Spatial Risk Factors for Pillar 1 COVID-19 Excess Cases and Mortality in Rural Eastern England, UK.
Ontology highlight
ABSTRACT:
SUBMITTER: Brainard J
PROVIDER: S-EPMC8661982 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Risk factors for increased risk of death from COVID-19 have been identified, but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015-2019. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
| S-EPMC8213785 | biostudies-literature
Project description:Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates.The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period.Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend.
| S-EPMC9465060 | biostudies-literature
Project description:BackgroundAn increasing proportion of malaria cases diagnosed in UK residents with a history of travel to malaria endemic areas are due to Plasmodium falciparum.MethodsIn order to identify travellers at most risk of acquiring malaria a proportional hazards model was used to estimate the risk of acquiring malaria stratified by purpose of travel and age whilst adjusting for entomological inoculation rate (EIR) and duration of stay in endemic countries.ResultsTravellers visiting friends and relatives and business travellers were found to have significantly higher hazard of acquiring malaria (adjusted hazard ratio (HR) relative to that of holiday makers 7.4, 95% CI 6.4-8.5, p < 0. 0001 and HR 3.4, 95% CI 2.9-3.8, p < 0. 0001, respectively). All age-groups were at lower risk than children aged 0-15 years.ConclusionsThese estimates of the increased risk for business travellers and those visiting friends and relatives should be used to inform programmes to improve awareness of the risks of malaria when travelling.
| S-EPMC4132200 | biostudies-literature
Project description:Mortality due to massive events like the COVID-19 pandemic is underestimated because of several reasons, among which the impossibility to track all positive cases and the inadequacy of coding systems are presumably the most relevant. Therefore, the most affordable method to estimate COVID-19-related mortality is excess mortality (EM). Very often, though, EM is calculated on large spatial units that may entail different EM patterns and without stratifying deaths by age or sex, while, especially in the case of epidemics, it is important to identify the areas that suffered a higher death toll or that were spared. We developed the Stata COVID19_EM.ado procedure that estimates EM within municipalities in six subgroups defined by sex and age class using official data provided by ISTAT (Italian National Statistics Bureau) on deaths occurred from 2015 to 2020. Using simple linear regression models, we estimated the mortality trend in each age-and-sex subgroup and obtained the expected deaths of 2020 by extrapolating the linear trend. The results are then displayed using choropleth maps. Subsequently, local autocorrelation maps, which allow to appreciate the presence of local clusters of high or low EM, may be obtained using an R procedure that we developed.•We focused on estimating excess mortality in small-scale spatial units (municipalities) and in population strata defined by age and sex.•This method gives a deeper insight on excess mortality than summary figures at regional or national level, enabling to identify the local areas that suffered the most and the high-risk population subgroups within them.•This type of analysis could be replicated on different time frames, which might correspond to successive epidemic waves, as well as to periods in which containment measures were enforced and for different age classes; moreover, it could be applied in every instance of mortality crisis within a region or a country.
| S-EPMC8105045 | biostudies-literature
Project description:The Covid-19 pandemic has claimed many lives in the UK and globally. The objective of this paper is to study whether the number of deaths not registered as Covid-19-related has increased compared to what would have been expected in the absence of the pandemic. Reasons behind this might include Covid-19 underreporting, avoiding visits to hospitals or GPs, and the effects of the lockdown. I used weekly ONS data on the number of deaths in England and Wales that did not officially involve Covid-19 over the period 2015-2020. Simply observing trends is not sufficient as spikes in deaths may occasionally occur. I thus followed a difference-in-differences econometric approach to study whether there was a relative increase in deaths not registered as Covid-19-related during the pandemic, compared to a control. Results suggest that there were an additional 968 weekly deaths that officially did not involve Covid-19, compared to what would have otherwise been expected. It is possible that some people are dying from Covid-19 without being diagnosed, and/or that there are excess deaths due to other causes as a result of the pandemic. Analysing the cause of death for any excess non-covid-19 deaths will shed light upon the reasons for the increase in such deaths and will help design appropriate policy responses to save lives.
