Social inequalities in heat-attributable mortality in the city of Turin, northwest of Italy: a time series analysis from 1982 to 2018

Abstract

Background Understanding context specific heat-health risks in urban areas is important, especially given anticipated severe increases in summer temperatures due to climate change effects. We investigate social inequalities in the association between daily temperatures and mortality in summer in the city of Turin for the period 1982–2018 among different social and demographic groups such as sex, age, educational level, marital status and household occupants. Methods Mortality data are represented by individual all-cause mortality counts for the summer months between 1982 and 2018. Socioeconomic level and daily mean temperature were assigned to each deceased. A time series Poisson regression with distributed lag non-linear models was fitted to capture the complex nonlinear dependency between daily mortality and temperature in summer. The mortality risk due to heat is represented by the Relative Risk (RR) at the 99th percentile of daily summer temperatures for each population subgroup. Results All-cause mortality risk is higher among women (1.88; 95% CI = 1.77, 2.00) and the elderly (2.13; 95% CI = 1.94, 2.33). With regard to education, the highest significant effects for men is observed among higher education levels (1.66; 95% CI = 1.38, 1.99), while risks for women is higher for the lower educational level (1.93; 95% CI = 1.79, 2.08). Results on marital status highlighted a stronger association for widower in men (1.66; 95% CI = 1.38, 2.00) and for separated and divorced in women (2.11; 95% CI = 1.51, 2.94). The risk ratio of household occupants reveals a stronger association for men who lived alone (1.61; 95% CI = 1.39, 1.86), while for women results are almost equivalent between alone and not alone groups. Conclusions The associations between heat and mortality is unequal across different aspects of social vulnerability, and, inter alia, factors influencing the population vulnerability to temperatures can be related to demographic, social, and economic aspects. A number of issues are identified and recommendations for the prioritisation of further research are provided. A better knowledge of these effect modifiers is needed to identify the axes of social inequality across the most vulnerable population sub-groups.

Selecting Thresholds of Heat-Warning Systems with Substantial Enhancement of Essential Population Health Outcomes for Facilitating Implementation

“…As for assessing health impacts using WBGT on different sex groups, our analysis showed mixed results of either higher heat-health risks for males or females in morbidity or mortality, similar to those presented in the literature [ 40 , 45 , 46 , 47 ]. A review study has concluded that heat-related health impacts could be higher for males or females, in terms of different health outcomes [ 37 ].…”

Using GAM regression analysis to identify proper heat-warning thresholds: a population-based observational study

“…As for health impacts with WBGT on different sex groups, our analysis showed mixed results of either higher heat-health risks for males or females in morbidity or mortality, similar to those presented in the literature (44,(46)(47)(48). For example, a study found that males were more affected than females in heat-related illness (29).…”

“…It was speculated that males may suffer more from heat partly due to their participation in more outdoor activities (29). However, other studies found higher heat-health impacts in either sex in different cities (44,(47)(48). A review study concluded that heat-related health impacts could be higher for males or females for different health outcomes (29).…”

“…A review study concluded that heat-related health impacts could be higher for males or females for different health outcomes (29). Other socio-demographic factors may play a role such as education level, marital status, and number of household occupants (47).…”

“…For heat and mortality/morbidity in different age groups, most studies found the elderly to be the most vulnerable group, followed by the youth (6,9,27,29,36,47,49,50). Our results showed that children had the highest RaRR for emergency visits on lag 0 (1.87) while the elderly had the highest RaRR for all-cause mortality on lag 0 (1.04), the highest RaRR for hospital visits on lag 1 (1.23), and the highest RaRR for emergency visits on lag 2 (1.38).…”