Laina D. Mercer

BACKGROUND: The mechanism(s) responsible for the efficacy of WHO-recommended malaria vaccine RTS,S/AS01 are not completely understood. We previously identified RTS,S vaccine–induced Plasmodium falciparum circumsporozoite protein–specific (PfCSP-specific) antibody measures associated with protection from controlled human malaria infection (CHMI). Here, we tested the protection-predicting capability of these measures in independent CHMI studies. METHODS: Vaccine-induced total serum antibody (immunoglobulins, Igs) and subclass antibody (IgG1 and IgG3) responses were measured by biolayer interferometry and the binding antibody multiplex assay, respectively. Immune responses were compared between protected and nonprotected vaccinees using univariate and multivariate logistic regression. RESULTS: Blinded prediction analysis showed that 5 antibody binding measures, including magnitude-avidity composite of serum Ig specific for PfCSP, major NANP repeats and N-terminal junction, and PfCSP- and NANP-specific IgG1 subclass magnitude, had good prediction accuracy (area under the receiver operating characteristic curves [ROC AUC] > 0.7) in at least 1 trial. Furthermore, univariate analysis showed a significant association between these antibody measures and protection (odds ratios 2.6–3.1). Multivariate modeling of combined data from 3 RTS,S CHMI trials identified the combination of IgG1 NANP binding magnitude plus serum NANP and N-junction Ig binding magnitude-avidity composite as the best predictor of protection (95% confidence interval for ROC AUC 0.693–0.834). CONCLUSION: These results reinforce our previous findings and provide a tool for predicting protection in future trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT03162614, NCT03824236, NCT01366534, and NCT01857869. FUNDING: This study was supported by Bill & Melinda Gates Foundation’s Global Health-Discovery Collaboratory grants (INV-008612 and INV-043419) to GDT.

Urban population growth in Nigeria may exceed the availability of affordable housing and basic services, resulting in living conditions conducive to vector breeding and heterogeneous malaria transmission. Understanding the link between community-level factors and urban malaria transmission informs targeted interventions. We analyzed Demographic and Health Survey Program cluster-level data, alongside geospatial covariates, to describe variations in malaria prevalence in children under 5 years of age. Univariate and multivariable models explored the relationship between malaria test positivity rates at the cluster level and community-level factors. Generally, malaria test positivity rates in urban areas are low and declining. The factors that best predicted malaria test positivity rates within a multivariable model were post-primary education, wealth quintiles, population density, access to improved housing, child fever treatment-seeking, precipitation, and enhanced vegetation index. Malaria transmission in urban areas will likely be reduced by addressing socioeconomic and environmental factors that promote exposure to disease vectors. Enhanced regional surveillance systems in Nigeria can provide detailed data to further refine our understanding of these factors in relation to malaria transmission.

Background: Previous studies show a positive association between household wealth and overweight in sub-Saharan African (SSA) countries; however, the manner in which this relationship differs in the presence of educational attainment has not been well-established. This study examined the multiplicative effect modification of educational attainment on the association between middle-income and rich household wealth and overweight status among adult females in 22 SSA countries. We hypothesized that household wealth was associated with a greater likelihood of being overweight among middle income and rich women with lower levels of educational attainment compared to those with higher levels of educational attainment. Methods: Demographic and Health Survey (DHS) data from 2006 to 2016 for women aged 18–49 years in SSA countries were used for the study. Overweight was defined as a body mass index (BMI) ≥ 25 kg/m². Household wealth index tertile was the exposure and educational attainment, the effect modifier. Potential confounders included age, ethnicity, place of residence, and parity. Descriptive analysis was conducted, and separate logistic regression models were fitted for each of the 22 SSA countries to compute measures of effect modification and 95% confidence intervals. Analysis of credibility (AnCred) methods were applied to assess the intrinsic credibility of the study findings and guide statistical inference. Results: The prevalence of overweight ranged from 12.6% in Chad to 56.6% in Swaziland. Eighteen of the 22 SSA countries had measures of effect modification below one in at least one wealth tertile. This included eight of the 12 low-income countries and all 10 middle income countries. This implied that the odds of overweight were greater among middle-income and rich women with lower levels of educational attainment than those with higher educational attainment. On the basis of the AnCred analysis, it was found that the majority of the study findings across the region provided some support for the study hypothesis. Conclusions: Women in higher wealth strata and with lower levels of educational attainment appear to be more vulnerable to overweight compared to those in the same wealth strata but with higher levels of educational attainment in most low- and middle- income SSA countries.

Purpose: Given incomplete data reporting by race, we used data on COVID-19 cases and deaths in US counties to describe racial disparities in COVID-19 disease and death and associated determinants. Methods: Using publicly available data (accessed April 13, 2020), predictors of COVID-19 cases and deaths were compared between disproportionately (≥13%) black and all other (<13% black) counties. Rate ratios were calculated and population attributable fractions (PAF) were estimated using COVID-19 cases and deaths via zero-inflated negative binomial regression model. National maps with county-level data and an interactive scatterplot of COVID-19 cases were generated. Results: Nearly ninety-seven percent of disproportionately black counties (656/677) reported a case and 49% (330/677) reported a death versus 81% (1987/2,465) and 28% (684/ 2465), respectively, for all other counties. Counties with higher proportions of black people have higher prevalence of comorbidities and greater air pollution. Counties with higher proportions of black residents had more COVID-19 diagnoses (RR 1.24, 95% CI 1.17-1.33) and deaths (RR 1.18, 95% CI 1.00-1.40), after adjusting for county-level characteristics such as age, poverty, comorbidities, and epidemic duration. COVID-19 deaths were higher in disproportionally black rural and small metro counties. The PAF of COVID-19 diagnosis due to lack of health insurance was 3.3% for counties with <13% black residents and 4.2% for counties with ≥13% black residents. Conclusions: Nearly twenty-two percent of US counties are disproportionately black and they accounted for 52% of COVID-19 diagnoses and 58% of COVID-19 deaths nationally. County-level comparisons can both inform COVID-19 responses and identify epidemic hot spots. Social conditions, structural racism, and other factors elevate risk for COVID-19 diagnoses and deaths in black communities.