A model-based scan statistic with enhanced specificity for detecting spatial clusters of high mortality risk

Mortality during the SARS-CoV-2 pandemic was studied in many countries. The results were strongly influenced by the chosen calculation method, the adjustment to the ageing of the population and the reference periods used. Smaller-scale studies sometimes showed considerable differences within countries, but it is unclear whether the differences within a country were due to the fact that the studies were small (sampling error) or whether they were true differences. In an earlier small-scale study in Frankfurt, we examined mortality during the first two years of the pandemic. Our aim was to continue this analysis until the end of 2023, for the first time taking into account other factors influencing mortality such as influenza and heat. We obtained population data for Frankfurt am Main for 2016-2023 from the Municipal Office of Statistics, City of Frankfurt/Main, mortality data from 2016 to 2023 from the Hessian State Office for Health and Care, data on SARS-CoV-2 and influenza notifications from the homepage of the Robert Koch-Institute and weather data from the homepage of the German Meteorological Office. For calculating standardized mortality ratios (SMR= observed number of deaths divided by the expected number of deaths), we multiplied the mean mortality rate for 5 age groups from 2016-2019 with the total numer of residents in those age groups in the further years or periods, and finally added the numbers of expected deaths per age group. The update of the assessment of mortality adjusted for age and population trend in the years 2020-2023 in Frankfurt am Main shows an excess mortality (SMR 1.029; 95% CI 1.004-1.054, +185 excess deaths) in 2022, followed by a negative excess mortality in 2023 (SMR 0.972; 95% CI 0.948-0.996). In the years 2020 and 2021 however, no increase in excess mortality had been found (2020: SMR 0.976; 95% CI 0.951-1.001; 2021: 0.998; 95% CI 0,973-1.023). In the second wave of the SARS CoV-2 pandemic with the Wuhan type (fall 2020), a significantly increased mortality was found (SMR 1.106; 95% CI 1.066-1.147, +274 deaths), as well as during the first four waves overall (Wuhan, Alpha and Delta type) (SMR 1.023; 95 CI 1.001-1.045), whereas no increased mortality occurred during the further waves with the Omikron variant in 2022 (SMR 0.988; 95% CI 0.963-1.014). The increased mortality in 2022 was associated with an influenza wave in the last 6 weeks of the year, which had led to a strong increase in mortality (SMR 1.250; 95% CI 1.170-1.330). During the SARS-CoV-2 pandemic, significant excess mortality occurred in Frankfurt am Main only in the second wave at the end of 2020 before vaccination was introduced; in all other waves, no significant excess mortality was recorded. Overall, there was a non-significant negative excess mortality in Frankfurt am Main in 2020 and 2021 and a significant negative excess mortality in 2023. In 2022, however, a significant excess mortality was observed, which could not be attributed to SARS-CoV-2 but to a short, intense wave of influenza in the last 6 weeks at the end of that year, which had also led to a significant increase in mortality throughout Germany. This influenza wave was associated with an excess mortality rate in Frankfurt am Main, which was higher than in any wave of the SARS-CoV-2 pandemic in Frankfurt am Main. The number of excess deaths during that influenza waves was larger than the excess deaths during all SARS-CoV-2 waves altogether. This remarkable fact should be taken into account when dealing with the evaluation of the pandemic, a process which is increasingly beeing called for in many ways in Germany but is still pending.

National mortality data for Germany before and throughout the pandemic: There is an excess mortality exceeding COVID-19-attributed fatalities

