Death Counts Research Articles

A new time-varying coefficient regression approach for analyzing infectious disease data

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Widening rural–urban gap in life expectancy in China since COVID-19

IntroductionDisparities in life expectancy between rural and urban populations are well established but how it varies with epidemics and pandemics remains poorly understood. We aimed to quantify the rural–urban differences...

A framework of zero-inflated Bayesian negative binomial regression models for spatiotemporal data

Spatiotemporal data analysis with massive zeros is widely used in many areas such as epidemiology and public health. We use a Bayesian framework to fit zero-inflated negative binomial models and employ a set of latent variables from Pólya-Gamma distributions to derive an efficient Gibbs sampler. The proposed model accommodates varying spatial and temporal random effects through Gaussian process priors, which have both the simplicity and flexibility in modeling nonlinear relationships through a covariance function. To conquer the computation bottleneck that GPs may suffer when the sample size is large, we adopt the nearest-neighbor GP approach that approximates the covariance matrix using local experts. For the simulation study, we adopt multiple settings with varying sizes of spatial locations to evaluate the performance of the proposed model such as spatial and temporal random effects estimation and compare the result to other methods. We also apply the proposed model to the COVID-19 death counts in the state of Florida, USA from 3/25/2020 through 7/29/2020 to examine relationships between social vulnerability and COVID-19 deaths.

Assessment of short-term heat effects on cardiovascular mortality and vulnerability factors using small area data in Europe

BackgroundShort-term associations between heat and cardiovascular disease (CVD) mortality have been examined mostly in large cities. However, different vulnerability and exposure levels may contribute to spatial heterogeneity. This study assessed heat effects on CVD mortality and potential vulnerability factors using data from three European countries, including urban and rural settings. MethodsWe collected daily counts of CVD deaths aggregated at the small-area level in Norway (small-area level: municipality), England and Wales (lower super output areas), and Germany (district) during the warm season (May-September) from 1996 to 2018. Daily mean air temperatures estimated by spatial–temporal models were assigned to each small area. Within each country, we applied area-specific Quasi-Poisson regression using distributed lag nonlinear models to examine the heat effects at lag 0–1 days. The area-specific estimates were pooled by random-effects meta-analysis to derive country-specific and overall heat effects. We examined individual- and area-level heat vulnerability factors by subgroup analyses and meta-regression, respectively. ResultsWe included 2.84 million CVD deaths in analyses. For an increase in temperature from the 75th to the 99th percentile, the pooled relative risk (RR) for CVD mortality was 1.14 (95% CI: 1.03, 1.26), with the country-specific RRs ranging from 1.04 (1.00, 1.09) in Norway to 1.24 (1.23, 1.26) in Germany. Heat effects were stronger among women [RRs (95% CIs) for women and men: 1.18 (1.08, 1.28) vs. 1.12 (1.00, 1.24)]. Greater heat vulnerability was observed in areas with high population density, high degree of urbanization, low green coverage, and high levels of fine particulate matter. ConclusionThis study provides evidence for the heat effects on CVD mortality in European countries using high-resolution data from both urban and rural areas. Besides, we identified individual- and area-level heat vulnerability factors. Our findings may facilitate the development of heat-health action plans to increase resilience to climate change.

Short-term exposure to ultrafine particles and mortality and hospital admissions due to respiratory and cardiovascular diseases in Copenhagen, Denmark

Ultrafine particles (UFP; particulate matter <0.1 μm in diameter) may be more harmful to human health than larger particles, but epidemiological evidence on their health effects is still limited. In this study, we examined the association between short-term exposure to UFP and mortality and hospital admissions in Copenhagen, Denmark. Daily concentrations of UFP (measured as particle number concentration in a size range 11–700 nm) and meteorological variables were monitored at an urban background station in central Copenhagen during 2002–2018. Daily counts of deaths from all non-accidental causes, as well as deaths and hospital admissions from cardiovascular and respiratory diseases were obtained from Danish registers. Mortality and hospital admissions associated with an interquartile range (IQR) increase in UFP exposure on a concurrent day and up to six preceding days prior to the death or admission were examined in a case-crossover study design. Odds ratios (OR) with 95% confidence intervals (CI) per one IQR increase in UFP were estimated after adjusting for temperature and relative humidity. We observed 140,079 deaths in total, 236,003 respiratory and 342,074 cardiovascular hospital admissions between 2002 and 2018. Hospital admissions due to respiratory and cardiovascular diseases were significantly positively associated with one IQR increase in UFP (OR: 1.04 [95% CI: 1.01, 1.07], lag 0–4, and 1.02 [1.00, 1.04], lag 0–1, respectively). Among the specific causes, the strongest associations were found for chronic obstructive pulmonary disease (COPD) mortality and asthma hospital admissions and two-day means (lag 0–1) of UFP (OR: 1.13 [1.01, 1.26] and 1.08 [1.00, 1.16], respectively, per one IQR increase in UFP). Based on 17 years of UFP monitoring data, we present novel findings showing that short-term exposure to UFP can trigger respiratory and cardiovascular diseases mortality and morbidity in Copenhagen, Denmark. The strongest associations with UFP were observed with COPD mortality and asthma hospital admissions.

