Daniel Ayoubkhani’s research while affiliated with University of Leicester and other places

Objective Evaluate the impact of bariatric surgery on monthly earnings and employee status among working-age adults, and examine variations across sociodemographic characteristics. Design Retrospective longitudinal cohort study using national, linked administrative datasets. Setting Hospital inpatient services in England between 1 April 2014 and 31 December 2022. Participants 40,662 individuals who had a bariatric surgery procedure and obesity diagnosis during the study period, with no bariatric surgery history in the previous 5 years, and were 25 to 64 years old at the date of surgery. We also included 49,921 individuals sampled from the general population who had not had bariatric surgery matched by age and sex to those in the cohort who had bariatric surgery. Main outcome measures Monthly employee pay - for all months and only months where the individual was in paid employment - expressed in 2023 prices; paid employee status. Results Among people living with obesity who had bariatric surgery, there was a sustained increase in monthly employee pay from six months after surgery with a mean increase of 84 pound sterling per month 5 years after surgery compared with the six months before surgery. Among those in paid employment, there was a sustained increase in the probability of being a paid employee from 4 months after bariatric surgery, with a mean increase of 4.3 percentage points 5 years after surgery. The increases in pay and probability of employment were greater for males. The increase in employee pay was not sustained over the 5-year follow up time for the youngest age groups. Conclusions Bariatric surgery is associated with an increased probability of being employed, resulting in increased earnings. These findings suggest that living with obesity negatively impacts labour market outcomes and that obesity management interventions are likely to generate economic benefits both to individuals and on a macroeconomic level by increasing the likelihood of employment of people living with obesity.

People suffering from common mental disorders (CMD) such as depression and anxiety are more likely to be economically inactive. Psychological therapies are highly effective at treating CMDs, but less is known about their impact on long-term labour market outcomes. Using national treatment programme data in England, NHS Talking Therapies (NHSTT), with unique linkage to administration data on employment and census records, we estimated the causal effects of NHSTT on employment and earnings. Overall, completing treatment led to a maximum average increase of £17 in monthly earnings (year two) and likelihood of paid employment by 1.5 percentage points (year seven). Those ′Not working, seeking work′ saw a maximum average increase in pay of £63 per month (year seven) and likelihood of paid employment by 3.1 percentage points (year four). Our findings demonstrate the economic benefits of treating CMDs, and how investing in mental health can impact labour market participation.

Introduction Endometriosis is a chronic disease and the second most common gynaecological condition in the UK, affecting approximately 1.5 million women. It is characterised by the growth of endometrial tissue outside the uterus, causing varying symptoms and having far reaching socioeconomic impacts. We utilise population level hospital admissions data and Census 2011 to examine the characteristics of women diagnosed with endometriosis in England. Methods Using a retrospective cohort design, we used Hospital Episode Statistics (HES) between 2011 and 2021, we linked health data to detailed sociodemographic information from Census 2011, providing individual population-level information on self-reported characteristics. Our outcome of interest was an endometriosis diagnosis in hospital. Our exposures were age on Census Day (five-year age bands), ethnic group, Index of Multiple Deprivation (IMD) decile, household National Statistics Socio-economic Classification (NS-SEC), highest qualification, country of birth, main language, self-reported general health, self-reported disability, rural/urban classification, region, and upper tier local authority (UTLA). We calculated crude and age-standardised rates, and odds of receiving a diagnosis using logistic regression models adjusted sequentially for age and health. Results Our results highlight differences in underlying prevalence of endometriosis by sociodemographic characteristic, as well as capturing differences in access to services for women receiving a diagnosis of endometriosis in an NHS hospital. The likelihood of receiving an endometriosis diagnosis was highest in the "White British", "Black Caribbean" and "Mixed White and Black Caribbean" ethnic groups, and lowest in the "Chinese", "Arab" and "Black African" ethnic groups. Women living in the most and least deprived areas were least likely to have an endometriosis diagnosis, possibly reflecting lower access to healthcare services in the most deprived group and more use of private healthcare in the least deprived group. Women self-reporting to be in bad health, or disabled, were more likely to have had an endometriosis diagnosis compared those in very good health or non-disabled women, respectively. Conclusions Our results demonstrate significant sociodemographic differences between groups of women receiving an endometriosis diagnosis in England. These results should be used to inform healthcare policies to better support groups of women who are most affected by endometriosis and barriers to receiving a diagnosis. Subsequent work should explore presentations in primary care, as well as the broader socioeconomic ramifications of endometriosis. Research in context What is already known on this topic Endometriosis is a common gynaecological condition which has debilitating impacts across many domains, including physical, psychological, social and economic. It is estimated to affect 1 in 10 reproductive age women in England, however evidence on the differences in endometriosis diagnosis by sociodemographic characteristics is lacking. What this study adds Our study utilises population-level Census and HES data for England to estimate crude and age-standardised rates of endometriosis diagnosis, and odds of receiving an endometriosis diagnosis by a range of sociodemographic characteristics. We estimate the prevalence of an endometriosis diagnosis to be approximately 2% of reproductive age women in our linked population, with an average age at diagnosis of 35 years. Women living in the most and least deprived areas were least likely to have an endometriosis diagnosis; this possibly reflects less access to healthcare services in the most deprived group and more use of private healthcare in the least deprived group. The likelihood of receiving an endometriosis diagnosis was highest in the "White British", "Black Caribbean", and "Mixed White and Black Caribbean" ethnic groups, and lowest in the "Chinese", "Arab", and "Black African" ethnic groups. This study is the most comprehensive analysis of the characteristics of women with an endometriosis diagnosis in England to date. How this study might affect research, practice or policy This research provides important information to gynaecologists, clinicians and other allied health professionals, as well as policy makers, to illustrate the prevalence of endometriosis and the groups most affected by endometriosis and barriers to receiving a diagnosis. In the Women’s Health Strategy for England, menstrual health and gynaecological conditions were identified as one of the priority areas, with a call for evidence and investment in women’s health research.

