 0.001) and diagnosis with an NCD (Model 1: OR = 3.336, 95% CI: 1.611–6.906; Model 2: OR = 3.600, 95% CI: 1.737–7.460; Model 3: OR = 3.873, 95% CI: 1.861–8.058; all p ≤ 0.001) were consistently associated with higher odds of hospitalization. These findings offer contextually bounded insights highlighting that health vulnerability among these populations is manifested less through differential disease prevalence but more through recurrent illness and NCD diagnosis necessitating hospitalization. This underscores the need for early detection, continuous care, and effective outpatient NCD management. Introduction Global migration has been transformed by climate change, economic fragility, political unrest, and human rights abuses, with the effects evident in the intensification of both voluntary and forced movements within and across countries [ 1 – 6 ]. Of great concern is that a proportion of these movements involves minors migrating without parental or legal guardianship and people who lack recognized nationality under the operation of the laws of any country [ 1 , 7 , 8 ]. Constrained by precarious living conditions, economic instability, restricted access to formal health systems, these populations experience structurally mediated health disadvantages that heighten exposure to preventable illness [ 9 – 12 ]. Evidence shows that migrants morbidity spans both communicable diseases such as tuberculosis, malaria, and cholera and non-communicable diseases (NCDs) including diabetes, hypertension, and asthma [ 13 – 15 ]. Beyond exposure, barriers to timely and appropriate care further intensify these risks. Consistently, delayed care-seeking, treatment interruption, and avoidable hospitalization driven by documentation insecurity, financial constraints, and perceived discrimination within health systems have been documented [ 1 , 14 , 16 , 17 ]. Existing studies including those by Aljadeeah et al [ 18 ], Osman et al [ 19 ], Boakye et al [ 20 ], Scales et al [ 21 ], Allegri et al [ 22 ], Vignier et al [ 23 ], Chavan et al [ 24 ], and Dalmau-Bueno et al [ 25 ] have examined these patterns among the general migrant population and specific subgroups such as undocumented migrants, asylum seekers, and older adults. Studies from the European context, for example, indicate that undocumented migrants experience approximately 19% higher risk of hospitalization for chronic conditions, 65% higher risk for acute illnesses, and more than double the risk for vaccine-preventable diseases compared with host populations [ 22 ]. Across diverse contexts, intersecting structural and institutional constraints have been documented to translate into measurable morbidity disparities, underscoring how legal precarity and social marginalization operate as fundamental determinants of health in displaced and nationality-insecure populations [ 18 , 19 , 21 , 23 – 25 ]. In Ghana, existing evidence indicates that hospitalization is shaped by chronic illness, functional limitation, and demographic factors, yet socio-economically disadvantaged groups are less likely to be admitted despite elevated need [ 20 , 26 , 27 ]. However, this evidence is confined to specific populations including adults,, and diabetic patients, leaving the morbidity burden and predictors of hospitalization among other marginalized groups such as unaccompanied migrants and persons prone to statelessness unexplored. Unaccompanied migrants refer to minors migrating without parental or legal guardianship whilst persons prone to statelessness refer to individuals who do not have recognized nationality under the operation of the laws of any country [ 1 , 7 , 8 ]. This gap constrains progress towards Sustainable Development Goal three (SDG 3), particularly targets 3.3 (infectious diseases), 3.4 (non-communicable diseases) and 3.8 (universal health coverage). This study addresses this gap by assessing the morbidity burden and predictors of hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana. Practically, the findings are intended to inform actions toward advancing SDG 3 and its related targets in Ghana and comparable contexts. Methods Ethics statement This study received ethical approval from the Committee on Human Research, Publication, and Ethics (CHRPE) at the School of Medical Sciences, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana (Reference: CHRPE/AP/058/24). Essential ethical safeguards upheld in the conduct of this study included confidentiality, voluntary participation, and informed consent procedures in accordance with CHRPE’s guidelines on research involving human subjects. Participants were informed about the study’s objectives, the voluntary nature of their participation, and autonomy to discontinue or decline answering questions deemed sensitive without any repercussions prior to the commencement of the exercise. Depending on contextual appropriateness and literacy level, written or verbal consent was obtained directly from non-minors. For minors under the care of relatives or guardians, consent was obtained from their designated caregivers. Verbal consent was specifically obtained from participants unable to provide written consent. All verbal consents were witnessed and documented in the field assistant’s consent log. Inclusivity in global research Additional information regarding the ethical, cultural, and scientific considerations specific to inclusivity in global research is included in the Supporting Information ( S1 Checklist ). Data and methods This study employed data from a cross-sectional survey conducted from March 2024 to May 2024, among unaccompanied migrants and individuals prone to statelessness residing in the Greater Kumasi Metropolitan Area (GKMA) and the Awutu Senya East Municipal Area (ASEMA). The unique characteristics and relevance of the study sites to migrants and vulnerable populations are detailed elsewhere [ 28 ]. These characteristics made the sites analytically relevant for examining morbidity and hospitalization patterns in the target population. A multi-stage sampling technique was employed. First, districts with concentrations of the study population were purposefully selected informed by existing literature [ 29 – 32 ]. Within the purposively selected districts, all individuals who met the predefined inclusion criteria and consented to participate were enrolled. Participant selection was therefore non-random but prioritized inclusivity and feasibility. Operationally, eligible participants included (i) individuals below 18 years who had lived outside their place of birth for more than six months under the care of persons other than a biological parent or legally recognized guardian, in line with Section 47 of the Children’s Act [ 33 ] categorized as unaccompanied migrants, and (ii) individuals resident in Ghana for over five years, as stipulated in the Citizens Act [ 34 ], who are without documentary evidence of a Ghanaian or foreign nationality and are unable to reasonably establish entitlement to Ghanaian citizenship through parentage, birth registration, or naturalization pathways were classified as persons prone to statelessness. Individuals who were undocumented but could demonstrate a plausible entitlement to nationality through parentage were excluded from this category. This approach is consistent with UNHCR guidance, which emphasizes that determination of statelessness requires examination of the nationality laws of relevant States and their application in practice [ 35 ]. The adjusted target sample size was 444, calculated using Cochran’s formula with a 15% allowance for non-response, 95% desired confidence level, a 0.5 conservative estimated population proportion as posited by Lwanga and Lemeshow [ 36 ] and a 0.05 margin of error. A visual