Excess Deaths of Gastrointestinal, Liver, and Pancreatic Diseases During the COVID-19 Pandemic in the United States.

Objectives: To evaluate excess deaths of gastrointestinal, liver, and pancreatic diseases in the United States during the COVID-19 pandemic. Methods: We retrieved weekly death counts from National Vital Statistics System and fitted them with a quasi-Poisson regression model. Cause-specific excess deaths were calculated by the difference between observed and expected deaths with adjustment for temporal trend and seasonality. Demographic disparities and temporal-spatial patterns were evaluated for different diseases. Results: From March 2020 to September 2022, the increased mortality (measured by excess risks) for Clostridium difficile colitis, gastrointestinal hemorrhage, and acute pancreatitis were 35.9%; 24.8%; and 20.6% higher than the expected. For alcoholic liver disease, fibrosis/cirrhosis, and hepatic failure, the excess risks were 1.4-2.8 times higher among younger inhabitants than older inhabitants. The excess deaths of selected diseases were persistently observed across multiple epidemic waves with fluctuating trends for gastrointestinal hemorrhage and fibrosis/cirrhosis and an increasing trend for C. difficile colitis. Conclusion: The persistently observed excess deaths of digestive diseases highlights the importance for healthcare authorities to develop sustainable strategies in response to the long-term circulating of SARS-CoV-2 in the community.

Impact of heat waves and cold spells on mortality in cities located in the Russian Arctic macroregion

BACKGROUND: Climate warming in Russia is most pronounced in the Arctic, where it is accompanied by an increase in the frequency and duration of heat waves. This emerging risk factor for the health of the population in major cities has not yet received adequate scientific attention. To effectively plan for adaptation to these phenomena, it is crucial to understand their specific impacts in regions with different climate types. Therefore, comprehensive analysis of the associations between climatic factors and health is needed for informed decision-making and strategic adaptation planning.&#x0D; AIM: To assess the impact of heat waves and cold spells on excess age-specific mortality within the urban populations of the Arctic macroregion. Furthermore, we analyzed the potential differences in the studied outcomes across climate types, ranging from marine to continental.&#x0D; METHODS: We analyzed the daily counts of deaths in Arkhangelsk, Magadan, Murmansk, and Yakutsk from 1999 through 2019. Poisson generalized linear regression models were employed to determine the relative mortality risks during heat waves and cold spells. All models accounted for potential lagged effects, seasonal and weekly patterns, and long-term mortality trends. Pearsons chi-squared tests were used to study the differences between the effects of heat and cold, as well as site-specific and age-specific variations in mortality.&#x0D; RESULTS: Effects of the studied climatic phenomena were more pronounced in continental than in marine climate. The age group 65+ years was more susceptible to the effects of extreme heat and cold than the middle-age group. Cold spells had a greater impact on the health of the residents of Murmansk, Arkhangelsk, and Magadan than heat waves, while the opposite was observed in Yakutsk. Cerebrovascular mortality during heath waves was 1.69 (95% CI: 1.342.13) times as high as during periods with normal temperature in the age-group 65+ in Yakutsk while cold spells were associated with 1.54 (95% CI: 1.182.01) greater risk of death from respiratory causes in the same age-group in Arkhangelsk.&#x0D; CONCLUSION: Our findings can be utilized by public health authorities to effectively prevent further fatalities among subarctic populations during heatwaves and cold spells.

Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread

BackgroundBayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.MethodsIn this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted using MCMC at 28 time horizons to mimic prospective prediction. A range of goodness of prediction measures were analyzed across the different time horizons.ResultsA particularly important result is that the peak intensity of case load is often under-estimated, while random spikes in case load can be mimicked using time dependent random effects. It is also clear that during the early wave of the pandemic simpler model forms are favored, but subsequently lagged spatial dependence models for cases are favored, even if the sophisticated models perform better overall.DiscussionThe models fitted mimic the situation where at a given time the history of the process is known but the future must be predicted based on the current evolution which has been observed. Using an overall ‘best’ model for prediction based on retrospective fitting of the complete pandemic waves is an assumption. However it is also clear that this case count model is well favored over other forms. During the first wave a simpler time series model predicts case counts better for counties than a spatially dependent one. The picture is more varied for morality.ConclusionsFrom a predictive point of view it is clear that spatio-temporal models applied to county level Covid-19 data within the US vary in how well they fit over time and also how well they predict future events. At different times, SIR case count models and also mortality models with cumulative counts perform better in terms of prediction. A fundamental result is that predictive capability of models varies over time and using the same model could lead to poor predictive performance.In addition it is clear that models addressing the spatial context for case counts (i.e. with lagged neighborhood terms) and cumulative case counts for mortality data are clearly better at modeling spatio-temporal data which is commonly available for the Covid-19 pandemic in different areas of the globe.

Better data for decision-making through Bayesian imputation of suppressed provisional COVID-19 death counts.

To facilitate use of timely, granular, and publicly available data on COVID-19 mortality, we provide a method for imputing suppressed COVID-19 death counts in the National Center for Health Statistic's 2020 provisional mortality data by quarter, county, and age. We used a Bayesian approach to impute suppressed COVID-19 death counts by quarter, county, and age in provisional data for 3,138 US counties. Our model accounts for multilevel data structures; numerous zero death counts among persons aged <50 years, rural counties, early quarters in 2020; highly right-skewed distributions; and different levels of data granularity (county, state or locality, and national levels). We compared three models with different prior assumptions of suppressed COVID-19 deaths, including noninformative priors (M1), the same weakly informative priors for all age groups (M2), and weakly informative priors that differ by age (M3) to impute the suppressed death counts. After the imputed suppressed counts were available, we assessed three prior assumptions at the national, state/locality, and county level, respectively. Finally, we compared US counties by two types of COVID-19 death rates, crude (CDR) and age-standardized death rates (ASDR), which can be estimated only through imputing suppressed death counts. Without imputation, the total COVID-19 death counts estimated from the raw data underestimated the reported national COVID-19 deaths by 18.60%. Using imputed data, we overestimated the national COVID-19 deaths by 3.57% (95% CI: 3.37%-3.80%) in model M1, 2.23% (95% CI: 2.04%-2.43%) in model M2, and 2.96% (95% CI: 2.76%-3.16%) in model M3 compared with the national report. The top 20 counties that were most affected by COVID-19 mortality were different between CDR and ASDR. Bayesian imputation of suppressed county-level, age-specific COVID-19 deaths in US provisional data can improve county ASDR estimates and aid public health officials in identifying disparities in deaths from COVID-19.

Search-and-rescue in the Central Mediterranean Route does not induce migration: Predictive modeling to answer causal queries in migration research

State- and private-led search-and-rescue are hypothesized to foster irregular migration (and thereby migrant fatalities) by altering the decision calculus associated with the journey. We here investigate this ‘pull factor’ claim by focusing on the Central Mediterranean route, the most frequented and deadly irregular migration route towards Europe during the past decade. Based on three intervention periods—(1) state-led Mare Nostrum, (2) private-led search-and-rescue, and (3) coordinated pushbacks by the Libyan Coast Guard—which correspond to substantial changes in laws, policies, and practices of search-and-rescue in the Mediterranean, we are able to test the ‘pull factor’ claim by employing an innovative machine learning method in combination with causal inference. We employ a Bayesian structural time-series model to estimate the effects of these three intervention periods on the migration flow as measured by crossing attempts (i.e., time-series aggregate counts of arrivals, pushbacks, and deaths), adjusting for various known drivers of irregular migration. We combine multiple sources of traditional and non-traditional data to build a synthetic, predicted counterfactual flow. Results show that our predictive modeling approach accurately captures the behavior of the target time-series during the various pre-intervention periods of interest. A comparison of the observed and predicted counterfactual time-series in the post-intervention periods suggest that pushback policies did affect the migration flow, but that the search-and-rescue periods did not yield a discernible difference between the observed and the predicted counterfactual number of crossing attempts. Hence we do not find support for search-and-rescue as a driver of irregular migration. In general, this modeling approach lends itself to forecasting migration flows with the goal of answering causal queries in migration research.

