Mental illness and increased vulnerability to negative health effects from extreme heat events: a systematic review.

RATIONALE: Across countries, extreme heat events are projected to increase in frequency and intensity because of climate change. Exposure to extreme heat events can have a substantial negative impact on human health, and extant research suggests that individuals with mental illness are particularly vulnerable. To date, there has been no review of evidence regarding this vulnerability to inform response strategies and future research. OBJECTIVE: A systematic review was undertaken to investigate mental illness as an effect modifier of the relationship between heat exposure and morbidity or mortality. METHODS: Six databases (Medline, Embase, Global Health, PsychInfo, CINAHL and Scopus) were searched for studies published between the years 2000 to 2022. Twenty-two observational studies that met the inclusion criteria were investigated through narrative synthesis. The RoBANS tool, ROBIS and GRADE were used to assess the certainty of evidence including the risk of bias. RESULTS: Individuals with mental illness experience worse morbidity and mortality outcomes compared to their counterparts without mental illness in all studies investigating high temperature over a single day. This did not hold for studies examining heatwaves, which reported mixed findings. CONCLUSIONS AND IMPLICATIONS: People with diagnosed mental illness should be targeted for policy and service attention during high temperature days. Further research should investigate specific mental illness and adjust for a wider range of confounding factors.

Predictive modelling of Ross River virus using climate data in the Darling Downs.

Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 - June 2014 were used for model training. Model predictions were tested using data for July 2014 - June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.