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. 2025 Sep 25;9(5):e424. doi: 10.1097/EE9.0000000000000424

Temporal changes in the high-temperature–mortality association in the Netherlands and the potential impact of the implementation of the national heat plan

Jochem O Klompmaker a,*, Werner I Hagens b
PMCID: PMC12466905  PMID: 41019641

Abstract

Background:

To mitigate the health impact of high temperatures, heat plans (HPs) have become widespread around the world. Our aim was to evaluate the temperature–mortality associations and estimate the temperature-related deaths in the Netherlands in the years before (2000–2009) and after (2010–2019) the first activation of the national HP.

Methods:

We obtained data about daily all-cause mortality (2000–2019) for the entire Dutch population, and by age, sex, neighborhood socioeconomic status, and urbanization. We linked the daily maximum temperature based on 23 monitoring stations across the Netherlands. Time-series Poisson regression models with a distributed lag nonlinear model, adjusted for long-term and seasonal trends and day of the week, were used to assess relative risks (RRs, 95% confidence intervals [CIs]) in the warm months (May–September). Temperature-attributable mortality fractions for high-temperature exposures and potential HP days were calculated.

Results:

We observed positive associations between daily maximum temperature and mortality in 2000–2009 and in 2010–2019. Associations of high temperatures (28.9 °C—95th percentile) were weaker in 2010–2019 (RR: 1.07, 95% CI: 1.05, 1.09) than in 2000–2009 (RR: 1.17, 95% CI: 1.15, 1.20). The attenuation in temperature–mortality risk was strongest for the elderly, women, and individuals living in low-socioeconomic status neighborhoods. The estimated mortality attributable fractions of high temperatures (≥28.9 °C) were lower in 2010–2019 (0.72, 95% CI: 0.60, 0.84) than in 2000–2009 (1.21%, 95% CI: 1.07, 1.33).

Conclusion:

The impact of high temperatures on mortality attenuated in the Netherlands. This might be due to the implementation of the national HP, but other factors may have played a role as well.

Keywords: heat, heatplan, mortality, temperature, time-series

What this study adds.

In the Netherlands, the national heat plan (HP) was developed in 2007 and first activated in 2010. We observed that associations of high temperatures with mortality were weaker in 2010–2019 compared with 2000–2009, especially for the elderly, women, and individuals living in low-socioeconomic status neighborhoods. In addition, the estimated mortality attributable fractions of high temperatures and potential HP days were lower in 2010–2019 than in 2000–2009. This might be due to the implementation of the national HP, but other factors may have played a role as well.

Introduction

In the past decades, populations across the world have been severely affected by episodes of extreme high temperatures (i.e., heatwaves).1 Exposure to high temperatures imposes overwhelming thermal stress on the human body that could trigger organ dysfunction and may lead to increased hospitalizations and premature deaths.2 To mitigate the health impact of high temperatures, heat plans (HPs) have become widespread in Europe.3 HPs trigger a variety of public health interventions designed to mitigate the health impacts of heat and are typically activated when (consecutive) days of high temperatures are expected.3 As climate change will lead to more frequent and extreme heatwaves in the coming decades,4 HPs will become increasingly important.

Several studies have evaluated HPs' efficiency in reducing heat-related deaths and morbidities.5–10 In some countries, attenuations of the heat–mortality associations were observed after the implementation of an HP.7–9,11 In Spain, the mortality attributable fraction due to extreme heat decreased after the implementation of the HP.6 However, in the United Kingdom, they found no evidence that associations between temperature and mortality and emergency hospital admissions changed in the years since the introduction of the HP.5 Differences in findings between these studies could be due to differences in data, study design, and statistical analyses. In addition, HP elements and implementations may differ between countries and could also affect their efficiency in mitigating adverse health outcomes.12

In the Netherlands, the national HP was developed in 2007 and first activated in 2010. Previous studies reported associations of high temperatures with mortality in the Netherlands;13,14 however, temporal changes in the temperature–mortality associations were not evaluated. The goal of this study was to evaluate the temperature–mortality associations and estimate the temperature-related deaths in the warm season (May–September) in the Netherlands in the years before (2000–2009) and after (2010–2019) the first activation of the national HP. We studied whether associations differed by age, sex, neighborhood socioeconomic status (SES), and degree of urbanization.

