# Association between temperature and occupational injuries in Spain: The role of contextual factors in workers' adaptation

Constanza Vielma, Hicham Achebak, Marcos Quijal-Zamorano, Simon Lloyd, Guillaume Chevance, Joan Ballester

► To cite this version:

Constanza Vielma, Hicham Achebak, Marcos et al.. Association between temperature contextual factors in workers’ adaptation. 10.1016/j.envint.2024.109006. hal-05041940

Quijal-Zamorano, Simon Lloyd, Guillaume Chevance, and occupational injuries in Spain: The role of Environment International, 2024, 192, pp.109006.

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Full length article

Association between temperature and occupational injuries in Spain: The
role of contextual factors in workers’ adaptation

Constanza Vielma<sup>a,b,*</sup>, Hicham Achebak<sup>a,c</sup>, Marcos Quijal-Zamorano<sup>a,b</sup>, Simon J Lloyd<sup>a</sup>, Guillaume Chevance<sup>a</sup>, Joan Ballester<sup>a</sup>

<figure><img src="image_2.png" /></figure>

A B S T R A C T

**Background:** Extensive evidence links both cold and hot temperatures to an increased incidence of occupational injuries. Contextual modifiers of the temperature-injury association have been scarcely researched. The present study addresses temporal and spatial variations to identify factors associated with (mal)adaptation to heat and cold among Spanish workers.

**Methods:** We assessed the association between daily mean temperature and work injuries using quasi-Poisson time-series regression models in 48 Spanish provinces over the period 1988–2019, with comparative analyses with census and economic data for the sub-periods 1989–1993, 1999–2003, 2009–2013 and 2015–2019. We explored the spatial and spatiotemporal modification of the association by demographic and socioeconomic variables via cross-sectional and longitudinal meta-regressions.

**Findings:** We found an increased risk of work-injuries by 4 % [95 % CI: 3 %-6 %] and 12 % [95 % CI: 10 %-13 %], for the 1st and 99th percentiles of temperature, respectively, for period 1988–2019. Heat had a greater overall impact than cold, and the groups more vulnerable to heat were male workers, under 35 years, and working in agriculture, construction and hostelry. Vulnerability to heat was highest in the earliest sub-period, while vulnerability to cold rose during periods of both economic expansion and recession. High educational attainment emerged as a protective factor during the warm months in the cross-sectional meta-regressions.

**Conclusions:** Our findings suggest an adaptation of Spanish workers to high temperatures over time. However, preventive measures are needed for traditionally exposed workers (agriculture and construction), non-traditionally vulnerable sectors (hostelry), and young, male, and less educated workers during warm months. For cold vulnerability, targeted measures should focus on women, the elderly, and tertiary service workers, especially in colder regions. Addressing temperature vulnerability would enhance worker safety, reduce injuries, and yield economic benefits.

# 1. Introduction

Occupational injuries affect 395 million workers worldwide every year, contributing to 6.7 % of global fatalities according to the International Labour Organization. (International Labour Organization, 2023) Extensive evidence links both cold and hot temperatures to an increased incidence of occupational injuries worldwide, (Spector et al., 2019; Fatima et al., 2021; Binazzi et al., 2019) with one systematic review suggesting a pooled risk of work injuries raised by 1 % for a 1 °C increase in temperature, and of 17.4 % during heat waves, with a significant lagged effect of 1–2 days. (Fatima et al., 2021) Other studies have demonstrated U-shaped and reversed U-shaped associations between temperature exposure and occupational injuries, with different risk profiles in different industries and settings (Spector et al., 2019; Fatima et al., 2021; Martínez-Solanas et al., 2018).

Workers engaged in physically demanding tasks, especially if

constrained by workwear and exposed to outdoor temperatures, are more susceptible to heat, and often lack autonomy to shield themselves from exposure to temperature extremes. (Ioannou et al., 2022) Key mechanisms leading to temperature-related injuries include sleep loss, fatigue, dehydration, diminished psychomotor performance, concentration loss, and reduced alertness due to heat strain during their work hours. (Uehli et al., 2014; Varghese et al., 2018) One meta-analysis showed that individuals working a single work shift under heat stress (defined as wet-bulb globe temperature beyond 22–25 °C) were 4 times more likely to experience occupational heat strain, while their core temperature was increased by 0.7 °C and their urine specific gravity was increased by 14.5 % (Spector et al., 2019).

Various factors contribute to the vulnerability and patterns in occupational injuries, including economic cycles, labour legislation and demographic characteristics. (Takala et al., 2014; de la Fuente et al., 2014 Feb) In heat-related mortality studies using population level data,

<footnote><sup>a</sup> ISGlobal, Barcelona, Spain</footnote>

<footnote>b Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote>c Inserm, France Cohortes, Paris, France</footnote>

<footnote>* Corresponding author at: Barcelona Institute for Global Health-Campus MAR, Doctor Aiguader, 88, 1st floor, 08003 Barcelona, Spain.
E-mail address: constanza.vielma@isglobal.org (C. Vielma).</footnote>
the heat-mortality association has been found to be modified by factors such as gross domestic product (GDP), inequality, green space, and access to air conditioning. Yet, despite this knowledge, limited attention has been given to potential modifiers of the temperature-occupational injury relation. Previous literature found higher risk of injury in male workers under 25 years, those employed in agriculture, forestry, construction and manufacturing (Fatima et al., 2021; Binazzi et al., 2019; Martínez-Solanas et al., 2018) during hot temperatures, while women, who are traditionally more present in indoor and regulated work settings, are still found to be at increased risk of occupational injury at hot temperatures (0.8 % increased risk associated to a 1 °C increase compared to 1.8 % for men) (Fatima et al., 2021). Another review found an increased risk of traumatic injury with increasing heat exposure in industrialized settings (Spector et al., 2019) However, less attention has been given to specific socioeconomic modifiers, such as education, public health investment, local industry characteristics and job security.

The analysis of the relation between ambient temperatures and occupational accidents is particularly relevant in Spain due to several factors including: relatively high summer temperatures, economic cycles with intense and prolonged recessions, climate and socioeconomic diversity, precarious working conditions, and a significantly faster temperature rise than the global average. (Agencia Estatal de Meteorología, 2021) Spain has been reported to have a weak occupational safety system within the European context, with prevention practices less rigorously implemented in high-incidence sectors. (Fernández-Muñiz et al., 2018) The Law on Prevention of Occupational Risks (LPRL), effective since 1997, has undergone several amendments, with mixed evidence of its success in improving worker safety and health. The legislation aimed to create a new framework in Spain aligned with EU regulation. Key policies included:

1) Designating the Labour and Social Security Inspectorate (ITSS) to monitor and enforce occupational hazard regulations through routine inspections and by addressing complaints from workers and unions.

2) Ensuring workers' rights to personal protective equipment, theoretical and practical training in preventive measures, and regular health monitoring.

3) Emphasizing workers' obligations in preventive matters.

