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Multimorbidity is of increasing concern for healthcare systems globally, particularly in the context of ageing population structures, such as in the European Union and the UK. Although there is growing attention on developing strategies to manage the health and healthcare burden of older patients with multimorbidity, little research or policy focus has been placed on how to best prevent the development of multimorbidity in future generations. In this research agenda piece, we argue for a shift from a sole focus on the management of multimorbidity in old age to a multimorbidity agenda that considers prevention and management throughout the life-course.

Multimorbidity is projected to continue increasing in England and many other countries. Here, we use a validated microsimulation model to quantify the potential impact of improving exposure levels of eight risk factors on the burden of major illness among adults aged 30+ in England between 2023-2043. We find that the biggest contributors to incident major illness are body mass index, smoking, systolic blood pressure, and physical inactivity. Theoretical minimum risk exposure levels of all risk factors could reduce 2043 major illness prevalence by 2 percentage points (95% uncertainty intervals: 1.3, 2.7) compared to the continuing trends (base-case) scenario; under a 10% improvement in all risk factors, we project a 0.3 percentage points (0.2, 0.4) reduction in major illness. The impact on health inequalities is mixed. Our findings show that large improvements in risk factors are unlikely to substantially reduce the major illness burden by 2043 due to population ageing. Burden of major disease is projected to increase in England and many other countries. Here the authors model the impact of reducing eight known risk factors for disease burden, such as BMI and smoking, between 2023-2043 in England and report that reductions in risk factors may not substantially reduce the major illness burden by 2043 due to population ageing.

Undiagnosed chronic disease has serious health consequences, and variation in rates of underdiagnosis between populations can contribute to health inequalities. We aimed to estimate the level of undiagnosed disease of 11 common conditions and its variation across sociodemographic characteristics and regions in England. We used linked primary care, hospital and mortality data on approximately 1.3 million patients registered at a GP practice for more than one year from 01/04/2008-31/03/2020 from Clinical Practice Research Datalink. We created a dynamic state model with six states based on the diagnosis and mortality of 11 conditions: coronary heart disease (CHD), stroke, hypertension, chronic obstructive pulmonary disease, type 2 diabetes, dementia, breast cancer, prostate cancer, lung cancer, colorectal cancer, and depression/anxiety. Undiagnosed disease was conceptualised as those who died with a condition but were not previously diagnosed. This was combined with observed data on the incidence of diagnosis, the case fatality rate in the diagnosed, and an assumption about how that rate varies with diagnosis to estimate the number of undiagnosed disease cases over the total number of disease cases (underdiagnosis) in each population group. We estimated underdiagnosis by year, sex, 10-year age group, relative deprivation, and administrative region. We then applied small-area estimation techniques to derive underdiagnosis estimates for health planning areas (CCGs). Levels of underdiagnosis varied between 16% for stroke and 69% for prostate cancer in 2018. For all diseases, the level of underdiagnosis declined over time. Underdiagnosis was not consistently concentrated in areas with high deprivation. For depression/anxiety and stroke, underdiagnosis was estimated to be higher in less deprived CCGs, whilst for CHD and T2DM, it was estimated to be higher in more deprived CCGs, with no apparent relationships for other conditions. We found no uniform spatial patterns of underdiagnosis across all diseases, and the relationship between age, deprivation and the probability of being undiagnosed varied greatly between diseases. Our findings suggest that underdiagnosis is not consistently concentrated in areas with high deprivation, nor is there a uniform spatial underdiagnosis pattern across diseases. This novel method for estimating the burden of underdiagnosis within England depends on the quality of routinely collected data, but it suggests that more research is needed to understand the key drivers of underdiagnosis.

Background Understanding the prevalence of diseases and where it is detected and recorded in healthcare settings is important for planning effective prevention and care provision. We examined inequalities in the prevalence of 205 chronic conditions and in the care setting where the related diagnoses were recorded in the English National Health Service. Methods We used data from the Clinical Practice Research Datalink Aurum linked with Hospital Episode Statistics for 12.8 million patients registered with 1406 general practices in 2018. We mapped diagnoses recorded in primary and secondary care in the previous 12 years. We used linear regressions to assess associations of ethnicity, deprivation, and general practice with a diagnosis being recorded in primary care only, secondary care only, or both settings. Results 72.65% of patients had at least one diagnosis recorded in any care setting. Most diagnoses were reported only in primary care (62.56%) and a minority only in secondary care (15.24%) or in both settings (22.18%). Black (− 0.08 percentage points (pp)), Asian (− 0.08 pp), mixed (− 0.13 pp), and other ethnicity patients (− 0.31 pp) were less likely than White patients to have a condition recorded. Patients in most deprived areas were 0.27 pp more likely to have a condition recorded (+ 0.07 pp in secondary care only, + 0.10 pp in both primary and secondary care, and + 0.10 pp in primary care only). Differences in prevalence by ethnicity were driven by diagnostic recording in primary care. Higher recording of diagnoses in more deprived areas was consistent across care settings. There were large differences in prevalence and diagnostic recording between general practices after adjusting for patient characteristics. Conclusions Linked primary and secondary care records support the identification of disease prevalence more comprehensively. There are inequalities in the prevalence and setting of diagnostic recording by ethnicity, deprivation, and providers on average across conditions. Further research should examine inequalities for each specific condition and whether they reflect also differences in access or recording as well as disease burden. Improving recording where needed and making national linked records accessible for research are key to understanding and reducing inequalities in disease prevention and management.