Methods

Study area and period

The Netherlands is a small country (total area of ~41,850 km2), bordered by the North Sea, and is relatively flat. The Netherlands has a temperate oceanic climate with mild temperatures in summer and no dry season. The study period was from 2000 to 2019, and we only focused on the warm season (May–September). The number of inhabitants in the Netherlands increased from 15.9 million in 2000 to 17.3 million in 2019.15

Heatwave plan

The national HP was developed in 2007 in collaboration between the National Institute for Public Health and the Environment (RIVM), the Royal Netherlands Meteorological Institute (KNMI), the Ministry of Health, Welfare and Sport, the Netherlands Municipal Public Health Services and Medical Assistance in Accidents and Disasters, the Dutch Red Cross, and several healthcare organizations.16 The Dutch National HP is a warning system and is generally activated when a period of 4 or more days with a maximum temperature higher than 27 °C is expected in the Netherlands. Other factors, such as humidity and temperatures at night, are also taken into account by the experts when activating the national HP. Temperature 27 °C corresponds to the ~90th percentile of the 2010–2019 warm season temperature distribution. RIVM uses the HP to inform (healthcare) organizations and other professionals and informal carers about the expected heat. The HP was activated for the first time in 2010. Therefore, we refer to the 2000–2009 period as the period without a national HP and to the 2010–2019 period as the period with a national HP. More information about the national HP can be found elsewhere.16

Data

Outcome

We obtained data about daily all-cause mortality for the Dutch population from 2000 to 2019 from Statistics Netherlands. We calculated daily all-cause mortality by age groups (0–64, 65–74, 75–79, 80–84, 85–89, and 90+ years), sex (men, women), neighborhood SES (tertiles of percent of the population with an income below the 40% income level of the total Dutch population), and degree of urbanization (≥2500 addresses per km2 [extremely urbanized], 2499–1500 addresses per km2 [strongly urbanized], 1499–1000 addresses per km2 [moderately urbanized], 999–500 addresses per km2 [hardly urbanized], <500 addresses per km2 [not urbanized]). For neighborhood SES and urbanization, we used data from 1999 (linked to 2000–2004), 2005 (linked to 2005–2009), 2010 (linked to 2010–2014), and 2015 (linked to 2015–2019). If neighborhood-level (buurt) data were missing, we used district-level (wijk) data. If both were missing, we used municipality-level (gemeente) data. Mortality data for 17 July 2014 were set to missing as this was a relatively warm day in the Netherlands and 196 Dutch citizens died because of the Malaysia Airlines Flight 17 (MH17/MAS17) crash.

Exposure

Daily maximum air temperature data were obtained from the Royal Netherlands Meteorological Institute (KNMI). They calculated the daily maximum, mean, and minimum Central Netherlands Temperature based on 23 monitoring stations across the Netherlands. More information about the Central Netherlands Temperature can be found elsewhere.17

We defined potential HP days as days within a period of 4 or more consecutive days with a maximum temperature higher than 27 °C. For sensitivity analyses, we additionally defined HP days as days within a period of 4 or more days with a maximum temperature higher than 26.5 °C and of 27.5 °C.

Covariates

We obtained data about daily humidity, pollen count, and air pollution concentrations from monitoring stations in the Netherlands. Daily average relative humidity data were obtained from a monitoring station located in De Bilt, in the center of the Netherlands. Daily pollen counts were obtained from two monitoring stations in the West (Leiden) and the South (Helmond) of the Netherlands. We focused on Poaceae, Artemisia, and Rumex pollen and averaged daily pollen counts from both stations. Daily concentrations of fine particulate matter (PM10, particles with a diameter of 10 µm or less), nitrogen dioxide (NO2), and ozone (O3) concentrations were obtained from several background monitoring stations across the country and used to calculate daily average air pollution concentrations.