4) Encouraging Spanish regions since 1999 to develop specific preventive interventions, known as preferential action plans (PAPs), targeting companies with high injury rates. These plans were primarily implemented by the Autonomous Communities (CCAA).

In regards to economic cycles, between 1992 and 1993, the Spanish economy experienced a severe contraction, resulting in the loss of nearly half of the jobs created during the preceding expansive period of 1986-1991. From the late 1990s to 2008, Spain entered a robust economic growth phase that witnessed the creation of millions of jobs, concentrated mainly in the construction and services sectors, which also attracted millions of foreign immigrants. However, the collapse of the "housing bubble" triggered the economic crisis of 2008-2013, leading Spain to record the highest unemployment rate among Western economies (Rahman et al., 2017).

Previous efforts to describe temperature effects on work injuries in Spain revealed a non-linear, U-shaped association, with relative risks increasing below and above the optimum temperature. The association appeared to be stable over time, with no significant differences between periods 1994-2000 and 2001-2013 (Martínez-Solanas et al., 2018). In this study, we focused on unexplored aspects of the association between temperatures and work injuries in Spain, specifically addressing temporal variations and effect modifiers. We covered a 32-year period (1988-2019), capturing the sub-period before the LPRL, the 1992-1993 and 2008-2013 recessions, the 2000-2007 economic and immigration boom, and the 2014-2019 pre-pandemic economic recovery. This extended timeframe enabled a comprehensive analysis of potential

longitudinal changes in the temperature-injuries association. Additionally, we performed cross-sectional and longitudinal analyses to explore, respectively, the spatial and spatiotemporal modification of this association by demographic and socioeconomic variables. We stratified all analyses by sex, age and economic sector, in order to identify factors associated with (mal)adaptation to heat and cold among Spanish workers.

## 2. Methods

### 2.1. Data sources

We collected data on occupational injuries, weather and socioeconomic indicators for 48 provinces of mainland Spain and the Balearic Islands, excluding the Canary Islands, Ceuta and Melilla. We extracted daily counts of occupational injuries from the Work Accident Statistics dataset ("Accidentes de trabajo") belonging to the Spanish Ministry of Work and Social Economy (Ministerio de Trabajo y Economía Social, MITES) and provided under request for the years 1988-2019. Injuries included all cases with at least one day of sick leave, excluding extensions due to relapses. High-resolution gridded temperature observations (0.1° × 0.1°) for 1988-2019 were obtained from E-OBS of the European Climate Assessment and Dataset (Cornes et al., 2018). We derived daily mean temperature estimates at the province level by population-weighting gridded temperatures with population data at 1 km resolution (Instituto Nacional de Estadística, 2024). Socioeconomic indicators for Spanish provinces were obtained from the Spanish National Statistics Institute (see Supplementary table 1 for specific sources, and Supplementary table 2.A and 2.B of the Supplementary data for a description of the indicators by sub-period and region). These included:

(a) annual variation of GDP (to account for the size and direction of the economic fluctuations),

(b) GDP per capita (converted to 2017 euros, as a measure of comparative economic performance and average living standards of the regions),

(c) sex-specific unemployment rates (both as a measure of economic cycles and job quality),

(d) percentage of temporary contracts (to account for average quality and security of the jobs),

(e) percentage of workers by economic sector (to represent differences in the magnitude of the risks across jobs),

(f) population percentage with tertiary educational attainment (an indicator of higher quality jobs and more risk-informed workers),

(g) percentage of foreign population (to control for a higher exposure to occupational risks), and

(h) percentage of the cost of medical prescriptions funded by social security, to represent public health investment (see Supplementary Fig. 1 of the Supplementary data for correlations between indicators).

The total number of workers for each trimester and province was extracted from the Affiliation of Workers to the Social Security System of the Spanish Ministry of Labour and Social Economy. (Ministerio de Trabajo y Economía Social, 2024) We extracted data for 4 time points for which socioeconomic data from the Spanish Population and Housing Census conducted by INE was available: years 1991, 2001 and 2011. Non-census indicators (see Supplementary table 1 of the Supplementary data for specific sources) were also collected for these years with most indicators available yearly, but some for a partial study period.

### 2.2. Statistical analysis

The statistical analysis consisted of three types of analyses for the temperature-injuries association covering the whole year, the cold
season only (December-March), and the warm season only (April-November). The minimum injury temperature (MIT, i.e., optimum temperature) when assessing the association for the entire year is around 10-11 °C. Temperatures of 10-11 °C and higher were found in months in the seasons of spring, summer and fall, and therefore, we included all these months in order to reach all days with hotter than optimal temperatures.

Each analysis was done for the entire study period (1988–2019) and four sub-periods (1989–1993, 1999–2003, 2009–2013 and 2015–2019). Sub-periods were selected to represent distinct phases of the economic cycle and working conditions, with datasets large enough to provide adequate statistical power for the estimations, and centered around the years with socioeconomic data available. A three-step analysis was conducted, following a methodology similar to other studies (Sera et al., 2019; Achebak et al., 2018) A comparison of the whole-year and seasonal association curves is available in Supplementary Fig. 2 of the Supplementary data.

In the first step, province-specific temperature-injury associations were estimated using a quasi-Poisson generalized linear regression combined with distributed lag non-linear models (Gasparrini, 2014):

$$ \log(E(inj)) = \text{intercept} + ns(dos) \times \text{factor(year)} + \text{cross-basis(temp)} + \text{dow} + \text{hol} + \text{easter} + \text{dec} + \text{aug} + \text{march11} + \text{offset(workers)}. $$

The models included (i) an intercept, (ii) smooth functions of time, (iii) several categorical variables, and (iv) a cross-basis function of temperature (temp).

The smooth functions of time included a natural cubic spline of the day of the year or day of season (dos, corresponding with the sequential number of each day in each single year for the whole year analyses, or the day of the season in each year for the seasonal analyses). We used 8, 5 and 3 degrees of freedom multiplied by the number of years for the whole-year, warm and cold seasons analyses, respectively, to control for the seasonality of work-injuries.

The cross-basis included a quadratic B-spline with one internal knot placed at the 50th percentile of temperature for the whole-year analyses, and a natural cubic spline modelled with one internal knot placed at the 95th and 10th percentiles of temperature, for the warm and cold seasons, respectively. We chose the placement of the knots by combining a set of criteria, namely: i) parameters used in previous similar works (Martínez-Solanas et al., 2018; Achebak et al., 2023) and ii) the results of our sensitivity analyses (see Supplementary table 1), where we compared the Akaike Information Criterion (AIC) obtained in the different models and sub-periods alternating the knot placement. We also used an unconstrained distributed lag function, with the lag period extending to 4 days to account for the delayed effect of temperatures.