presentation of participant screening, eligibility, and final inclusion is provided in Fig 1 . Ultimately, data were obtained from 481 respondents using structured questionnaires developed for this study (see S1 Text ). The instrument captured demographic and socioeconomic characteristics, along with information on morbidity and hospitalization. Data were digitally collected with Kobo Collect on smart devices, enabling real-time entry and verification. Fig 1. Participants sampling flowchart. Open in a new tab A pilot survey conducted in the New Juaben South Municipality (NJSM) tested the clarity, reliability, and contextual appropriateness of the instrument. NJSM was selected for the pretesting exercise due to its sociodemographic and health system comparability to the study sites. Insights from this exercise informed revisions including wording and question sequence. To accommodate linguistic diversity, the questionnaire was translated into Twi, and interpreters trusted by participants were engaged where necessary. Interviews lasted 30–50 minutes. Data collection was carried out by six trained field assistants, all with tertiary-level education. Training which was facilitated by the principal investigator focused on the study objectives, inclusion and exclusion criteria, ethical considerations, survey administration, confidentiality, and strategies for maximizing participation. Practical sessions, including role-plays and mock interviews, were conducted to ensure competence and consistency. Fieldwork was supervised by the principal investigator and monitored by the research team to ensure data quality. Theoretical framework This study employed Andersen’s Behavioral Model of Health Services Use (BM) to examine predictors of hospitalization among the study population. Since its introduction in 1968, the model has remained a seminal framework for analyzing healthcare access and utilization [ 37 – 41 ]. This model organizes determinants into three domains, namely, predisposing, enabling, and need factors [ 42 , 43 ]. Predisposing factors reflect demographic attributes such as age, gender, marital status, education, and religion [ 44 ]. Enabling factors refer to the financial and logistical resources that facilitate healthcare access [ 43 ]. In Ghana, hospitalization is often contingent on health insurance [ 37 , 38 ], financial capacity [ 38 , 45 ], and proximity to healthcare facilities [ 46 ]. Yet documentation requirements for insurance registration and the burden of out-of-pocket payment systematically disadvantage vulnerable groups [ 38 , 47 ]. Among unaccompanied migrants and persons prone to statelessness, language barriers may additionally reduce health literacy and awareness of available services, further restricting access to preventive and primary care. Need factors on the other hand capture both self-perceived health status and clinically assessed illness for which medical attention is required [ 42 ]. Among vulnerable populations, economic survival strategies may often delay symptom recognition and healthcare use. Many may engage in physically demanding informal labor, normalizing chronic pain and untreated symptoms, which reduces their likelihood of timely hospital visits. Consequently, hospitalizations may occur only under emergency conditions, leading to prolonged stays, higher treatment costs, and poorer health outcomes. The model’s relevance in the Ghanaian context has been demonstrated in studies by Kumah et al [ 37 ], Sekyi et al [ 38 ] and Braimah et al [ 39 ]. In this study, the model guided the selection of predictor variables and the formulation of the following hypotheses: (1) need factors (frequency of illness and disease diagnosis) would significantly explain higher variation in hospitalization among unaccompanied migrants and persons prone to statelessness; (2) predisposing factors (respondent status, sex, and marital status) would significantly predict hospitalization after adjusting for need-related factors; and (3) enabling factors (district, insurance coverage, and employment status) would contribute additional explanatory power to hospitalization after accounting for need and predisposing factors. Measures The dependent variable was hospitalization, defined as admission for inpatient care at a formal healthcare facility. Operationally, respondents were asked whether they had spent more than one full week on admission in a health facility. Responses were coded as binary (0 = No, 1 = Yes). Although a ≥ 24-hour threshold is conventionally employed in defining hospitalization, self-reported healthcare utilization varies in accuracy according to population characteristics, recall period, and event salience [ 48 ]. In vulnerable populations, brief inpatient stays are particularly prone to recall decay and misclassification compared to longer admissions [ 49 , 50 ]. The > 1-week threshold was therefore purposefully used to capture health conditions necessitating extended medical treatment or management and to improve recall and classification accuracy. Independent variables were structured in accordance with BM. However, guided by Kumah et al [ 37 ], Sekyi et al [ 38 ] and Braimah et al [ 39 ], only theoretically salient predictors were considered. Predisposing factors included respondent status (1 = unaccompanied migrant, 2 = prone to statelessness, 3 = both), sex (1 = male, 2 = female), marital status (recoded 1 = single, 2 = married, 3 = divorced/widowed), religion (recoded 1 = Christianity, 2 = Islam, 3 = Others) to capture dominant faith-based affiliations, formal education (recoded 1 = none, 2 = primary-SHS, 3 = tertiary) to reflect functional educational attainment, and age (recoded 1 = ≤ 24, 2 = > 24). This categorization of age was adapted from the WHO demographic classification [ 51 ] and life course transitions [ 52 , 53 ]. Enabling factors included health insurance coverage (1 = insured, 0 = uninsured), employment status (recoded 1 = employed, 2 = student, 3 = unemployed/retired) to distinguish economic participation from dependency, district of residence (1 = GKMA, 2 = ASEMA), locality of residence (recoded 1 = rural/peri-urban/slum, 2 = urban) to capture differences in service access between urban and non-urban settings, income and social network. Income (in Ghana cedis) was recoded 1 = ≤971 and 2 = > 971 to reduce sparse cells. This threshold was chosen to align with the statutory minimum wage at the time (GH¢18.15/day; GH¢544.50/month; US$1 = GH¢12.67) and to distinguish between respondents at or below subsistence income and those above. Social network denoting individuals with whom respondents maintain close contact or share living arrangements was recoded (1 = blood relation, 2 = non-blood relation, 3 = self/alone) to reflect forms of social support. This variable was included to account for the role of social ties in resource mobilization, information exchange, resilience building and barrier navigation [ 54 – 57 ]. Need factors captured self-reported illness frequency, NCDs, and communicable diseases. Illness frequency in the past six months was measured on a five-point scale (1 = never, 2 = less frequent, 3 = frequent, 4 = very frequent, 5 = every day) but recoded into ‘infrequent’ (≤2) and ‘frequent’ (>2) to enhance model stability. Incidence of NCDs and communicable diseases were binary (0 = No, 1 = Yes) and based solely on respondents’ responses. Detailed measurement and coding of the study’s variables are provided in the supplementary file (see S1 Text ). Analytic framework Data analysis was conducted using Stata 14.2. Both descriptive and inferential techniques were applied to examine prevalent illnesses, frequency of hospital admissions, and predictors of hospitalization. Descriptive