Modelling malaria reduction in a highly endemic country: Evidence from household survey, climate, and program data in Zambia

Abstract

Substantial efforts have seen the reduction in malaria prevalence from 33% in 2006 to 19.4% in 2015 in Zambia. Many studies have used effect measures, such as odds ratios, of malaria interventions without combining this information with coverage levels of the interventions to assess how malaria prevalence would change if these interventions are scaled up. We contribute to filling this gap by combining intervention coverage information with marginal predictions to model the extent to which key interventions can bring down malaria in Zambia. We used logistic regression models and derived marginal effects using repeated cross-sectional survey data from the Malaria Indicator Survey (MIS) datasets for Zambia collected in 2010, 2012 and 2015. Average monthly temperature and rainfall data were obtained from climate explorer a satellite-generated database. We then conducted a counterfactual analysis using the estimated marginal effects and various hypothetical levels of intervention coverage to assess how different levels of coverage would affect malaria prevalence. Increasing IRS and ITNs from the 2015 levels of coverage of 28.9% and 58.9% respectively to at least 80% and rising standard housing to 20% from the 13.4% in 2015 may bring malaria prevalence down to below 15%. If the percentage of modern houses were increased further to 90%, malaria prevalence might decrease to 10%. Other than ITN and IRS, streamlining and increasing of the percentage of standard houses in malaria fight would augment and bring malaria down to the levels needed for focal malaria elimination. The effects of ITNs, IRS and Standard housing were pronounced in high than low epidemiological areas.

Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014–2020 Routine Data

“…Information on healthseeking behaviour among the population in the northwestern province with suspected symptoms of malaria is not available; however, it has been noted that the presence of chronic carriers of malaria parasites among the adult population may lead to underestimation of the true number of malaria cases in the population over the time period of the study [22]. Among the children under the age of five years, a national survey in 2015 found that only about 22% who had had a fever in the two weeks preceding the survey had sought treatment from health facilities [5]. Despite these limitations in available data, for planning purposes for those that seek medical care at health facilities, the monthly reported cases of confirmed malaria through the District Health Information System (DHIS) is helpful to health authorities.…”

“…Malaria remains a significant public health challenge in low and middle-income countries (LMICs) despite some advancements in diagnostic and treatment modalities and resources applied to prevention, control, and elimination [1][2][3]. Malaria is a disease caused by infection of parasites of Plasmodium species and transmitted through infected Anopheles mosquito bites [4,5]. However, though preventable and treatable, malaria continues to contribute significantly to global morbidity and mortality, especially in LMICs, mainly in sub-Saharan Africa, Latin America, and Asia [3,6,7].…”

“…With the support from international organizations and partners such as the Global Fund to fight AIDS, tuberculosis, and malaria, the United States President's Malaria Initiative, and the World Bank, Zambia, utilize vital interventions such as indoor residual spraying (IRS), distribution of long-lasting insecticidal nets (LLINs), prompt diagnosis and treatment of malaria cases, and intermittent preventive treatment of malaria in pregnancy (IPTp) among others to fight the disease [11,13]. However, in certain parts of the Zambia, such as the northwestern province, there has been a persistence of malaria cases, and in some years, even resurgence incidents have been recorded [5,14]. Malaria is still one of the leading causes of morbidity and mortality, especially in children aged below five years with the peak of transmission during the rainy season, between December and April every year [2,15].…”

“…e autoregressive integrated moving average (ARIMA) time series method can produce an estimation model with known monthly malaria cases [16]. Forecasting malaria cases is essential for allocating appropriate preventive control measures and eventual elimination strategies [5]. Time series analysis can predict malaria in a particular month based on preceding months [16,17].…”

Time-Varying Effects of Meteorological Variables on Malaria Epidemiology in the Context of Interrupted Control Efforts in the Amazon Rainforest, 2000–2017

“…In addition, governments need to monitor and enforce the implementation of malaria interventions ( 59 ). In Zambia, for example, many of the positive effects of control programs are being reduced by the negative impact of climatic conditions that favor the spread of malaria ( 60 ). These scenarios may lead to a more substantial resurgence, changes in seasonal trends, and complications for malaria control and elimination.…”