Categorical control variables: accounting for periods of abnormal number of work-injuries, such as day of the week (dow), national and regional holidays (hol), the entire week of Easter (easter), days 24 and 31 of December (dec), the month of August (aug) and an indicator for March 11th, 2004 in the province of Madrid (march11, the highest number of work-injuries in the series due to train bombings). Finally, the number of workers registered in the social security system was added as an offset term (workers). Sensitivity analyses for the choice of parameters are presented in Supplementary Table 3 of the Supplementary data.

In the second step, the estimations by province obtained in the previous stage were accumulated over all lags, and pooled together through a meta-analysis in order to report the average association of temperature and work-injuries across Spain for the whole study period and each sub-period. We estimated national level relative risks (RR), or alternatively, the percentage difference in risk of occupational injury at the 1st (extreme cold) and 99th (extreme heat) temperature percentiles, relative to the country-level MIT (Masselot et al., 2023; Gasparrini and Armstrong, 2013). We then assessed their magnitude when describing the results considering the following categories for difference in risk:

low (0 % to 5 %), moderate (5 % to 10 %), high (10 % to 15 %) and very high (over 15 %).

In the third step, we calculated the Best Linear Unbiased Prediction (BLUP) for each of the provinces. We transformed relative risks into attributable numbers and fractions of injuries due to heat and cold days, (Gasparrini and Leone, 2014) defined as the days with temperatures higher and lower than MIT according to the BLUP coefficients. Finally, the BLUP coefficients for heat and cold seasons were used as dependent variables in meta-regression models to evaluate the role of socioeconomic factors in predicting the temperature-injuries associations. Two sets of meta-regressions were separately performed: (i) a cross-sectional analysis for the entire period (1988–2019) using the whole-period average of the meta-predictors by province, (ii) a spatiotemporal longitudinal analysis using socioeconomic data by province of the mid-year of each sub-period (1991, 2001, 2011 and 2017), and the average of the (sub)period for data available yearly, as predictors of the sub-period BLUP coefficients. Region (i.e., autonomous community) random effects were included in all analyses, as well as sub-period as a factor in the longitudinal analyses. The effect of predictors was tested using a multivariate Wald test. Residual heterogeneity was assessed using the I² statistic.

For each set of meta regressions (cross-sectional and longitudinal), we first tested each predictor separately in univariable models, and later in bivariable models including average seasonal temperature as a covariate. We decided to control for seasonal temperature since we found previously moderate correlations with some of the socio-economic predictors (see Supplementary Fig. 1), and, as single predictor models, average seasonal temperature had the best performance according to the Akaike Information Criterion (AIC).

## 3. Results

Over 22.3 million occupational injuries were included in the study. For the period 1988–2019, we observed a U-shaped association with a 4 % [95 % CI: 3 %-6%] and 12 % [95 % CI: 10 %-13 %] rise in work-related injuries at the 1st and 99th temperature percentiles, respectively (Fig. 1.A). The MIT was at 11.1 °C, which is the 33rd percentile of the temperature distribution. The lag-response associations (Fig. 1.B and 1.C) showed increased injury risk on the exposure day and the previous day in the case of the 1st temperature percentile, and up to 4 days in the case of the 99th temperature percentile. For the sub-period analyses, the temperature-injury association was consistently U-shaped, but the patterns of risk differed. For extreme heat, the risk of work-injuries increased by 19 % [95 % CI: 16 %-21 %], 8 % [95 % CI: 6 %-10 %], 14 % [95 % CI: 12 %-16 %], and 13 % [95 % CI: 10 %-15 %] during sub-periods 1, 3, and 4, respectively; while the increases of risk for extreme cold were of 3 % [95 % CI: 2 %-4%], 10 % [95 % CI: 8 %-12 %], 8 % [95 % CI: 5 %-11 %], and 4 % [95 % CI: 2 %-6%], respectively (Fig. 1.D to 1. G).

The MIT fluctuated from a minimum of 9 °C in subperiod 1 to a maximum 13.4 °C in sub-period 2. According to the 95 % CIs, there were significant differences between the MIT of sub-periods 1 and 2, sub-periods 1 and 3, and sub-periods 2 and 4.

In the analysis for the whole-period, we identified several groups with higher vulnerability to work-injuries (see Supplementary Fig. 5 of the Supplementary data). Agriculture, hostelry and construction workers were the most vulnerable to extreme heat, the risk of work-injuries increasing by 18 %, 16 % and 13 %, respectively, at 99th percentile of temperature. Male workers, people aged under 35 years, and those with less than a year of experience, had higher risks of work-injuries during extreme heat than their counterparts. For extreme cold, women, people aged 55 years or older, and those with non-manual work were more at risk of work-injuries. Manual workers are those in agriculture, fishing, construction, manufacturing and transport industries, while non-manual workers are those in occupations of management, technicians, tertiary professions, office workers, counter clerks, catering
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Overall cumulative exposure-response whole period (1988–2019, panel a), and risk at lags 0–4 at percentile 1 and 99 of temperature (panel b and c, respectively), for the whole period. Panels d, e, f and g, show the overall cumulative exposure-response by period. Dotted lines represent 1st and 99th percentile of temperatures.</figcaption></figure>

and hostelry, health, security and service industries. Overall, the risk of work-injury during the entire period was higher at extreme heat than extreme cold for all groups except women.

**Fig. 2** depicts the percentage increase in risk of occupational injuries of different groups, by sub-period, for the 1st (cold effects) and the 99th (heat effects) percentiles of temperatures. Both women and men reduced their risk of work-injury to extreme heat from 1989 to 1993 to 2015–2019 (from 18 % to 14 % for men, and from 15 % to 8 % for women). Workers under 35 years were the most vulnerable group to extreme heat in 1989–1993 (22 %), showing similar risk of work-injuries to other age groups in 2015–2019 (13 %). Construction workers saw

their risk reduced from 1989 to 1993 to 2015–2019 (from 23 % to 11 %), while agriculture and hostelry workers remained at relatively high risk by 2015–2019 (17 % and 21 %, respectively). For extreme cold, all worker groups were at their highest risk of work-injuries during 1999–2003, except for women and non-manual workers who reached their highest risk at sub-period 2009–2013 (12 % and 9 %, respectively). Most groups reduced their vulnerability to extreme cold from 2009 to 2013 to 2015–2019, though women (6 %), workers older than 55 years (6 %), tertiary services (6 %), non-manual (5 %) and construction workers (5 %) maintained moderate risk of work-injuries at extremely cold temperatures in 2015–2019.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Percent increase in risk of occupational injuries relative to the minimum injuries temperature (MIT) with 95% confidence interval, by sub-period and group of workers. Cold and heat effects refer to the increase of risk when temperatures are in the 1st percentile and in the 99th percentile of mean temperature, respectively, versus the MIT.</figcaption></figure>
Regarding the estimates of the attributable injuries, 671 thousand occupational injuries (95 % CI 598.301–781.362) were attributable to non-optimal temperatures during the whole study period, i.e., 20,9 thousand injuries every year on average, accounting for 3.0% (95 % CI 2.5%-3.5%) of the total work-related injuries (see Table 1). Most injuries were attributable to temperatures hotter than the MIT, i.e., 2.7% (95 % CI 2.19 %-3.16 %), while colder temperatures only accounted for 0.3% of the total (95 % CI 0.26 %-0.39 %). The attributable number (AN) of injuries decreased consistently from the first to the last sub-periods, with attributable fractions (AF) of 4.9% (95 % CI 4.3%-5.5%), 3.2% (95 % CI 2.7%-3.6%), 3.9% (95 % CI 3.6%-4.3%), and 3.2% (95 % CI 2.7%-3.6%) in sub-periods 1989–1993, 1999–2003, 2009–2013, and 2015–2019, respectively. The impact of heat was higher than that of cold in all sub-periods, with agriculture and hostelry workers having the highest overall fractions of occupational injuries to heat (8.6% [95 % CI 7.5%-9.6%] and 4.4% [95 % CI 3.7%-5.1%], respectively).