BackgroundApproximately 32,000 people take their own lives every year in the United States. In Kentucky, suicide mortality rates have been steadily increasing since 1999. Few studies in the United States have assessed spatial clustering of suicides. The purpose of this study was to identify high-risk clusters of suicide at the county level in Kentucky and assess the characteristics of those suicide cases within the clusters.MethodsA spatial epidemiological study was undertaken using suicide data for the period January 1, 1999 to December 31, 2008, obtained from the Kentucky Office of Vital Statistics. Descriptive analyses using Pearson's chi-square test and t-test were performed to determine whether differences existed in age, marital status, year, season, and suicide method between males and females, and between cases inside and outside high-risk spatial clusters. Annual age-adjusted cumulative incidence rates were also calculated. Suicide incidence rates were spatially smoothed using the Spatial Empirical Bayesian technique. Kulldorff's spatial scan statistic was applied on all suicide cases at the county level to identify counties with the highest risks of suicide. Temporal cluster analysis was also performed.ResultsThere were a total of 5,551 suicide cases in Kentucky from 1999 to 2008, of which 5,237 (94%) were included in our analyses. The majority of suicide cases were males (82%). The average age of suicide victims was 45.4 years. Two statistically significant (p < 0.05) high-risk spatial clusters, involving 15 counties, were detected. The county level cumulative incidence rate in the most likely high-risk cluster ranged from 12.4 to 21.6 suicides per 100,000 persons. The counties inside both high-risk clusters had relative risks ranging from 1.24 to 1.38.ConclusionsStatistically significant high-risk spatial clusters of suicide were detected at the county level. This study may be useful for guiding future research and intervention efforts. Future studies will need to focus on these high-risk clusters to investigate reasons for these occurrences.

BackgroundDespite widespread vaccination efforts, significant excess mortality continued in various countries following the COVID-19 pandemic. This study aims to estimate excess mortality during 2022 in 21 countries and regions, and to examine the relationship of governmental control measures and vaccination rates with excess mortality during 2021–2 at an ecological level.MethodsExcess mortality for 2022 was estimated by analysing weekly mortality data from January 2020 to December 2022 across 21 countries and regions participating in the C-MOR consortium. This was achieved by comparing the observed age-standardized mortality rates per 100 000 population to a baseline derived from historical data (2015–19). Governmental control measures and vaccination efforts were investigated for their association with weekly excess mortality during 2021–2 in multilevel models with country as a random effect.ResultsAll 21 countries experienced excess mortality in 2022, ranging from 8.6 (Peru) to 116.2 (Georgia) per 100 000 population, noting that rates were not directly comparable across countries. Many countries had higher excess mortality in 2022 compared with previous years. Mauritius showed a significant excess mortality for the first time in 2022. The proportion of COVID-19 deaths relative to total deaths decreased in 2022 for most countries, except Australia. Governmental control measures and vaccinations were associated with reduced excess mortality in 2021 and 2022, respectively.ConclusionThe study reveals sustained excess mortality throughout 2022. Excess deaths were mainly non-COVID-19-related, likely due to displaced mortality or to broader long-term impacts of the pandemic response. Governmental control policies and vaccination efforts were associated with lower excess mortality. These findings provide critical insights into pandemic mortality dynamics and emphasize the need for continued vigilance and adaptive public health strategies.

Decision letter: Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

Decision letter: Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022

Editor's evaluation: Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022

The primary aim of this paper is to expose the use and the value of spatial statistical analysis in business and especially in designing economic policies in rural areas. Specifically, we aim to present under a unified framework, the use of both point and area‐based methods, in order to analyze in‐depth economic data, as well as, to drive conclusions through interpreting the analysis results. The motivating problem is related to the establishment of women‐run enterprises in a rural area of Greece. Moreover, in this article, the spatial scan statistic is successfully applied to the spatial economic data at hand, in order to detect possible clusters of small women‐run enterprises in a rural mountainous and disadvantaged region of Greece. Then, it is combined with Geographical Information System based on Local Indicator of Spatial Autocorrelation scan statistic for further exploring and interpreting the spatial patterns. The rejection of the random establishment of women‐run enterprises and the interpretation of the clustering patterns are deemed necessary, in order to assist government in designing policies for rural development. Copyright © 2014 John Wiley &amp; Sons, Ltd.

