Projecting Future Heat-Related Mortality under Climate Change Scenarios: A Systematic Review

Background: Heat-related mortality is a matter of great public health concern, especially in the light of climate change. Although many studies have found associations between high temperatures and mortality, more research is needed to project the future impacts of climate change on heat-related mortality

Assessment of Heat-Related Health Impacts in Brisbane, Australia: Comparison of Different Heatwave Definitions

Background: There is no global definition of a heatwave because local acclimatisation and adaptation influence the impact of extreme heat. Even at a local level there can be multiple heatwave definitions, based on varying temperature levels or time periods. We investigated the relationship between heatwaves and health outcomes using ten different heatwave definitions in Brisbane, Australia. ---------- Methodology/Principal Findings: We used daily data on climate, air pollution, and emergency hospital admissions in Brisbane between January 1996 and December 2005; and mortality between January 1996 and November 2004. Case-crossover analyses were used to assess the relationship between each of the ten heatwave definitions and health outcomes. During heatwaves there was a statistically significant increase in emergency hospital admissions for all ten definitions, with odds ratios ranging from 1.03 to 1.18. A statistically significant increase in the odds ratios of mortality was also found for eight definitions. The size of the heat-related impact varied between definitions.---------- Conclusions/Significance Even a small change in the heatwave definition had an appreciable effect on the estimated health impact. It is important to identify an appropriate definition of heatwave locally and to understand its health effects in order to develop appropriate public health intervention strategies to prevent and mitigate the impact of heatwaves

Target and actual sample sizes for studies from two trial registries from 1999 to 2020: an observational study

OBJECTIVES: To investigate differences between target and actual sample sizes, and what study characteristics were associated with sample sizes. DESIGN: Observational study. SETTING: The large trial registries of clinicaltrials.gov (starting in 1999) and ANZCTR (starting in 2005) through to 2021. PARTICIPANTS: Over 280 000 interventional studies excluding studies that were withheld, terminated for safety reasons or were expanded access. MAIN OUTCOME MEASURES: The actual and target sample sizes, and the within-study ratio of the actual to target sample size. RESULTS: Most studies were small: the median actual sample sizes in the two databases were 60 and 52. There was a decrease over time in the target sample size of 9%–10% per 5 years, and a larger decrease of 18%–21% per 5 years for the actual sample size. The actual-to-target sample size ratio was 4.1% lower per 5 years, meaning more studies (on average) failed to hit their target sample size. CONCLUSION: Registered studies are more often under-recruited than over-recruited and worryingly both target and actual sample sizes appear to have decreased over time, as has the within-study gap between the target and actual sample size. Declining sample sizes and ongoing concerns about underpowered studies mean more research is needed into barriers and facilitators for improving recruitment and accessing data

Examination of CIs in health and medical journals from 1976 to 2019:An observational study

Objectives: Previous research has shown clear biases in the distribution of published p values, with an excess below the 0.05 threshold due to a combination of p-hacking and publication bias. We aimed to examine the bias for statistical significance using published confidence intervals. Design: Observational study. Setting: Papers published in Medline since 1976. Participants: Over 968 000 confidence intervals extracted from abstracts and over 350 000 intervals extracted from the full-text. Outcome measures: Cumulative distributions of lower and upper confidence interval limits for ratio estimates. Results: We found an excess of statistically significant results with a glut of lower intervals just above one and upper intervals just below 1. These excesses have not improved in recent years. The excesses did not appear in a set of over 100 000 confidence intervals that were not subject to p-hacking or publication bias. Conclusions: The huge excesses of published confidence intervals that are just below the statistically significant threshold are not statistically plausible. Large improvements in research practice are needed to provide more results that better reflect the truth.</p

Effect of temperature and precipitation on salmonellosis cases in South-East Queensland, Australia: An observational study

Objective \ud \ud Foodborne illnesses in Australia, including salmonellosis, are estimated to cost over $A1.25 billion annually. The weather has been identified as being influential on salmonellosis incidence, as cases increase during summer, however time series modelling of salmonellosis is challenging because outbreaks cause strong autocorrelation. This study assesses whether switching models is an improved method of estimating weather–salmonellosis associations.\ud \ud Design \ud \ud We analysed weather and salmonellosis in South-East Queensland between 2004 and 2013 using 2 common regression models and a switching model, each with 21-day lags for temperature and precipitation.\ud \ud Results \ud \ud The switching model best fit the data, as judged by its substantial improvement in deviance information criterion over the regression models, less autocorrelated residuals and control of seasonality. The switching model estimated a 5°C increase in mean temperature and 10 mm precipitation were associated with increases in salmonellosis cases of 45.4% (95% CrI 40.4%, 50.5%) and 24.1% (95% CrI 17.0%, 31.6%), respectively.\ud \ud Conclusions \ud \ud Switching models improve on traditional time series models in quantifying weather–salmonellosis associations. A better understanding of how temperature and precipitation influence salmonellosis may identify where interventions can be made to lower the health and economic costs of salmonellosis

Scatter plots of the relationship between maximum temperature and mortality (A and C) and emergency hospital admissions (B and D) between 1996 and 2005.

<p>Fitted regression line (and 95% confidence interval) from a non-linear (quadratic) regression. EHAs: Emergency Hospital Admissions.</p

Heatwave definitions (HWDs) and heatwave days during 1996–2005 in Brisbane, Australia.

a<p>The first three definitions were widely used in the literature and the remainder (HWDs 4–10) were extended definitions developed for this study.</p

Summary statistics for daily temperature and health outcomes, 1996–2005 in Brisbane, Australia.

a<p>Emergency Hospital Admissions.</p