Fig. 3 displays the spatiotemporal variation of the attributable fraction. For cold, spatial patterns were visible in sub-period 2009–2013, showing a higher AF to cold in the north and centre of the country. This pattern was still visible by the next sub-period of 2015–2019, but the maximum regional estimates of AF to cold decreased from 2.8 % in 2009–2013 to 1.2 % in 2015–2019. It’s important to highlight that average temperature for cold months during the sub-periods did not largely change (see Supplementary Table 2.A), but there was a fall in winter mean temperature from 1999 to 2003 (8.8 °C on average) to 2009–2013 (8.2 °C on average), which then increased again in 2015–2019 (8.7 °C), potentially explaining some of these spatiotemporal differences in AF. Changes in the MIT throughout time (on average decreasing on the last 2 sub-periods compared to 1999–2003) are another potential explanation.

For heat, the spatial pattern of AF shifted from higher impact in the southern provinces in sub-periods 1989–1993 to 2009–2013, to a more even spatial distribution with a slight increase (over 3 %) in the north-eastern provinces from 2009 to 2013 to 2015–2019. However, in most of the provinces the impact of heat decreased from the first to the last sub-periods during the warm months, despite average temperatures steadily increasing between subperiods, rising from 17.2 °C in 1989–1993 to 18.8 °C in 2015–2019.

The cross-sectional meta-regression analysis (Table 2) for the hot season revealed that educational attainment significantly modified the temperature-injury association (p = 0.001). A higher proportion of tertiary-educated adults was related to a decreased risk of occupational injury during extremely hot conditions. In bivariable models, when each of the meta-predictors were introduced in a model along with average mean temperature, only educational attainment (p-value = 0.007) and the proportion of agriculture workers (p-value = 0.010) variables showed a significant association with heat-related risks, though the last was not significant when controlling for education. For cold months (December to March), no meta predictor was found to be significant when controlling for average mean temperature.

Supplementary Table 4 of the Supplementary data shows the results for the longitudinal (spatiotemporal) *meta*-regression analyses. The only significant *meta*-predictor of the temperature-injury association was average mean temperature for the cold season analyses. Models with only the sub-period variable as *meta*-predictor showed significant differences in the temperature-injury association coefficients for both cold and hot season analyses at different time intervals.

## 4. Discussion

The current research is, to our knowledge, the first to study the time-varying association between temperature and work injuries and its contextual modifiers. Our analyses covered 32 years with over 22 million injuries across 48 Spanish provinces, and explored the spatial and longitudinal heterogeneity of this association, considering a range of demographic and socio-economic characteristics. Our findings

showed vulnerability to extreme heat has decreased significantly with time when comparing the first and last sub-periods, (though the impact has increased slightly when comparing AF for sub-periods 1999–2003 and 2015–2019). In turn, vulnerability increased for extreme cold significantly during the mid-periods (economic expansion and recession periods). These results suggest an adaptation of workers to temperatures over time, observed in the injury risk due to heat declining, as well as the overall fraction and number of injuries attributable to temperatures. It’s important to highlight that heat consistently posed a greater risk than cold, and remains a threat to workers.

Compared to previous analyses by Martínez-Solanes et al., (Martínez-Solanas et al., 2018) we extended the study period, and found significant differences in the temperature-injury association between sub-periods. A strength of our study is that we account for days that, though not legally designated as holidays, are commonly observed as such in practice during December, March or April (Easter) and August, finding a higher vulnerability to heat than previously estimated. August is the month when a majority of Spanish workers take their annual vacations, resulting in a period of elevated temperatures but a scarce number of injuries, which, if not appropriately controlled for, has the potential to confound the estimations. Our inclusion of years prior to the LPRL also contributed to our findings of a higher heat vulnerability, since 1989–1993 was the period with the highest RR for extreme heat. This implies that the legislation could have been an effective tool in reducing heat vulnerability to heat for workers. Previous literature found a significant decrease in injury rates in Spanish regions after the implementation of the Preferential Action Plans (PAPs) established by the LRPL (Benavides et al., 2009) though additional analyses are needed to confirm this association.

In our sensitivity analyses, we found a significant increase of injuries at lags of up to 4 days for extreme heat, and 1 day for extreme cold. This contrasts with associations between temperature and mortality outcomes, where the lagged effect of cold is much longer (of up to 21 days) than heat (5–7 days) (Gasparrini, 2014; Achebak et al., 2023). A recent study about the impact of heat over hospitalisations in Spain found that, for most diagnoses, the lagged effect of heat exposure is limited to 7 days (Achebak et al., 2024). This highlights the differences in the mechanisms through which temperature affects mortality, work-injuries and other health outcomes. A work-injury is more comparable to a hospitalisation than to a death:⁵ individuals may die after many weeks battling an infection or the consequences of an accident, but they’ll be hospitalised or placed on work-injury leave much sooner.

Overall, we found similar patterns of vulnerability by groups as compared to previous studies, with noteworthy sex differences in vulnerability to temperatures (Fatima et al., 2021; Martínez-Solanas et al., 2018). In our analyses, working women exhibited lower heat-attributable fraction of injuries than their male peers, as well as lower RR to heat, in line with lower heat-related mortality rates of women below 65 years. (Ballester et al., 2023; Charkoudian and Stachenfeld, 2016) This is probably a direct result of male prevalence in environmentally exposed, physically demanding and high-risk jobs like construction and agriculture, and also potentially related to gender behavioural differences such as women being more likely to undertake preventive measures, to engage in less risk intake, and generally having greater risk awareness than their male peers (Khare et al., 2015; Li et al., 2016). Our findings suggest that societal gender differences (both in the gender gap for manual, exposed jobs and gendered behaviour) have an important impact on the effect of heat on health.