statistics, including frequencies and percentages, were used to summarize sample characteristics, illness types, and hospitalization patterns. To address the primary objective, proportions were computed to estimate disease prevalence and cross-tabulations were performed to examine morbidity patterns across respondent characteristics. Statistical significance of observed differences was assessed using Pearson’s chi-square tests of independence (or Fisher’s exact tests where cell counts were small) at a 5% significance level. For predictors of hospitalization, inferential analysis employed complementary log-log regression (CLLR). This approach was appropriate given the binary nature of the outcome variable (0 = No, 1 = Yes) and the relative rarity of hospitalization events, for which CLLR is recommended [ 26 ]. Three regression models were specified to systematically assess the effects of predictors. In the base model, need variables were included. In the second model, predisposing factors were added. In the final model, enabling factors were added. This structure was adopted to assess the effect of the study’s primary predictors (need factors) controlling for the incremental explanatory effects of predisposing and enabling confounders, respectively. To enhance model stability and interpretability, univariate analysis (see S1 Table ) with hospitalization as outcome variable was estimated to assess crude associations between predictors and the outcome variable. Based on the analysis, significant predictors (p ≤ 0.05) were considered candidates for inclusion in the multivariate analysis. Additionally, predisposing variables were deliberately minimized to include only respondent status, sex, and marital status. Respondent status is central to the study as it distinguishes between unaccompanied migrants, persons prone to statelessness, and those with intersecting vulnerabilities, thereby directly shaping exposure to structural exclusion and health risks [ 58 ]. Sex was included given consistent evidence of sex-based disparities in access and use of health service, particularly in contexts where cultural norms constrain women’s healthcare access [ 59 ]. Marital status was also retained, as social support derived from marriage/cohabitation or its absence has been shown to significantly influence health behaviour globally [ 60 ]. Among enabling factors, health insurance coverage, employment status, and district of residence were prioritized. Health insurance coverage is a decisive determinant of healthcare seeking behaviour in low- and middle-income countries, where out-of-pocket payments remain a barrier to care [ 38 , 47 ]. Employment status reflects both economic capacity and access to financial resources, which is a common barrier to healthcare seeking in Ghana and similar context [ 45 ]. District of residence was retained to capture geographical inequities in healthcare infrastructure, as spatial disparities in service distribution are a persistent determinant of healthcare use in Ghana and similar developing contexts [ 46 , 61 ]. Associations were reported using odds ratios (OR), with statistical significance set at p ≤ 0.05. Model fit was evaluated using Wald’s Chi-square to assess the explanatory contribution of each model. Results Table 1 presents the descriptive characteristics of the study population. Hospitalization was reported by 8.7% of the respondents. The sample was predominately composed of persons prone to statelessness (61.5%), individuals under 25 years (55.7%), male (61.7%), and single (76.1%). Additionally, the majority had primary to secondary education (76.5%), earned below GH₵971 ( 1 dollar = 12.67 cedis at the time of data collection ) (92.1%), and without health insurance (64.4%). In relation to morbidity, 23.3% reported being diagnosed with a communicable disease, while 8.1% reported an NCD. In relation to frequency of illness, majority reported infrequent illness (91.1%). Table 1. Descriptive statistics. Themes Variables Response Frequency Percent (%) Outcome Hospitalization No 439 91.3 Yes 42 8.7 Predisposing Respondent Status Unaccompanied Migrant 112 23.3 Prone to Statelessness 296 61.5 Unaccompanied & Stateless 73 15.2 Sex Male 297 61.7 Female 184 38.3 Marital Status Single 366 76.1 Married 95 19.8 Divorced/Widowed 20 4.2 Age ≤ 24 268 55.7 > 24 213 44.3 Religion Christianity 279 58.0 Islam 155 32.2 Others 47 9.8 Formal education None 80 16.6 Primary-SHS 368 76.5 Tertiary 33 6.9 Enabling Employment Status Employed 131 27.2 Student 174 36.2 Unemployed/Retired 176 36.6 Health Insurance Uninsured 310 64.4 Insured 171 35.6 District of Residence GKMA 231 48.0 ASEMA 250 52.0 Income ≤ 971 443 92.1 > 971 38 7.9 Locality Rural/Peri-Urban/Slum 245 50.9 Urban 236 49.1 Social network Blood Relation 176 36.6 Non-Blood Relation 197 41.0 Alone 108 22.5 Need Frequency of illness Infrequent 438 91.1 Frequent 43 8.9 NCDs No 442 91.9 Yes 39 8.1 Communicable Disease No 369 76.7 Yes 112 23.3 Open in a new tab Distribution of reported NCDs is presented in Table 2 . Among the reported NCD conditions, diabetes prevalence was 33%. Diabetes prevalence was significant among persons prone to statelessness and GKMA residents, with no significant differences by sex, age, or locality. Asthma with an overall prevalence of 21% was prevalent among unaccompanied migrants and younger respondents (≤24 years). Eye problems (15%) were significantly common among rural/peri-urban/slum residents, while ear problems (15%) were significant among ASEMA residents. Hypertension (13%) showed a significant association with urban residence only. All remaining conditions occurred at extremely low prevalence (≤5%), with no statistically significant associations. Table 2. Prevalence and sociodemographic associations of NCD among respondents (N = 39). Condition Prevalence n (%) 95% CI Explanatory Variable Test Statistic (df) p-value Diabetes 13 (33) 0.199 - 0.501 Respondent status χ²(2) = 6.53 0.038 Age group χ²(1) = 3.28 0.070 Sex χ²(1) = 0.52 0.819 District of residence χ²(1) = 7.43 0.006 Locality of residence χ²(1) = 0.848 0.357 Asthma 8 (21) 0.103 - 0.368 Respondent status χ²(2) = 7.90 0.019 Age group χ²(1) = 5.28 0.022 Sex χ²(1) = 0.17 0.682 District of residence χ²(1) = 3.37 0.066 Locality of residence χ²(1) = 1.92 0.166 Eye Problem 6 (15) 0.068 - 0.311 Respondent status χ²(2) = 1.81 0.404 Age group χ²(1) = 0.00 0.946 Sex χ²(1) = 1.54 0.215 District of residence χ²(1) = 0.47 0.493 Locality of residence χ²(1) = 5.25 0.022 Ear problems 6 (15) 0.068 - 0.311 Respondent status χ²(2) = 4.55 0.103 Age group χ²(1) = 2.92 0.088 Sex χ²(1) = 0.30 0.058 District of residence χ²(1) = 8.27 0.004 Locality of residence χ²(1) = 0.17 0.677 Hypertension 5 (13) 0.052 - 0.282 Respondent status χ²(2) = 1.52 0.468 Age group χ²(1) = 2.25 0.134 Sex χ²(1) = 0.03 0.864 District of residence χ²(1) = 1.58 0.209 Locality of residence χ²(1) = 3.99 0.046 Insomnia 2 (5) 0.012 - 0.193 Respondent status χ²(2) = 1.63 0.443 Age group χ²(1) = 0.00 0.970 Sex χ²(1) = 0.04 0.851 District of residence χ²(1) = 1.81 0.179 Locality of residence χ²(1) = 0.70 0.791 Mental disorder 1 (3) 0.003 - 0.174 Respondent status χ²(2) = 6.98 0.031 Age group χ²(1) = 0.98 0.323 Sex χ²(1) = 0.79 0.373 District of residence χ²(1) = 0.88 0.348 Locality of residence χ²(1) = 0.71 0.398 Stroke 1 (3) 0.003 - 0.174 Respondent status χ²(2) = 0.79 0.673 Age group χ²(1) = 1.08 0.299 Sex χ²(1) = 0.79 0.373 District of residence χ²(1) = 0.88 0.348 Locality of residence χ²(1) = 0.71 0.398 Depression 1 (3) 0.003 - 0.174 Respondent status χ²(2) = 2.31 0.315 Age group χ²(1) = 0.98 0.323 Sex χ²(1) = 1.33 0.249 District of residence χ²(1) = 1.20 0.274 Locality of residence χ²(1) = 1.48 0.225 Arthritis 1 (3) 0.003 - 0.174 Respondent status χ²(2) = 0.79 0.673 Age group χ²(1) = 1.08 0.299 Sex χ²(1) = 0.79 0.373 District of residence χ²(1) = 1.20 0.274 Locality of residence χ²(1) = 1.48 0.225 Cancer No cases observed Kidney disease No cases observed Open in a new tab Notes 1. Prevalence is reported as number (percentage) of respondents diagnosed with each condition. 