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Record W4400015618 · doi:10.2196/58584

Estimating the Size of Populations at High Risk of Malaria in 2 Operational Districts in Cambodia: Household-Based Survey

2024· article· en· W4400015618 on OpenAlexvenueno aff
Jerry O. Jacobson, Dyna Doum, Neil F. Lobo, Siv Sovannaroth, Allison Tatarsky, David J. McIver

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious Diseases
KeywordsMalariaGeographyBed netsEnvironmental healthSocioeconomicsPopulationMosquito controlDistribution (mathematics)Environmental protectionMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cambodia is targeting the elimination of malaria by 2025. The last remaining pockets of malaria in Cambodia are concentrated among populations exposed to forested areas, but the size of these populations is not well understood. To plan for the procurement and distribution of vector-control tools, chemoprophylaxis, and other commodities for malaria prevention and surveillance, robust estimates of the population at greatest risk are required. OBJECTIVE: This study aims to estimate the number of forest-exposed individuals residing in Cambodia's highest-burden operational districts (ODs) in 2 provinces with active malaria transmission. METHODS: In April 2023, a multistage, in-person survey was conducted among residents in the 2 ODs in Cambodia with the highest malaria burden: Sen Monorom in Mondulkiri province and Phnom Srouch in Kampong Speu province. In each OD, 10 villages were randomly selected, and 35 households were randomly selected from each village. To estimate the number of individuals at high risk of malaria-defined as residing within 1 km of a forest or traveling at least once per week to the forest-respondents were asked about the distance from their household to the nearest forested area, and their travel patterns to forested areas. To account for mobility (ie, to avoid double-counting), respondents also provided information on overnight stays at other households in the selected villages in the past month. In the 4 selected villages in Sen Monorom OD where Project BITE forest packs (an intervention in the larger research program) had been distributed prior to the survey, respondents were also asked questions to determine if they had received such a pack, to develop smaller scale "multiplier method" estimates of at-risk individuals in each of those villages. RESULTS: In Sen Monorom, 138 households and 872 individuals were enrolled in the survey, and in Phnom Srouch, 163 households and 844 individuals were enrolled. The estimated percentage of female householders was 49.7% (852/1716) across both ODs; the median age was 22 (IQR 12-37) years in Sen Monorom and 24.5 (IQR 16.0-40.5) years in Phnom Srouch (total age range 3-86). Based on mobility-adjusted survey estimates alone, 32% (280/706; 95% CI 19.9-47.2) of residents in Sen Monorom (an estimated 12,133-20,135 individuals) and 36% (68/198; 95% CI 24.5-45.5) of residents in Phnom Srouch (an estimated 1717-2203 individuals), met risk criteria for forest exposure. Between 125 and 186 individuals were estimated to be at risk in each of the 4 villages where the multiplier method could be applied. CONCLUSIONS: This study provides estimates of the number of individuals potentially at high risk for malaria infection due to forest exposure in 2 ODs in Cambodia. These estimates can support planning for malaria control and elimination efforts. The straightforward methods of household surveys and multipliers should be feasible for many national malaria control programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.332
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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