How Data Quality Shapes the Measured Impact of Indoor Residual Spraying Against Malaria in Côte d’Ivoire

When indoor residual spraying (IRS) was introduced in two health districts of Côte d’Ivoire in 2020, it joined a growing arsenal of vector control tools deployed against one of the country’s leading causes of illness and death. Malaria accounts for roughly a third of outpatient visits at Ivorian health facilities, and the National Malaria Control Program had embedded IRS in its strategic plans for 2016–2020 and 2021–2025 precisely to reduce transmission in high-burden districts. Yet a new analysis published in the International Journal of Public Health reveals a sobering methodological truth: the measured success of such campaigns depends heavily on the quality of the routine surveillance data used to evaluate them, and flawed data can inflate the apparent benefit of an intervention by a factor of two or more.

The research, led by Christian Selinger of the Swiss Tropical and Public Health Institute in collaboration with the National Malaria Control Program (NMCP) of Côte d’Ivoire and partners, tackles a problem that has long troubled malaria evaluators. Randomized controlled trials remain the gold standard for establishing efficacy, but they are expensive and slow. Real-world evidence drawn from routine health information systems is cheaper, locally specific, and immediately useful to decision-makers—but it typically lacks the control groups and counterfactuals that retrospective impact evaluations critically require. The Ivorian team set out to quantify exactly how much these data quality limitations distort the picture.

The setting was a pair of districts, Nassian and Sakassou, where IRS campaigns were carried out in 2020 and 2021 with support from the US President’s Malaria Initiative (PMI). Two neighboring districts, Dabakala and Beoumi, served as unsprayed controls. Because routine data quality was deemed insufficient, PMI and the NMCP also re-digitized patient records directly from health facility consultation registers covering September 2018 to April 2022. This created a rare opportunity: the same malaria burden, recorded twice—once through the national District Health Information Software (DHIS2) database, in place since 2015, and once through carefully curated registry abstraction.

The researchers first asked which factors explained the discrepancies between the two data sources. Using mixed-effect linear models on log-transformed incidence differences, with a random intercept for each health district and backward stepwise selection, they found that the presence of the first IRS campaign, test positivity rates, and increased coverage of intermittent preventive treatment for pregnant women were statistically associated with a narrowing gap between the datasets. Health facility reporting rates entered the picture once environmental and entomological variables were added to the model. In other words, the very act of spraying—and the diagnostic and reporting behaviors surrounding it—left fingerprints on the surveillance record itself.

Correlation analysis sharpened this concern. When the team compared monthly incidence against remote-sensed covariates from the ERA5 reanalysis—rainfall, temperature, humidity, vegetation cover—alongside entomological measures such as biting rates and the entomological inoculation rate (EIR), a stark asymmetry emerged. Incidence derived from the re-digitized PMI data tracked expected seasonal patterns and correlated significantly with biting rates (coefficients of 0.53 and 0.57) and with surveillance quality indicators. The DHIS2 data, by contrast, showed no significant correlation with any entomological variable and only weaker links to rainfall and humidity. The authors conclude that routine DHIS2 incidence likely does not faithfully represent transmission dynamics at monthly scales, and that coarser temporal aggregation may be more appropriate for such data.

The heart of the study lies in its causal impact quantification. The team employed Bayesian structural time series analysis, a method that combines state-space modeling with Bayesian feature selection through spike-and-slab priors—probabilistic switches that decide which covariates belong in the regression at each iteration. The model is trained on pre-intervention data to forecast a counterfactual: what incidence would have been in the absence of spraying. Causal impact is then the difference between that counterfactual and the observed post-intervention incidence, averaged over one year, with causality deemed established if fewer than 5 percent of 10,000 simulated counterfactual draws fall below the observed values.