2. Conditions with zero prevalence were excluded from inferential analysis and are presented descriptively. 3. Given the sample size, findings should be interpreted as exploratory. Distribution of reported communicable diseases is presented in Table 3 . Among communicable diseases reported, malaria was the most prevalent condition (90%), with no statistically significant variation across respondent status, age, sex, district, or locality. Flu/cold (30%) and typhoid (27%) were also common conditions. Typhoid prevalence was significant among ASEMA residents, while flu/cold showed no significant subgroup differences. Cholera (8%) was significantly common among younger respondents (≤24 years), females, and ASEMA residents. Measles/chickenpox (6%) was significantly associated with dual vulnerability status, while STIs/STDs (4%) were significant among GKMA and urban residents. Yellow fever was rare (3%) with no statistically significant associations. Table 3. Prevalence and sociodemographic associations of communicable diseases among respondents (N = 112). Condition Prevalence n (%) 95% CI Explanatory Variable Test Statistic (df) p-value Malaria 101 (90) 0.830 - 0.945 Respondent status χ²(2) = 2.73 0.255 Age group χ²(1) = 0.26 0.613 Sex χ²(1) = 0.24 0.623 District of residence χ²(1) = 3.04 0.081 Locality of residence χ²(1) = 0.00 0.995 Flu/Cold 34 (30) 0.224 - 0.396 Respondent status χ²(2) = 1.86 0.395 Age group χ²(1) = 2.59 0.107 Sex χ²(1) = 3.76 0.053 District of residence χ²(1) = 0.75 0.386 Locality of residence χ²(1) = 3.42 0.064 Typhoid 30 (27) 0.193 - 0.359 Respondent status χ²(2) = 2.10 0.350 Age group χ²(1) = 0.12 0.731 Sex χ²(1) = 0.28 0.595 District of residence χ²(1) = 9.67 0.002 Locality of residence χ²(1) = 0.51 0.477 Cholera 9 (8) 0.042 - 0.149 Respondent status χ²(2) = 1.79 0.408 Age group χ²(1) = 8.79 0.003 Sex χ²(1) = 4.68 0.031 District of residence χ²(1) = 5.29 0.021 Locality of residence χ²(1) = 1.76 0.184 Measles/ Chickenpox 7 (6) 0.030 - 0.127 Respondent status χ²(2) = 10.60 0.005 Age group χ²(1) = 3.27 0.071 Sex χ²(1) = 1.80 0.180 District of residence χ²(1) = 2.37 0.124 Locality of residence χ²(1) = 0.02 0.883 STI/STDs 4 (4) 0.013 - 0.093 Respondent status χ²(2) = 2.31 0.316 Age group χ²(1) = 1.28 0.259 Sex χ²(1) = 2.13 0.144 District of residence χ²(1) = 13.06 0.000 Locality of residence χ²(1) = 4.96 0.026 Yellow Fever 3 (3) 0.009 - 0.081 Respondent status χ²(2) = 5.87 0.053 Age group χ²(1) = 2.78 0.096 Sex χ²(1) = 1.47 0.225 District of residence χ²(1) = 0.98 0.322 Locality of residence χ²(1) = 0.19 0.667 Tuberculosis No cases observed Hepatitis No cases observed Open in a new tab Notes 1. Prevalence is reported as number (percentage) of respondents diagnosed with each condition. 2. Conditions with zero prevalence were excluded from inferential analysis and are presented descriptively. 3. Given the sample size, findings should be interpreted as exploratory. Table 4 summarizes the predictors of inpatient care. In Model 1, frequent illness, and diagnosis with an NCD were both significant predictors of hospitalization. Succinctly, respondents reporting frequent illness had over four times higher odds of hospitalization compared with those reporting infrequent illness (OR = 4.097; 95% CI: 2.056–8.163; p < 0.001). Similarly, respondents diagnosed with an NCD had over three times higher odds of hospitalization than those without such a diagnosis (OR = 3.336; 95% CI: 1.611–6.906; p = 0.001). Diagnosis of a communicable disease was not statistically associated with hospitalization. Table 4. Predictors of hospitalization. Parameter Model 1 Model 2 Final Model (3) OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value Frequency of illness Infrequent (Ref) Frequent 4.097* (2.056-8.163) 0.000 3.724* (1.830-7.576) 0.000 4.224* (2.002-8.913) 0.000 NCDs No (Ref) Yes 3.336* (1.611-6.906) 0.001 3.600* (1.737-7.460) 0.001 3.873* (1.861-8.058) 0.000 Communicable Disease No (Ref) Yes 1.756 (0.920-3.353) 0.088 1.771 (0.924-3.394) 0.085 1.335 (0.659-2.706) 0.423 Respondent status Unaccompanied Migrant (Ref) Prone to Statelessness 0.385* (0.163-0.909) 0.030 0.558 (0.188-1.652) 0.292 Unaccompanied & Stateless 1.003 (0.415-2.426) 0.994 1.477 (0.574-3.804) 0.419 Sex Male (Ref) Female 0.516 (0.258-1.030) 0.061 0.469* (0.233-0.946) 0.035 Marital Status Single (Ref) Married 1.245 (0.433-3.581) 0.684 1.301 (0.437-3.872) 0.636 Divorced/Widowed 4.042* (1.278-12.784) 0.017 4.808* (1.423-16.247) 0.011 District GKMA (Ref) ASEMA 1.790 (0.737-4.342) 0.198 Insurance Coverage Uninsured (Ref) Insured 1.676 (0.838-3.351) 0.144 Employment Status Employed (Ref) Student 0.721 (0.208-2.506) 0.607 Unemployed/Retired 0.416 (0.155-1.111) 0.080 (Intercept) 0.050 (0.032-0.077) 0.000 0.090 (0.046-0.177) 0.000 0.062 (0.017-0.220) 0.000 Model Fitness Wald’s Chi-Square (p-value) 36.57 (0.000) 47.69 (0.000) 55.58 (0.000) Open in a new tab Note 1. Model 1 includes need variables only (frequency of illness, NCD and communicable illness diagnosis). 2. Model 2 adjusts Model 1 for predisposing factors (respondent status, gender, marital status). 3. Model 3 further adjusts Model 2 for enabling factors (district of residence, insurance coverage, employment status). 4. Statistical significance set at p < 0.05. 5. OR > 1 indicates higher odds of hospitalization; OR < 1 indicates lower odds. 6. 42 respondents (8.7%) reported hospitalization while 439 (91.3%) reported no hospitalization. In Model 2, after adjusting for demographic characteristics, the associations for frequent illness (OR = 3.724; 95% CI: 1.830–7.576; p < 0.001) and NCD diagnosis (OR = 3.600; 95% CI: 1.737–7.460; p = 0.001) remained statistically significant with minimal attenuation. Additionally, individuals prone to statelessness exhibited significantly lower odds of hospitalization compared with unaccompanied migrants (OR = 0.385; 95% CI: 0.163–0.909; p = 0.030). Furthermore, divorced, or widowed respondents exhibited over four times higher odds of hospitalization compared with single respondents (OR = 4.042; 95% CI: 1.278–12.784; p = 0.017). Sex and communicable disease diagnosis were not statistically significant in this model. In the final model which further adjusted for contextual and healthcare access factors, frequent illness (OR = 4.224; 95% CI: 2.002–8.913; p < 0.001) and NCD diagnosis (OR = 3.873; 95% CI: 1.861–8.058; p < 0.001) remained robust predictors of hospitalization. The previously observed association between being prone to statelessness and lower odds of hospitalization was attenuated (OR = 0.558; 95% CI: 0.188–1.652; p = 0.292). However, female respondents demonstrated significantly lower odds of hospitalization compared with males (OR = 0.469; 95% CI: 0.233–0.946; p = 0.035). Divorced or widowed status remained associated with over four times higher odds of hospitalization (OR = 4.808; 95% CI: 1.423–16.247; p = 0.011). District of residence, insurance, and employment status were not significantly associated with hospitalization. Across models, likelihood ratio tests indicated progressive improvement in model fit, with the final model demonstrating the best overall fit (Wald χ² = 55.58; p < 0.001). Discussion This study provides a novel assessment of morbidity burden and hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana. The results indicate a higher prevalence of communicable diseases than NCDs. Malaria, flu/cold, typhoid, diabetes and asthma emerged as the most