Sex differences in temperature-related all-cause mortality in the Netherlands

Purpose: Over the last few decades, a global increase in both cold and heat extremes has been observed with significant impacts on human mortality. Although it is well-identified that older individuals (> 65 years) are most prone to temperature-related mortality, there is no consensus on the effect of sex. The current study investigated if sex differences in temperature-related mortality exist in the Netherlands. Methods: Twenty-three-year ambient temperature data of the Netherlands were combined with daily mortality data which were subdivided into sex and three age classes (< 65 years, 65–80 years, ≥ 80 years). Distributed lag non-linear models were used to analyze the effect of ambient temperature on mortality and determine sex differences in mortality attributable to the cold and heat, which is defined as mean daily temperatures below and above the Minimum Mortality Temperature, respectively. Results: Attributable fractions in the heat were higher in females, especially in the oldest group under extreme heat (≥ 97.5th percentile), whilst no sex differences were found in the cold. Cold- and heat-related mortality was most prominent in the oldest age group (≥ 80 years) and to a smaller extent in the age group between 65–80 years. In the age group < 65 years temperature-related mortality was only significant for males in the heat. Conclusion: Mortality in the Netherlands represents the typical V- or hockey-stick shaped curve with a higher daily mortality in the cold and heat than at milder temperatures in both males and females, especially in the age group ≥ 80 years. Heat-related mortality was higher in females than in males, especially in the oldest age group (≥ 80 years) under extreme heat, whilst in the cold no sex differences were found. The underlying cause may be of physiological or behavioral nature, but more research is necessary

Comparing Pandemic to Seasonal Influenza Mortality: Moderate Impact Overall but High Mortality in Young Children

Background: We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed - and thus an underestimation - whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality. Methods and Findings: We used data on all-cause mortality (1999-2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266-958) influenza-attributed deaths; for seasonal influen

Explaining spatial homogamy. Compositional, spatial and regional cultural determinants of regional patterns of spatial homogamy

Abstract Spatial homogamy, or sharing a similarity in geographical origin, is an under-researched dimension in homogamy studies. In the Netherlands, people tend to choose spatially homogamous partners. Moreover, there is considerable regional variation in spatial homogamy, even when residential location and population density are controlled for. This study aims to explain the regional variation in spatial homogamy by means of a spatial regression. Three sets of explanations are taken into account: compositional effects, spatial determinants, and regional cultural differences. The data used consists of a unique geo-coded micro dataset on all new cohabiters in the Netherlands in 2004 (N=289,248), combined with other data from varying sources. In the spatial regression, the dependent variable is the standardized distance coefficient, based on the distance between partners before cohabitation, standardised for the average distance to other inhabitants. We find that especially educational, income and cultural differences contribute to the regional variation in spatial homogamy

Belgen in Nederland en Nederlanders in België

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Geography Matters: Patterns of Spatial Homogamy in the Netherlands

'Cupid may have wings, but apparently they are not adapted for long flights.' Studies on the spatial dimension of the partner market have found that the number of marriages declines as the distance between potential spouses increases. This paper explores the role of geographical distance in partner choice in the Netherlands. The availability of unique integral micro data from the population register enables us to study spatial homogamy among all new cohabiters. Spatial homogamy is measured by calculating distances between partners before cohabitation. The explorative study shows that geography matters: Dutch persons choose spatially homogamous partners. Spatial homogamy is influenced by demographic factors. With increasing age, spatial homogamy increases. Moreover, those who live with their parents and those who are single parents before cohabitation live significantly shorter to their future partners. Spatial homogamy also exhibits a distinct spatial pattern. However, conditional on population size and geographical location, long distances between partners in peripheral areas become insignificant. Finally, the distance between partners decreases as urbanisation increases. The findings stimulate the discussion on the role of cultural factors in partner choice. Copyright (c) 2008 John Wiley & Sons, Ltd

Inequalities in COVID-19 deaths by migration background during the first wave, interwave period and second wave of the COVID-19 pandemic: A closed cohort study of 17 million inhabitants of the Netherlands

Background: It is not known how differences in COVID-19 deaths by migration background in the Netherlands evolved throughout the pandemic, especially after introduction of COVID-19 prevention measures targeted at populations with a migration background (in the second wave). We investigated associations between migration background and COVID-19 deaths across first wave of the pandemic, interwave period and second wave in the Netherlands. Methods: We obtained multiple registry data from Statistics Netherlands spanning from 1 March 2020 to 14 March 2021 comprising 17.4 million inhabitants. We estimated incidence rate ratios for COVID-19 deaths by migration background using Poisson regression models and adjusted for relevant sociodemographic factors. Results: Populations with a migration background, especially those with Turkish, Moroccan and Surinamese background, exhibited higher risk of COVID-19 deaths than the Dutch origin population throughout the study periods. The elevated risk of COVID-19 deaths among populations with a migration background (as compared with Dutch origin population) was around 30% higher in the second wave than in the first wave. Conclusions: Differences in COVID-19 deaths by migration background persisted in the second wave despite introduction of COVID-19 prevention measures targeted at populations with a migration background in the second wave. Research on explanatory mechanisms and novel prevention measures are needed to address the ongoing differences in COVID-19 deaths by migration background