Statistical analyses

We linked daily mortality data to daily temperature and covariate data. Analyses were performed separately for the period 2000–2009 (before the first activation of the HP) and for the period 2010–2019 (since the first activation of the HP). To evaluate associations (relative risks) with mortality, we used a time-series Poisson regression model for seasonal data, allowing for overdispersed death counts. As exposures of interest, we focused on daily maximum temperature (continuous indicator) and potential HP days (binary indicator). For daily maximum temperature, we used distributed lag nonlinear models (dlnm)18 to capture potential nonlinear and lagged associations. The exposure–response relation was modeled using a natural cubic spline with two internal knots placed at the 50th and 90th percentiles of the period-specific temperature distribution. The choice for natural splines allows the log-linear extrapolation of the function beyond the boundaries of period-specific temperature distribution, a step needed to project risks to periods with higher temperatures.19 For daily maximum temperature and potential HP days, the exposure–lag–response relation was modeled using a natural cubic spline with 4 degrees of freedom to capture the distributed lag effect over time up to 10 days. Two internal knots were placed at equally spaced log values of the lag, plus intercept. To account for long-term trends, we used a linear term for date. To account for seasonal trends, we used an interaction term between year and a natural cubic spline for day of the season with 4 degrees of freedom per year. The interaction term was specified to relax the assumption of a constant seasonal trend. We used a categorical variable to control for day of the week. This is a standard setup for covariate adjustment and specification in the literature. We performed separate analyses by age groups, sex, tertiles of neighborhood SES, and degree of urbanization.

For sensitivity analyses, we additionally adjusted for PM10 (lag 0–1), NO2 (lag 0–1) and O3 (lag 0–1), for relative humidity (lag 0–1) using a natural spline with 3 degrees of freedom, for categorical (0–75th percentile, 75th–95th percentile, 95th–100th percentile) Poaceae (lag 0–1), Artemisia (lag 0–1), and Rumex (lag 0–1), and for categorical (0–75th percentile, 75th–95th percentile, 95th–100th percentile) Poaceae (lag 0–4), Artemisia (lag 0–4), and Rumex (lag 0–4). In addition, we used a month–year combination to control for seasonal and long-term trends instead of the date and day of the season. We modeled the exposure–response relation using a B-spline with two internal knots placed at the 50th and 90th percentiles of the period-specific temperature distribution instead of the natural spline. We used the daily mean temperature instead of the daily maximum temperature. We also evaluated the daily maximum temperature–mortality association per 5-year interval (2000–2004, 2005–2009, 2010–2014, 2015–2019), before and after the development of the national HP (2002–2006, 2007–2011), and used 26.5 °C and 27.5 °C as thresholds for potential HP days.

To assess the temperature-attributable mortality fractions, we used a method described elsewhere.20 As the national HP focuses on high temperatures, we calculated the temperature-attributable mortality fractions for high-temperature exposures (≥95th percentile [28.9 °C] of the 2010–2019 temperature distribution) and for potential HP days. We calculated the fraction of deaths attributable to temperature in the period from 2000 to 2009 and in the period from 2010 to 2019 based on the population, temperature exposure, and exposure–response function from the corresponding period. We also assessed the fraction of deaths attributable to temperature in a counterfactual scenario; in this scenario, we used the exposure–response function from 2000 to 2009 and the population and temperature exposure of 2010 to 2019. This scenario shows the fraction of deaths attributable to temperature in 2010 to 2019 when the exposure–response function would not have changed between 2000–2009 and 2010–2019. In addition, we calculated the temperature-attributable mortality fractions for high-temperature exposures (≥97.5 percentile [31.3 °C] of the 2010–2019 temperature distribution). To assess 95% confidence intervals (CIs) for the temperature-attributable mortality fractions, we used Monte Carlo simulations (1000 simulations).

Results

The average (standard deviation) daily maximum temperature was similar in 2000–2009 as in 2010–2019 (Table 1). The highest temperatures were generally observed in July in both periods (Figure 1). There were 56 (3.7%) potential HP days in 2000–2009 and 65 (4.2%) in 2010–2019. The average number of daily deaths increased over time (Figure 1), which is likely due to an increase in the total population and in the proportion of the elderly. Daily maximum temperature was positively correlated with PM10, NO2, O3, Poaceae, Artemisia, and Rumex and weakly negatively correlated with relative humidity in both periods (Figure S1; https://links.lww.com/EE/A375).