common conditions. Despite this morbidity profile, hospitalization was rare (8.7%). Across all the models, frequent illness, and diagnosis with an NCD were significantly associated with higher odds of hospitalization. Morbidity prevalence in this study was modest, particularly for NCD, diverging from international evidence which documents elevated disease burdens among migrant populations due to socioeconomic constraints and restricted healthcare access [ 22 , 23 , 62 – 67 ]. In the Ghanaian context, where population-based studies report higher NCD prevalence [ 25 ], the comparatively low rates observed here are more plausibly interpreted as reflecting limited detection within marginalized populations rather than a lower underlying disease burden. This interpretation is consistent with existing literature documenting persistent barriers to formal healthcare access among uninsured and socioeconomically vulnerable groups including migrants, refugees, older adult populations and persons prone to statelessness [ 9 – 12 , 26 , 68 , 69 ]. Within the observed morbidity profiles, diabetes emerged as the most prevalent NCD, aligning with national evidence of Ghana’s epidemiological transition [ 70 – 73 ]. Its higher prevalence among persons prone to statelessness and urban residents is consistent with evidence that place-based and socioeconomic contexts structure NCD risk. In the Ghanaian context for example, significant variations in NCD multimorbidity have been attributed to neighborhood-level factors such as income [ 74 ], with community socioeconomic conditions including level of poverty observed to be independently associated with NCD risk [ 75 ]. Similar chronic disease burdens have been documented among displaced populations such as refugees in other contexts including America, the Middle East and Asia [ 76 , 77 ]. Similarly, malaria’s dominance among communicable diseases is consistent with national and global evidence of its persistence in underserved populations [ 78 – 80 ]. The concentration of typhoid in ASEMA can plausibly be attributed to spatial inequalities in environmental health systems including adequate sanitation and access to safe water. Similar patterns have been documented in comparable Ghanaian settings where typhoid, diarrhea, and other waterborne infections are major contributors to disease burden [ 81 , 82 ]. Importantly, most communicable and non-communicable conditions showed no statistically significant variation across key socio-demographic subgroups, suggesting that morbidity exposure in this population is broadly distributed rather than concentrated within defined demographic strata. Across all models, frequent illness and NCD diagnosis were the dominant predictors of hospitalization. This indicates that hospitalization in this population is driven primarily by need factors as has been observed among population of older adults [ 26 , 68 , 83 ]. This finding accords closely with the BM and further suggests that need factors exert the strongest and most proximate influence on hospitalization. In contrast, enabling factors such as health insurance coverage, employment status, and district of residence were not independently associated with hospitalization after adjustment. This suggests that structural barriers may constrain access to care earlier in the illness trajectory rather than at the point of severe morbidity requiring admission. Finally, predisposing factors demonstrated limited and model-specific effects. While being prone to statelessness was associated with lower odds of hospitalization in the partially adjusted model, this association was attenuated after accounting for contextual and access-related variables. This indicates that documentation vulnerability does not independently shape hospitalization once need and enabling factors are considered. Sex and marital status showed independent associations only in the fully adjusted model, with females exhibiting lower odds of hospitalization and divorced or widowed respondents showing higher odds. These findings reflect differential patterns of hospitalization [ 26 , 60 , 68 ] rather than differential morbidity risk. This further reinforces the BM’s proposition that predisposing characteristics conditions responses to illness rather than disease occurrence itself. Policy and practice implications The findings of this study offer contextually bounded implications that warrant a cautious generalization and application. The predominance of communicable diseases, particularly malaria relative to NCDs, indicates that infectious disease prevention remains a critical public health priority within the study population. While these results are not generalizable, it supports the continued prioritization of established preventive interventions, including vector control, sanitation, and basic health education, in communities hosting socioeconomically marginalized groups. This has direct implications for adopted strategies towards the achievement of targets 3.3 and 3.4 of SDG 3. Also, the minimal hospitalization rate and consistent association of hospitalization with illness frequency and NCD diagnosis highlight that inpatient care in this population is largely triggered by advanced or recurrent morbidity rather than routine healthcare use. This pattern highlights the potential value of strengthening early detection and outpatient management of chronic and recurrent illnesses within primary care and community health systems serving vulnerable groups. Importantly, the absence of independent associations between hospitalization and enabling factors such as health insurance enrolment or employment status cautions against over-interpreting structural access variables as direct predictors of hospitalization among socioeconomically marginalized groups. Finally, the limited and model-dependent associations observed for sex and marital status indicate that predisposing characteristics may shape responses to illness once hospitalization becomes necessary, rather than influencing morbidity risk per se. These suggest that service delivery approaches should remain attentive to heterogeneity in hospitalization patterns without assuming uniform vulnerability across demographic subgroups. However, given the study’s cross-sectional design, self-reported measures, and geographically restricted sample, such implications should be viewed as hypothesis-generating rather than prescriptive. Limitations Despite the useful insights and implications, this study is not without limitations. First, morbidity and hospitalization were self-reported, introducing potential recall bias and misclassification. Secondly, classification of persons prone to statelessness relied on documentation status and self-reported legal entitlement and may have misclassified some undocumented individuals. Furthermore, the use of an extended inpatient threshold as a measure of hospitalization improved recall but likely underestimated prevalence and may have influenced effect estimates. Moreso, the binary outcome of hospitalization yielded a markedly low success rate relative to non-hospitalization due to the restrictive threshold. This could have constrained statistical power and influenced estimates. However, this limitation was methodologically moderated through the application of CLLR with hierarchical variable entry, an analytic approach well suited for modelling rare events and mitigating estimation bias under such conditions. Additionally, the reliance on purposive site selection and non-probability participant selection within the selected districts limits representativeness and generalizability. Again, several disease categories had small cell counts, which constrained statistical power and may have influenced