Mortality surveillance in the Netherlands: winter 2015/2016 of moderate severity

ObjectiveWeekly numbers of deaths are monitored to increase the capacityto deal with both expected and unusual (disease) events such aspandemic influenza, other infections and non-infectious incidents.The monitoring information can potentially be used to detect, trackand estimate the impact of an outbreak or incident on all-causemortality.IntroductionThe mortality monitoring system (initiated in 2009 during theinfluenza A(H1N1) pandemic) is a collaboration between the Centrefor Infectious Disease Control (CIb) and Statistics Netherlands.The system monitors nation-wide reported number of deaths(population size 2014: 16.8 million) from all causes, as cause ofdeath information is not available real-time. Data is received fromStatistics Netherlands by weekly emails.MethodsOnce a week the number of reported deaths is checked for excessabove expected levels at 2 different time-lags: within 1 and 2 weeksafter date of death (covering a median 43% and 96% of all deathsrespectively). A weekly email bulletin reporting the findings is sentto the Infectious Disease Early Warning Unit (at CIb) and a summaryof results is posted on the RIVM website (National Institute for PublicHealth and the Environment). Any known concurrent and possiblyrelated events are also reported. When excess deaths coincide withhot temperatures, the bulletin is sent to the Heat Plan Team (also atRIVM). Data are also sent to EuroMOMO which monitors excessmortality at a European level. For the Dutch system baselines andprediction limits are calculated using a 5 year historical period(updated each July). A serfling-like algorithm based on regressionanalysis is used to produce baselines which includes cyclical seasonaltrends (models based on historical data in which weeks with extremeunderreporting have been removed. Also periods with high excessmortality in winter and summer were removed so as not to influencethe baseline with previous outbreaks).ResultsIncreased mortality occurred during the entire influenza epidemicand up to three weeks thereafter (weeks 1-14 of 2016), except for adrop in week 7 (figure1). Excess mortality was primarily observedin persons 75 or older. Additionally, in several weeks mortality wasincreased in 65-74 year olds, (weeknr 4-6; peaking in week 4 with564 deaths, when 468 baseline deaths were predicted). Also, inweek 4, mortality in the 25-34 year-old age group was significantlyincreased (25 deaths, while 14 were expected as baseline). Cumulativeexcess mortality was estimated at 3,900 deaths occurring duringthe 11 weeks of the 2015/2016 influenza epidemic and at 6,085during the total winter season (44 weeks running from week 40 up toweek 20).ConclusionsIn terms of number of deaths during the winter season (weeks40-20) and during the influenza epidemic (weeks 1-11), the 2015/2016season in the Netherlands was of moderate severity compared with theprevious five years (and was of similar magnitude as the 2011/2012winter). Notable was the short three-week time span with a higherpeak in mortality in 65-74 year olds than has been observed in recentyears. Although the influenza epidemic reached its peak in week7, the mortality data showed a dip in week 7. The reason for thetemporary decrease is unknown but there was a partial overlap witha public holiday

Mortality surveillance in the Netherlands: winter 2015/2016 of moderate severity

ObjectiveWeekly numbers of deaths are monitored to increase the capacityto deal with both expected and unusual (disease) events such aspandemic influenza, other infections and non-infectious incidents.The monitoring information can potentially be used to detect, trackand estimate the impact of an outbreak or incident on all-causemortality.IntroductionThe mortality monitoring system (initiated in 2009 during theinfluenza A(H1N1) pandemic) is a collaboration between the Centrefor Infectious Disease Control (CIb) and Statistics Netherlands.The system monitors nation-wide reported number of deaths(population size 2014: 16.8 million) from all causes, as cause ofdeath information is not available real-time. Data is received fromStatistics Netherlands by weekly emails.MethodsOnce a week the number of reported deaths is checked for excessabove expected levels at 2 different time-lags: within 1 and 2 weeksafter date of death (covering a median 43% and 96% of all deathsrespectively). A weekly email bulletin reporting the findings is sentto the Infectious Disease Early Warning Unit (at CIb) and a summaryof results is posted on the RIVM website (National Institute for PublicHealth and the Environment). Any known concurrent and possiblyrelated events are also reported. When excess deaths coincide withhot temperatures, the bulletin is sent to the Heat Plan Team (also atRIVM). Data are also sent to EuroMOMO which monitors excessmortality at a European level. For the Dutch system baselines andprediction limits are calculated using a 5 year historical period(updated each July). A serfling-like algorithm based on regressionanalysis is used to produce baselines which includes cyclical seasonaltrends (models based on historical data in which weeks with extremeunderreporting have been removed. Also periods with high excessmortality in winter and summer were removed so as not to influencethe baseline with previous outbreaks).ResultsIncreased mortality occurred during the entire influenza epidemicand up to three weeks thereafter (weeks 1-14 of 2016), except for adrop in week 7 (figure1). Excess mortality was primarily observedin persons 75 or older. Additionally, in several weeks mortality wasincreased in 65-74 year olds, (weeknr 4-6; peaking in week 4 with564 deaths, when 468 baseline deaths were predicted). Also, inweek 4, mortality in the 25-34 year-old age group was significantlyincreased (25 deaths, while 14 were expected as baseline). Cumulativeexcess mortality was estimated at 3,900 deaths occurring duringthe 11 weeks of the 2015/2016 influenza epidemic and at 6,085during the total winter season (44 weeks running from week 40 up toweek 20).ConclusionsIn terms of number of deaths during the winter season (weeks40-20) and during the influenza epidemic (weeks 1-11), the 2015/2016season in the Netherlands was of moderate severity compared with theprevious five years (and was of similar magnitude as the 2011/2012winter). Notable was the short three-week time span with a higherpeak in mortality in 65-74 year olds than has been observed in recentyears. Although the influenza epidemic reached its peak in week7, the mortality data showed a dip in week 7. The reason for thetemporary decrease is unknown but there was a partial overlap witha public holiday