Table 1.

Descriptive statistics of mortality, temperature, and other variables in 2000–2009 and in 2010–2019

Mean (standard deviation)/n (%) Mean (standard deviation)/n (%)
Years 2000–2009 2010–2019
Daily mortality 354.9 (28.9) 368.8 (28.8)
Daily maximum temperature (°C) 21.0 (4.3) 21.2 (4.7)
Potential heat plan days (27 °C), n (%) 56 (3.7) 65 (4.2)
Potential heat plan days (26.5 °C), n (%) 70 (4.6) 103 (6.7)
Potential heat plan days (27.5 °C), n (%) 54 (3.5) 43 (2.8)
Relative humidity (%) 78.2 (9.3) 76.3 (9.1)
NO2 (µg/m3) 19.1 (6.2) 15.7 (5)
O3 (µg/m3) 79.9 (23.1) 77.7 (20.4)
PM10 (µg/m3) 24.7 (8.8) 17.3 (5.5)
Poaceae (pollen/m3)a
 Low (<75th percentile) 1,095 (71.6) 1,153 (75.4)
 Mid (75th–95th percentile) 321 (21) 330 (21.6)
 High (>95th percentile) 114 (7.5) 47 (3.1)
Artemisia (pollen/m3)b
 Low (<75th percentile) 1,078 (70.5) 1,150 (75.2)
 Mid (75th–95th percentile) 344 (22.5) 325 (21.2)
 High (>95th percentile) 108 (7.1) 55 (3.6)
Rumex (pollen/m3)c
 Low (<75th percentile) 989 (64.6) 1162 (75.9)
 Mid (75th–95th percentile) 433 (28.3) 320 (20.9)
 High (>95th percentile) 108 (7.1) 48 (3.1)
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NO2, O3, PM10, Poaceae, Artemisia, and Rumex values are given for a lag of 0–1.

a

The 75th percentile was 21.4 pollen/m3, the 95th percentile was 73.5 pollen/m3.

b

The 75th percentile was 0.8 pollen/m3, the 95th percentile was 4.8 pollen/m3.

c

The 75th percentile was 1.3 pollen/m3, the 95th percentile was 4.8 pollen/m3.

Figure 1.

Figure 1.

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Boxplots of the daily maximum temperature and daily mortality in the Netherlands from 2000 to 2019 by year and by month.

Temperature–– mortality associations

We observed positive associations between daily maximum temperature and mortality in 2000–2009 and in 2010–2019 (Figure 2). Associations of high temperatures were weaker in 2010–2019 than in 2000–2009. The attenuation in temperature–mortality risk was strongest for the elderly (80–84, 85–89, 90+ years), women, and individuals living in low-SES neighborhoods; no clear pattern was found by urbanization (Figure 3). Analyses based on potential HP days showed similar patterns; associations of high temperatures were weaker in 2010–2019 than in 2000–2009. For potential HP days, we found a relative risks (95% CI) of 1.22 (1.18, 1.26) in 2000–2009 and of 1.10 (1.07, 1.13) in 2010–2019. Similar to associations of daily maximum temperature with mortality, the attenuation in temperature–mortality risk was strongest for the elderly, women, and individuals living in low-SES neighborhoods.

Figure 2.

Figure 2.

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Cumulative exposure–response curves for the daily maximum temperature–all-cause mortality association in the Netherlands in 2000–2009 and in 2010–2019. The Poisson regression models were adjusted for long-term and seasonality trends and day of the week.

Figure 3.

Figure 3.

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Cumulative relative risks (RRs, 95% CI) for the daily maximum temperature (95th percentile of the 2010–2019 distribution)–all-cause mortality association and the potential HP days–all-cause mortality association in the Netherlands in 2000–2009 and in 2010–2019. The Poisson regression models were adjusted for long-term and seasonality trends and day of the week. nSES indicates neighborhood SES; RR, relative risk.

