Background: There are many different traps available for studying fly populations. The aim of this study was to find the most suitable trap to collect synanthropic fly populations to assess the impact of increased latrine coverage in the state of Odisha, India. Methods: Different baits were assessed for use in sticky pot traps (60% sucrose solution, 60 g dry sucrose, half a tomato and an non-baited control), followed by different colours of trap (blue versus yellow) and finally different types of trap (baited sticky pot trap versus sticky card traps). The experiments were undertaken in a semi-urban slum area of Bhubaneswar, the capital of Odisha. The first experiment was conducted in 16 households over 30 nights while experiments 2 and 3 were conducted in 5 households over 30 nights. Results: The traps predominantly caught adult Musca domestica and M. sorbens (78.4, 62.6, 83.8% combined total in experiments 1-3 respectively). Non-baited traps did not catch more flies (median 7.0, interquartile range, IQR: 0.0-24.0) compared with baited traps (sucrose solution: 6.5, 1.0-27.0; dry sucrose: 5.0, 0.5-14.5; tomato: 5.0, 1.5-17.5). However, there were significantly more flies collected on blue sticky pot traps, which caught nearly three times as many flies as yellow sticky pot traps (Incidence Rate Ratio, IRR = 2.91; 95% CI: 1.77-4.79); P < 0.001). Sticky card traps (27, 8-58) collected significantly more flies than the non-baited sticky pot traps (10, 1.5-30.5). Conclusions: Blue sticky card traps can be recommended for the capture of synanthropic fly species as they are non-intrusive to residents, easy to use, readily allow for species identification, and collect sufficient quantities of flies over 12 hours for use in monitoring and control programmes.
Environmental surveillance can be used for monitoring enteric disease in a population by detecting pathogens, shed by infected people, in sewage. Detection of pathogens depends on many factors: infection rates and shedding in the population, pathogen fate in the sewerage network, and also sampling sites, sample size, and assay sensitivity. This complexity makes the design of sampling strategies challenging, which creates a need for mathematical modeling to guide decision making. In the present study, a model was developed to simulate pathogen shedding, pathogen transport and fate in the sewerage network, sewage sampling, and detection of the pathogen. The simulation study used Salmonella enterica serovar Typhi (S. Typhi) as the target pathogen and two wards in Kolkata, India as the study area.
Five different sampling strategies were evaluated for their sensitivity of detecting S. Typhi, by sampling unit: sewage pumping station, shared toilet, adjacent multiple shared toilets (primary sampling unit), pumping station + shared toilets, pumping station + primary sampling units. Sampling strategies were studied in eight scenarios with different geographic clustering of risk, pathogen loss (decay, leakage), and sensitivity of detection assays. A novel adaptive sampling site allocation method was designed, that updates the locations of sampling sites based on their performance. We then demonstrated how the simulation model can be used to predict the performance of environmental surveillance and how it is improved by optimizing the allocation of sampling sites.
The results are summarized as a decision tree to guide the sampling strategy based on disease incidence, geographic distribution of risk, pathogen loss, and the sensitivity of the detection assay. The adaptive sampling site allocation method consistently outperformed alternatives with fixed site locations in most scenarios. In some cases, the optimum allocation method increased the median sensitivity from 45% to 90% within 20 updates.
Background In Rwanda, pneumonia and diarrhea are the first and second leading causes of death, respectively, among children under five. Household air pollution (HAP) resultant from cooking indoors with biomass fuels on traditional stoves is a significant risk factor for pneumonia, while consumption of contaminated drinking water is a primary cause of diarrheal disease. To date, there have been no large-scale effectiveness trials of programmatic efforts to provide either improved cookstoves or household water filters at scale in a low-income country. In this paper we describe the design of a cluster-randomized trial to evaluate the impact of a national-level program to distribute and promote the use of improved cookstoves and advanced water filters to the poorest quarter of households in Rwanda. Methods/Design We randomly allocated 72 sectors (administratively defined units) in Western Province to the intervention, with the remaining 24 sectors in the province serving as controls. In the intervention sectors, roughly 100,000 households received improved cookstoves and household water filters through a government-sponsored program targeting the poorest quarter of households nationally. The primary outcome measures are the incidence of acute respiratory infection (ARI) and diarrhea among children under five years of age. Over a one-year surveillance period, all cases of acute respiratory infection (ARI) and diarrhea identified by health workers in the study area will be extracted from records maintained at health facilities and by community health workers (CHW). In addition, we are conducting intensive, longitudinal data collection among a random sample of households in the study area for in-depth assessment of coverage, use, environmental exposures, and additional health measures. Discussion Although previous research has examined the impact of providing household water treatment and improved cookstoves on child health, there have been no studies of national-level programs to deliver these interventions at scale in a developing country. The results of this study, the first RCT of a large-scale programmatic cookstove or household water filter intervention, will inform global efforts to reduce childhood morbidity and mortality from diarrheal disease and pneumonia. Trial registration This trial is registered at Clinicaltrials.gov (NCT02239250).