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BACKGROUND: The mortality impact of COVID-19 has thus far been described in terms of crude death counts. We aimed to calibrate the scale of the modelled mortality impact of COVID-19 using age-standardised mortality rates and life expectancy contribution against other, socially determined, causes of death in order to inform governments and the public. METHODS: We compared mortality attributable to suicide, drug poisoning and socioeconomic inequality with estimates of mortality from an infectious disease model of COVID-19. We calculated age-standardised mortality rates and life expectancy contributions for the UK and its constituent nations. RESULTS: Mortality from a fully unmitigated COVID-19 pandemic is estimated to be responsible for a negative life expectancy contribution of −5.96 years for the UK. This is reduced to −0.33 years in the fully mitigated scenario. The equivalent annual life expectancy contributions of suicide, drug poisoning and socioeconomic inequality-related deaths are −0.25, −0.20 and −3.51 years, respectively. The negative impact of fully unmitigated COVID-19 on life expectancy is therefore equivalent to 24 years of suicide deaths, 30 years of drug poisoning deaths and 1.7 years of inequality-related deaths for the UK. CONCLUSION: Fully mitigating COVID-19 is estimated to prevent a loss of 5.63 years of life expectancy for the UK. Over 10 years, there is a greater negative life expectancy contribution from inequality than around six unmitigated COVID-19 pandemics. To achieve long-term population health improvements it is therefore important to take this opportunity to introduce post-pandemic economic policies to 'build back better'.

Background: The mortality impact of COVID-19 has thus far been described in terms of crude death counts. We aimed to calibrate the scale of the modelled mortality impact of COVID-19 using age-standardised mortality rates and life expectancy contribution against other, socially determined, causes of death in order to inform governments and the public. Methods: We compared mortality attributable to suicide, drug poisoning and socioeconomic inequality with estimates of mortality from an infectious disease model of COVID-19. We calculated age-standardised mortality rates and life expectancy contributions for the UK and its constituent nations. Results: Mortality from a fully unmitigated COVID-19 pandemic is estimated to be responsible for a negative life expectancy contribution of −5.96 years for the UK. This is reduced to −0.33 years in the fully mitigated scenario. The equivalent annual life expectancy contributions of suicide, drug poisoning and socioeconomic inequality-related deaths are −0.25, −0.20 and −3.51 years, respectively. The negative impact of fully unmitigated COVID-19 on life expectancy is therefore equivalent to 24 years of suicide deaths, 30 years of drug poisoning deaths and 1.7 years of inequality-related deaths for the UK. Conclusion: Fully mitigating COVID-19 is estimated to prevent a loss of 5.63 years of life expectancy for the UK. Over 10 years, there is a greater negative life expectancy contribution from inequality than around six unmitigated COVID-19 pandemics. To achieve long-term population health improvements it is therefore important to take this opportunity to introduce post-pandemic economic policies to 'build back better'.

Background: Outcomes for children who have been received into state care due to maltreatment or neglect are very poor in comparison to the general population. A mental health focussed intervention is being trialled in the UK which seeks to improve outcomes for such children. Although the main study is concerned with outcome evaluation (child mental health measures), a robust process evaluation adds nuance to the findings by examining the relationship between the interventions, the participants and the context-thereby determining what works, for whom and in what context. This paper sets out the protocol for that process evaluation. Methods: The process evaluation is embedded within a Randomised Control Trial and uses Realist Evaluation as its theoretical framework. It sets out the mechanisms of change that are used to effect positive outcomes and outlines the various aspects of the context (including service provision and the legal system that provides the statutory basis for involvement with families). Data collection is primarily qualitative and takes place with a large group of stakeholders. The analysis of the interplay between context, mechanisms and outcomes will provide a richer understanding of the main trial outcomes. Discussion: The nature of the interaction between the social, legal and practice context is complex. The use of a variety of methods including case studies, focus groups and analysis of routine data are justified and it is argued that they will provide for greater understanding of the nature of the interactions within such a challenging context.

Background: In the UK, lone parents must seek work as a condition of receiving welfare benefits once their youngest child reaches a certain age. Since 2008, the lower age limit at which these Lone Parent Obligations (LPO) apply has been reduced in steps. We used data from a nationally representative, longitudinal, household panel study to analyse the health effects of increased welfare conditionality under LPO. Methods: From the Understanding Society survey, we used data for lone mothers who were newly exposed to LPO when the age cutoff was reduced from 7 to 5 years in 2012 (intervention group 1) and from 10 to 7 years in 2010 (intervention group 2), as well as lone mothers who remained unexposed (control group 1) or continuously exposed (control group 2) at those times. We did difference-in-difference analyses that controlled for differences in the fixed characteristics of participants in the intervention and control groups to estimate the effect of exposure to conditionality on the health of lone mothers. Our primary outcome was the difference in change over time between the intervention and control groups in scores on the Mental Component Summary (MCS) of the 12-item Short-Form Health Survey (SF-12). Findings: The mental health of lone mothers declined in the intervention groups compared with the control groups. For intervention group 1, scores on the MCS decreased by 1·39 (95% CI −1·29 to 4·08) compared with control group 1 and by 2·29 (0·00 to 4·57) compared with control group 2. For intervention group 2, MCS scores decreased by 2·45 (−0·57 to 5·48) compared with control group 1 and by 1·28 (−1·45 to 4·00) compared with control group 2. When pooling the two intervention groups, scores on the MCS decreased by 2·13 (0·10 to 4·17) compared with control group 1 and 2·21 (0·30 to 4·13) compared with control group 2. Interpretation: Stringent conditions for receiving welfare benefits are increasingly common in high-income countries. Our results suggest that requiring lone parents with school-age children toseek work as a condition of receiving welfare benefits adversely affects their mental health. Funding: UK Medical Research Council, Scottish Government Chief Scientist Office, and National Health Service Research Scotland.