regression estimates. Finally, the cross-sectional design precludes causal inference. Conclusion This study provides novel insights on the morbidity patterns and predictors of hospitalization among unaccompanied migrants and persons prone to statelessness in Ghana—groups whose invisibility in data translates in invisibility in health policy, planning and response in Ghana and similar context. The findings suggests that vulnerability in these populations is manifested less through differences in disease prevalence but more through recurrent illness and NCD diagnosis necessitating hospitalization. This underscores the importance of early detection, continuity of care, and outpatient management for marginalized migrant populations, among whom unmet health needs may remain obscured until hospitalization becomes unavoidable. While the evidence does not justify definitive conclusions, it suggests that health strategies in Ghana and similar context could be strengthened by integrating targeted support for vulnerable groups within the broader primary healthcare and social protection frameworks. In the Ghanaian context, the Ministry of Health (MoH), Ghana Health Services (GHS), non-governmental organizations (NGO’s), faith-based organizations, and research institutions could implement regular community-based NCD screening targeting high-risk age groups, socioeconomically marginalized populations and provide subsidized medication for detected diseases in the short-medium term. Supporting information S1 Text. Data collection instrument (questionnaire). (DOCX) pgph.0006316.s001.docx (24.5KB, docx) S1 Table. Univariate regression results. (DOCX) pgph.0006316.s002.docx (17KB, docx) S1 Checklist. Inclusivity in global research. (DOCX) pgph.0006316.s003.docx (66.1KB, docx) Abbreviations ASEMA Awutu Senya East Municipal Area BM Behavioral Model CHPS Community-based Health Planning and Services CHRPE Committee on Human Research Publication Ethics CLLR Complementary Log Log Regression GHS Ghana Health Services GKMA Greater Kumasi Metropolitan Area KNUST Kwame Nkrumah University of Science and Technology MoH Ministry of Health NDPC National Development Planning Commission NGO Non-Governmental Organization NHIS National Health Insurance Scheme SDG Sustainable Development Goal UNCHR United Nations High Commissioner for Refugees WHO World Health Organization Data Availability The datasets used and/or analyzed during the present study cannot be shared publicly because of 1) ethical restrictions with the protocol approved by the ethics board for the study and 2) the fact that it is coming from a broader PhD study which as per the requirements future publications are expected from it to meet the PhD requirements. 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PMC Copyright notice 15 Oct 2025 Attachment Submitted filename: Response to Editors Comment_October_2025.doc pgph.0006316.s004.doc (31.5KB, doc) PLOS Glob Public Health. doi: 10.1371/journal.pgph.0006316.r002 Decision Letter 0 Ryan Essex Ryan Essex Academic Editor Find articles by Ryan Essex Author information Copyright and License information Roles Ryan Essex : Academic Editor © 2026 Ryan Essex This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 26 Jan 2026 PGPH-D-25-03075 Morbidity Burden and Predictors of Hospitalization Among Unaccompanied Migrants and Persons Prone to Statelessness in Ghana PLOS Global Public Health Dear Dr. Aboagye, Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 12 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at [email protected]. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. We look forward to receiving your revised manuscript. Kind regards, Ryan Essex Academic Editor PLOS Global Public Health Journal Requirements: 1. Please include a complete copy of PLOS’ questionnaire on inclusivity in global research in your revised manuscript. Our policy for research in this area aims to improve transparency in the reporting of research performed outside of researchers’ own country or community. The policy applies to researchers who have travelled to a different country to conduct research, research with Indigenous populations or their lands, and research on cultural artefacts. The questionnaire can also be requested at the journal’s discretion for any other submissions, even if these conditions are not met.  Please find more information on the policy and a link to download a blank copy of the questionnaire here: https://journals.plos.org/globalpublichealth/s/best-practices-in-research-reporting . Please upload a completed version of your questionnaire as Supporting Information when you resubmit your manuscript. 2. Please ensure that your Ethics Statement is available in its entirety at the beginning of your Methods section, under a subheading 'Ethics Statement'. 3. In the online submission form, you indicated that “Datasets for this study are available from the corresponding author upon reasonable request.”. All PLOS journals now require all data underlying the findings described in their manuscript to be freely available to other researchers, either 1. In a public repository, 2. Within the manuscript itself, or 3. Uploaded as supplementary information. This policy applies to all data except where public deposition would breach compliance with the protocol approved by your research ethics board. If your data cannot be made publicly available for ethical or legal reasons (e.g., public availability would compromise patient privacy), please explain your reasons by return email and your exemption request will be escalated to the editor for approval. Your exemption request will be handled independently and will not hold up the peer review process, but will need to be resolved should your manuscript be accepted for publication. 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Appendix 2 contains a logo. We are not permitted to publish this under our CC-BY 4.0 license, even with permission. We ask that you please remove or replace it. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. Additional Editor Comments (if provided): [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Does this manuscript meet PLOS Global Public Health’s publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?> Reviewer #1: Yes Reviewer #2: Partly ********** 2. Has the statistical analysis been performed appropriately and rigorously?-->?> Reviewer #1: Yes Reviewer #2: No ********** 3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: No ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ********** Reviewer #1: This manuscript addresses an important and understudied topic: the morbidity burden and predictors of hospitalization among unaccompanied migrants and individuals prone to statelessness in Ghana. despite its relevance, the manuscript requires substantial revisions before it can be considered for publication. • Revise and justify the definition of Statelessness using established international frameworks (e.g., UNHCR, Ghana Nationality Act). Clarify how the study distinguishes undocumented but legally entitled individuals from those genuinely at risk of statelessness. • Hospitalization is defined as inpatient stay for more than one full week, which is inconsistent with global health services research (typically ≥24 hours). This definition excludes a large proportion of real hospitalizations, artificially depresses prevalence (8.7%), and may bias regression results. Revise the definition of Hospitalization or provide a strong methodological justification for using a 7-day threshold. • Present a clear sampling flowchart. Justify the selection of districts and communities. Explain how randomness was ensured at the participant level. • Provide justification for each recoding decision