In general, sensitivity analyses showed that associations were robust to additional adjustment for air pollution, relative humidity, and pollen (Tables S1 and S2; https://links.lww.com/EE/A375). Associations of potential HP days attenuated after adjustment for air pollution. In addition, associations were similar in models adjusted for month–year combinations instead of seasonal and long-term trends, in models using B-splines instead of natural cubic splines for daily maximum temperature, and when using alternative HP day thresholds. In 2010–2019, associations of daily mean temperature were similar to associations of daily maximum temperature; in 2000–2009, associations of daily mean temperature were slightly stronger. Associations of daily maximum temperature with mortality per 5-year periods showed that associations in 2000–2004 and 2005–2009 were relatively similar, as well as associations in 2010–2014 and 2015–2019 (Figure S2; https://links.lww.com/EE/A375). An attenuation of the daily maximum temperature–mortality association was observed between 2005–2009 and 2010–2014 and also between 2002–2006 and 2007–2011.

Temperature-related mortality

The estimated mortality attributable fractions (95% CI) of high temperatures (≥95th percentile) were 1.21 (1.07, 1.33) in 2000–2009, 0.72 (0.60, 0.84) in 2010–2019, and 1.57 (1.39, 1.74) in the counterfactual scenario (Figure 4). The estimated mortality attributable fractions (95% CI) of potential HP days were 0.72 (0.60, 0.84) in 2000–2009, 0.40 (0.29, 0.51) in 2010–2019, and 0.80 (0.68, 0.92) in the counterfactual scenario. Reductions in estimated mortality attributable fractions of high temperatures and potential HP days were strongest for women and the 80–84 and 85–89 years groups. In sensitivity analyses using the 97.5th percentile instead of the 95th percentile of the 2010–2019 temperature distribution, patterns of reductions of the estimated mortality attributable fractions between the counterfactual scenario and 2010–2019 were fairly similar, but differences of the estimated mortality attributable fractions between 2000–2009 and 2010–2019 became less clear for some subgroups (Figure S3; https://links.lww.com/EE/A375).

Figure 4.

Figure 4.

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Mortality attributable fraction (95% CI) due to high temperatures (95th–100th percentile of the 2010–2019 distribution) and potential HP days in 2000–2009, 2010–2019, and in the counterfactual scenario. The counterfactual scenario shows the fraction of deaths attributable to temperature in 2010–2019 when the exposure–response function would not have changed between 2000–2009 and 2010–2019.

Discussion

Associations of high temperatures with mortality in the Netherlands were weaker in 2010–2019 compared with 2000–2009, especially for the elderly, women, and individuals living in low-SES neighborhoods. In addition, the estimated mortality attributable fractions of high temperatures and potential HP days were lower in 2010–2019 than in 2000–2009.

A review showed that most studies that examined associations of temperatures with morbidity and mortality reported decreases in heat sensitivity over time.21 However, changes in heat sensitivity differed between counties,22 and some studies conducted in Europe reported similar or stronger associations of high temperatures with mortality in more recent years compared with earlier years.23–25 Our findings are in line with previous studies from Italy, Switzerland, and Australia;7–9,11 they also observed weaker high-temperature–mortality associations after the implementation of an HP.7–9 In Spain, the mortality attributable fraction due to extreme high temperatures also decreased in the period after the implementation of the HP.6 However, in the United Kingdom and the United States, they found no evidence that associations between temperature and mortality weakened in the years since the introduction of the HP.5,10 We note that differences in data, study design, statistical analyses, and HP elements and implementation may have led to differences in associations. We speculate that due to the temperate oceanic climate in the Netherlands, heat adaptation measures and awareness of heat risks in the past were limited, and therefore, potential impacts of the HP and heat awareness on reducing risks may have been high. We emphasize that although the high temperature–mortality associations attenuated, high temperatures still remain associated with increased mortality risks. As it is expected that the proportion of the elderly and the number of warm days will increase in the future in the Netherlands,26,27 the mortality attributable fraction of high temperature may increase in the future.