BACKGROUND: Unintentional injury is a leading cause of death/disability, with more disadvantaged children at greater risk. Understanding how inequalities vary by injury type, age, severity, and place of injury, can inform prevention. METHODS: For all Scotland-born children 2009-2013 (n=195,184), hospital admissions for unintentional injury (HAUI) were linked to socioeconomic circumstances (SECs) at birth: area deprivation via the Scottish Index of Multiple Deprivation (SIMD), mother's occupational social class, parents' relationship status. HAUI was examined from birth-five, and during infancy. We examined HAUI frequency, severity, injury type, and injury location (home vs. elsewhere). We estimated relative inequalities using the relative indices of inequality (RII, 95% CIs), before and after adjusting for demographics and other non-mediating SECs. FINDINGS: More disadvantaged children were at greater risk of any HAUI from birth-five, RII: 1•59(1•49-1•70), 1•74(1•62-1•86), 1•97(1•84-2•12) for area deprivation, maternal occupational social class, and relationship status respectively. These attenuated after adjustment (1•15 [1•06-1•24], 1.22 [1•12-1•33], 1.32 [1•21-1•44]). Inequalities were greater for severe (vs. non-severe), multiple (vs. one-off) and home (vs. other location) injuries. Similar patterns were seen in infancy, excluding SIMD-inequalities in falls, where infants living in more disadvantaged neighbourhoods were at lower risk (0•79 [0•62-1•00]). After adjustment, reverse SIMD-gradients were also observed for all injuries and poisonings. INTERPRETATION: Children living in more disadvantaged households are more likely to be injured across multiple dimensions of HAUI in Scotland. Upstream interventions which tackle family-level disadvantage may be most effective at reducing childhood HAUI. FUNDING: Wellcome Trust, Medical Research Council, Scottish Government Chief Scientist Office.

IntroductionMany early years interventions, e.g. UK's Family Nurse Partnership, aim to provide additional support to higher need families to prevent poor health and development. Most prediction studies using cohort surveys have had limited success in identifying which families to target for such interventions.
Objectives and ApproachTo examine whether the breadth and volume of information available in linked administrative data can predict childhood overweight (defined using International Obesity TaskForce cut-offs) at age 5 years. Data for all children born in Scotland 2011-2012 (n~120,000) were sourced from: birth registration records, maternity hospital records, health visitor and school health checks, immunisation records, prescription data. Predictors spanned socio-economic (e.g. neighbourhood deprivation, occupational status), demographic (e.g. mother's age, number of children), birth (e.g. birthweight, APGAR score), service planning (allocation to core/additional services), and health (e.g. smoking in pregnancy, overweight at age 3y). Sensitivity, specificity and positive predictive values (PPV) for each predictor was considered. The collective predictive power of >20 variables (selected a-priori) (complete case sample, n~22000) was examined using conventional area under the curve (AUC) and three machine learning methods: decision tree classification, random forest, gradient boosted trees.
ResultsOverweight at 3y was the strongest predictor of overweight at 5y (sensitivity:0.53; specificity:0.89; PPV:0.51). AUC and the machine learning approaches prioritised different sets of predictors, but all included overweight at 3y. All produced 'Moderate-Good' predictive power, falling to 'Poor-Moderate' when overweight at 3y was excluded. Multiple imputation will be carried out to address item missingness.
ConclusionOverweight at 3y was the most informative predictor, yet 49% who were overweight at 3y were not by 5y. Models excluding overweight at 3y had poor predictive power, indicating the challenge of targeting interventions to prevent overweight. Thus, universal interventions addressing the upstream determinants of childhood overweight are likely to have greatest success in supporting child health.

his project is funded by the Economic and Social Research Council grant number ES/L007487/1 (Administrative Data Research Centre–Scotland). FP,RD, PC, and MHB are also funded by the Medical Research Council [MC_UU_12017/13, MC_UU_12017/15] and the Scottish Government Chief Scientist Office [SPHSU13, SPHSU15] ; Peer reviewed ; Publisher PDF