as Variable Recoding reduces Analytical Precision, including statistical (model stability) or theoretical reasons. • tables 2 and 3 show no statistically significant differences for any NCD or communicable disease across demographic and geographic subgroups (all p > 0.1). Despite this, the narrative repeatedly describes patterns as “higher,” “more common,” or “notable.” This is inappropriate and misleading. Rewrite the Results section to strictly reflect statistically supported findings. Where no significant differences exist, state this clearly and avoid narrative speculation based on raw counts. • Regression tables lack 95% confidence intervals, which are mandatory for odds ratio interpretation. Include confidence intervals for all ORs in all models. • Interpretations across Models 1–3 are sometimes incorrect. For example, “prone to statelessness” is statistically significant in Model 2 but not in Model 3; however, the discussion presents it as a consistent predictor. Revise narrative to accurately reflect model-specific findings and note attenuation or loss of significance. • The Discussion section restates results extensively and occasionally presents speculative explanations (e.g., lifestyle shifts, informal care behaviors) that were not measured in the study. While Andersen’s Behavioral Model is cited, its application is superficial. The discussion does not meaningfully connect predisposing, enabling, and need factors to the results. Given that most subgroup differences were non-significant, this should be a central part of the Discussion, yet it is not addressed. • Reframe policy recommendations to align directly with supported findings and emphasize caution due to limited generalizability. • While some limitations are acknowledged, several important ones are missing: Potential misclassification of statelessness, Highly restrictive hospitalization definition, Non-representative sampling from only two districts, Very small cell counts for diseases (1–11 cases in some categories), Instability of regression estimates due to sparse data. Expand the Limitations section to address these substantive concerns. • The Introduction is overly long, repetitive, and contains multiple overlapping paragraphs about migrant vulnerabilities. Reduce length by 30-40%, remove repetition, and sharpen the logical flow toward the research gap. • Many interpretive comments appear in the Results section; others reappear in the exact same words in the Discussion. Ensure strict separation between empirical results and interpretation. Reviewer #2: 1. In abstract, Methods section needs to be elaborated focusing on the flow of selection of these two areas along with participants, exposure and outcome assessment. Results section lacks frequency and percentages to get meaningful interpretation of the results. 2. In Introduction, the authors have claimed disproportionate health risk faced by migrant population, but statistics related to it is missing in this section. (line number 42-45) 3. In line number 78-79, what SDG goal is affected from the objective authors claims to assess? 4. In line number 83-85, the study objective doesn't inform inclusive healthcare policies and interventions. Rephrase the sentence and give the expected outcome of the study. 5. The theoretical framework given from line number 86 to 110 can be included under methodology section. 6. In the line number 119-121, the authors have claimed the areas to be distinctive for target population, need some more information of the target population distribution in these areas and what does these areas represent as in organizational hierarchy? 7. From line number 131-133, on what basis, the proportion of 0.5 was determined for sample size calculation? Sample size calculation for the determinants is missing. 8. Operational definition of Persons Prone to Statelessness, Unaccompanied Migrants is not defined explicitly. 9. In line number 202, the authors have mentioned three tier hierarchical regression analysis, what are the three tiers in that regression analysis? 10. The Inference statistics for primary objective is missing in the statistical analysis plan. 11. Information about the univariate regression and multivariable regression analysis is missing. How the variables were selected into the model is missing too. 12. The interpretation given below the table 1 is repetition of information given in the table. Mention only the relevant findings of the table 1 13. The construction of Table 2 is poor, what does the p-value indicate? is the p-value specific to one self-reported morbidity status or it is for the entire morbidity status? Frequency for each morbidity is given, percentage is missing. 95% Confidence Interval for overall prevalence for each morbidity status is missing. 14. The comment 13 applies for table 3 too. 15. In table 4, what does Infrequent illness indicate, should be given in the table. The variables included in each model should be explicitly mentioned and the descriptive statistics about the hospitalization is missing in Table 4. Only the point estimate of Odds ratio is explicitly given, 95% CI is missing. 16. Interpretation of the multivariable regression analysis for each covariates is wrong from line number 272 to 291. ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public. For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: No Reviewer #2: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] PLOS Glob Public Health. 2026 Apr 17;6(4):e0006316. doi: 10.1371/journal.pgph.0006316.r003 Author response to Decision Letter 1 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 10 Feb 2026 Attachment Submitted filename: Plos_Response to Reviewers.doc pgph.0006316.s005.doc (70KB, doc) PLOS Glob Public Health. doi: 10.1371/journal.pgph.0006316.r004 Decision Letter 1 Ryan Essex Ryan Essex Academic Editor Find articles by Ryan Essex Author information Copyright and License information Roles Ryan Essex : Academic Editor © 2026 Ryan Essex This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 23 Feb 2026 PGPH-D-25-03075R1 Morbidity Burden and Predictors of Hospitalization Among Unaccompanied Migrants and Persons Prone to Statelessness in Ghana PLOS Global Public Health Dear Dr. Aboagye, Thank you for submitting your manuscript to PLOS Global Public Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Global Public Health’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 25 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at [email protected]. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pgph/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. We look forward to receiving your revised manuscript. Kind regards, Ryan Essex Academic Editor PLOS Global Public Health Journal Requirements: 1. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. Additional Editor Comments (if provided): [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ********** publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?> Reviewer #1: Yes Reviewer #2: No ********** 3. Has the statistical analysis been performed appropriately and rigorously?-->?