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      Spatial and epidemiological drivers of Plasmodium falciparum malaria among adults in the Democratic Republic of the Congo

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          Abstract

          Background

          Adults are frequently infected with malaria and may serve as a reservoir for further transmission, yet we know relatively little about risk factors for adult infections. In this study, we assessed malaria risk factors among adults using samples from the nationally representative, cross-sectional 2013–2014 Demographic and Health Survey (DHS) conducted in the Democratic Republic of the Congo (DRC). We further explored differences in risk factors by urbanicity.

          Methods

          Plasmodium falciparum infection was determined by PCR. Covariates were drawn from the DHS to model individual, community and environmental-level risk factors for infection. Additionally, we used deep sequencing data to estimate the community-level proportions of drug-resistant infections and included these estimates as potential risk factors. All identified factors were assessed for differences in associations by urbanicity.

          Results

          A total of 16 126 adults were included. Overall prevalence of malaria was 30.3% (SE=1.1) by PCR; province-level prevalence ranged from 6.7% to 58.3%. Only 17% of individuals lived in households with at least one bed-net for every two people, as recommended by the WHO. Protective factors included increasing within-household bed-net coverage (Prevalence Ratio=0.85, 95% CI=0.76–0.95) and modern housing (PR=0.58, 95% CI=0.49–0.69). Community-level protective factors included increased median wealth (PR=0.87, 95% CI=0.83–0.92). Education, wealth, and modern housing showed protective associations in cities but not in rural areas.

          Conclusions

          The DRC continues to suffer from a high burden of malaria; interventions that target high-risk groups and sustained investment in malaria control are sorely needed. Areas of high prevalence should be prioritised for interventions to target the largest reservoirs for further transmission.

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          Most cited references45

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          The table 2 fallacy: presenting and interpreting confounder and modifier coefficients.

          It is common to present multiple adjusted effect estimates from a single model in a single table. For example, a table might show odds ratios for one or more exposures and also for several confounders from a single logistic regression. This can lead to mistaken interpretations of these estimates. We use causal diagrams to display the sources of the problems. Presentation of exposure and confounder effect estimates from a single model may lead to several interpretative difficulties, inviting confusion of direct-effect estimates with total-effect estimates for covariates in the model. These effect estimates may also be confounded even though the effect estimate for the main exposure is not confounded. Interpretation of these effect estimates is further complicated by heterogeneity (variation, modification) of the exposure effect measure across covariate levels. We offer suggestions to limit potential misunderstandings when multiple effect estimates are presented, including precise distinction between total and direct effect measures from a single model, and use of multiple models tailored to yield total-effect estimates for covariates.
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            The silent threat: asymptomatic parasitemia and malaria transmission.

            Scale-up of malaria control interventions has resulted in a substantial decline in global malaria morbidity and mortality. Despite this achievement, there is evidence that current interventions alone will not lead to malaria elimination in most malaria-endemic areas and additional strategies need to be considered. Use of antimalarial drugs to target the reservoir of malaria infection is an option to reduce the transmission of malaria between humans and mosquito vectors. However, a large proportion of human malaria infections are asymptomatic, requiring treatment that is not triggered by care-seeking for clinical illness. This article reviews the evidence that asymptomatic malaria infection plays an important role in malaria transmission and that interventions to target this parasite reservoir may be needed to achieve malaria elimination in both low- and high-transmission areas.
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              Urbanization, malaria transmission and disease burden in Africa.

              Many attempts have been made to quantify Africa's malaria burden but none has addressed how urbanization will affect disease transmission and outcome, and therefore mortality and morbidity estimates. In 2003, 39% of Africa's 850 million people lived in urban settings; by 2030, 54% of Africans are expected to do so. We present the results of a series of entomological, parasitological and behavioural meta-analyses of studies that have investigated the effect of urbanization on malaria in Africa. We describe the effect of urbanization on both the impact of malaria transmission and the concomitant improvements in access to preventative and curative measures. Using these data, we have recalculated estimates of populations at risk of malaria and the resulting mortality. We find there were 1,068,505 malaria deaths in Africa in 2000 - a modest 6.7% reduction over previous iterations. The public-health implications of these findings and revised estimates are discussed.
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                Author and article information

                Journal
                BMJ Glob Health
                BMJ Glob Health
                bmjgh
                bmjgh
                BMJ Global Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-7908
                2020
                29 June 2020
                : 5
                : 6
                : e002316
                Affiliations
                [1 ]departmentDepartment of Epidemiology, Gillings School of Global Public Health , University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA
                [2 ]departmentDivision of Infectious Diseases, Department of Medicine, School of Medicine , University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA
                [3 ]National AIDS Control Program , Kinshasa, Congo (the Democratic Republic)
                [4 ]departmentSchool of Public Health , University of Kinshasa Faculty of Medicine , Kinshasa, Congo (the Democratic Republic)
                [5 ]departmentDepartment of Pathology and Laboratory Medicine , Brown University Warren Alpert Medical School , Providence, Rhode Island, USA
                [6 ]departmentMedical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology , Imperial College London , London, UK
                [7 ]departmentDepartment of Geography , University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA
                [8 ]departmentCurriculum in Genetics and Molecular Biology , University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, USA
                Author notes
                [Correspondence to ] Molly Deutsch-Feldman; mollydf1@ 123456live.unc.edu
                Author information
                http://orcid.org/0000-0001-6191-930X
                Article
                bmjgh-2020-002316
                10.1136/bmjgh-2020-002316
                7326263
                32601091
                08410d53-04d9-4505-a435-28adcf0dabfa
                © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 13 January 2020
                : 22 April 2020
                : 25 April 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01AI107949
                Award ID: R01AI139520
                Award ID: T32AI070114
                Funded by: FundRef http://dx.doi.org/10.13039/501100001713, European and Developing Countries Clinical Trials Partnership;
                Funded by: FundRef http://dx.doi.org/10.13039/100000861, Burroughs Wellcome Fund;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000278, Department for International Development;
                Categories
                Original Research
                1506
                Custom metadata
                unlocked

                malaria,pcr,epidemiology,cross-sectional survey
                malaria, pcr, epidemiology, cross-sectional survey

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