BackgroundA large proportion of chronic conditions are undiagnosed, preventing early treatment, and leading to poorer outcomes. Understanding how levels of underdiagnosis vary between diseases and population groups over time is crucial for effectively allocating resources and targeting interventions to increase diagnosis rates.MethodsWe used two annual national surveys: the Health Survey for England (cross-sectional) and the UK Household Longitudinal Survey, to identify people with diabetes, hypertension and depression. Diagnosed cases were defined as a self-report of being told by a nurse or doctor as having a condition; undiagnosed cases were defined as those where screening tools used in the survey identified clinical signs of the condition but the individual did not self-report a diagnosis. We used logistic regression to estimate the proportion of people with these three conditions who are undiagnosed for 540 population segments defined by age group, sex, deprivation quintile and region between 2011 and 2019. These predicted probabilities were applied to population estimates using microsimulation to model the proportion undiagnosed for each disease in each Clinical Commissioning Group (local health planning areas) in England.ResultsThe proportion of people with diabetes and depression who were undiagnosed reduced between 2011 and 2019, with no change in the proportion of hypertensives undiagnosed. For hypertension, people in more deprived areas were less likely to be undiagnosed than those in less deprived areas. The opposite was true for depression. Younger men with hypertension or diabetes were less likely to be diagnosed than older men. Both those aged under 30 and those over 70 with depression were less likely to be diagnosed compared with those aged 30–70.ConclusionStrategies aiming to improve undiagnosed hypertension case finding need to understand the reasons for little progress over the past decade. For depression, strategies to increase early diagnosis should prioritise deprived areas. Case finding for all three diseases would benefit from targeting younger age groups.

ObjectiveExamine the association between country-level gender social norms and (1) cardiovascular disease mortality rates; (2) female to male cardiovascular disease mortality ratios; and (3) life expectancy.DesignEcological study with the country as the unit of analysis.SettingGlobal, country-level data.ParticipantsGlobal population of countries with data available on gender social norms as measured by the Gender Social Norms Index (developed by the United Nations Development Programme).Main outcome measuresCountry-level female and male age-standardised cardiovascular disease mortality rates, population age-standardised cardiovascular disease mortality rates, female to male cardiovascular disease mortality ratios, female and male life expectancy at birth. Outcome measure data were retrieved from the WHO and the Institute for Health Metrics and Evaluation. Multivariable linear regression models were fitted to explore the relationship between gender social norms and the outcome variables.ResultsHigher levels of biased gender social norms, as measured by the Gender Social Norms Index, were associated with higher female, male and population cardiovascular disease mortality rates in the multivariable models (β 4.86, 95% CIs 3.18 to 6.54; β 5.28, 95% CIs 3.42 to 7.15; β 4.89, 95% CIs 3.18 to 6.60), and lower female and male life expectancy (β −0.07, 95% CIs −0.11 to −0.03; β −0.05, 95% CIs −0.10 to −0.01). These results included adjustment within the models for potentially confounding country-level factors including gross domestic product per capita, population mean years of schooling, physicians per 1000 population, year of Gender Social Norms Index data collection and maternal mortality ratio.ConclusionsOur analysis suggests that higher levels of biased gender social norms are associated with higher rates of population cardiovascular disease mortality and lower life expectancy for both sexes. Future research should explore this relationship further, to define its causal role and promote public health action.