We compared the effects of hot and cold spells on cardiovascular mortality in the Czech Republic over 1986-2006 and examined differences between population groups. We used analogous definitions for hot and cold spells that are based on quantiles of daily average temper- ature anomalies and do not incorporate any location-specific threshold. Epidemics of influenza/ acute respiratory infections were identified, and corresponding periods were excluded from the analysis. Both hot and cold spells are associated with significant excess cardiovascular mortality. The effects of hot spells are more direct (unlagged) and typically concentrated in a few days of a hot spell, while cold spells are associated with indirect (lagged) mortality impacts persisting after a cold spell ends. Although the mortality peak is less pronounced for cold spells, the cumulative magnitude of excess mortality is larger for cold than hot spells. Gender differences consist mainly of much larger excess mortality of females in hot spells and more lagged effects in females than males associated with cold spells. Effects of hot spells have a similar temporal pattern in all age groups but much larger magnitude in the elderly. For cold spells, by contrast, relative excess mor- tality is largest in the middle-aged population (25-59 yr). The results suggest that mechanisms playing the dominant role in inducing cold-related mortality differ between this age group (in which the effects are unlagged) and older age groups (significant excess mortality at lags of around 7 d and longer). For both high and low temperature extremes, the formulation of preven- tive measures (implemented by means of warning systems and biometeorological forecast alerts) should take into account the varied effects in individual population groups.

BackgroundIn 1955, an outbreak of arsenic poisoning caused by ingestion of arsenic-contaminated dry milk occurred in western Japan. We assessed the excess mortality among Japanese who were poisoned during this episode as infants.MethodsWe identified and enrolled 6104 survivors (mean age at enrollment, 27.4 years) who had ingested contaminated milk when they were age 2 years or younger; they were followed until 2006 (mean duration of follow-up, 24.3 years). Death certificates of subjects who died between 1982 and 2006 were examined to calculate cause-specific standardized mortality ratios (SMRs) using the mortality rate among Osaka residents as the standard.ResultsThere was no significant excess overall mortality (SMR: 1.1, 95% confidence interval: 1.0–1.2). However, significant excess mortality in both sexes was observed from diseases of the nervous system (3.7, 1.9–6.2). Excess mortality from all causes of death decreased to unity beyond 10 years after study enrollment. The 408 men who were unemployed at the time of enrollment in the study had a significantly elevated risk of death from diseases of the nervous system (25.3, 10.8–58.8), respiratory diseases (8.6, 3.1–16.8), circulatory diseases (3.2, 1.6–5.2), and external causes (2.6, 1.4–4.1).ConclusionsAs compared with the general population, survivors of arsenic poisoning during infancy had a significantly higher mortality risk from diseases of the nervous system.

Workers exposed to dimethylformamide (DMF) and/or acrylonitrile (ACN) were observed from 1950 through 1982 for mortality. The wage-roll workers exposed to DMF showed significant excess in total deaths attributable mainly to ischemic heart disease and external causes when compared with rates from E. I. Du Pont de Nemours & Co. However, there were no significant excesses in mortality when expected numbers were based on US or local statistics. No dose-response relationships were observed between DMF or ACN exposure and mortality. The significant excesses in mortality among employees exposed to DMF and/or ACN could be due to statistical chance or life-style factors

Spatial scan statistics are commonly used for geographic disease cluster detection and evaluation. We propose and implement a modified version of the simulated annealing spatial scan statistic that incorporates the concept of “non-compactness” in order to penalize clusters that are very irregular in shape. We evaluate its power for the simulated annealing scan and compare it with the circular and elliptic spatial scan statistics. We observe that, with the non-compactness penalty, the simulated annealing method is competitive with the circular and elliptic scan statistic, and both have good power performance. The elliptic scan statistic is computationally faster and is well suited for mildly irregular clusters, but the simulated annealing method deals better with highly irregular cluster shapes. The new method is applied to breast cancer mortality data from northeastern United States.

The Brazilian National Hansen's Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters. The study area included 373 municipalities in the four Brazilian states Maranhão, Pará, Tocantins and Piauí. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases/100,000 population, (ii) rate of cases <15 years/100,000 population, (iii) new cases with grade-2 disability/100,000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic. A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates >40 cases/100,000 inhabitants). There was a concentration of municipalities with higher detection rates in Pará and in the center of Maranhão. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate <15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2/population overlap with those of new cases detection and detection of children <15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters. Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

BackgroundA common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.ResultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.ConclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

ACOMCD: A multiple cluster detection algorithm based on the spatial scan statistic and ant colony optimization