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      Health insurance and care-seeking behaviours of female migrants in Accra, Ghana

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          Abstract

          People working in Ghana’s informal sector have low rates of enrolment in the publicly funded National Health Insurance Scheme. Informal sector workers, including migrant girls and women from northern Ghana working as head porters ( kayayei), report challenges obtaining insurance and seeking formal health care. This article analyses how health insurance status affects kayayei migrants’ care-seeking behaviours. This mixed-methods study involved surveying 625 migrants using respondent-driven sampling and conducting in-depth interviews with a sub-sample of 48 migrants. Analyses explore health status and health seeking behaviours for recent illness/injury. Binary logistic regression modelled the effects of selected independent variables on whether or not a recently ill/injured participant ( n = 239) sought health care. Although recently ill/injured participants (38.4%) desired health care, less than half (43.5%) sought care. Financial barriers overwhelmingly limit kayayei migrants from seeking health care, preventing them from registering with the National Health Insurance Scheme, renewing their expired health insurance policies, or taking time away from work. Both insured and uninsured migrants did not seek formal health services due to the unpredictable nature of out-of-pocket expenses. Catastrophic and impoverishing medical expenses also drove participants’ migration in search of work to repay loans and hospital bills. Health insurance can help minimize these expenditures, but only 17.4% of currently insured participants (58.2%) reported holding a valid health insurance card in Accra. The others lost their cards or forgot them when migrating. Access to formal health care in Accra remains largely inaccessible to kayayei migrants who suffer from greater illness/injury than the general female population in Accra and who are hindered in their ability to receive insurance exemptions. With internal migration on the rise in many settings, health systems must recognize the varied needs of populations in multi-ethnic and multilingual countries to ensure that internal migrants can access affordable, quality health services across domestic borders.

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          Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

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            Variance Estimation, Design Effects, and Sample Size Calculations for Respondent-Driven Sampling

            Hidden populations, such as injection drug users and sex workers, are central to a number of public health problems. However, because of the nature of these groups, it is difficult to collect accurate information about them, and this difficulty complicates disease prevention efforts. A recently developed statistical approach called respondent-driven sampling improves our ability to study hidden populations by allowing researchers to make unbiased estimates of the prevalence of certain traits in these populations. Yet, not enough is known about the sample-to-sample variability of these prevalence estimates. In this paper, we present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use. We also use simulations and real data to estimate the design effects for respondent-driven sampling in a number of situations. We conclude with practical advice about the power calculations that are needed to determine the appropriate sample size for a study using respondent-driven sampling. In general, we recommend a sample size twice as large as would be needed under simple random sampling.
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              Assessing respondent-driven sampling.

              Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.
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                Author and article information

                Journal
                Health Policy Plan
                Health Policy Plan
                heapol
                Health Policy and Planning
                Oxford University Press
                0268-1080
                1460-2237
                May 2018
                15 February 2018
                15 February 2018
                : 33
                : 4
                : 505-515
                Affiliations
                Department of Social Policy, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK
                Author notes
                Corresponding author. Department of Social Policy, London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK. E-mail: s.lattof@ 123456lse.ac.uk

                Country of expertise: Ghana; sub-Saharan Africa

                Article
                czy012
                10.1093/heapol/czy012
                5894076
                29462305
                e04ea3d9-6a98-4ce6-b66b-1d5e1e43bb10
                © The Author(s) 2018. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 January 2018
                Page count
                Pages: 11
                Funding
                Funded by: London School of Economics and Political Science 10.13039/100011326
                Categories
                Original Articles

                Social policy & Welfare
                health insurance,health care-seeking behaviour,determinants,perceptions,poverty,access,urban health,gender,informal sector,population movement

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