Malaria is one of the leading causes of morbidity and mortality in sub-Saharan Africa, causing over 200 million cases and 405,000 deaths in 2017 (1). The World Health Organization states that quality malaria surveillance is essential to target regions and populations at highest risk, accurately measure changes in disease burden, and evaluate the impact of interventions (2). Surveillance data can also be used to produce meaningful indicators of malaria burden in order to identify determinants of disease and produce high resolution maps of risk (3, 4). This is essential because malaria transmission is heterogenous across space and time (5). Despite the critical need for high quality data, malaria surveillance is particularly poor in high burden countries (1).

The ideal indicator for estimating malaria morbidity in high transmission areas is case incidence, which is defined as the number of cases of malaria per unit time divided by the population at risk (3, 6, 7). The approach currently employed by most countries in sub-Saharan Africa to estimate malaria incidence involves cartographic methods that link parasite prevalence to incidence rates (8, 9). This method has several weaknesses. It relies on cross-sectional surveys that are costly, time-consuming, and infrequently performed. In addition, its accuracy is limited because the relationship between prevalence and incidence is variable and non-linear, particularly in high transmission settings. An alternative method of estimating incidence that is more direct, inexpensive, and comprehensive is the use of data reported through routine health management information systems (HMIS). Unfortunately, this is challenging because HMIS data are typically not available at an individual patient level and lack information on where patients come from. Cases of malaria detected by the HMIS often lack laboratory confirmation. Furthermore, quantifying the denominator for incidence using these data is ambiguous because catchment areas around health facilities are not well defined.

The overall goals of this dissertation are to leverage surveillance data routinely collected at public health facilities in Uganda to improve measurement of malaria burden and to accurately capture changes in malaria burden across space and time. This dissertation is organized into three chapters. The first chapter used enhanced HMIS data to estimate care-seeking populations around health facilities and, with these estimates, to generate measures of malaria incidence over time. These estimates were then compared to “gold standard” measures of incidence measured concurrently in cohorts. Through developing this new method to generate catchment area populations, we found that our estimates of incidence accurately captured gold standard incidence and its dynamics over time. The second chapter aimed to map malaria incidence across Uganda using this new measure of incidence captured through enhanced HMIS data. Using spatio-temporal modeling, we generated predictions of malaria incidence in 2019-2020 at the parish-level in Uganda. The model performed well, particularly in areas where sentinel surveillance sites were more densely located across space. The findings from this analysis suggest that routinely collected health facility data could be used for risk mapping purposes. The third chapter used enhanced HMIS data to evaluate the impact of indoor residual spraying (IRS) in Uganda. We used data from 5 districts that received over 7 years of sustained IRS and compared monthly malaria burden to a baseline pre-IRS period. Our findings suggest that malaria burden declined 85% in the 4th and 5th year of sustained IRS, but increased in the 6th and 7th years. The timing of the increase in burden coincided with a switch in IRS active ingredient to a new chemical, clothianidin. These findings are an important call for more research aimed at studying the real world effectiveness of IRS with clothianidin.

Taken together, these chapters underscore the potential for routinely collected health facility surveillance data to answer important scientific questions and to inform policy.

This dissertation describes the discovery of highly informative serologic biomarkers of recent exposure to Plasmodium falciparum, the most deadly species causing malaria. An innovative approach that combined detailed individual-level exposure data, high-throughput screening of hundreds of antibody responses, and robust statistical methods was used to identify the most informative signatures of exposure. The novel antigens described here and, more importantly, the outlined approach for biomarker discovery will allow for the development of public health and research tools that are imperative for the control and elimination of malaria. Additionally, the methodologies outlined here are highly applicable to the discovery of biomarkers of exposure for other infectious diseases.

Serology has been used for decades to measure exposure to Plasmodium species and other infectious diseases. While useful, most existing assays for measuring P. falciparum exposure have been based on population-level responses to a few target antigens chosen by convenience rather than utility, resulting in relatively coarse exposure estimates. Here, detailed assessments of malaria exposure in Malian and Ugandan children were used to identify novel serologic biomarkers of malaria exposure and calibrate responses to quantitative estimates of individual exposure. The power of obtaining these individual-level estimates was illustrated by their ability to accurately identify individuals with infection in the recent past; to obtain precise estimates of malaria incidence in a population from cross-sectional samples of as few as 20 individuals; and to accurately estimate heterogeneity in recent exposure within a community using data from a single time point.

Interest in the development of improved serologic assays has increased in recent years as more investment is made in malaria control and elimination. There is need for widely available, accurate estimates of malaria exposure that will allow for targeting and evaluation of public health interventions. This dissertation initiates a response to the call to develop accurate field-based assays for rapid and cost-effective assessment of malaria exposure, with the ultimate goal of putting cohort-quality data into the hands of malaria control programs.

Surveillance plays a key role in malaria control and elimination efforts by allowing for informed and effective allocation of often limited resources. Current methods for estimating malaria exposure are limited either by cost or accuracy. Serological data offers the potential to provide inexpensive and accurate estimates of exposure, but to date there is no consensus on which antibody responses are informative and how they can be interpreted. Previous studies have been limited to single age groups, transmission intensities, and geographic locations that are not generalizable across populations. This dissertation describes an investigation into serologic biomarkers of recent exposure to Plasmodium falciparum, the most deadly species causing malaria, using samples from 8 cohort studies representing diverse populations. Using an innovative approach that combined detailed individual-level exposure data, high-throughput screening of hundreds of antibody responses, and robust statistical methods, we were unable to identify a set of antibody responses predictive of recent exposure that was generalizable across settings. Although universal seromarkers may not exist, we found that accurate prediction of recent exposure was possible in a cohort of return travelers, suggesting the potential for seromarkers to be developed for specific settings, particularly those with limited cumulative exposure.

Background: The use of genotyping to distinguish recrudescent from new infections is currently recommended for all clinical antimalarial efficacy trials by the World Health Organization. However, genotyping-adjusted drug efficacy estimates may vary between trials due to the use of different genotyping methods and to the different settings in which these methods are applied. Methods: A systematic review of all clinical antimalarial efficacy trials published from 1995-2005 was performed to characterize the use of genotyping, including the methods used and the effect of these methods on estimates of drug efficacy. Results: In a multivariate analysis, the method of interpretation of genotyping results, the studied therapy, the location of the trial, and the duration of study follow-up all had statistically significant effects on the percent of genotyped outcomes classified as new infections. Conclusion: Criteria for defining appropriate, standardized genotyping methods for use in different settings are needed to enable more accurate estimates of antimalarial drug efficacy and better comparison between trials. The advantages and disadvantages of different genotyping methods and their potential impact in various settings are discussed.