The effect of cold waves on daily mortality in districts in Madrid considering sociodemographic variables

López-Bueno, J.A.; Linares, Cristina; Sánchez, Carmen Sánchez-Guevara; Martínez, Gerardo Sánchez; Mirón, I.J.; Peiró, Miguel Núñez; Valero, Ignacio; Díaz, Julio

doi:10.1016/j.scitotenv.2020.142364

Cited by 8 publications

(8 citation statements)

References 51 publications

(39 reference statements)

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“…The univariate models show that the percentage of the population over age 64 and the IP are statistically significant risk factors. These results coincide with what has been observed in the neighborhoods of Madrid and are in line with scientific consensus [2,29].…”

Section: Discussionsupporting

confidence: 92%

“…In the global model (Table 4), the percentage of population over age 64 displaced the rest of the socioeconomic context variables considered. This result agrees with the etiology of cold, for which the most vulnerable population is those over age 64 [20,24,29,40,52].…”

Section: Discussionsupporting

confidence: 85%

“…Although many countries in the European Union do not have these plans in place at the national level (for example, in Spain), evaluations of Heat Health Action Plans (HHAP) have provided conclusions that can be extrapolated to the development of action plans to address cold [55]. First, it is important to note that these plans have been effective [56,57], although they could be improved by making them more specific and directing them more to vulnerable populations according to income, gender or occupational health risks, among other characteristics [29,[58][59][60][61][62]. In addition to the implementation of these plans at the national level, it is also important to adapt them to the local level to make them more precise and efficient [63,64].…”

Section: Discussionmentioning

confidence: 99%

“…One area for improvement is the promoting a "Cold culture" which have gotten good results with the heat waves [1,66,67]. Furthermore, they must imply vulnerable people's dwellings rehabilitations plans and ensure the right to access to heater systems in coldest months [29]. An taking account of the recent heat health action plan assessment [68], which is also suitable for cold waves, these plans must be implemented at the local scale and periodically updated.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the housing of people with low economic status tends to present worse thermal properties [28], such that both factors impact the vulnerability of the population to low temperatures. These social inequalities are cause of concern in the different urban and rural zones of Madrid, along with the predicted negative health impact of the cold [27,29].…”

Section: Introductionmentioning

confidence: 99%

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The effect of cold waves on mortality in urban and rural areas of Madrid

et al. 2021

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Background While many studies analyze the effect of extreme thermal events on health, little has been written about the effects of extreme cold on mortality. This scarcity of papers is particularly relevant when we search studies about extreme cold on the health of rural population. Therefore, we tried to analyze the effect of cold waves on urban areas and rural areas from Madrid and to test whether differentiated effects exist between both population classes. For this purpose, we analyzed data from the municipalities with over 10,000 inhabitants for the period from January 1, 2000 through December 31, 2013. Municipalities were classified as urban or rural (Eurostat), and they were grouped into similar climatological zones: Urban Metropolitan Centre (UMC), Rural Northern Mountains (RNM), Rural Centre (RC) and Southern Rural (SR). The dependent variable was the daily mortality rate due to natural causes per million inhabitants (CIE-X: A00-R99) that occurred between the months of November and March for the period. The independent variable was minimum daily temperature (ºC) (Tmin). Social and demographic contextual variables were used, including: population > age 64 (%), deprivation index and housing indicators. The analysis was carried out in three phases: (1) determination of the threshold temperature (Tthreshold) which defines the cold waves; (2) determination of the relative risk (RR) for cold waves using Poisson linear regression (GLM); and (3) using GLM of the binomial family, Odds Ratios (OR) were calculated to analyze the relationship between the frequency of the appearance of cold waves and the socioeconomic variables. Results The UMC zone experienced 585 extreme cold events related to attributable increases in the mortality rate. The average number of cold waves in the rural zones was 319. The primary risk factor was the percentage of population over age 64, and the primary protective factor was housing rehabilitation. As a whole, the period experienced more cold waves (1542) than heat waves (1130). Conclusion The UMC was more vulnerable than the rural areas. Furthermore, the results support the development of prevention policies, especially considering the fact that cold wave events were more frequent than heat waves.

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Section: Discussionsupporting

confidence: 92%