| S-EPMC7836531 | biostudies-literature
Project description:The epidemiological features of Cryptosporidium infection among school-age children in China still remain unclear. Hereby, a cross-sectional study of 1637 children aged 3⁻9 years was designed to investigate the risk factors and spatial clusters of Cryptosporidium infection in a rural region of Eastern China. Stool specimens collected from participants were examined using the auramine-phenol and modified acid-fast staining. Univariable and multivariable analyses were performed to identify the risk factors of Cryptospordium infection. The spatial clusters were analyzed by a discrete Poisson model using SaTScan software. Our results showed that the overall prevalence of Cryptosporidium infection was 11‰ in the research region. At the age of 3⁻6 years (odds ratios (OR) = 3.072, 95% confidence intervals (CI): 1.001⁻9.427), not washing hands before eating and after defecation (OR = 3.003, 95% CI: 1.060⁻8.511) were recognized as risk factors. Furthermore, a high-risk spatial cluster (relative risk = 4.220, p = 0.025) was identified. These findings call for effective sustainable interventions including family and school-based hygienic education to reduce the prevalence of Cryptosporidium infection. Therefore, an early warning system based spatiotemporal models with risk factors is required to further improve the effectiveness and efficiency of cryptosporidiosis control in the future.
| S-EPMC5981963 | biostudies-literature
Project description:BackgroundMortality in Scotland is higher than in the rest of west and central Europe and is improving more slowly. Relative to England and Wales, the excess is only partially explained by area deprivation. We tested the extent to which sociodemographic, behavioural, anthropometric and biological factors explain the higher mortality in Scotland compared with England.MethodsPooled data from 18 nationally representative cohort studies comprising the Health Surveys for England (HSE) and the Scottish Health Survey (SHS). Cox regression analysis was used to quantify the excess mortality risk in Scotland relative to England with adjustment for baseline characteristics.ResultsA total of 193,873 participants with a mean of 9.6?years follow-up gave rise to 21,345 deaths. The age-adjusted and sex-adjusted all-cause mortality HR for Scottish respondents compared with English respondents was 1.40 (95% CI 1.34 to 1.47), which attenuated to 1.29 (95% CI 1.23 to 1.36) with the addition of the baseline socioeconomic and behavioural characteristics. Cause-specific mortality HRs attenuated only marginally to 1.43 (95% 1.28 to 1.60) for ischaemic heart disease, 1.37 (95% CI 1.15 to 1.63) for stroke, 1.41 (95% CI 1.30 to 1.53) for all cancers, 3.43 (95% CI 1.85 to 6.36) for illicit drug-related poisoning and 4.64 (95% CI 3.55 to 6.05) for alcohol-related mortality. The excess was greatest among young adults (16-44?years) and was observed across all occupational social classes with the greatest excess in the unskilled group.ConclusionsOnly a quarter of the excess mortality among Scottish respondents could be explained by the available baseline risk factors. Greater understanding is required on the lived experience of poverty, the role of social support, and the historical, environmental, cultural and political influences on health in Scotland.
| S-EPMC4283682 | biostudies-literature
Project description:ObjectivesTo examine magnitude of the impact of the COVID-19 pandemic on inequalities in premature mortality in England by deprivation and ethnicity.DesignA statistical model to estimate increased mortality in population subgroups during the COVID-19 pandemic by comparing observed with expected mortality in each group based on trends over the previous 5 years.SettingInformation on deaths registered in England since 2015 was used, including age, sex, area of residence and cause of death. Ethnicity was obtained from Hospital Episode Statistics records linked to death data.ParticipantsPopulation study of England, including all 569 824 deaths from all causes registered between 21 March 2020 and 26 February 2021.Main outcome measuresExcess mortality in each subgroup over and above the number expected based on trends in mortality in that group over the previous 5 years.ResultsThe gradient in excess mortality by area deprivation was greater in the under 75s (the most deprived areas had 1.25 times as many deaths as expected, least deprived 1.14) than in all ages (most deprived had 1.24 times as many deaths as expected, least deprived 1.20). Among the black and Asian groups, all area deprivation quintiles had significantly larger excesses than white groups in the most deprived quintiles and there were no clear gradients across quintiles. Among the white group, only those in the most deprived quintile had more excess deaths than deaths directly involving COVID-19.ConclusionThe COVID-19 pandemic has widened inequalities in premature mortality by area deprivation. Among those under 75, the direct and indirect effects of the pandemic on deaths have disproportionately impacted ethnic minority groups irrespective of area deprivation, and the white group the most deprived areas. Statistics limited to deaths directly involving COVID-19 understate the pandemic's impact on inequalities by area deprivation and ethnic group at younger ages.