There is uncertainty around the costs and health impacts of undiagnosed mental health problems. Using survey data, we aim to understand the costs and health-related quality-of-life decrements from undiagnosed anxiety/depression. We analysed survey data from two waves of the North West Coast Household Health Survey, which included questions on disease, medications, and Patient Health Questionnaire 9 (PHQ-9) and Generalised Anxiety Disorder 7 (GAD-7) scores (depression and anxiety scales). People were judged as having undiagnosed anxiety/depression problems if they scored ≥5 on the PHQ-9 or GAD-7, and did not declare a mental health issue or antidepressant prescription. Linear regression for EuroQol 5-Dimension 3-Level (EQ-5D-3L) index scores, and Tweedie regression for health and social care costs, were used to estimate the impact of undiagnosed mental health problems, controlling for age, gender, deprivation and other health conditions. Around 26.5% of participants had undiagnosed anxiety/depression. The presence of undiagnosed anxiety/depression was associated with reduced EQ-5D-3L index scores (0.040 lower on average) and increased costs (£250 ($310) per year on average). Using a higher cut-off score of 10 on the PHQ-9 and GAD-7 for undiagnosed anxiety/depression had similar increased costs but a greater reduction in EQ-5D-3L index scores (0.076 on average), indicating a larger impact on health-related quality of life. Having undiagnosed anxiety or depression increases costs and reduces health-related quality of life. Reducing stigma and increasing access to cost-effective treatments will have population health benefits.

Tobacco control models are mathematical models predicting tobacco-related outcomes in defined populations. The policy simulation model is considered as a subcategory of tobacco control models simulating the potential outcomes of tobacco control policy options. However, we could not identify any existing tool specifically designed to assess the quality of tobacco control models.BACKGROUNDTobacco control models are mathematical models predicting tobacco-related outcomes in defined populations. The policy simulation model is considered as a subcategory of tobacco control models simulating the potential outcomes of tobacco control policy options. However, we could not identify any existing tool specifically designed to assess the quality of tobacco control models.The aims of this systematic methodology review are to: (1) identify best modeling practices, (2) highlight common pitfalls, and (3) develop recommendations to assess the quality of tobacco control policy simulation models. Crucially, these recommendations can empower model users to assess the quality of current and future modeling studies, potentially leading to better tobacco policy decision-making for the public. This protocol describes the planned systematic review stages, paper inclusion and exclusion criteria, data extraction, and analysis.OBJECTIVEThe aims of this systematic methodology review are to: (1) identify best modeling practices, (2) highlight common pitfalls, and (3) develop recommendations to assess the quality of tobacco control policy simulation models. Crucially, these recommendations can empower model users to assess the quality of current and future modeling studies, potentially leading to better tobacco policy decision-making for the public. This protocol describes the planned systematic review stages, paper inclusion and exclusion criteria, data extraction, and analysis.Two reviewers searched five databases (Embase, EconLit, PsycINFO, PubMed, and CINAHL Plus) to identify eligible studies published between July 2013 and August 2019. We included papers projecting tobacco-related outcomes with a focus on tobacco control policies in any population and setting. Eligible papers were independently screened by two reviewers. The data extraction form was designed and piloted to extract model structure, data sources, transparency, validation, and other qualities. We will use a narrative synthesis to present the results by summarizing model trends, analyzing model approaches, and reporting data input and result quality. We will propose recommendations to assess the quality of tobacco control policy simulation models using the findings from this review and related literature.METHODSTwo reviewers searched five databases (Embase, EconLit, PsycINFO, PubMed, and CINAHL Plus) to identify eligible studies published between July 2013 and August 2019. We included papers projecting tobacco-related outcomes with a focus on tobacco control policies in any population and setting. Eligible papers were independently screened by two reviewers. The data extraction form was designed and piloted to extract model structure, data sources, transparency, validation, and other qualities. We will use a narrative synthesis to present the results by summarizing model trends, analyzing model approaches, and reporting data input and result quality. We will propose recommendations to assess the quality of tobacco control policy simulation models using the findings from this review and related literature.Data collection is in progress. Results are expected to be completed and submitted for publication by April 2021.RESULTSData collection is in progress. Results are expected to be completed and submitted for publication by April 2021.This systematic methodological review will summarize the best practices and pitfalls existing among tobacco control policy simulation models and present a recommendation list of a high-quality tobacco control simulation model. A more standardized and quality-assured tobacco control policy simulation model will benefit modelers, policymakers, and the public on both model building and decision making.CONCLUSIONSThis systematic methodological review will summarize the best practices and pitfalls existing among tobacco control policy simulation models and present a recommendation list of a high-quality tobacco control simulation model. A more standardized and quality-assured tobacco control policy simulation model will benefit modelers, policymakers, and the public on both model building and decision making.PROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146.TRIAL REGISTRATIONPROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146.DERR1-10.2196/26854.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)DERR1-10.2196/26854.