> Reviewer #1: Yes Reviewer #2: No ********** 4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: No Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ********** Reviewer #1: After reviewing the revised manuscript carefully, the following concerns remain either not fully addressed or only partially addressed: • The definition of statelessness is grounded in the Ghana Citizenship Act, but it is not explicitly aligned with established international frameworks such as the 1954 Convention or UNHCR’s formal definition. The manuscript does not clearly situate its operational definition within global legal standards. • The hospitalization definition (>1 week inpatient stay) is justified on recall grounds, but no sensitivity analysis using the conventional ≥24-hour definition is provided. The manuscript does not empirically demonstrate how this restrictive threshold may have influenced prevalence or regression estimates. • A clear sampling flowchart is still missing. Although the text now clarifies purposive district selection and non-random inclusion, there is no visual presentation of participant screening, eligibility, and final inclusion. • The use of the term “multi-stage sampling” may remain misleading because the final participant inclusion was non-probability and inclusive rather than randomized. The terminology could still imply greater methodological rigor than was actually used. • Some residual narrative language in the Results section still uses comparative terms such as “more prevalent” or “higher” in contexts where statistical support is weak or exploratory. While improved, a few phrases could be further neutralized. • The Discussion still contains speculative explanations (for example, sanitation or urban poverty mechanisms) that were not directly measured in the study. These interpretations are softer than before but remain somewhat inferential. • The Introduction, although shortened, but Some repetition around migrant vulnerability remains. Everything else, including regression interpretation corrections, confidence intervals, recoding justifications, expanded limitations, and improved model-specific interpretation, appears to have been adequately addressed. Reviewer #2: 1. Sample size calculation for the primary objective - proportion was taken from Sample Size Determination in Health Studies: A Practical Guide by Lwanga and Lemeshow. But, the guide recommends to use 0.5 as proportion if previous published literature is not available. So, the author claim that there is no published literature related to proportion of communicable and NCD among the selected study population? Sample size calculation for predictors of hospitalization is not explicitly stated. 2. Operational definition of Persons Prone to Statelessness, Unaccompanied Migrants is not defined explicitly. 3. The Inference statistics for primary objective is still missing in the statistical analysis plan. 4. Line number 219-234 - The authors have given rationale for variable selection in description. But, it would be better to include Directed acyclic graphs (DAG) or statistical principle of variable selection in the multivariable regression model. 5. Table 2 Prevalence and Sociodemographic Associations of NCD Among Respondents (N = 39) and Table 3 Prevalence and Sociodemographic Associations of Communicable Diseases Among Respondents (N = 112) along with their interpretation are not required as per your objective of interest. Univariate analysis should be performed with hospitalization as outcome of interest. 6. In Table 4, as per the frequency of outcome of interest i.e (hospitalization), it is advisable to include 4 to 5 variables in the model. But the authors have included more than the advisable predictor variables in the model. ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public. For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: No Reviewer #2: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] PLOS Glob Public Health. 2026 Apr 17;6(4):e0006316. doi: 10.1371/journal.pgph.0006316.r005 Author response to Decision Letter 2 Article notes Copyright and License information Collection date 2026. PMC Copyright notice 1 Mar 2026 Attachment Submitted filename: Response to Reviewers_01-03-2026.docx pgph.0006316.s006.docx (29.4KB, docx) PLOS Glob Public Health. doi: 10.1371/journal.pgph.0006316.r006 Decision Letter 2 Ryan Essex Ryan Essex Academic Editor Find articles by Ryan Essex Author information Copyright and License information Roles Ryan Essex : Academic Editor © 2026 Ryan Essex This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMC Copyright notice 31 Mar 2026 Morbidity Burden and Predictors of Hospitalization Among Unaccompanied Migrants and Persons Prone to Statelessness in Ghana PGPH-D-25-03075R2 Dear Mr Aboagye, We are pleased to inform you that your manuscript 'Morbidity Burden and Predictors of Hospitalization Among Unaccompanied Migrants and Persons Prone to Statelessness in Ghana' has been provisionally accepted for publication in PLOS Global Public Health. Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests. Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated. IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact [email protected]. Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Global Public Health. Best regards, Ryan Essex Academic Editor PLOS Global Public Health *********************************************************** Associate editor comments While one reviewer has identified that further amendments be made, I believe the authors have responded appropriately in the last round of reviews and provided a rationale for changes they did not incorporate. *********************************************************** Reviewer Comments (if any, and for reference): Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ********** publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.-->?> Reviewer #1: Yes Reviewer #2: No ********** 3. Has the statistical analysis been performed appropriately and rigorously?-->?> Reviewer #1: I don't know Reviewer #2: No ********** 4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)??> The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ********** Reviewer #1: NA Reviewer #2: 1. Checking the association of sociodemographic characteristics for each individual NCD and communicable disorders is not required as per the objectives of the study and there is no need for authors to present these results as the numbers for each individual disorders are less. 2. Authors are requested to address the comments given during previous revision ********** what does this mean? ). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files. Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public.If you choose “no”, your identity will remain anonymous but your review may still be made public. For information about this choice, including consent withdrawal, please see our Privacy Policy ..--> Reviewer #1: No Reviewer #2: No ********** Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials S1 Text. Data collection instrument (questionnaire). (DOCX) pgph.0006316.s001.docx (24.5KB, docx) S1 Table. Univariate regression results. (DOCX) pgph.0006316.s002.docx (17KB, docx) S1 Checklist. Inclusivity in global research. (DOCX) pgph.0006316.s003.docx (66.1KB, docx) Attachment Submitted filename: Response to Editors Comment_October_2025.doc pgph.0006316.s004.doc (31.5KB, doc) Attachment Submitted filename: Plos_Response to Reviewers.doc pgph.0006316.s005.doc (70KB, doc) Attachment Submitted filename: Response to Reviewers_01-03-2026.docx pgph.0006316.s006.docx (29.4KB, docx) Data Availability Statement The datasets used and/or analyzed during the present study cannot be shared publicly because of 1) ethical restrictions with the protocol approved by the ethics board for the study and 2) the fact that it is coming from a broader PhD study which as per the requirements future publications are expected from it to meet the PhD requirements. The data can, however, be made available upon reasonable written request. The contact details of the ethics committee that approved the conduct of this study are as follows: Committee on Human Research, Publication, and Ethics (CHRPE), School of Medicine and Dentistry, KNUST, University Post Office, Kumasi, Ghana. Email: [email protected] / [email protected] . 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