Child mortality is a common measure of the overall health of society. Maternal characteristics, such as ethnicity and socioeconomic status are known to contribute to inequalities in health and mortality in babies and children. Overall, in the UK babies born to non-white mothers have a higher risk of mortality, and there is also a strong association between deprivation and risk of death. However, population data is currently lacking for England and Wales disentangling the various socioeconomic risk factors which are known to contribute to increased risk. We utilise population level data to assess the association between socioeconomic status, ethnicity, and child mortality. Our cohort consisted of all live singleton births in England and Wales between 2011 and 2016. We linked birth notifications to mothers’ 2011 Census records using NHS number, which provides person-level sociodemographic information. Death registration data was linked to identify all-cause and cause-specific deaths in babies. Babies were followed from date-of-birth for up to 10-years, or date-of-death. We report cumulative incidence of all-cause mortality for a 10-year follow-up for neonatal, infant and child deaths based on maternal socioeconomic and ethnic groups. Cox proportional hazard models are used to estimate hazard ratios for neonatal, infant and child deaths, with both minimally adjusted (maternal age and baby sex) and fully-adjusted models (accounting for other maternal, household and birth characteristics). This presentation will give an overview of our unique linked data sources, the statistical techniques employed, the latest available analytical results, and the emerging implications of the research.

Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK’s national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.

Background Evidence on the long-term employment consequences of SARS-CoV-2 infection is lacking. We used data from a large, community-based sample in the UK to estimate associations between Long Covid and employment outcomes. Methods This was an observational, longitudinal study using a pre–post design. We included survey participants from 3 February 2021 to 30 September 2022 when they were aged 16–64 years and not in education. Using conditional logit modelling, we explored the time-varying relationship between Long Covid status ≥12 weeks after a first test-confirmed SARS-CoV-2 infection (reference: pre-infection) and labour market inactivity (neither working nor looking for work) or workplace absence lasting ≥4 weeks. Results Of 206 299 participants (mean age 45 years, 54% female, 92% white), 15% were ever labour market inactive and 10% were ever long-term absent during follow-up. Compared with pre-infection, inactivity was higher in participants reporting Long Covid 30 to <40 weeks [adjusted odds ratio (aOR): 1.45; 95% CI: 1.17–1.81] or 40 to <52 weeks (aOR: 1.34; 95% CI: 1.05–1.72) post-infection. Combining with official statistics on Long Covid prevalence, and assuming a correct statistical model, our estimates translate to 27 000 (95% CI: 6000–47 000) working-age adults in the UK being inactive because of Long Covid in July 2022. Conclusions Long Covid is likely to have contributed to reduced participation in the UK labour market, though it is unlikely to be the sole driver. Further research is required to quantify the contribution of other factors, such as indirect health effects of the pandemic.

Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1–5, give rise to highly divergent lineages6–8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as ‘persistent infections’ as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1–0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11–14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.

Background The risk of suicide is complex and often a result of multiple interacting factors. Understanding which groups of the population are most at risk of suicide is important to inform the development of targeted public health interventions. Methods We used a novel linked dataset that combined the 2011 Census with the population-level mortality data in England and Wales. We fitted generalized linear models with a Poisson link function to estimate the rates of suicide across different sociodemographic groups and to identify which characteristics are independent predictors of suicide. Results Overall, the highest rates of suicide were among men aged 40–50 years, individuals who reported having a disability or long-term health problem, those who were unemployed long term or never had worked, and those who were single or separated. After adjusting for other characteristics such as employment status, having a disability or long-term health problem, was still found to increase the incidence of suicide relative to those without impairment [incidence rate ratio minimally adjusted (women) = 3.5, 95% confidence interval (CI) = 3.3–3.6; fully adjusted (women) 3.1, 95% CI = 3.0–3.3]. Additionally, while the absolute rate of suicide was lower in women compared with men, the relative risk in people reporting impairments compared with those who do not was higher in women compared with men. Conclusions The findings of this work provide novel population-level insights into the risk of suicide by sociodemographic characteristics in England and Wales. Our results highlight several sociodemographic groups who may benefit from more targeted suicide prevention policies and practices.

SARS-CoV-2 reinfections increased substantially after Omicron variants emerged. Large-scale community-based comparisons across multiple Omicron waves of reinfection characteristics, risk factors, and protection afforded by previous infection and vaccination, are limited. Here we studied ~45,000 reinfections from the UK’s national COVID-19 Infection Survey and quantified the risk of reinfection in multiple waves, including those driven by BA.1, BA.2, BA.4/5, and BQ.1/CH.1.1/XBB.1.5 variants. Reinfections were associated with lower viral load and lower percentages of self-reporting symptoms compared with first infections. Across multiple Omicron waves, estimated protection against reinfection was significantly higher in those previously infected with more recent than earlier variants, even at the same time from previous infection. Estimated protection against Omicron reinfections decreased over time from the most recent infection if this was the previous or penultimate variant (generally within the preceding year). Those 14–180 days after receiving their most recent vaccination had a lower risk of reinfection than those >180 days from their most recent vaccination. Reinfection risk was independently higher in those aged 30–45 years, and with either low or high viral load in their most recent previous infection. Overall, the risk of Omicron reinfection is high, but with lower severity than first infections; both viral evolution and waning immunity are independently associated with reinfection.