Regarding other groups of workers, we found a higher risk for workers younger than 35 years, but this was only significant during the period 1989–1993. This aligns with previous studies, which found small, non-significant differences, but did not include this period (Martínez-Solanas et al., 2018). Although agricultural workers were identified as the most vulnerable sector to heat over the entire period, our temporal analyses showed that this difference was significant only during 1989–1993. One explanation for this is the greater uncertainty in
<caption>**Table 1**</caption>

Attributable measures of occupational injuries for temperatures below and above the minimum injury temperature (MIT), for period 1988–2019, and sub-periods (AF: Attributable fraction, AN: Attributable number).

<table><thead><tr><th rowspan="2">Group</th><th rowspan="2">Period</th><th rowspan="2">Total injuries</th><th rowspan="2">AN total /<br>Avg. AN annual</th><th colspan="2">AF total</th><th colspan="2">Cold AF</th><th colspan="2">Heat AF</th></tr><tr><th>%</th><th>CI 95 %</th><th>%</th><th>CI 95 %</th><th>%</th><th>CI 95 %</th></tr></thead><tbody><tr><td rowspan="5">All groups</td><td>1988–2019</td><td>22,441,991</td><td>671,772 /<br>20,992</td><td>3.0 %</td><td>[2.5 %-3.5 %]</td><td>0.3 %</td><td>[0.3 %-0.4 %]</td><td>2.7 %</td><td>[2.1 %-3.2 %]</td></tr><tr><td>1989–1993</td><td>3,279,508</td><td>160,842 /<br>32,168</td><td>4.9 %</td><td>[4.3 %-5.5 %]</td><td>0.2 %</td><td>[0.2 %-0.3 %]</td><td>4.7 %</td><td>[4.1 %-5.3 %]</td></tr><tr><td>1999–2003</td><td>4,688,076</td><td>148,060 /<br>29,612</td><td>3.2 %</td><td>[2.7 %-3.6 %]</td><td>1.4 %</td><td>[1.0 %-1.8 %]</td><td>1.8 %</td><td>[1.4 %-2.1 %]</td></tr><tr><td>2009–2013</td><td>2,725,550</td><td>107,176 /<br>21,435</td><td>3.9 %</td><td>[3.6 %-4.3 %]</td><td>0.9 %</td><td>[0.7 %-1.1 %]</td><td>3.0 %</td><td>[2.6 %-3.3 %]</td></tr><tr><td>2015–2019</td><td>2,821,861</td><td>89,659 /<br>17,931</td><td>3.2 %</td><td>[2.7 %-3.6 %]</td><td>0.3 %</td><td>[0.2 %-0.5 %]</td><td>2.9 %</td><td>[2.4 %-3.2 %]</td></tr><tr><td rowspan="5">Women</td><td>1988–2019</td><td>4,941,347</td><td>131,325 /<br>4,103</td><td>2.7 %</td><td>[2.3 %-3.0 %]</td><td>0.9 %</td><td>[0.7 %-1.2 %]</td><td>1.7 %</td><td>[1.4 %-2.0 %]</td></tr><tr><td>1989–1993</td><td>410,926</td><td>17,092 /<br>3,418</td><td>4.2 %</td><td>[3.2 %-4.9 %]</td><td>0.6 %</td><td>[0.3 %-0.8 %]</td><td>3.6 %</td><td>[2.6 %-4.5 %]</td></tr><tr><td>1999–2003</td><td>926,136</td><td>30,684 /<br>6,136</td><td>3.3 %</td><td>[2.5 %-4.1 %]</td><td>1.8 %</td><td>[1.2 %-2.4 %]</td><td>1.5 %</td><td>[0.9 %-2.1 %]</td></tr><tr><td>2009–2013</td><td>852,552</td><td>33,746 /<br>6,749</td><td>4.0 %</td><td>[3.1 %-4.8 %]</td><td>1.6 %</td><td>[1.3 %-1.9 %]</td><td>2.4 %</td><td>[1.5 %-3.1 %]</td></tr><tr><td>2015–2019</td><td>926,588</td><td>21,265 /<br>4,253</td><td>2.3 %</td><td>[1.5 %-3.0 %]</td><td>0.5 %</td><td>[0.2 %-0.8 %]</td><td>1.8 %</td><td>[1.0 %-2.4 %]</td></tr><tr><td rowspan="5">Men</td><td>1988–2019</td><td>17,500,644</td><td>597,321 /<br>18,666</td><td>3.4 %</td><td>[2.8 %-3.9 %]</td><td>0.2 %</td><td>[0.2 %-0.3 %]</td><td>3.2 %</td><td>[2.6 %-3.7 %]</td></tr><tr><td>1989–1993</td><td>2,868,582</td><td>142,942 /<br>28,588</td><td>5.0 %</td><td>[4.4 %-5.5 %]</td><td>0.2 %</td><td>[0.2 %-0.3 %]</td><td>4.8 %</td><td>[4.2 %-5.3 %]</td></tr><tr><td>1999–2003</td><td>3,761,940</td><td>117,164 /<br>23,432</td><td>3.1 %</td><td>[2.7 %-3.6 %]</td><td>1.3 %</td><td>[1.0 %-1.7 %]</td><td>1.8 %</td><td>[1.4 %-2.1 %]</td></tr><tr><td>2009–2013</td><td>1,872,998</td><td>83,117 /<br>16,623</td><td>4.4 %</td><td>[3.9 %-4.9 %]</td><td>0.7 %</td><td>[0.5 %-0.9 %]</td><td>3.7 %</td><td>[3.1 %-4.3 %]</td></tr><tr><td>2015–2019</td><td>1,895,273</td><td>72,560 /<br>14,512</td><td>3.8 %</td><td>[3.4 %-4.2 %]</td><td>0.3 %</td><td>[0.2 %-0.4 %]</td><td>3.6 %</td><td>[3.2 %-3.9 %]</td></tr><tr><td rowspan="5">Age 35&lt;</td><td>1988–2019</td><td>10,795,254</td><td>360,761 /<br>11,273</td><td>3.3 %</td><td>[2.7 %-3.9 %]</td><td>0.2 %</td><td>[0.1 %-0.3 %]</td><td>3.1 %</td><td>[2.5 %-3.7 %]</td></tr><tr><td>1989–1993</td><td>1,790,401</td><td>99,010 /<br>19,802</td><td>5.5 %</td><td>[4.7 %-6.2 %]</td><td>0.2 %</td><td>[0.1 %-0.3 %]</td><td>5.3 %</td><td>[4.6 %-6.0 %]</td></tr><tr><td>1999–2003</td><td>2,606,900</td><td>82,504 /<br>16,501</td><td>3.2 %</td><td>[2.7 %-3.6 %]</td><td>1.2 %</td><td>[0.8 %-1.6 %]</td><td>2.0 %</td><td>[1.6 %-2.3 %]</td></tr><tr><td>2009–2013</td><td>1,030,430</td><td>34,930 /<br>6,986</td><td>3.4 %</td><td>[3.0 %-3.7 %]</td><td>0.9 %</td><td>[0.6 %-1.1 %]</td><td>2.5 %</td><td>[2.2 %-2.8 %]</td></tr><tr><td>2015–2019</td><td>832,376</td><td>33,476 /<br>6,695</td><td>4.0 %</td><td>[3.1 %-4.9 %]</td><td>0.2 %</td><td>[0.0 %-0.4 %]</td><td>3.8 %</td><td>[2.9 %-4.7 %]</td></tr><tr><td>Age 35–54</td><td>1988–2019</td><td>9,735,647</td><td>252,956 /<br>7,904</td><td>2.6 %</td><td>[%-%]</td><td>0.4 %</td><td>[%-%]</td><td>2.2 %</td><td>[%-%]</td></tr><tr><td rowspan="5">Age 55&gt;</td><td>1988–2019</td><td>5,978,107</td><td>151,044 /<br>4,720</td><td>2.5 %</td><td>[2.3 %-2.8 %]</td><td>0.5 %</td><td>[0.4 %-0.6 %]</td><td>2.0 %</td><td>[1.7 %-2.3 %]</td></tr><tr><td>1989–1993</td><td>783,892</td><td>28,420 /<br>5,684</td><td>3.7 %</td><td>[2.9 %-4.3 %]</td><td>0.5 %</td><td>[0.3 %-0.6 %]</td><td>3.2 %</td><td>[2.3 %-3.9 %]</td></tr><tr><td>1999–2003</td><td>1,004,630</td><td>26,692 /<br>5,338</td><td>2.7 %</td><td>[2.3 %-3.0 %]</td><td>1.6 %</td><td>[1.3 %-1.9 %]</td><td>1.1 %</td><td>[0.8 %-1.3 %]</td></tr><tr><td>2009–2013</td><td>889,047</td><td>36,545 /<br>7,309</td><td>4.1 %</td><td>[3.6 %-4.6 %]</td><td>1.2 %</td><td>[0.9 %-1.4 %]</td><td>2.9 %</td><td>[2.4%-3.2 %]</td></tr><tr><td>2015–2019</td><td>1,117,900</td><td>37,233 /<br>7,446</td><td>3.3 %</td><td>[2.9 %-3.8 %]</td><td>0.5 %</td><td>[0.4 %-0.7 %]</td><td>2.8 %</td><td>[2.4 %-3.2 %]</td></tr><tr><td rowspan="2">Experience &lt; 1 yr</td><td>1988–2019</td><td>10,588,445</td><td>419,691 /<br>13,115</td><td>4.0 %</td><td>[3.4 %-4.5 %]</td><td>0.2 %</td><td>[0.2 %-0.3 %]</td><td>3.7 %</td><td>[3.1 %-4.3 %]</td></tr><tr><td>1988–2019</td><td>11,853,546</td><td>268,712 /<br>8,397</td><td>2.3 %</td><td>[2.0 %-2.5 %]</td><td>0.4 %</td><td>[0.3 %-0.6 %]</td><td>1.8 %</td><td>[1.6 %-2.1 %]</td></tr><tr><td>Agricultural sector</td><td>1988–2019</td><td>1,151,771</td><td>101,393 /<br>3,168</td><td>8.8 %</td><td>[7.7 %-9.7 %]</td><td>0.2 %</td><td>[0.1 %-0.4 %]</td><td>8.6 %</td><td>[7.5 %-9.6 %]</td></tr><tr><td rowspan="5">Sub-period</td><td>1989–1993</td><td>182,616</td><td>21,060 /<br>4,212</td><td>11.5 %</td><td>[9.7 %-12.7 %]</td><td>0.4 %</td><td>[0.1 %-0.6 %]</td><td>11.2 %</td><td>[9.4 %-12.4 %]</td></tr><tr><td>1999–2003</td><td>203,350</td><td>16,568 /<br>3,313</td><td>8.1 %</td><td>[6.4 %-9.6 %]</td><td>0.6 %</td><td>[0.3 %-0.8 %]</td><td>7.6 %</td><td>[5.8 %-8.9 %]</td></tr><tr><td>2009–2013</td><td>137,838</td><td>12,832 /<br>2,566</td><td>9.3 %</td><td>[7.6 %-10.6 %]</td><td>0.4 %</td><td>[0.1 %-0.6 %]</td><td>8.9 %</td><td>[7.3 %-10.4 %]</td></tr><tr><td>2015–2019</td><td>177,946</td><td>12,881 /<br>2,576</td><td>7.2 %</td><td>[6.1 %-8.4 %]</td><td>0.1 %</td><td>[0.0 %-0.2 %]</td><td>7.1 %</td><td>[5.9 %-8.2 %]</td></tr><tr><td>Manufacturing sector</td><td>1988–2019</td><td>5,123,259</td><td>91,968 /<br>2,874</td><td>1.8 %</td><td>[1.0 %-2.6 %]</td><td>0.5 %</td><td>[0.3 %-0.6 %]</td><td>1.3 %</td><td>[0.4 %-2.1 %]</td></tr><tr><td>Construction sector</td><td>1988–2019</td><td>4,356,519</td><td>140,862 /<br>4,401</td><td>3.2 %</td><td>[2.7 %-3.8 %]</td><td>0.2 %</td><td>[0.1 %-0.3 %]</td><td>3.0 %</td><td>[2.4 %-3.6 %]</td></tr><tr><td>Transport & Storage sector</td><td>1988–2019</td><td>1,284,053</td><td>34,996 /<br>1,093</td><td>2.7 %</td><td>[2.2 %-3.2 %]</td><td>0.7 %</td><td>[0.5 %-0.9 %]</td><td>2.0 %</td><td>[1.5 %-2.5 %]</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th rowspan="2">Group</th><th rowspan="2">Period</th><th rowspan="2">Total injuries</th><th rowspan="2">AN total /<br>Avg. AN annual</th><th colspan="2">AF total</th><th colspan="2">Cold AF</th><th colspan="2">Heat AF</th></tr><tr><th>%</th><th>CI 95 %</th><th>%</th><th>CI 95 %</th><th>%</th><th>CI 95 %</th></tr></thead><tbody><tr><td>Hostelry sector</td><td>1988-2019</td><td>1,286,289</td><td>57,615 /<br>1,801</td><td>4.5 %</td><td>[3.8 %-5.2 %]</td><td>0.1 %</td><td>[0.0 %-0.2 %]</td><td>4.4 %</td><td>[3.6 %-5.1 %]</td></tr><tr><td rowspan="5">Sub-period</td><td>1989-1993</td><td>112,472</td><td>8,442 /<br>1,688</td><td>7.5 %</td><td>[6.0 %-8.7 %]</td><td>0.0 %</td><td>[0.0 %-0.0 %]</td><td>7.5 %</td><td>[6.2 %-8.7 %]</td></tr><tr><td>1999-2003</td><td>245,496</td><td>14,158 /<br>2,831</td><td>5.8 %</td><td>[2.7 %-8.0 %]</td><td>0.2 %</td><td>[0.0 %-0.5 %]</td><td>5.5 %</td><td>[2.6 %-8.0 %]</td></tr><tr><td>2009-2013</td><td>198,840</td><td>8,214 /<br>1,642</td><td>4.1 %</td><td>[3.5 %-4.7 %]</td><td>0.6 %</td><td>[0.2 %-1.0 %]</td><td>3.6 %</td><td>[2.8 %-4.3 %]</td></tr><tr><td>2015-2019</td><td>267,065</td><td>17,673 /<br>3,534</td><td>6.6 %</td><td>[4.1 %-8.7 %]</td><td>0.1 %</td><td>[0.0 %-0.3 %]</td><td>6.5 %</td><td>[4.1 %-8.6 %]</td></tr><tr><td>Other tertiary services</td><td>1988-2019</td><td>7,419,645</td><td>210,984 /<br>6,593</td><td>2.8 %</td><td>[2.5 %-3.2 %]</td><td>0.6 %</td><td>[0.4 %-0.8 %]</td><td>2.2 %</td><td>[1.9 %-2.7 %]</td></tr><tr><td>Class: Manual</td><td>1988-2019</td><td>13,529,717</td><td>421,955 /<br>13,186</td><td>3.1 %</td><td>[2.7 %-3.5 %]</td><td>0.2 %</td><td>[0.1 %-0.4 %]</td><td>2.9 %</td><td>[2.4 %-3.3 %]</td></tr><tr><td>Class: Non-manual</td><td>1988-2019</td><td>6,141,591</td><td>168,254 /<br>5,257</td><td>2.7 %</td><td>[2.0 %-3.4 %]</td><td>0.3 %</td><td>[0.2 %-0.5 %]</td><td>2.4 %</td><td>[1.8 %-3.1 %]</td></tr></tbody></table>

