The Effect of a Community Health Volunteer Led Mobile Phone Technology in Public Health Surveillance of Bed Bug Infestation among Households in N akuru County; Kenya
Bibliographic record
Abstract
Globally there has been a resurgence of bed bug infestation after decades of suppression using modem pesticides such as pyrethroids. The dramatic rise in bed bug infestation has been reported in Canada, USA, Australia and Africa causing panic and significant public attention. Bed bug is widely found in temperate and in sub-tropical countries and is broadly distributed in regions north and south of the equator. Bed bug infestation is shallowly studied and thus limited information regarding the parasite especially in developing countries such as Kenya. The objective of this study was to establish effect of a Community Health Volunteer (CHV) led mobile phone intervention in Public Health Surveillance of bed bug Infestation among households in N akuru County. The study deign was a quasi-experiment conducted in intervention and control sites. Flamingo and Kivumbini wards where intervention sites while Menengai and Kiratina were control sites respectively. Sample size for intervention and control sites were354 and 362 households respectively. Purposive and systematic sampling methods were used to identify the study participants. Ethical approval will be sought from Mt. Kenya University Ethical approval board and the National Council of Science and Technology (NACOSTI). At baseline, crude and adjusted Odds Ratios indicated that there was no significant difference in public health surveillance of bedbugs [Crude OR=l.005, 95% CI=0.812-1.244, P>0.05)] and [(Adj. OR=l.41995%CI=0.797-2.524, P>0.05)]. However, in the end term survey, both crude and adjusted ORs indicated a significance difference in public health surveillance of bed bugs in intervention site compared to control site. Crude OR. =O. 339, 95% CI: 0.184-0.623; P<0.05)], and adjusted OR=l.621, 95%CI: 1.064-2.468)]. The Community Health worker Mobile led Intervention increased the probability of bedbug detection and reporting (Surveillance) by 62% in the intervention site. The community health worker based mobile application is effective in carrying out public health surveillance of bedbugs. The ministry of health should adopt such technologies and scale up to entire Country to help in controlling the menace caused by bedbugs in Kenya.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".