Reports of deaths are an exaggeration: all-cause and NAA-test-conditional mortality in Germany during the SARS-CoV-2 era.

Counts of SARS-CoV-2-related deaths have been key numbers for justifying severe political, social and economical measures imposed by authorities world-wide. A particular focus thereby was the concomitant excess mortality (EM), i.e. fatalities above the expected all-cause mortality (AM). Recent studies, inter alia by the WHO, estimated the SARS-CoV-2-related EM in Germany between 2020 and 2021 as high as 200 000. In this study, we attempt to scrutinize these numbers by putting them into the context of German AM since the year 2000. We propose two straightforward, age-cohort-dependent models to estimate German AM for the 'Corona pandemic' years, as well as the corresponding flu seasons, out of historic data. For Germany, we find overall negative EM of about -18 500 persons for the year 2020, and a minor positive EM of about 7000 for 2021, unveiling that officially reported EM counts are an exaggeration. In 2022, the EM count is about 41 200. Further, based on NAA-test-positive related death counts, we are able to estimate how many Germans have died due to rather than with CoViD-19; an analysis not provided by the appropriate authority, the RKI. Through 2020 and 2021 combined, our due estimate is at no more than 59 500. Varying NAA test strategies heavily obscured SARS-CoV-2-related EM, particularly within the second year of the proclaimed pandemic. We compensated changes in test strategies by assuming that age-cohort-specific NAA-conditional mortality rates during the first pandemic year reflected SARS-CoV-2-characteristic constants.

Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016).

Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to investigate drug-related deaths in Iran in 2014-2016 and forecast the death toll by 2019. In this longitudinal study, Box-Jenkins time series analysis was used to forecast drug-related deaths. To this end, monthly counts of drug-related deaths were obtained from March 2014 to March 2017. After data processing, to obtain stationary time series and examine the stability assumption with the Dickey-Fuller test, the parameters of the Autoregressive Integrated Moving Averages (ARIMA) model were determined using autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. Based on Akaike statistics, ARIMA (0, 1, 1) was selected as the best-fit model. Moreover, the Dickey-Fuller test was used to confirm the stationarity of the time series of transformed observations. The forecasts were made for the next 36 months using the ARIMA (0,1,2) model and the same confidence intervals were applied to all months. The final extracted data were analyzed using R software, Minitab, and SPSS-23. According to the Iranian Ministry of Health and the Legal Medicine Organization, there were 8883 drug-related deaths in Iran from March 2014 to March 2017. According to the time series findings, this count had an upward trend and did not show any seasonal pattern. It was forecasted that the mean drug-related mortality rate in Iran would be 245.8 cases per month until 2019. This study showed a rising trend in drug-related mortality rates during the study period, and the modeling process for forecasting suggested this trend would continue until 2019 if proper interventions were not instituted.

State-level homelessness and drug overdose mortality: Evidence from US panel data

BackgroundAlthough homelessness is a well-documented risk factor for drug overdose at the individual level, less is known about state-level homelessness and overdose mortality in the United States (US). MethodsThis study used 2007–2020 panel data for all US states and the District of Columbia, from the following sources: US Department of Housing and Urban Development (homelessness data); Centers for Disease Control and Prevention (drug overdose death counts, population estimates, and opioid prescribing rates); Bureau of Labor Statistics (unemployment rates); and the National Forensic Laboratory Information System (drug seizure data). Two-way (state and year) fixed effects models regressed log-transformed drug overdose mortality rates on homelessness prevalence, in nested models adding demographic composition and unemployment measures, as well as drug supply measures. Models were weighted by state population size, and standard errors (SEs) were clustered at the state level. ResultsHomelessness prevalence was significantly and positively associated with rates of drug overdose mortality after adjusting for nationwide trends, time-invariant differences between states, demographic composition, and unemployment rates (b[SE]=0.98[0.36], p=0.009). The positive association between homelessness prevalence and overdose mortality was attenuated at higher levels of fentanyl availability (fentanyl involvement in drug seizures; interaction term b[SE]=−0.02[0.01], p=0.001). ConclusionThe positive association between state-level homelessness and drug overdose mortality suggests that policies and programs to prevent and reduce homelessness represent fundamental elements of a comprehensive response to the US overdose crisis.

