Commissioned by the Healthcare Quality Improvement Partnership (HQIP) on behalf of NHS England, this report includes analysis of 3,347 children who died in England between 1 April 2019 and 31 March 2020 and investigates the characteristics of their deaths to identify if socio-economic deprivation is associated with childhood mortality.

BackgroundAdverse socioeconomic conditions at the individual and regional levels are associated with an increased risk of mortality. However, few studies have examined this relationship using multilevel analysis and, if so, only within a single country. This study aimed to examine this relationship using data from several European countries.MethodsIndividual-level data were obtained from Waves 5 to 9 of the Survey of Health, Ageing and Retirement in Europe, while regional-level data were obtained from the Luxembourg Income Study Database. Cox regression analysis with gamma-shared frailty and a random intercept for country of residence was used to examine the association between individual mortality from all causes, cancer, heart attack, and stroke and measures of socioeconomic deprivation at the individual level, including material and social deprivation indices, and at the area level, including the Gini index.ResultsThe risk of mortality from all causes was increased for respondents with material deprivation (hazard ratio (HR) = 1.77, 95% CI = [1.60, 1.96]) and social deprivation (HR = 7.63, 95% CI = [6.42, 9.07]) compared with those without. A similar association was observed between individual deprivation and the risk of mortality from cancer, heart attack, or stroke. Regional deprivation had a modest contextual effect on the individual risk of death from all causes and cancer. However, when individual-level deprivation was included in the models, no contextual effects were found.ConclusionThe results indicate that individual socioeconomic conditions significantly predict causes of death in older European adults, with those with material deprivation and social deprivation having a higher risk of death from all causes, including cancer, heart attack, and stroke, while the Gini index has a minimal effect, although the Gini index reflects regional disparities across Europe.

ABSTRACT Objectives: to analyze social inequalities in spatial distribution of fetal and infant mortality by avoidable causes and identify the areas of greater risk of occurrence. Methods: avoidable deaths of fetal and infant residents of Recife/Brazil were studied. The rates of avoidable fetal and infant mortality were calculated for two five-year periods, 2006-2010 and 2011-2015. The scan statistics was used for spatial analysis and related to the social deprivation index. Results: out of the total 2,210 fetal deaths, 80% were preventable. Avoidable fetal mortality rates increased by 8.1% in the five-year periods. Of the 2,846 infant deaths, 74% were avoidable, and the infant mortality rate reduced by 0.13%. Conclusions: in the spatial analysis, were identified clusters with higher risk for deaths. The social deprivation index showed sensibility with areas of worse living conditions.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. Notes: 1. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). This new organisation structure is reflected from this publication onwards. 2. There is a shortfall in the number of records for Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), The Shrewsbury and Telford Hospital NHS Trust (trust code RXW), and Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

This indicator measures inequalities in life expectancy at birth within England as a whole, each English region, and each local authority. Life expectancy at birth is calculated for each deprivation decile of lower super output areas within each area and then the slope index of inequality (SII) is calculated based on these figures.

The SII is a measure of the social gradient in life expectancy, i.e., how much life expectancy varies with deprivation. It takes account of health inequalities across the whole range of deprivation within each area and summarises this in a single number. This represents the range in years of life expectancy across the social gradient from most to least deprived, based on a statistical analysis of the relationship between life expectancy and deprivation across all deprivation deciles.

Life expectancy at birth is a measure of the average number of years a person would expect to live based on contemporary mortality rates. For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.

The SII for England and for regions have been presented alongside the local authority figures in order to improve the display of the indicators on the overview page. However, they should not be considered as comparators for the local authority figures. The SII for England takes account of the full range of deprivation and mortality across the whole country. This does not therefore provide a suitable benchmark with which to compare local authority results, which take into account the range of deprivation and mortality within much smaller geographies.

