Efficacy of Wolbachia-Infected Mosquito Deployments for the Control of Dengue.

BACKGROUND: Aedes aegypti mosquitoes infected with the wMel strain of Wolbachia pipientis are less susceptible than wild-type A. aegypti to dengue virus infection. METHODS: We conducted a cluster-randomized trial involving releases of wMel-infected A. aegypti mosquitoes for the control of dengue in Yogyakarta, Indonesia. We randomly assigned 12 geographic clusters to receive deployments of wMel-infected A. aegypti (intervention clusters) and 12 clusters to receive no deployments (control clusters). All clusters practiced local mosquito-control measures as usual. A test-negative design was used to assess the efficacy of the intervention. Patients with acute undifferentiated fever who presented to local primary care clinics and were 3 to 45 years of age were recruited. Laboratory testing was used to identify participants who had virologically confirmed dengue (VCD) and those who were test-negative controls. The primary end point was symptomatic VCD of any severity caused by any dengue virus serotype. RESULTS: After successful introgression of wMel into the intervention clusters, 8144 participants were enrolled; 3721 lived in intervention clusters, and 4423 lived in control clusters. In the intention-to-treat analysis, VCD occurred in 67 of 2905 participants (2.3%) in the intervention clusters and in 318 of 3401 (9.4%) in the control clusters (aggregate odds ratio for VCD, 0.23; 95% confidence interval [CI], 0.15 to 0.35; P = 0.004). The protective efficacy of the intervention was 77.1% (95% CI, 65.3 to 84.9) and was similar against the four dengue virus serotypes. The incidence of hospitalization for VCD was lower among participants who lived in intervention clusters (13 of 2905 participants [0.4%]) than among those who lived in control clusters (102 of 3401 [3.0%]) (protective efficacy, 86.2%; 95% CI, 66.2 to 94.3). CONCLUSIONS: Introgression of wMel into A. aegypti populations was effective in reducing the incidence of symptomatic dengue and resulted in fewer hospitalizations for dengue among the participants. (Funded by the Tahija Foundation and others; AWED ClinicalTrials.gov number, NCT03055585; Indonesia Registry number, INA-A7OB6TW.).

An evaluation of enhanced surveillance of hospitalised COVID-19 patients to inform the public health response in Victoria.

BACKGROUND: Public health surveillance is crucial for supporting a rapid and effective response to public health emergencies. In response to the coronavirus disease (COVID-19) pandemic, an enhanced surveillance system of hospitalised COVID-19 patients was established by the Victorian Department of Health and Human Services (DHHS) and the Victorian Healthcare Associated Infection Surveillance System Coordinating Centre. The system aimed to reduce workforce capacity constraints and increase situational awareness on the status of hospitalised patients. METHODS: The system was evaluated, using guidelines from the United States Centers for Disease Control and Prevention, against eight attributes: acceptability; data quality; flexibility; representativeness; simplicity; stability; timeliness; and usefulness. Evidence was generated from stakeholder consultation, participant observation, document review, systems review, issues log review and audits. Data were collected and analysed over a period of up to three months, covering pre- and post-implementation from March to June 2020. RESULTS: This system was rapidly established by leveraging established relationships and infrastructure. Stakeholders agreed that the system was important but was limited by a reliance on daily manual labour (including weekends), which impeded scalability. The ability of the system to perform well in each attribute was expected to shift with the severity of the pandemic; however, at the time of this evaluation, when there were an average 23 new cases per day (0.3 cases per 100,000 population per day), the system performed well. CONCLUSION: This enhanced surveillance system was useful and achieved its key DHHS objectives during the COVID-19 public health emergency in Victoria. Recommendations for improvement were made to the current and future systems, including the need to plan alternatives to improve the system's scalability and to maintain stakeholder acceptability.

Analysis of cluster-randomized test-negative designs: cluster-level methods.

Intervention trials of vector control methods often require community level randomization with appropriate inferential methods. For many interventions, the possibility of confounding due to the effects of health-care seeking behavior on disease ascertainment remains a concern. The test-negative design, a variant of the case-control method, was introduced to mitigate this issue in the assessment of the efficacy of influenza vaccination (measured at an individual level) on influenza infection. Here, we introduce a cluster-randomized test-negative design that includes randomization of the intervention at a group level. We propose several methods for estimation and inference regarding the relative risk (RR). The inferential methods considered are based on the randomization distribution induced by permuting intervention assignment across two sets of randomly selected clusters. The motivating example is a current study of the efficacy of randomized releases of Wolbachia-infected Aedes aegypti mosquitoes to reduce the incidence of dengue in Yogyakarta City, Indonesia. Estimation and inference techniques are assessed through a simulation study.

Cluster-Randomized Test-Negative Design Trials: A Novel and Efficient Method to Assess the Efficacy of Community-Level Dengue Interventions.

Cluster-randomized controlled trials are the gold standard for assessing efficacy of community-level interventions, such as vector-control strategies against dengue. We describe a novel cluster-randomized trial methodology with a test-negative design (CR-TND), which offers advantages over traditional approaches. This method uses outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We used simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. We demonstrated that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. Analytical considerations for an odds ratio-based effect estimate arising from clustered data and potential approaches to analysis are also discussed briefly. We concluded that application of the test-negative design to certain cluster-randomized trials could increase their efficiency and ease of implementation.

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks.

Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK.

Poor health and social outcomes for ex-prisoners with a history of mental disorder: a longitudinal study.

OBJECTIVE: To examine the association between self-reported lifetime diagnosis of mental disorder and health-related outcomes in prisoners during the first six months after release. METHODS: We interviewed 1,324 adult prisoners in Queensland, Australia, within six weeks of expected release and one, three and six months post-release. Outcomes of interest included health service access, housing, employment, substance use and criminal activity. We used multivariate logistic regression to investigate the association between self-reported, lifetime diagnosis of mental disorder and these health-related outcomes post-release, adjusting for pre-existing disadvantage. RESULTS: 43.4% of participants reported a lifetime diagnosis of mental disorder. This group had increased crude odds of poor outcomes across all evaluated domains. After adjusting for pre-existing disadvantage, significantly increased odds of poor outcomes persisted in the substance use, mental health, crime and health service access domains. CONCLUSIONS: People with a history of mental disorder experience particularly poor outcomes following release from prison that are not fully explained by pre-existing disadvantage. IMPLICATIONS: Evidence-based transitional programs for prisoners with a history of mental disorder should be provided at a level commensurate with need.