The Trends in Excess Mortality in Winter vs. Summer in a Sub-Tropical City and Its Association with Extreme Climate Conditions

The rise and fall of excess winter mortality in New Zealand from 1876 to 2020

Excess winter mortality (EWM) has been used as a measure of how well populations and policy moderate the health effects of cold weather. We aimed to investigate long-term changes in the EWM of Aotearoa New Zealand (NZ), and potential drivers of change, and to test for structural breaks in trends. We calculated NZ EWM indices from 1876 (4,698 deaths) to 2020 (33,310 deaths), total and by age-group and sex, comparing deaths from June to September (the coldest months) to deaths from February to May and October to January. The mean age and sex-standardised EWM Index (EWMI) for the full study period, excluding 1918, was 1.22. However, mean EWMI increased from 1.20 for 1886 to 1917, to 1.34 for the 1920s, then reduced over time to 1.14 in the 2010s, with excess winter deaths averaging 4.5% of annual deaths (1,450 deaths per year) in the 2010s, compared to 7.9% in the 1920s. Children under 5 years transitioned from a summer to winter excess between 1886 and 1911. Otherwise, the EWMI age-distribution was J-shaped in all time periods. Structural break testing showed the 1918 influenza pandemic strain had a significant impact on trends in winter and non-winter mortality and winter excess for subsequent decades. It was not possible to attribute the post-1918 reduction in EWM to any single factor among improved living standards, reduced severe respiratory infections, or climate change.

Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach

Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.

Modelling of Temperature-Attributable Mortality among the Elderly in Lisbon Metropolitan Area, Portugal: A Contribution to Local Strategy for Effective Prevention Plans

Epidemiological studies on the impact of determining environmental factors on human health have proved that temperature extremes and variability constitute mortality risk factors. However, few studies focus specifically on susceptible individuals living in Portuguese urban areas. This study aimed to estimate and assess the health burden of temperature-attributable mortality among age groups (0-64 years; 65-74 years; 75-84 years; and 85+ years) in Lisbon Metropolitan Area, from 1986-2015. Non-linear and delayed exposure-lag-response relationships between temperature and mortality were fitted with a distributed lag non-linear model (DLNM). In general, the adverse effects of cold and hot temperatures on mortality were greater in the older age groups, presenting a higher risk during the winter season. We found that, for all ages, 10.7% (95% CI: 9.3-12.1%) deaths were attributed to cold temperatures in the winter, and mostly due to moderately cold temperatures, 7.0% (95% CI: 6.2-7.8%), against extremely cold temperatures, 1.4% (95% CI: 0.9-1.8%). When stratified by age, people aged 85+ years were more burdened by cold temperatures (13.8%, 95% CI: 11.5-16.0%). However, for all ages, 5.6% of deaths (95% CI: 2.7-8.4%) can be attributed to hot temperatures. It was observed that the proportion of deaths attributed to exposure to extreme heat is higher than moderate heat. As with cold temperatures, people aged 85+ years are the most vulnerable age group to heat, 8.4% (95% CI: 3.9%, 2.7%), and mostly due to extreme heat, 1.3% (95% CI: 0.8-1.8%). These results provide new evidence on the health burdens associated with alert thresholds, and they can be used in early warning systems and adaptation plans.

Hot Weather and Suicide Deaths among Older Adults in Hong Kong, 1976-2014: A Retrospective Study

Findings of the association between hot weather and suicide in a subtropical city such as Hong Kong are inconsistent. This study aimed to revisit the association by identifying meteorological risk factors for older-adult suicides in Hong Kong using a time-series approach. A retrospective study was conducted on older-adult (aged ≥65) suicide deaths in Hong Kong from 1976 to 2014. Suicides were classified into those involving violent methods and those involving nonviolent methods. Meteorological data, including ambient temperature, were retrieved. Transfer function time-series models were fitted. In total, 7314 older-adult suicide deaths involving violent methods and 630 involving nonviolent methods were recorded. For violent-method suicides, a monthly average daily minimum ambient temperature was determined to best predict the monthly rate, and a daily maximum ambient temperature of 30.3 °C was considered the threshold. For suicide deaths involving nonviolent methods, the number of days in a month for which the daily maximum ambient temperature exceeded 32.7 °C could best predict the monthly rate. Higher ambient temperature was associated with more older-adult suicide deaths, both from violent and nonviolent methods. Weather-focused preventive measures for older-adult suicides are necessary, such as the provision of more public air-conditioned areas where older adults can shelter from extreme hot weather.

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009-2013

Background

The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear.

Objectives

Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam.

Design

We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model).

Results

High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11–1.83; low temperature effect, RR=2.0, 95% CI=1.13–3.52), females (low temperature effect, RR=2.19, 95% CI=1.14–4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91–6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15–2.22; low temperature effect, RR=1.99, 95% CI=0.92–4.28).

Conclusions

In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City.