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Record W2949849337 · doi:10.1016/s2542-5196(19)30071-3

Attribution of reductions in malaria prevalence in Dar es Salaam, Tanzania

2019· letter· en· W2949849337 on OpenAlexaff
Mathieu Maheu‐Giroux, Márcia C. Castro

Bibliographic record

VenueThe Lancet Planetary Health · 2019
Typeletter
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsTanzaniaMalariaDar es salaamScopusVector (molecular biology)MedicineEnvironmental healthPopulationGeographyDemographyTraditional medicineVeterinary medicineSocioeconomicsBiologyImmunologyMEDLINE

Abstract

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Dar es Salaam's Urban Malaria Control Project (UMCP) was launched in 2004 with the objective of implementing and assessing a microbial larviciding intervention. From 2004 to 2008, malaria prevalence decreased from 28% to less than 2%.1Maheu-Giroux M Castro M Impact of community-based larviciding on the prevalence of malaria infection in Dar es Salaam, Tanzania.PLoS One. 2013; 8: e71638Crossref PubMed Scopus (58) Google Scholar This collapse of malaria vector populations was also observed in neighbouring regions of Tanzania.2Meyrowitsch DW Pedersen EM Alifrangis M et al.Is the current decline in malaria burden in sub-Saharan Africa due to a decrease in vector population?.Malar J. 2011; 10: 188Crossref PubMed Scopus (80) Google Scholar Part of the reduction preceded the initiation of larviciding activities and the introduction of artemisinin-based combination therapies. Yet, previous analyses suggested that larviciding was effective in reducing malaria prevalence.1Maheu-Giroux M Castro M Impact of community-based larviciding on the prevalence of malaria infection in Dar es Salaam, Tanzania.PLoS One. 2013; 8: e71638Crossref PubMed Scopus (58) Google Scholar, 3Lawson AB Carroll R Castro M Joint spatial Bayesian modeling for studies combining longitudinal and cross-sectional data.Stat Methods Med Res. 2014; 23: 611-624Crossref PubMed Scopus (9) Google Scholar In their Article, Gerry Killeen and colleagues4Killeen GF Govella NJ Mlacha YP Chaki PP Suppression of malaria vector densities and human infection prevalence associated with scale-up of mosquito-proofed housing in Dar es Salaam, Tanzania: re-analysis of an observational series of parasitological and entomological surveys.Lancet Planet Health. 2019; 3: e132-e143Summary Full Text Full Text PDF PubMed Scopus (22) Google Scholar reanalysed the UMCP data. Although it is reassuring that most of their results confirm those previously reported, their paper raises important concerns. First, the rollout of larviciding followed a stepped-wedge design. It involved sequential crossover of the city's wards from control to intervention such that, ultimately, all wards received the larviciding intervention. This implies that calendar time is a potentially crucial confounder and, following standard practice, must be included in the analyses.5Hemming K Haines TP Chilton PJ Girling AJ Lilford RJ The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting.BMJ. 2015; 350: h391Crossref PubMed Scopus (661) Google Scholar Substituting another covariate for time in the model could grossly overestimate the intervention's impact. A second concern is that the authors mistakenly state that earlier analyses did not adjust for within-ward covariance. This is incorrect because previously reported odds ratios for larviciding included this adjustment in sensitivity analyses using ward-level fixed effects—confirming their robustness. The advantage of this specification is that it controls for “any time-invariant measured or unmeasured confounders of the larviciding-malaria relationship”,1Maheu-Giroux M Castro M Impact of community-based larviciding on the prevalence of malaria infection in Dar es Salaam, Tanzania.PLoS One. 2013; 8: e71638Crossref PubMed Scopus (58) Google Scholar which is especially important for the UMCP whose larviciding allocation sequence was not randomised. Using fixed effects therefore provides a more robust estimate than using a random effects specification. Third, attributing changes in an observed health outcome to one specific intervention requires an appropriate control group. Although it is plausible that improved house proofing had a role in reducing malaria transmission, attributing the reduction to this factor alone, as implied by their analyses substituting calendar time, does not follow principles of causal attribution. The authors disregard household-level variations in house proofing to use city-wide coverage (with unrealistic discrepancies between the two estimates) and do not consider potential confounders. From 2002 to 2012, Dar es Salaam's population increased from 2·5 million to 4·3 million people and its density nearly doubled. There were noticeable reductions in urban agriculture, increases in bednet usage, introduction of artemisinin-based combination therapy, and clear improvements in literacy and socioeconomic status during the UMCP. The decision to attribute the bulk of the malaria decline to improved housing seems arbitrary and could misguide public health efforts by overstating its potential benefits. We declare no competing interests. Suppression of malaria vector densities and human infection prevalence associated with scale-up of mosquito-proofed housing in Dar es Salaam, Tanzania: re-analysis of an observational series of parasitological and entomological surveysCommunity-wide mosquito proofing of houses might deliver greater impacts on vector populations and malaria transmission than previously thought. The spontaneous nature of the scale-up observed here is also encouraging with regards to practicality, acceptability, and affordability in low-income settings. Full-Text PDF Open AccessAttribution of reductions in malaria prevalence in Dar es Salaam, Tanzania – Authors' replyWe thank Mathieu Maheu-Giroux and Marcia Castro for their Correspondence about our Article.1 Regarding concerns about the stepped-wedge design of the larviciding scale-up in our study, we agree and also note that scale-up was not randomised, but rather introduced earliest to the best-prepared wards. However, such compromises are normal and healthy in pragmatic assessments of effectiveness under realistic programmatic conditions, rather than efficacy under artificially controlled experimental conditions. Full-Text PDF Open Access

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.740
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.316
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
Admission routes1
Has abstractyes

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