Entomological baseline data collection and power analyses in preparation of a mosquito swarm-killing intervention in south-western Burkina Faso.

BACKGROUND: Insecticides are currently the main tools used to reduce the transmission of malaria; therefore, the development of resistance to insecticides in malaria vectors is of major concern for malaria control. The resistance level to pyrethroids is particularly high in the Western region of Burkina Faso and may affect the efficacy of insecticidal bed nets and indoor residual spraying. Adult mosquito swarming and other nocturnal behaviours exhibit spatial and temporal patterns that suggest potential vulnerability to targeted space spraying with effective insecticides. Indeed, targeted space-spraying against adult mosquito swarms has been used to crash mosquito populations and disrupt malaria transmission. METHODS: Prior to impact assessment of swarm killing, a baseline data collection was conducted from June to November 2016 in 10 villages divided into two areas in western Burkina Faso. The data considered both ecological and demographic characteristics to monitor the key entomological parameters. RESULTS: The mean number of swarms observed was 35 per village, ranging from 25 to 70 swarms according to the village. Female density in both areas varied significantly as a function of the village and the period of collection. The human biting rate was significantly affected by the period of collection and depended upon whether the collection was carried out indoors or outdoors. Averages of parity rate were high in both areas for all periods of collection, ranging from 60 to 90%. These values ranged from 80 to 100% for inseminated females. Sporozoite rates ranged between 1.6 and 7.2% depending upon the village. The molecular identification of resting and swarming mosquitoes showed the presence of the three major malaria vectors in Burkina Faso, but in different proportions for each village. CONCLUSIONS: The distribution of the potential swarm markers and swarms in villages suggested that swarms are clustered across space, making intervention easier. Power simulations showed that the direct sampling of swarms provides the highest statistical power, thereby reducing the number of villages needed for a trial.

Futility of Cluster Designs at Individual Hospitals to Study Surgical Site Infections and Interventions Involving the Installation of Capital Equipment in Operating Rooms.

Anesthesia workspaces are integral components in the chains of many intraoperative bacterial transmission events resulting in surgical site infections (SSI). Matched cohort designs can be used to compare SSI rates among operating rooms (ORs) with or without capital equipment purchases (e.g., new anesthesia machines). Patients receiving care in intervention ORs (i.e., with installed capital equipment) are matched with similar patients receiving care in ORs lacking the intervention. We evaluate statistical power of an alternative design for clinical trials in which, instead, SSI incidences are compared directly among ORs (i.e., the ORs form the clusters) at single hospitals (e.g., the 5 ORs with bactericidal lights vs. the 5 other ORs). Data used for parameter estimates were SSI for 24 categories of procedures among 338 hospitals in the State of California, 2015. Estimated statistical power was ≅8.4% for detecting a reduction in the incidence of SSI from 3.6% to 2.4% over 1 year with 5 intervention ORs and 5 control ORs. For ≅80% statistical power, >20 such hospitals would be needed to complete a study in 1 year. Matched paired cluster designs pair similar ORs (e.g., 2 cardiac ORs, 1 to intervention and 1 to control). With 5 pairs, statistical power would be even less than the estimated 8.4%. Cluster designs (i.e., analyses by OR) are not suitable for comparing SSI among ORs at single hospitals. Even though matched cohort designs are non-randomized and thus have lesser validity, matching patients by their risk factors for SSI is more practical.

Pragmatic statistical issues in biological research: Introduction to special series.

This is the introduction to a series of articles will be published over the next year in JMCC on statistical issues which commonly arise in types of studies published in the journal. Each article will cover a specific statistical topic and be prefaced with a typical related question that is likely to arise in laboratory and biomedical studies. There will be a discussion of the underlying statistical concepts followed by several websites which may be used to perform the relevant analysis on data.

Analysis of network interventions with an application to hospital-acquired infections.

Regional interventions to prevent the spread of hospital-acquired infections, vaccination campaigns, and information dissemination strategies are examples of treatment interventions applied to members of a network with the intent of effecting a network-wide change. In designing clinical trials or determining policy changes, it may not be cost effective or otherwise possible to treat all actors of a network. There is a notable lack of study designs and statistical frameworks with which to plan a network-wide intervention in this context and analyze the resulting data. This paper builds off of the network autocorrelation model in order to provide such a framework for a pre-post study design. We derive key quantitative measures of the network-wide treatment effect, exact formulas for power analyses of these measures, and extensions for the context in which the network is unknown. As the treatment assignation is part of the network-wide treatment, we provide methods for determining the assignation which optimizes the overall treatment effect over all members of the network subject to any arbitrary set of implementation costs and cost constraint. We implement these methods on Clostridioides difficile data for the state of California, where the hospitals are linked through patient sharing.

Optimising survey effort to monitor environmental variables: A case study using New Zealand kiwifruit orchards.

Environmental monitoring is increasingly used to assess spatial and temporal trends in agricultural sustainability, and test the effectiveness of farm management policies. However, detecting changes in environmental variables is often technically and logistically challenging. To demonstrate how survey effort for environmental monitoring can be optimised, we applied the new statistical power analysis R package simr to pilot survey data. Specifically, we identified the amount of survey effort required to have an 80% chance of detecting specified trends (-1 to -4% pa) in 13 environmental variables on New Zealand kiwifruit orchards within an 11-year period. The variables assessed were related to soil status, agricultural pests (birds), or ecosystem composition (birds). Analyses were conducted on average values (for each orchard and year combination) to provide a consistent scale for comparison among variables. Survey frequency varied from annual (11 surveys) to every 5 years (3 surveys). Survey size was set at either 30, 60, 150 or 300 orchards. In broad terms, we show the power to detect a specified range of trends over an 11-year period in this sector is much higher for 'soil status' than for 'agricultural pest' or 'ecosystem composition'. Changes in one subset of native bird species (nectar-feeders) requiring a particularly high level of relative survey effort to detect with confidence. Monitoring soil status can thus be smaller and less frequent than those which also want to detect changes in agricultural pests or ecosystem composition (with the latter requiring the most effort) but will depend on the magnitude of changes that is meaningful to detect. This assessment thus allows kiwifruit industry in New Zealand to optimise survey design to the desired information, and provides a template for other industries to do likewise. Power analyses are now more accessible through the provision of the simr package, so deploying and integrating them into design and decision-making should be routine to reduce the risk of inefficiencies and opportunity costs.