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      Occurrence and Distribution of Nonfalciparum Malaria Parasite Species Among Adolescents and Adults in Malawi

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          Abstract

          Background

          Plasmodium falciparum malaria dominates throughout sub-Saharan Africa, but the prevalence of Plasmodium malariae, Plasmodium ovale spp., and Plasmodium vivax increasingly contribute to infection in countries that control malaria using P. falciparum-specific diagnostic and treatment strategies.

          Methods

          We performed quantitative polymerase chain reaction (qPCR) on 2987 dried blood spots from the 2015–2016 Malawi Demographic and Health Survey to identify presence and distribution of nonfalciparum infection. Bivariate models were used to determine species-specific associations with demographic and environmental risk factors.

          Results

          Nonfalciparum infections had broad spatial distributions. Weighted prevalence was 0.025 (SE, 0.004) for P. malariae, 0.097 (SE, 0.008) for P. ovale spp., and 0.001 (SE, 0.0005) for P. vivax. Most infections (85.6%) had low-density parasitemias ≤ 10 parasites/µL, and 66.7% of P. malariae, 34.6% of P. ovale spp., and 40.0% of P. vivax infections were coinfected with P. falciparum. Risk factors for P. malariae were like those known for P. falciparum; however, there were few risk factors recognized for P. ovale spp. and P. vivax, perhaps due to the potential for relapsing episodes.

          Conclusions

          The prevalence of any nonfalciparum infection was 11.7%, with infections distributed across Malawi. Continued monitoring of Plasmodium spp. becomes critical as nonfalciparum infections become important sources of ongoing transmission.

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          Most cited references39

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          The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

          The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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            Constructing inverse probability weights for marginal structural models.

            The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use.
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              The changing epidemiology of malaria elimination: new strategies for new challenges.

              Malaria-eliminating countries achieved remarkable success in reducing their malaria burdens between 2000 and 2010. As a result, the epidemiology of malaria in these settings has become more complex. Malaria is increasingly imported, caused by Plasmodium vivax in settings outside sub-Saharan Africa, and clustered in small geographical areas or clustered demographically into subpopulations, which are often predominantly adult men, with shared social, behavioural, and geographical risk characteristics. The shift in the populations most at risk of malaria raises important questions for malaria-eliminating countries, since traditional control interventions are likely to be less effective. Approaches to elimination need to be aligned with these changes through the development and adoption of novel strategies and methods. Knowledge of the changing epidemiological trends of malaria in the eliminating countries will ensure improved targeting of interventions to continue to shrink the malaria map. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                The Journal of Infectious Diseases
                Oxford University Press (OUP)
                0022-1899
                1537-6613
                January 15 2022
                January 18 2022
                July 09 2021
                January 15 2022
                January 18 2022
                July 09 2021
                : 225
                : 2
                : 257-268
                Affiliations
                [1 ]National Malaria Control Programme, Malawi Ministry of Health, Lilongwe, Malawi
                [2 ]Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, USA
                [3 ]University of North Carolina Project-Malawi, Lilongwe, Malawi
                [4 ]National HIV Reference Laboratory, Malawi Ministry of Health, Lilongwe, Malawi
                [5 ]Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
                [6 ]Institute for Global Health and Infectious Diseases, University of North Carolina, Chapel Hill, North Carolina, USA
                [7 ]Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
                Article
                10.1093/infdis/jiab353
                34244739
                700b0c32-7035-4d71-968d-8554f073ff34
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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