Modelling Sociodemographic Factors That Affect Malaria Prevalence in Sussundenga, Mozambique.

Malaria is still one of the leading causes of mortality and morbidity in Mozambique with 17 little progress in malaria control over the past 20 years. Sussundenga is one of most 18 affected areas. Malaria transmission has a strong association with environmental and 19 socio-demographic factors. The knowledge of sociodemographic factors that affects 20 malaria, may be used to improve the strategic planning for its control and, such studies 21 do not exist in Sussundenga. Hence, the objective of this study is to model the relationship 22 between malaria and sociodemographic factors in Sussundenga, Mozambique.

between malaria and sociodemographic factors in Sussundenga, Mozambique.

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Houses in the study area were digitalized and enumerated using GoogleEarth ProTM. 25 Hundred houses were randomly selected to conduct a community survey of P. falciparum 26 parasite prevalence using rapid diagnostic test (RDT). During the survey, a questionnaire 27 was conducted to assess the socio-demographic factors of the participants. Descriptive 28 statistics were analyzed and backward stepwise logistic regression was performed 29 establishing a relationship between positive cases and the factors. The analysis was 30 carried out using SPSS version 20 package.

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The overall P. falciparum prevalence was 31.6 %. Half of the malaria positive cases capital of Manica, close to Sussundenga Village, modelled the influence of climate on 72 malaria occurrence and indicated that selected environmental characteristics accounted 73 for malaria incidence by 72.5% implying that non-environmental factors such as 74 sociodemographic, economic, cultural and behavioral traits would account for the res 10 .

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While Mozambique has one of the highest incidences and prevalence of malaria in the 76 region and, it accounts for nearly half of childhood deaths, little is known about the 77 epidemiology to inform appropriate and effective interventions. This is one of two major 78 barriers to expanding control measures in the country with the other being limited funding.

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In the country, malaria transmission occurs all year round and, the knowledge of 80 sociodemographic factors that affect malaria is crucial for informing the implementation 81 of the most appropriate and effective malaria interventions to achieve control. In 82 Sussundenga no studies are known in this field. Therefore, the objective of this study was    Coordinates of the households were extracted using a GPS device and maps of the 103 selected households to conduct study visits. The study involved two visits to the selected 104 households. The first was a notification visit where the study team introduced themselves 105 to the head of the household and explained the objectives and procedures of the study.

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It is customary for the head of household to provide permission to the study team before  After obtaining consent from the household residents, the study team informed 113 participants when they will return the following day to conduct the study activities. The 114 only eligibility requirement was that the residents live in household full time. Data collectors verbally administered a questionnaire to collect the basic demographics. The 116 field study was carried out from December 2019 to January 2020.

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The study nurse collected current malaria specific symptoms by self-report and will took 118 participant's temperature using a digital thermometer (Mebaline). They then collected a 119 finger prick blood sample to administer a Rapid Diagnostic Test (RDT), RightSign Biotest

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This study was a cross-sectional community-based survey. The analyses were conducted 130 on datasets downloaded from REDCap to excel spread sheet (additional file 1). As 131 variables, a binary variable as the dependent variable malaria infection, that is whether 132 malaria was present (positive) to RDT or absent (negative) was used.

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The explanatory variables analyzed were the following sociodemographic factors: age, if 134 the person was an adult or child, age category, sex, history of malaria treatment, paid  identified and, was calculated as 16 : The model specificity (conditional probability of a negative test given that the patient is 164 well) of the final model measures the proportion of negative case correctly identified and 165 was calculated as 16 : Positive Predictive Value (PPV) that is, the conditional probability, whether the screened 168 people who tested positive do or do not actually have malaria was calculated as 16 : Negative Predicted Value (NPV) that is, the conditional probability that an individual with 171 a test indicative of No malaria is actually disease free, was calculated as 16 :

Association between malaria infection and sociodemographic factors.
The backward stepwise regression selection of predictors into the binary logistic model 204 produced a series of model and, in this study, we only present the relevant, initial models 205 and other outputs can be found in appendix 1.   Mozambique it was reported 52% of malaria cases in children under five 17 and, the 247 discrepancy may due to the fact that the present study was carried out at community level 248 while, the Chimoio study was carried out from health center data.

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This study suggests that recent diagnosis and treatment for malaria infection reduces the certain degree of immunity. Also, when re-infected, patients present a mild form of the 255 diseases without symptoms. Natural active immunity is established after ten or more P. 256 falciparum infections, which can be sufficient to suppress symptoms and clinical signs 34 .

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Different results were reported in Angola where women who had a previous malaria 258 infection during pregnancy also had a higher risk to contract malaria 35 . This is likely 259 because pregnant women may take SP rather than ACT.

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In this study population density was found as a significant predictor for an individual to

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Age category is a good proxy for age group and, household size for household category.     Malaria positive and negative cases in Sussundenga Village. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.