ObjectivesAdministrative data are primarily collected for operational processes and these processes can lead to sources of bias that may not be adequately considered by researchers. We provide a framework to help understand how biases might arise from using linked administrative data, and hopefully aid future study designs.
ApproachWe developed the conceptual framework based on the team's experiences with the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018, linked to other administrative data sources. We provide examples from the 100MCohort of where and how in the linkage process different forms of bias could arise. We make recommendations on how biases might be addressed using commonly available external data.
ResultsThe conceptual framework covers the whole data generating process from people and events occurring in the population through to deriving variables for analysis. The framework comprises three distinct stages: 1) Recording and registration of events in administrative systems such as Brazil's Mortality Information System (SIM) and the Hospital Information System (SIH); 2) Linkage of different data sources, for example using exact matching via the Social Identification Number (NIS) in Brazil's CadÚnico database or linkage algorithms; 3) Cleaning and coding data used both for analysis and linkage. The biases arising from linkage can be better understood by applying theory and making additional metadata available.
ConclusionMaximising the potential of administrative data for research requires a better understanding of how biases arise. This is best achieved by considering the entire data generating process, and better communication among all those involved in the data collection and linkage processes.

OBJECTIVE: To describe and assess currently used area-based measures of deprivation in Brazil for health research, to the purpose of informing the development of a future small area deprivation index. METHODS: We searched five electronic databases and seven websites of Brazilian research institutions and governmental agencies. Inclusion criteria were: studies proposing measures of deprivation for small areas (i.e., finer geography than country-level) in Brazil, published in English, Portuguese or Spanish. After data-extraction, results were tabulated according to the area level the deprivation measure was created for and to the dimensions of deprivation or poverty included in the measures. A narrative synthesis approach was used to summarize the measures available, highlighting their utility for public health research. RESULTS: A total of 7,199 records were retrieved, 126 full-text articles were assessed after inclusion criteria and a final list of 30 articles was selected. No small-area deprivation measures that have been applied to the whole of Brazil were found. Existing measures were mainly used to study infectious and parasitic diseases. Few studies used the measures to assess inequalities in mortality and no studies used the deprivation measure to evaluate the impact of social programs. CONCLUSIONS: No up-to-date small area-based deprivation measure in Brazil covers the whole country. There is a need to develop such an index for Brazil to measure and monitor inequalities in health and mortality, particularly to assess progress in Brazil against the Sustainable Development Goal targets for different health outcomes, showing progress by socioeconomic groups.

OBJECTIVE: To describe and assess currently used area-based measures of deprivation in Brazil for health research, to the purpose of informing the development of a future small area deprivation index. METHODS: We searched five electronic databases and seven websites of Brazilian research institutions and governmental agencies. Inclusion criteria were: studies proposing measures of deprivation for small areas (i.e., finer geography than country-level) in Brazil, published in English, Portuguese or Spanish. After data-extraction, results were tabulated according to the area level the deprivation measure was created for and to the dimensions of deprivation or poverty included in the measures. A narrative synthesis approach was used to summarize the measures available, highlighting their utility for public health research. RESULTS: A total of 7,199 records were retrieved, 126 full-text articles were assessed after inclusion criteria and a final list of 30 articles was selected. No small-area deprivation measures that have been applied to the whole of Brazil were found. Existing measures were mainly used to study infectious and parasitic diseases. Few studies used the measures to assess inequalities in mortality and no studies used the deprivation measure to evaluate the impact of social programs. CONCLUSIONS: No up-to-date small area-based deprivation measure in Brazil covers the whole country. There is a need to develop such an index for Brazil to measure and monitor inequalities in health and mortality, particularly to assess progress in Brazil against the Sustainable Development Goal targets for different health outcomes, showing progress by socioeconomic groups

ABSTRACT OBJECTIVE To describe and assess currently used area-based measures of deprivation in Brazil for health research, to the purpose of informing the development of a future small area deprivation index. METHODS We searched five electronic databases and seven websites of Brazilian research institutions and governmental agencies. Inclusion criteria were: studies proposing measures of deprivation for small areas (i.e., finer geography than country-level) in Brazil, published in English, Portuguese or Spanish. After data-extraction, results were tabulated according to the area level the deprivation measure was created for and to the dimensions of deprivation or poverty included in the measures. A narrative synthesis approach was used to summarize the measures available, highlighting their utility for public health research. RESULTS A total of 7,199 records were retrieved, 126 full-text articles were assessed after inclusion criteria and a final list of 30 articles was selected. No small-area deprivation measures that have been applied to the whole of Brazil were found. Existing measures were mainly used to study infectious and parasitic diseases. Few studies used the measures to assess inequalities in mortality and no studies used the deprivation measure to evaluate the impact of social programs. CONCLUSIONS No up-to-date small area-based deprivation measure in Brazil covers the whole country. There is a need to develop such an index for Brazil to measure and monitor inequalities in health and mortality, particularly to assess progress in Brazil against the Sustainable Development Goal targets for different health outcomes, showing progress by socioeconomic groups.