a

<figure><img src="image_5.png" /></figure>

b

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Attributable fraction of occupational injuries to hotter (panel A) and colder (panel B) temperatures than the minimum injury temperature (MIT), by sub-period.</figcaption></figure>

<caption>**Table 2**</caption>

Meta regressions for hot and cold seasons, for single and 2 predictor models for period 1988–2019. The association is considered statistically significant when the p-value < 0.05. IQR=Interquartile Range.

<table><thead><tr><th rowspan="3">Predictor</th><th colspan="6">Hot months (Apr-Nov)</th><th colspan="6">Cold months (Dec-Mar)</th></tr><tr><th colspan="3">Single predictor</th><th colspan="3">Predictor + temperature</th><th colspan="3">Single predictor</th><th colspan="3">Predictor + temperature</th></tr><tr><th>Wald</th><th>AIC</th><th>I2 (%)</th><th>Wald</th><th>AIC</th><th>I2 (%)</th><th>Wald</th><th>AIC</th><th>I2 (%)</th><th>Wald</th><th>AIC</th><th>I2 (%)</th></tr></thead><tbody><tr><td>GDP per capita</td><td>0.013</td><td>-325</td><td>7.8</td><td>0.465</td><td>-317</td><td>0</td><td>0.373</td><td>-209</td><td>53.5</td><td>0.718</td><td>-249</td><td>0</td></tr><tr><td>GDP, annual variation</td><td>0.718</td><td>-318</td><td>22.5</td><td>0.482</td><td>-317</td><td>2</td><td>0.083</td><td>-212</td><td>52.8</td><td>0.878</td><td>-249</td><td>0</td></tr><tr><td>Rate of accidents per 100.000 workers</td><td>0.453</td><td>-319</td><td>21.4</td><td>0.767</td><td>-316</td><td>0.3</td><td>0.335</td><td>-210</td><td>52.6</td><td>0.714</td><td>-249</td><td>0</td></tr><tr><td>Unemployment, all</td><td>0.023</td><td>-324</td><td>9.5</td><td>0.887</td><td>-316</td><td>4.1</td><td>0.003</td><td>-218</td><td>46.4</td><td>0.406</td><td>-250</td><td>0</td></tr><tr><td>Unemployment, men</td><td>0.007</td><td>-326</td><td>5.8</td><td>0.883</td><td>-316</td><td>2.8</td><td>0.000</td><td>-223</td><td>41.4</td><td>0.536</td><td>-250</td><td>0</td></tr><tr><td>Unemployment, women</td><td>0.095</td><td>-322</td><td>13.9</td><td>0.891</td><td>-316</td><td>5.1</td><td>0.070</td><td>-213</td><td>51.3</td><td>0.349</td><td>-250</td><td>0</td></tr><tr><td>Workers in agriculture (%)</td><td>0.008</td><td>-326</td><td>8.1</td><td>0.010</td><td>-324</td><td>0</td><td>0.574</td><td>-209</td><td>54.8</td><td>0.490</td><td>-250</td><td>0</td></tr><tr><td>Workers in industry (%)</td><td>0.001</td><td>-330</td><td>0</td><td>0.098</td><td>-320</td><td>0</td><td>0.013</td><td>-215</td><td>49.9</td><td>0.098</td><td>-252</td><td>0</td></tr><tr><td>Workers in construction (%)</td><td>0.485</td><td>-319</td><td>20</td><td>0.797</td><td>-316</td><td>4.4</td><td>0.733</td><td>-208</td><td>54.5</td><td>0.610</td><td>-249</td><td>0</td></tr><tr><td>Workers in tertiary services (%)</td><td>0.506</td><td>-319</td><td>22.1</td><td>0.328</td><td>-317</td><td>2.9</td><td>0.006</td><td>-217</td><td>49.6</td><td>0.577</td><td>-249</td><td>0</td></tr><tr><td>Temporary contracts (%)</td><td>0.003</td><td>-327</td><td>3</td><td>0.436</td><td>-317</td><td>0</td><td>0.000</td><td>-223</td><td>43.1</td><td>0.532</td><td>-249</td><td>0</td></tr><tr><td>Foreign population (%)</td><td>0.942</td><td>-317</td><td>23</td><td>0.677</td><td>-316</td><td>2.7</td><td>0.142</td><td>-211</td><td>52.2</td><td>0.812</td><td>-249</td><td>0</td></tr><tr><td>Population high educational attainment (%)</td><td><strong>0.000</strong></td><td><strong>-332</strong></td><td><strong>0</strong></td><td><strong>0.007</strong></td><td><strong>-324</strong></td><td><strong>0</strong></td><td>0.568</td><td>-209</td><td>54</td><td>0.450</td><td>-250</td><td>0</td></tr><tr><td>Public funding of prescription medicines (%)</td><td>0.092</td><td>-322</td><td>16.9</td><td>0.122</td><td>-320</td><td>0</td><td>0.997</td><td>-208</td><td>55</td><td>0.333</td><td>-250</td><td>0</td></tr><tr><td>Average Season Mean Temperature</td><td>0.037</td><td>-328</td><td>3.4</td><td></td><td></td><td></td><td>0.000</td><td>-260</td><td>0</td><td></td><td></td><td></td></tr><tr><td>IQR Mean Temperature</td><td>0.681</td><td>-318</td><td>22.5</td><td>0.568</td><td>-317</td><td>0.7</td><td>0.000</td><td>-245</td><td>11.4</td><td>0.451</td><td>-250</td><td>0</td></tr></tbody></table>
estimations due to the smaller number of years when assessing risk in different sub-periods. Additionally, the fraction of agricultural workers in the workforce has diminished over time (see Supplementary Table 2. A.). As other sectors increased their share, such as construction and tertiary services, this could add further uncertainty to the estimations due to smaller total numbers.

