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      Estimating district HIV prevalence in Zambia using small-area estimation methods (SAE)

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          Abstract

          Background

          The HIV/AIDS pandemic has had a very devastating impact at a global level, with the Eastern and Southern African region being the hardest hit. The considerable geographical variation in the pandemic means varying impact of the disease in different settings, requiring differentiated interventions. While information on the prevalence of HIV at regional and national levels is readily available, the burden of the disease at smaller area levels, where health services are organized and delivered, is not well documented. This affects the targeting of HIV resources. There is need, therefore, for studies to estimate HIV prevalence at appropriate levels to improve HIV-related planning and resource allocation.

          Methods

          We estimated the district-level prevalence of HIV using Small-Area Estimation (SAE) technique by utilizing the 2016 Zambia Population-Based HIV Impact Assessment Survey (ZAMPHIA) data and auxiliary data from the 2010 Zambian Census of Population and Housing and the HIV sentinel surveillance data from selected antenatal care clinics (ANC). SAE models were fitted in R Programming to ascertain the best HIV predicting model. We then used the Fay–Herriot (FH) model to obtain weighted, more precise and reliable HIV prevalence for all the districts.

          Results

          The results revealed variations in the district HIV prevalence in Zambia, with the prevalence ranging from as low as 4.2% to as high as 23.5%. Approximately 32% of the districts ( n = 24) had HIV prevalence above the national average, with one district having almost twice as much prevalence as the national level. Some rural districts have very high HIV prevalence rates.

          Conclusions

          HIV prevalence in Zambian is highest in districts located near international borders, along the main transit routes and adjacent to other districts with very high prevalence. The variations in the burden of HIV across districts in Zambia point to the need for a differentiated approach in HIV programming within the country. HIV resources need to be prioritized toward districts with high population mobility.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12963-022-00286-3.

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          Most cited references35

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          Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017

          HIV/AIDS is a leading cause of disease burden in sub-Saharan Africa. Existing evidence has demonstrated that there is substantial local variation in the prevalence of HIV; however, subnational variation has not been investigated at a high spatial resolution across the continent. Here we explore within-country variation at a 5 × 5-km resolution in sub-Saharan Africa by estimating the prevalence of HIV among adults (aged 15–49 years) and the corresponding number of people living with HIV from 2000 to 2017. Our analysis reveals substantial within-country variation in the prevalence of HIV throughout sub-Saharan Africa and local differences in both the direction and rate of change in HIV prevalence between 2000 and 2017, highlighting the degree to which important local differences are masked when examining trends at the country level. These fine-scale estimates of HIV prevalence across space and time provide an important tool for precisely targeting the interventions that are necessary to bringing HIV infections under control in sub-Saharan Africa.
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            Numerical ecology

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              Large-scale spatial-transmission models of infectious disease.

              During transmission of seasonal endemic diseases such as measles and influenza, spatial waves of infection have been observed between large distant populations. Also, during the initial stages of an outbreak of a new or reemerging pathogen, disease incidence tends to occur in spatial clusters, which makes containment possible if you can predict the subsequent spread of disease. Spatial models are being used with increasing frequency to help characterize these large-scale patterns and to evaluate the impact of interventions. Here, I review several recent studies on four diseases that show the benefits of different methodologies: measles (patch models), foot-and-mouth disease (distance-transmission models), pandemic influenza (multigroup models), and smallpox (network models). This review highlights the importance of the household in spatial studies of human diseases, such as smallpox and influenza. It also demonstrates the need to develop a simple model of household demographics, so that these large-scale models can be extended to the investigation of long-time scale human pathogens, such as tuberculosis and HIV.
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                Author and article information

                Contributors
                chris.muna@gmail.com
                Journal
                Popul Health Metr
                Popul Health Metr
                Population Health Metrics
                BioMed Central (London )
                1478-7954
                19 February 2022
                19 February 2022
                2022
                : 20
                : 8
                Affiliations
                [1 ]GRID grid.12984.36, ISNI 0000 0000 8914 5257, Department of Health Policy, Systems and Management, School of Public Health, , University of Zambia, ; Ridgeway Campus, P.O. Box 50110, Lusaka, Zambia
                [2 ]GRID grid.8652.9, ISNI 0000 0004 1937 1485, School of Public Health, , University of Ghana, ; P.O. Box LG 571, Accra, Ghana
                [3 ]GRID grid.12984.36, ISNI 0000 0000 8914 5257, Department of Economics, School of Humanities and Social Science, , University of Zambia, ; Great East Road Campus, P.O Box 32379, Lusaka, Zambia
                Author information
                http://orcid.org/0000-0002-6834-9896
                Article
                286
                10.1186/s12963-022-00286-3
                8858531
                35183216
                397575e0-b43d-450f-8333-dcaf93ef61dd
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 May 2021
                : 8 February 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000061, Fogarty International Center;
                Award ID: D43 TW009744
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Health & Social care
                sae,small-area estimation,hiv,prevalence,district,fay–herriot,auxiliary information
                Health & Social care
                sae, small-area estimation, hiv, prevalence, district, fay–herriot, auxiliary information

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