| S-EPMC8710653 | biostudies-literature
Project description:BackgroundThe associations of social isolation and loneliness with premature mortality are well known, but the risk factors linking them remain unclear. We sought to identify risk factors that might explain the increased mortality in socially isolated and lonely individuals.MethodsWe used prospective follow-up data from the UK Biobank cohort study to assess self-reported isolation (a three-item scale) and loneliness (two questions). The main outcomes were all-cause and cause-specific mortality. We calculated the percentage of excess risk mediated by risk factors to assess the extent to which the associations of social isolation and loneliness with mortality were attributable to differences between isolated and lonely individuals and others in biological (body-mass index, systolic and diastolic blood pressure, and handgrip strength), behavioural (smoking, alcohol consumption, and physical activity), socioeconomic (education, neighbourhood deprivation, and household income), and psychological (depressive symptoms and cognitive capacity) risk factors.Findings466 901 men and women (mean age at baseline 56·5 years [SD 8·1]) were included in the analyses, with a mean follow-up of 6·5 years (SD 0·8). The hazard ratio for all-cause mortality for social isolation compared with no social isolation was 1·73 (95% CI 1·65-1·82) after adjustment for age, sex, ethnic origin, and chronic disease (ie, minimally adjusted), and was 1·26 (95% CI 1·20-1·33) after further adjustment for socioeconomic factors, health-related behaviours, depressive symptoms, biological factors, cognitive performance, and self-rated health (ie, fully adjusted). The minimally adjusted hazard ratio for mortality risk related to loneliness was 1·38 (95% CI 1·30-1·47), which reduced to 0·99 (95% CI 0·93-1·06) after full adjustment for baseline risks.InterpretationIsolated and lonely people are at increased risk of death. Health policies addressing risk factors such as adverse socioeconomic conditions, unhealthy lifestyle, and lower mental wellbeing might reduce excess mortality among the isolated and the lonely.FundingAcademy of Finland, NordForsk, and the UK Medical Research Council.
| S-EPMC5463031 | biostudies-literature
Project description:IntroductionIncomplete understanding of TB transmission dynamics in high HIV prevalence settings remains an obstacle for prevention. Understanding where transmission occurs could provide a platform for case finding and interrupting transmission.MethodsFrom 2012-2015, we sought to recruit all adults starting TB treatment in a Ugandan community. Participants underwent household (HH) contact investigation, and provided names of social contacts, sites of work, healthcare and socializing, and two sputum samples. Mycobacterium tuberculosis culture-positive specimens underwent 24-loci MIRU-VNTR and spoligotyping. We sought to identify epidemiologic links between genotype-matched cases by analyzing social networks and mapping locations where cases reported spending ?12 hours over the one-month pre-treatment. Sites of spatial overlap (?100m) between genotype-matched cases were considered potential transmission sites. We analyzed social networks stratified by genotype clustering status, with cases linked by shared locations, and compared network density by location type between clustered vs. non-clustered cases.ResultsOf 173 adults with TB, 131 (76%) were enrolled, 108 provided sputum, and 84/131 (78%) were MTB culture-positive: 52% (66/131) tested HIV-positive. Of 118 adult HH contacts, 105 (89%) were screened and 3 (2.5%) diagnosed with active TB. Overall, 33 TB cases (39%) belonged to 15 distinct MTB genotype-matched clusters. Within each cluster, no cases shared a HH or reported shared non-HH contacts. In 6/15 (40%) clusters, potential epidemiologic links were identified by spatial overlap at specific locations: 5/6 involved health care settings. Genotype-clustered TB social networks had significantly greater network density based on shared clinics (p<0.001) and decreased density based on shared marketplaces (p<0.001), compared to non-clustered networks.ConclusionsIn this molecular epidemiologic study, links between MTB genotype-matched cases were only identifiable via shared locations, healthcare locations in particular, rather than named contacts. This suggests most transmission is occurring between casual contacts, and emphasizes the need for improved infection control in healthcare settings in rural Africa.
| S-EPMC5811029 | biostudies-literature