Another interesting case are hostelry workers, a subset of the tertiary services industry in constant expansion after the 2008 crisis, which stands out as one of the only sectors to maintain their attributable fraction to heat at high levels (around 7 %) from 1989 to 1993 to 2015–2019, while the AF for most other sectors significantly decreased in this period. Hostelry sectors are often depicted as heavily precarious jobs characterized by low entry barriers, risky environments, loose regulation and mostly absent union representation (Baum, 2015), the case of Spanish restaurant employees with long working hours and physically straining tasks, especially during the peak tourism season in the summer(Martínez-Gayo, 2019). It is important to also highlight that the share of tertiary services workers (of which hostelry workers are a part of) in relation to the total workforce has been in constant increase during the last 3 decades (see Supplementary table 2.A) which could account for the doubling in the AN of injuries to heat for this sector. In light of these challenges, understanding and addressing the unique vulnerabilities faced by Spanish hostelry workers in the context of rising temperatures becomes imperative, and to consider them along with other traditionally vulnerable groups to heat, like agriculture and construction workers.

Our analyses also found a lower vulnerability to heat in highly educated populations. Previous evidence suggests education might improve workers' ability to recognize work hazards and of safety compliance (Gyekye and Salminen, 2009); as well as potentially acting as a proxy for less exposed jobs, and for a better underlying health of the population in these provinces. It is encouraging that educational attainment emerges as a protective factor against the risk of injury under high temperatures. However, this finding also confirms that the effects of heat on health are not independent of socio-spatial inequalities. Workers in highly exposed jobs, such as agriculture, construction, and hostelry, are disproportionately at risk of injury during hot seasons. These jobs are more common in the less economically developed, southern (and therefore hotter) regions of Spain. This highlights the intersection of heat exposure and social inequality in determining health outcomes. We emphasize the importance of addressing the demand for greater equity, highlighting the need for both research and policy initiatives to tackle the inherent inequalities both among and within countries (van Daalen et al., 2023).

The lack of significant socioeconomic predictors in the spatiotemporal meta-regression analyses might be explained due to the complex interaction between the non-monotonic spatial and temporal variations of predictors and the temperature-injuries associations. While some socioeconomic factors improved only during expansions, others grew steadily independently of economic cycles, like the share of female workers (see Supplementary table 2.A of Supplementary data), and the proportion of tertiary service sector workers. Also, the spatial distribution of socioeconomic factors is not independent of temperatures, since southern and centre-south provinces (hotter regions) have historically shown lower wealth and social development indicators. At last, we might lack enough spatiotemporal heterogeneity, for which data from more countries and periods would be useful to distinguish significant meta predictors.

## 5. Conclusions

In order to reduce heat vulnerability, our findings suggest a need for preventive measures both for traditionally exposed workers (agriculture and construction) and non-traditionally vulnerable sectors (hostelry, and for young, male, and less educated workers during hot seasons, for whom legislation enforcement, and education on heat hazards are

crucial. In terms of vulnerability to cold, the health of women, the elderly, and tertiary service workers must be targeted during the winter, especially in the coldest regions. Social measures addressing temperature vulnerability would bring general benefits to worker safety, thus reducing not only heat-and-cold-related injuries (via relative risk reduction), but overall vulnerability to injuries. Reducing the burden of temperature-related occupational injuries would also have economic benefits, since the annual cost is estimated at an average of 0.03 % of Spain's GDP (Martínez-Solanas et al., 2018). Thus, vulnerability of workers to ambient temperature is not only a social and health issue, but also a relevant economic question.

## 6. Limitations

First, we assume an underestimation of work injuries since our primary data does not include informal workers, more vulnerable to all type of hazards because of the lack of regulation of their work conditions, and we also accept some degree of bias in the estimations due to using mean temperature at province level as the exposure, for which some degree of heterogeneity (both at the spatial and temperature level) is not being accounted for. Second, we omitted other exposure variables like air pollution and humidity, that could potentially modify the temperature-injuries association. We did not include these variables in the analyses due to data availability limitations, and because they are both beyond the scope of this paper, in which the temporal changes in the association between temperatures and injuries was the priority. Third, incomplete province-level socioeconomic data may have impacted the spatiotemporal meta-regression analyses. Our findings, though encompassing 48 regions, are representative of only one European country; it would be advisable to use our methods with data for different regions including other European countries, as well as non-western and southern hemisphere nations. At last, the ecological study design does not allow us to confirm causation between exposure and outcomes: this should be considered carefully when extrapolating our findings to other settings.

### CRediT authorship contribution statement

Constanza Vielma: **Hicham Achebak:** Writing – review & editing, Software, Methodology, Data curation, Conceptualization. **Marcos Quijal-Zamorano:** Writing – review & editing, Validation, Software, Methodology, Formal analysis. **Simon J Lloyd:** Writing – review & editing, Formal analysis, Conceptualization. **Guillaume Chevance:** Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization. **Joan Ballester:** Writing – review & editing, Supervision, Methodology, Formal analysis, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

The authors do not have permission to share data. Temperature data can be freely downloaded from E-OBS (https://www.ecad.eu/download/ensembles/download.php). Occupational injuries data can be obtained under request from the Spanish Ministry of Work and Social Economy.

### Acknowledgements

We acknowledge support from the grant CEX2023-0001290-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. CV, HA, MQ-Z,
SL, and JB were supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 865564 (European Research Council Consolidator Grant EARLY-ADAPT, https://www.early-adapt.eu/). The following individual grants also supported this work: CV acknowledges support from the grant PRE2021-097512 funded by MCIN/AEI /10.13039/ 501100011033 and by European Social Fund invests in your future. HA acknowledges funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101065876 (MSCA Postdoctoral Fellowship TEMP-MOMO). SL acknowledges funding from the Swedish Research Council (FORMAS) under grant agreement No. 2022-01845 (project ADATES). GC acknowledges funding with the grant RYC2021-033537-I, supported by MCIN/AEI/10.13039/501100011033 and by the European Union "Next Generation EU"/PRTR". We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program.

We thanked the Spanish Ministry of Labour and Social Economy (MITES) for providing on the dataset of occupational injuries, the main source of data of this study. The degree of accuracy or reliability of the information derived from the dataset provided to us is our sole responsibility. We also thank Raúl Méndez for the processing of the temperature data.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2024.109006.

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