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BackgroundIn order to tackle social inequalities in mortality, it is crucial to quantify them. We produced French deprivation-specific life tables for the period 2016–2018 to measure the social gradient in adult all-cause mortality.MethodsData from the Permanent Demographic Sample (EDP) were used to provide population and death counts by age, sex and deprivation quintile. The European Deprivation Index (EDI), applied at a sub-municipal geographical level, was used as an ecological measure of deprivation. Smoothed mortality rates were calculated using a one-dimensional Poisson counts smoothing method with P-Splines. We calculated life expectancies by age, sex and deprivation quintile as well as interquartile mortality rate ratios (MRR).ResultsAt the age of 30, the difference in life expectancy between the most and least deprived groups amounted to 3.9 years in males and 2.2 years in females. In terms of relative mortality inequalities, the largest gaps between extreme deprivation groups were around age 55 for males (MRR = 2.22 [2.0; 2.46] at age 55), around age 50 in females (MRR = 1.77 [1.48; 2.1] at age 47), and there was a decrease or disappearance of the gaps in the very older adults.ConclusionsThere is a strong social gradient in all-cause mortality in France for males and females. The methodology for building these deprivation-specific life tables is reproducible and could be used to monitor its development. The tables produced should contribute to improving studies on net survival inequalities for specific diseases by taking into account the pre-existing social gradient in all-cause mortality.

OBJECTIVES: To determine the association between area and individual measures of social disadvantage and infant health in the United Kingdom (UK). DESIGN: Systematic review and meta-analyses. DATA SOURCES: 26 databases and web sites, reference lists, experts in the field and hand-searching. STUDY SELECTION: 36 prospective and retrospective observational studies with socio-economic data and health outcomes for infants in the UK, published from 1994 to May 2011. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers assessed the methodological quality of the studies and abstracted data. Where possible, study outcomes were reported as odds ratios for the highest versus the lowest deprivation quintile. RESULTS: In relation to the highest versus lowest area deprivation quintiles the odds of adverse birth outcomes were 1.81 (1.71 to 1.92) for low birth weight, 1.67 (1.42 to 1.96) for premature birth and 1.54 (1.39 to 1.72) for still birth. For infant mortality rates the odds ratios were 1.72 (1.37 to 2.15) overall, 1.61 (1.08 to 2.39) for neonatal and 2.31 (2.03 to 2.64) for post-neonatal mortality. For lowest versus highest social class, the odds were 1.79 (1.71 to 1.92) for premature birth, 1.52 (1.44 to 1.61) for overall infant mortality, 1.42 (1.33 to1.51) for neonatal and 1.69 (1.53 to 1.87) for post-neonatal mortality. There are similar patterns for other infant health outcomes with the possible exception of failure to thrive, where there is no clear association. CONCLUSIONS: This review quantifies the influence of social disadvantage on infant outcomes in the UK. The magnitude of effect is similar across a range of area and individual deprivation measures and birth and mortality outcomes. Further research should explore the factors that are more proximal to mothers and infants, to help throw light on the most appropriate times to provide support and the form(s) that this support should take.

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. The COVID-19 pandemic has wider impacts on individuals' health, and their use of healthcare services, than those that occur as the direct result of infection. Reasons for this may include: * Individuals being reluctant to use health services because they do not want to burden the NHS or are anxious about the risk of infection. * The health service delaying preventative and non-urgent care such as some screening services and planned surgery. * Other indirect effects of interventions to control COVID-19, such as mental or physical consequences of distancing measures. This dataset provides information on trend data regarding the wider impact of the pandemic on the number of deaths in Scotland, derived from the National Records of Scotland (NRS) weekly deaths registration data. Data show recent trends in deaths (2020), whether COVID or non-COVID related, and historic trends for comparison (five-year average, 2015-2019). The recent trend data are shown by age group and sex, and the national data are also shown by broad area deprivation category (Scottish Index of Multiple Deprivation, SIMD). This data is also available on the COVID-19 Wider Impact Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications.

Additional file 2: Characteristics of participants included in the analysis of NDI/RSI/SVI and excluded due to missingness.

Estimated excess YLL (95% confidence intervals), of direct, indirect, and total excess deaths, weeks 11 to 52, 7 March 2020 to 25 December 2020.

Additional file 7: The result of pathway enrichment analysis for neighborhood sociodemographic environment-related metabolites.