Changes in spatial clusters of cancer incidence and mortality over 15 years in South Korea: Implication to cancer control.

The temporal investigation of high-risk areas of cancer incidence and mortality can provide practical implications in cancer control. We aimed to investigate the changes in spatial clusters of incidence and mortality from 1999 through 2013 by major cancer types in South Korea. We applied flexible scan statistics to identify spatial clusters of cancer incidence and mortality by three 5-year periods and seven major cancer types using the counts of new cases and deaths and population in 244 districts during 1999-2013. Then, we compared the changes across three periods in the locations of primary clusters of incidence and mortality by cancer types. To explore the determinants that possibly affect cancer cluster areas, we compared geographic characteristics between clustered and non-clustered areas. While incidence clusters for lung, stomach, and liver cancer remained in the same areas over 15 years, mortality clusters were relocated to the areas similar to those of incidence clusters. In contrast, colorectal, breast, cervical, and prostate cancer displayed consistently different locations of clusters over time, indicating the disappearance of existing clusters and the appearance of new clusters. Cluster areas tended to show higher portions of older population, unemployment, smoking, and cancer screening compared to non-cluster areas particularly for mortality. Our findings of diverse patterns of changes in cancer incidence and mortality clusters over 15 years can indicate the degree of effectiveness in cancer prevention and treatment depending on the area and suggest the need for area-specific applications of different cancer control programs.

A Sequential Cross-Sectional Analysis Producing Robust Weekly COVID-19 Rates for South East Asian Countries.

The COVID-19 pandemic has expanded fast over the world, affecting millions of people and generating serious health, social, and economic consequences. All South East Asian countries have experienced the pandemic, with various degrees of intensity and response. As the pandemic progresses, it is important to track and analyse disease trends and patterns to guide public health policy and treatments. In this paper, we carry out a sequential cross-sectional study to produce reliable weekly COVID-19 death (out of cases) rates for South East Asian countries for the calendar years 2020, 2021, and 2022. The main objectives of this study are to characterise the trends and patterns of COVID-19 death rates in South East Asian countries through time, as well as compare COVID-19 rates among countries and regions in South East Asia. Our raw data are (daily) case and death counts acquired from "Our World in Data", which, however, for some countries and time periods, suffer from sparsity (zero or small counts), and therefore require a modelling approach where information is adaptively borrowed from the overall dataset where required. Therefore, a sequential cross-sectional design will be utilised, that will involve examining the data week by week, across all countries. Methodologically, this is achieved through a two-stage random effect shrinkage approach, with estimation facilitated by nonparametric maximum likelihood.

A baseline study on mortality and air pollution in Kabyle village (northern Algeria) under COVID-19

The present work is a preliminary study of mortality data and fine particulate matter (≤ 2.5 µm, PM2.5) concentration in Kabyle village (northern Algeria) under COVID-19. Three epidemiological measures were used to quantify COVID-19-attributable mortality during the years 2019, 2020, 2021 and the first half of 2022, namely monthly death count, crude excess mortality (EM) and P-score (%). In addition, PM2.5 concentration was measured three times minimum in the morning (between 6:00 and 10:00 a.m.) for 2 to 25 days depending on the month, from August to October 2021 and from January to June 2022 to look for a possible correlation between air quality and monthly excess mortality. The P-score, as the most interesting parameter for comparison purposes, was found to range from approximately -67 % (in 2021, for women 65 years and older) to 133 % (in 2021, for men 65 years and older). For those under 65, the P-score is undefined, either because their EM is zero (the case in 2021 for men under 65) or because expected mortality is zero. The daily PM2.5 concentration varied from 0.5 to 150 µg/m3 during the entire observation period. A moderate positive correlation was found between monthly EM and monthly PM2.5 concentration, for women of all ages only (Kendall’s tau (τ) ~ 0.22, p = 0.00). This work could inspire other larger studies, which should include, for example, all the 23 villages of the municipality, considering other air pollution parameters with, in addition, a more consequent recording frequency.

Tracking the Uneven Outcomes of COVID-19 on Racial and Ethnic Groups: Implications for Health Policy.

