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      Does mHealth influence community health worker performance in vulnerable populations? A mixed methods study in a multinational refugee settlement in Uganda

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          Abstract

          Community Health Workers (CHWs) provide healthcare in under-served communities, including refugee settlements, despite various challenges hindering their performance. Implementers have adopted mobile wireless technologies (m-Health) to improve the performance of CHWs in refugee settlements. We assessed the CHWs’ performance and associated factors in a multi-national refugee settlement, operating mHealth and paper-based methods. This cross-sectional study employed quantitative and qualitative data collection methods. Data for 300 CHWs was collected from implementing partners’ (IPs) databases. Nine focus group discussions (FGDs) with the CHWs and community members, two in-depth interviews (IDIs) with CHW leaders, and eight key informant interviews (KIIs) with six IPs and two local leaders were conducted. The qualitative data were analysed thematically using AtlasTi version 9 while the quantitative data were analysed at the univariate, bivariate and multivariable levels using Stata version14. The study found that only 17% of the CHWs performed optimally. The factors that significantly influenced CHW performance included education level: secondary and above (APR: 1.83, 95% CI: 1.02–3.30), having a side occupation (APR: 2.02, 95% CI: 1.16–3.52) and mHealth use (APR: 0.06, 95% CI: 0.02-.0.30). The qualitative data suggested that performance was influenced by the number of households assigned to CHWs, monetary incentives, adequacy of materials and facilitation. Particularly, mHealth was preferred to paper-based methods. Overall, the CHWs’ performance was sub-optimal; only 2 in 10 performed satisfactorily. The main factors that influenced performance included the level of education, use of mHealth, having another occupation, workload and incentivisation. CHWs and IPs preferred mHealth to paper-based methods. IPs should work to improve refugee settlement working conditions for the CHWs and adopt mHealth to improve CHW performance.

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          User Acceptance of Computer Technology: A Comparison of Two Theoretical Models

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            Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

            Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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              Estimating the relative risk in cohort studies and clinical trials of common outcomes.

              Logistic regression yields an adjusted odds ratio that approximates the adjusted relative risk when disease incidence is rare (<10%), while adjusting for potential confounders. For more common outcomes, the odds ratio always overstates the relative risk, sometimes dramatically. The purpose of this paper is to discuss the incorrect application of a proposed method to estimate an adjusted relative risk from an adjusted odds ratio, which has quickly gained popularity in medical and public health research, and to describe alternative statistical methods for estimating an adjusted relative risk when the outcome is common. Hypothetical data are used to illustrate statistical methods with readily accessible computer software.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLOS Glob Public Health
                PLOS Glob Public Health
                plos
                PLOS Global Public Health
                Public Library of Science (San Francisco, CA USA )
                2767-3375
                29 December 2023
                2023
                : 3
                : 12
                : e0002741
                Affiliations
                [1 ] Department of Community Health and Behavioral Sciences, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
                [2 ] Department of Disease Control and Environmental Health, School of Public Health, College of Health Sciences, Makerere University, Kampala, Uganda
                PLOS: Public Library of Science, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0009-0002-8622-6993
                https://orcid.org/0000-0003-3262-3918
                https://orcid.org/0009-0001-2549-5228
                Article
                PGPH-D-23-00744
                10.1371/journal.pgph.0002741
                10756529
                38157328
                5cf5dee8-cbc8-447d-8a5f-0be7abb15357
                © 2023 Wagaba 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.

                History
                : 9 May 2023
                : 30 November 2023
                Page count
                Figures: 1, Tables: 2, Pages: 18
                Funding
                Funded by: Kakande Ministries, Department of Philanthropy, Health and Welfare
                Award Recipient :
                This study was supported by the Kakande Ministries, under the Department of Philanthropy, Health and Welfare to MTW. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                People and Places
                Demography
                Refugees
                People and Places
                Population Groupings
                Professions
                Social Sciences
                Sociology
                Education
                Educational Attainment
                Research and Analysis Methods
                Research Design
                Qualitative Studies
                Medicine and Health Sciences
                Health Care
                Health Care Facilities
                Computer and Information Sciences
                Data Management
                Medicine and Health Sciences
                Health Care
                Socioeconomic Aspects of Health
                Medicine and Health Sciences
                Public and Occupational Health
                Socioeconomic Aspects of Health
                Social Sciences
                Economics
                Finance
                Custom metadata
                Data are available from https://doi.org/10.17026/dans-zz5-9fvq.

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