Background: Sudden Unexpected Death in Infancy (SUDI) is a leading cause of preventable infant mortality and strongly associated with social adversity. While this has been noted over many decades, most previous studies have used single economic markers in social disadvantage analyses. To date there have been no previous attempts to analyze the cumulative effect of multiple adversities in combination on SUDI risk.Methods: Based on sociological theories of social exclusion, a multidimensional framework capable of producing an overall measure of family-level social vulnerability was developed, accounting for both increasing disadvantage with increasing prevalence among family members and effect of family structures. This framework was applied retrospectively to all cases of SUDI that occurred in Queensland between 2010 and 2014. Additionally, an exploratory factor analysis was performed to investigate whether differing “types” of vulnerability could be identified.Results: Increased family vulnerability was associated with four major known risk factors for sudden infant death: smoking, surface sharing, not-breastfeeding and use of excess bedding. However, families with lower levels of social vulnerability were more likely to display two major risk factors: prone infant sleep position and not room-sharing. There was a significant positive relationship between family vulnerability and the cumulative total of risk factors. Exploratory factor analysis identified three distinct vulnerability types (chaotic lifestyle, socioeconomic and psychosocial); the first two were associated with presence of major SUDI risk factors. Indigenous infants had significantly higher family vulnerability scores than non-Indigenous families.Conclusion: A multidimensional measure that captures adversity across a range of indicators highlights the need for proportionate universalism to reduce the stalled rates of sudden infant death. In addition to information campaigns continuing to promote the importance of the back-sleeping position and close infant-caregiver proximity, socially vulnerable families should be a priority population for individually tailored or community based multi-model approaches.

Exponentiated coefficients and 95% confidence intervals from negative binomial regression.Estimates of the effects of social determinants on common factor mortality.

BackgroundSocial determinants of health (SDoH) affect congenital heart disease (CHD) mortality across all forms and age groups. We sought to evaluate risk of mortality from specific SDoH stratified across CHD to guide interventions to alleviate this risk.MethodsWe searched electronic databases between January 1980 and June 2019 and included studies that evaluated occurrence of CHD deaths and SDoH in English articles. Meta-analysis was performed if SDoH data were available in >3 studies. We included race/ethnicity, deprivation, insurance status, maternal age, maternal education, single/multiple pregnancy, hospital volume, and geographic location of patients as SDoH. Data were pooled using random-effects model and outcome was reported as odds ratio (OR) with 95% confidence interval (CI).ResultsOf 17,716 citations reviewed, 65 met inclusion criteria. Sixty-three were observational retrospective studies and two prospective. Of 546,981 patients, 34,080 died. Black patients with non-critical CHD in the first year of life (Odds Ratio 1.62 [95% confidence interval 1.47–1.79], I2 = 7.1%), with critical CHD as neonates (OR 1.27 [CI 1.05-1.55], I2 = 0%) and in the first year (OR 1.68, [1.45-1.95], I2 = 0.3%) had increased mortality. Deprived patients, multiple pregnancies, patients born to mothers

aHRs for 90-day case fatality in septic shock patients with SMI compared to those without (1:up to 4 patients matched, within hospital, for age (5-year range), sex, degree of social deprivation, and year of hospitalization).

Case fatality in septic shock patients with versus without SMI (1:up to 4 patients matched, within hospital, for age (5-year range), sex, degree of social deprivation, and year of hospitalization).

Total number of estimated avoidable deaths and survivors in each quintile of the composite CSE Index and the Socioeconomic and Education domains in 2007 (year of registration).

BackgroundRecent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data exclude Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between the island and the mainland, it may not be appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the US to Puerto Rico. Thus, we aim to (1) examine the types of neighborhood environments older Puerto Rican adults reside in and (2) explore the association between neighborhood environments and all-cause mortality.MethodsWe linked data from the 2000 US Census to the longitudinal Puerto Rican Elderly Health Conditions Project (PREHCO) with mortality follow-up through 2021 to examine the effects of the baseline neighborhood environment on all-cause mortality among 3,469 participants. Latent profile analysis, a model-based clustering technique, classified Puerto Rican neighborhoods based on 19 census block group indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. The associations between the latent classes and all-cause mortality were assessed using multilevel mixed-effects parametric survival models with a Weibull distribution.ResultsA five-class model was fit on 2,477 census block groups in Puerto Rico with varying patterns of social (dis)advantage. Our results show that older adults residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico were at higher risk of death over the 19-year study period relative to the Urban Low Deprivation cluster, controlling for individual-level covariates.ConclusionsConsidering Puerto Rico's socio-structural reality, we recommend that policymakers, healthcare providers, and leaders across industries to (1) understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and (2) make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico.

Total number of estimated avoidable deaths and survivors in each quintile of the Connectedness, Housing and Health services domains in 2007 (year of registration).

Factor loading and alpha-reliability from principal components analysis.