The results are striking in their divergence. When the model was trained only on incidence from the sprayed districts themselves, DHIS2 data suggested a 30 percent reduction in Nassian (95% credible interval 11–43 percent) and 36 percent in Sakassou (20–48 percent), while PMI data showed no significant impact. When incidence from the control districts was incorporated into model training, the estimates converged toward modesty: 12 percent (3–19 percent) for Nassian and 19 percent (10–27 percent) for Sakassou using the high-quality PMI data, versus 30 percent (19–38 percent) and 43 percent (34–50 percent) using DHIS2. The low-quality data thus yielded impact estimates roughly two and a half times larger than those based on carefully validated records. Notably, the PMI-based figures of 12 and 19 percent align well with a previously published independent analysis reporting reductions of 16 and 15.8 percent in the same districts, whereas the DHIS2-based estimates resemble the 44 and 47 percent reductions reported in DHIS2-based evaluations from Ghana and Uganda.

Which covariates drove these discrepancies? The posterior regression coefficients—extracted only when they maintained a consistent sign across 95 percent of model runs—told a coherent story. Incidence from control districts carried consistently positive coefficients with high inclusion probability, meaning that rising transmission in unsprayed neighbors raises the counterfactual and thereby tempers the apparent impact of spraying. For PMI data, health facility reporting rates showed consistently negative coefficients: periods of improved reporting during the intervention would lower the counterfactual and thus shrink the estimated effect. For DHIS2 data, test positivity rates, the number of active health facilities, and rainfall carried positive coefficients, suggesting that fluctuations in these surveillance-related quantities were the main drivers of the inflated impact estimates. The authors caution that these coefficients are high-dimensional and interdependent, so causality likely reflects a combination of factors rather than any single one.

The analysis extended to entomology with remarkable results. Using the same Bayesian framework on EIR data—excluding incidence covariates because they are statistically dependent on EIR—the team estimated a strong, statistically significant causal impact of IRS: roughly a 70 percent decrease in the entomological inoculation rate averaged over the first post-intervention year. This corroborates end-of-spray reports documenting more than 80 percent vector mortality on treated walls. Transmission modeling suggests that a 90 percent EIR reduction in highly endemic settings like Nassian and Sakassou would translate into about a 38 percent decline in clinical incidence under active case detection, hinting that entomological surveillance could serve as a surrogate metric for intervention impact—though the authors warn against generalizing from the limited spatial and temporal scope of entomological sampling.

The implications reach well beyond Côte d’Ivoire. The authors argue that scrutinizing test positivity rates and health facility reporting completeness before running impact analyses can prevent overly optimistic conclusions, and that carefully chosen control districts remain indispensable for factoring out confounders that surrogate variables like rainfall cannot capture. They acknowledge limitations: their causal graph remains elementary, modeling relationships between covariates was beyond scope, and findings may not extrapolate to health systems with stronger active surveillance or near-universal care-seeking, where baseline detection is less vulnerable to reporting gaps. Still, with malaria funding under strain—IRS in these very districts was halted after 2022 when PMI support ended—the message is urgent. Overestimating intervention impact leads to misallocated resources that could instead strengthen the surveillance systems on which all future evaluations depend. As climate change alters vector dynamics and malaria seasonality, the integration of remote-sensed data and rigorous counterfactual modeling with honest, high-quality routine data may determine whether the next decade of malaria control progress stalls or resumes.

Subject of Research: The influence of routine malaria surveillance data quality on estimates of indoor residual spraying impact in Côte d'Ivoire

Article Title: Evaluating indoor residual spraying in Côte d’Ivoire: the role of routine malaria data quality

Article References: Selinger, C., Appeti, K. D., Thawer, S., Angoa, G., Aïmain, S. A., Assi, S. B., Pothin, E., Hilton, E. R., & Tanoh, A. M. (2026). Evaluating indoor residual spraying in Côte d’Ivoire: the role of routine malaria data quality. International Journal of Public Health, 71, Article 1608766. https://doi.org/10.3389/ijph.2026.1608766

Image Credits: AI Generated

DOI: 10.3389/ijph.2026.1608766

Keywords: malaria, indoor residual spraying, Côte d'Ivoire, data quality, Bayesian structural time series, DHIS2, surveillance, vector control, entomological inoculation rate, counterfactual modeling, President's Malaria Initiative, health information systems