Enabling the examination of long-term mortality trends by educational level for England and Wales in a time-consistent and internationally comparable manner.

BACKGROUND: Studying long-term trends in educational inequalities in health is important for monitoring and policy evaluation. Data issues regarding the allocation of people to educational groups hamper the study and international comparison of educational inequalities in mortality. For the UK, this has been acknowledged, but no satisfactory solution has been proposed. OBJECTIVE: To enable the examination of long-term mortality trends by educational level for England and Wales (E&W) in a time-consistent and internationally comparable manner, we propose and implement an approach to deal with the data issues regarding mortality data by educational level. METHODS: We employed 10-year follow-ups of individuals aged 20+ from the Office for National Statistics Longitudinal Study (ONS-LS), which include education information from each decennial census (1971-2011) linked to individual death records, for a 1% representative sample of the E&W population. We assigned the individual cohort data to single ages and calendar years, and subsequently obtained aggregate all-cause mortality data by education, sex, age (30+), and year (1972-2017). Our data adjustment approach optimised the available education information at the individual level, and adjusts-at the aggregate level-for trend discontinuities related to the identified data issues, and for differences with country-level mortality data for the total population. RESULTS: The approach resulted in (1) a time-consistent and internationally comparable categorisation of educational attainment into the low, middle, and high educated; (2) the adjustment of identified data-quality related discontinuities in the trends over time in the share of personyears and deaths by educational level, and in the crude and the age-standardised death rate by and across educational levels; (3) complete mortality data by education for ONS-LS members aged 30+ in 1972-2017 which aligns with country-level mortality data for the total population; and (4) the estimation of inequality measures using established methods. For those aged 30+ , both absolute and relative educational inequalities in mortality first increased and subsequently decreased. CONCLUSION: We obtained additional insights into long-term trends in educational inequalities in mortality in E&W, and illustrated the potential effects of different data issues. We recommend the use of (part of) the proposed approach in other contexts.

Comparing the Costs and Diagnostic Outcomes of Replacing Cytology with the QIAsure DNA Methylation Test as a Triage within HPV Primary Cervical Cancer Screening in The Netherlands.

Detecting hypermethylation of tumour suppressor genes could provide an alternative to liquid-based cytology (LBC) triage within HPV primary cervical screening. The impact of using the QIAsure® FAM19A4/mir124-2 DNA Methylation Test (QIAGEN, N.V, Hilden, Germany) on CIN3+ diagnoses, retention, unnecessary colposcopies, and programme costs is unknown. A decision-tree model was developed to compare LBC with the QIAsure Methylation testing to guide colposcopy referral. Incorporating clinician- and self-sampling pathways the model was informed by the Dutch cervical cancer screening programme, published studies, and manufacturer data. Clinical and cost outcomes were assessed using two scenarios for DNA methylation testing and LBC relative performance. Sensitivity analyses (deterministic and probabilistic) were performed to assess model and parameter uncertainty. A range of self-sampling uptake was assessed in scenario analyses. For the screening cohort (n = 807,269) where 22.1% self-sampled, the number of unnecessary colposcopies and CIN3+ diagnoses varied according to the relative performance of methylation testing and LBC. Irrespective of relative performance, the cost per complete screen was lower and fewer people were lost to follow-up when using DNA methylation testing. The results indicate that, within an HPV primary screening programme that incorporates self-sampling, using the QIAsure Methylation Test for triage reduces the cost per screen compared to LBC.

Long-Term Trends in Obesity Prevalence by Socio-Economic Group in Five European Countries and the USA: The Relevance of the Diffusion of Innovations Theory.

INTRODUCTION: Cross-national comparison suggests that the timing of the obesity epidemic differs across socio-economic groups (SEGs). Similar to the smoking epidemic, these differences might be described by the diffusion of innovations theory, which states that health behaviours diffuse from higher to lower SEGs. However, the applicability of the diffusion of innovations theory to long-term time trends in obesity by SEG is unknown. We studied long-term trends in the obesity prevalence by SEG in England, France, Finland, Italy, Norway, and the USA and examined whether trends are described by the diffusion of innovations theory. METHODS: Obesity prevalence from 1978 to 2019 by educational level, sex, and age group (25+ years) from health surveys was harmonized, age-standardized, Loess-smoothed, and visualized. Prevalence rate differences were calculated, and segmented regression was performed to obtain annual percentage changes, which were compared over time and across SEGs. RESULTS: Obesity prevalence among lower educated groups has exceeded that of higher educated groups, except among American men, in all countries throughout the study period. A comparable increase across educational levels was observed until approximately 2000. Recently, obesity prevalence stagnated among higher educated groups in Finland, France, Italy, and Norway and lower educated groups in England and the USA. DISCUSSION: Recent trends in obesity prevalence by SEG are mostly in line with the diffusion of innovations theory; however, no diffusion from higher to lower SEGs at the start of the epidemic was found. The stagnation among higher SEGs but not lower SEGs suggests that the latter will likely experience the greatest future burden.

Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia.

Familial hypercholesterolaemia increases circulating LDL-C levels and leads to premature cardiovascular disease when undiagnosed or untreated. Current guidelines support genetic testing in patients complying with clinical diagnostic criteria and cascade screening of their family members. However, most of hyperlipidaemic subjects do not present pathogenic variants in the known disease genes, and most likely suffer from polygenic hypercholesterolaemia, which translates into a relatively low yield of genetic screening programs. This study aims to identify new biomarkers and develop new approaches to improve the identification of individuals carrying monogenic causative variants. Using a machine-learning approach in a paediatric dataset of individuals, tested for disease causative genes and with an extended lipid profile, we developed new models able to classify familial hypercholesterolaemia patients with a much higher specificity than currently used methods. The best performing models incorporated parameters absent from the most common FH clinical criteria, namely apoB/apoA-I, TG/apoB and LDL1. These parameters were found to contribute to an improved identification of monogenic individuals. Furthermore, models using only TC and LDL-C levels presented a higher specificity of classification when compared to simple cut-offs. Our results can be applied towards the improvement of the yield of genetic screening programs and corresponding costs.