The national HP may be one of the factors that have contributed to the weaker temperature–mortality associations in 2010–2019 compared with 2000–2009. The strongest attenuations in risk were found for the elderly, a group that is specifically targeted by the national HP. Strong attenuations in risks were also observed for women and in low-SES groups. The national HP does not specifically focus on these groups, but they may be more aware of HP activations or follow precautionary measures better than other populations. Analyses per 5-year periods indicated differences in associations between 2005–2009 and 2010–2014; the first activation of the HP was in 2010. This implies the importance of the first activation of the national HP. However, associations also attenuated between 2002–2006 and 2007–2011. As most people may not have been aware of the national HP in 2007–2009, we note that other factors may have played a role in the attenuation of the mortality risks. The 2006 heatwave was one of the most intense heatwaves in the Netherlands and may have led to increased awareness of heat risks apart from the national HP. In addition, temporal changes in demographics, improvements in healthcare, and increased use of air conditioning—unrelated to the national HP—may have affected the temperature–mortality risk. Hence, we emphasize that the attenuation of the high-temperature–mortality risk after the first activation of the national HP is not proof of a causal effect of the national HP.

This study has several strengths and some limitations in addition to those discussed earlier. We used a continuous temperature indicator and a potential HP day indicator to assess temporal changes in the temperature–mortality associations. Potential HP days were days in a period of 4 or more consecutive days with a maximum temperature higher than 27 °C, similar to the HP in the Netherlands. We note that the HP is activated based on weather forecasts, while we used measured temperature data. We used a one-stage study design and aggregated temperature and death data for the entire country. We acknowledge that averaging daily maximum temperatures across 23 stations may dilute regional heterogeneity in exposure. However, given the relatively small size and absence of mountains in the Netherlands, we believe that the impact on the estimated associations is minor. We performed several sensitivity analyses, and our findings were generally robust to adjustment for potential confounders and alternative model specifications. We aggregated daily air pollution and pollen data. This may have led to exposure misclassification if temporal variations strongly differ across regions. A previous study showed that daily average PM10 concentrations measured at different stations were highly correlated in the Netherlands.28 Hence, we do not have strong indications that nation-wide averages are nonrepresentative estimates in the Netherlands nor that it would affect the temperature–mortality association strongly. There are only two pollen monitoring stations in the Netherlands and these are located in cities. Therefore, daily pollen counts may not be representative of other parts of the Netherlands. The lower number of high pollen days in 2010–2019 compared with 2000–2009 is probably due to increasing levels of urbanization around both monitoring stations.29 We evaluated associations with mortality by age, sex, nSES, and urbanization. Other factors, such as humidity, seasonality, and temperature at night, may be taken into account by the experts when activating the national HP. However, we did not look at potential additive or interaction effects of these indicators. For the analyses per 5-year period, the number of days with high maximum temperatures differs, which makes it harder to compare mortality risks across periods. We only focused on mortality and we do not know whether associations of high temperatures with other health outcomes have changed over time.

In conclusion, high temperature–mortality associations attenuated in the Netherlands, especially for the elderly, women, and individuals living in low-SES neighborhoods. The estimated mortality attributable fractions of high temperatures were generally lower in 2010–2019 than in 2000–2009. This might be due to the implementation of the national HP, but other factors may have played a role as well.

Conflicts of interest statement

The authors declare that they have no conflicts of interest with regard to the content of this report.

Acknowledgments

We would like to thank Gerard van der Schrier (KNMI) for providing CNT data and Letty de Weger (LUMC) and Mieke Koenders (Elkerliek Hospital) for providing pollen count data.

Supplementary Material

ee9-9-e424-s001.pdf (451.6KB, pdf)
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Footnotes

This study was supported by the Dutch Ministry of Public Health, Welfare and Sport (project number: V200320).

The mortality data that has been used is confidential. The temperature and humidity data are freely accessible at https://www.knmi.nl/nederland-nu/klimatologie/daggegevens. The air pollution data is freely accessible at https://www.luchtmeetnet.nl/.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.environepidem.com).

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