Confounding by indication poses a significant threat to the validity of nonexperimental studies assessing effectiveness and safety of medical interventions. While no different from other forms of confounding in theory, confounding by indication often requires specific methods to address the bias it creates in addition to common epidemiological adjustment or restriction methods. Clinical indication influencing treatment prescription is patient-specific and complex, making it challenging to measure within nonexperimental research. Restriction of the study population to patients with the indication for treatment would effectively mitigate confounding by indication and bring about comparability between exposure and comparator populations with respect to probability of the exposure. Active comparators are often an effective practical solution to restrict the study population in this manner when indication cannot be measured accurately. This article discusses various forms of confounding by indication, the utility of active comparators for nonexperimental studies of treatment effects, and the active comparator, new user (ACNU) study design to implicitly condition on indication. Considerations for selecting active comparators and conducting an ACNU study design are discussed to enable increased adoption of these methods, improve quality of nonexperimental studies, and ultimately strengthen our evidence base for intended and unintended treatment effects in relevant target populations.

Achieving malaria elimination requires considering both Plasmodium falciparum and non– P. falciparum infections. We determined prevalence and geographic distribution of 4 Plasmodium spp. by performing PCR on dried blood spots collected within 8 regions of Tanzania during 2017. Among 3,456 schoolchildren, 22% had P. falciparum, 24% had P. ovale spp., 4% had P. malariae , and 0.3% had P. vivax infections . Most (91%) schoolchildren with P. ovale infections had low parasite densities; 64% of P. ovale infections were single-species infections, and 35% of those were detected in low malaria endemic regions. P. malariae infections were predominantly (73%) co-infections with P. falciparum. P. vivax was detected mostly in northern and eastern regions. Co-infections with > 1 non– P. falciparum species occurred in 43% of P. falciparum infections. A high prevalence of P. ovale infections exists among schoolchildren in Tanzania, underscoring the need for detection and treatment strategies that target non– P. falciparum species.

Efforts to achieve malaria elimination need to consider both falciparum and non-falciparum infections. The prevalence and geographic distribution of four Plasmodium species were determined by real-time PCR using dried blood spots collected during the 2017 School Malaria Parasitological Survey of eight regions of Tanzania. Among 3,456 schoolchildren, 22% had P. falciparum, 24% P. ovale spp., 4% P. malariae, and 0.3% P. vivax. Ninety-one percent of P. ovale infections had very low parasite densities, based on amplification at later cycle thresholds. Sixty-four percent of P. ovale infections were single-species, and 35% of these were detected in low malaria endemicity regions. P. malariae infections were predominantly co-infections with P. falciparum (73%). P. vivax was largely detected in northern and eastern regions. Overall, 43% of children with P. falciparum were co-infected with at least one non-falciparum species. A large, previously under-appreciated burden of P. ovale spp. infection exists among Tanzanian schoolchildren.Article SummaryA previously unrecognized burden of non-falciparum malaria infections was detected among Tanzanian schoolchildren in a 2017 cross-sectional study, with P. ovale spp. prevalence comparable to P. falciparum, and low-level prevalences of P. malariae and P. vivax detected.

Background: Increasing reports suggest that non-falciparum species are an underappreciated cause of malaria in sub-Saharan Africa, but their epidemiology is not well-defined. This is particularly true in regions of highP. falciparumendemicity such as the Democratic Republic of Congo (DRC), where 12% of the world's malaria cases and 13% of deaths occur. Methods and Findings: The cumulative incidence and prevalence ofP. malariaeandP. ovalespp. infection detected by real-time PCR were estimated among children and adults within a longitudinal study conducted in seven rural, peri-urban, and urban sites from 2015-2017 in Kinshasa Province, DRC. Participants were sampled at biannual household survey visits (asymptomatic) and during routine health facility visits (symptomatic). Participant-level characteristics associated with non-falciparum infections were estimated for single- and mixed- species infections. Among 9,089 samples collected from 1,565 participants over a 3-year period, the incidence ofP. malariaeandP. ovalespp. infection was 11% (95% CI: 9%-12%) and 7% (95% CI: 5%-8%) by one year, respectively, compared to a 67% (95% CI: 64%-70%) one-year cumulative incidence ofP. falciparuminfection. Incidence continued to rise in the second year of follow-up, reaching 26% and 15% in school-age children (5-14yo) forP. malariaeandP. ovalespp., respectively. Prevalence ofP. malariae,P. ovalespp., andP. falciparuminfections during household visits were 3% (95% CI: 3%-4%), 1% (95% CI: 1%-2%), and 35% (95% CI: 33%-36%), respectively. Non-falciparum malaria was more prevalent in rural and peri-urban vs. urban sites, in school-age children, and among those withP. falciparumco-infection. A crude association was detected betweenP. malariaeand any anemia in the symptomatic clinic population, although this association did not hold when stratified by anemia severity. No crude associations were detected between non-falciparum infection and fever prevalence. Conclusions:P. falciparumremains the primary driver of malaria morbidity and mortality in the DRC. However, non-falciparum species also pose an infection risk across sites of varying urbanicity and malaria endemicity within Kinshasa, DRC, particularly among children under 15 years of age. AsP. falciparuminterventions gain traction in high-burden settings like the DRC, continued surveillance and improved understanding of non-falciparum infections are warranted.

Malaria programs rely upon a variety of diagnostic assays, including rapid diagnostic tests (RDTs), microscopy, polymerase chain reaction (PCR), and bead-based immunoassays (BBA), to monitor malaria prevalence and support control and elimination efforts. Data comparing these assays are limited, especially from high-burden countries like the Democratic Republic of the Congo (DRC). Using cross-sectional and routine data, we compared diagnostic performance andPlasmodium falciparum prevalence estimates across health areas of varying transmission intensity to illustrate the relevance of assay performance to malaria control programs. Data and samples were collected between March-June 2018 during a cross-sectional household survey across three health areas with low, moderate, and high transmission intensities within two health zones of Kinshasa province, DRC. Samples from 1,431 participants were evaluated using RDT, microscopy, PCR, and BBA.P. falciparumparasite prevalence varied between diagnostic methods across all health areas, with the highest prevalence estimates observed in Bu (57.4-72.4% across assays), followed by Kimpoko (32.6-53.2%), and Voix du Peuple (3.1-8.4%). Using latent class analysis to compare these diagnostic methods against an “alloyed gold standard,” the most sensitive diagnostic method was BBA in Bu (high prevalence) and Voix du Peuple (low prevalence), while PCR diagnosis was most sensitive in Kimpoko (moderate prevalence). RDTs were consistently the most specific diagnostic method in all health areas. Among 9.0 million people residing in Kinshasa Province in 2018, the estimatedP. falciparum prevalence by microscopy, PCR, and BBA were nearly double that of RDT. Comparison of malaria RDT, microscopy, PCR, and BBA results confirmed differences in sensitivity and specificity that varied by endemicity, with PCR and BBA performing best for detecting anyP. falciparuminfection. Prevalence estimates varied widely depending on assay type for parasite detection. Inherent differences in assay performance should be carefully considered when using community survey and surveillance data to guide policy decisions.