The socioeconomic shocks of the first COVID-19 pandemic wave disproportionately affected vulnerable groups. But did that trend continue to hold during the Delta and Omicron waves? Leveraging data from the Johns Hopkins Coronavirus Resource Center, this paper examines whether demographic inequalities persisted across the waves of COVID-19 infections. The current study utilizes fixed effects regressions to isolate the marginal relationships between socioeconomic factors with case counts and death counts. Factors include levels of urbanization, age, gender, racial distribution, educational attainment, and household income, along with time- and state-specific COVID-19 restrictions and other time invariant controls captured via fixed effects controls. County-level health outcomes in large metropolitan areas show that despite higher incidence rates in suburban and exurban counties, urban counties still had disproportionately poor outcomes in the latter COVID-19 waves. Policy makers should consider health disparities when developing long-term public health regulatory policies to help shield low-income households from the adverse effects of COVID-19 and future pandemics.

Estimating Drug Involvement in Fatal Overdoses With Incomplete Information

Death certificates provide incomplete information on the specific drug categories involved in fatal overdoses. The accuracy of previously developed corrections for this and modifications to them was examined. Uncorrected mortality rates were compared with rates from the preferred correction models. Data were obtained for the universe of 932,364 drug overdoses in the U.S. between 1999 and 2020, including 769,982 (82.6%) with a drug classification and 162,382 (17.4%) without, from the National Center for Health Statistics Multiple Cause of Death files. Opioid and cocaine involvement for unclassified overdoses were estimated using multiple approaches. Prediction accuracy was assessed as the mean absolute deviation between actual and estimated involvement for a test sample with known drug involvement. Corrected death rates from preferred models were compared with uncorrected rates. Analyses were conducted in 2022-2023. Previous regression-based corrections that controlled for decedent characteristics can be improved upon by adding state-fixed effects as covariates. Once this is done, supplementary controls for county characteristics or contributory causes of death do not materially improve prediction accuracy. Properly designed naïve models, which distribute unspecified drug deaths proportionately to specified deaths, often provide similar results and, for county-level analyses, supply the most accurate predictions. Uncorrected results substantially understate the levels of opioid and cocaine involvement and can either understate or overstate changes over time. Failing to correct for incomplete information on death certificates leads to inaccurate counts of deaths from specific categories of drugs, such as opioids. However, relatively simple corrections are available that substantially improve accuracy.

Counting bodies, preventing war: Future conflict and the ethics of fatality numbers

With conflict prevention as a commonplace agenda of international organisations, numerous instruments for gathering knowledge about potential armed conflict have emerged. This article focuses on the mode of knowing war through quantification in the form of fatality statistics or ‘death counts’, which are taken by analysts and policymakers to indicate the severity and extent of conflicts. Drawing on official documents and interviews, I argue that fatality numbers are productive of the reality of violent conflict as they shape what counts as conflict and what does not. In the reporting by prevention actors, such numbers indicate past and future trends of armed violence and, in this way, bolster the imperative to prevent by creating quantified futures of conflict. However, fatality numbers also normalise deadly violence as a baseline criterion, thus also limiting the scope of what is known as future conflict and omitting lived experiences from the abstractions behind such numbers.

Heat-related mortality in Europe during the summer of 2022

Over 70,000 excess deaths occurred in Europe during the summer of 2003. The resulting societal awareness led to the design and implementation of adaptation strategies to protect at-risk populations. We aimed to quantify heat-related mortality burden during the summer of 2022, the hottest season on record in Europe. We analyzed the Eurostat mortality database, which includes 45,184,044 counts of death from 823 contiguous regions in 35 European countries, representing the whole population of over 543 million people. We estimated 61,672 (95% confidence interval (CI) = 37,643–86,807) heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths; 95% CI = 13,793–22,225), Spain (11,324; 95% CI = 7,908–14,880) and Germany (8,173; 95% CI = 5,374–11,018) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million, 95% CI = 226–364), Greece (280, 95% CI = 201–355), Spain (237, 95% CI = 166–312) and Portugal (211, 95% CI = 162–255) had the highest heat-related mortality rates. Relative to population, we estimated 56% more heat-related deaths in women than men, with higher rates in men aged 0–64 (+41%) and 65–79 (+14%) years, and in women aged 80+ years (+27%). Our results call for a reevaluation and strengthening of existing heat surveillance platforms, prevention plans and long-term adaptation strategies.