Socioeconomic inequalities in accumulation of multimorbidity in England from 2019 to 2049: a microsimulation projection study.

BACKGROUND: There are socioeconomic inequalities in the prevalence of multimorbidity and its accumulation across the life course. Estimates of multimorbidity prevalence in English primary care increased by more than two-thirds from 2004 to 2019. We developed a microsimulation model to quantify current and projected multimorbidity inequalities in the English adult population. METHODS: We used primary care data for adults in England from the Clinical Practice Research Datalink Aurum database between 2004 and 2019, linked to the 2015 English Index of Multiple Deprivation (IMD), to model time individuals spent in four health states (healthy, one chronic condition, basic multimorbidity [two or more chronic conditions], and complex multimorbidity [three or more chronic conditions affecting three or more body systems]) by sex, age, IMD quintile, birth cohort, and region. We applied these transition times in a stochastic dynamic continuous-time microsimulation model to Office for National Statistics population estimates for adults aged 30-90 years. We calculated projected prevalence and cumulative incident cases from 2019 to 2049 by IMD quintile, age group (younger than 65 years vs 65 years and older), and years to be lived without multimorbidity at age 30 years. FINDINGS: Under the assumption that all chronic conditions were lifelong, and that once diagnosed there was no recovery, we projected prevalence of multimorbidity (basic or complex) increases by 34% from 53·8% in 2019 to 71·9% (95% uncertainty interval 71·8-72·0) in 2049. This rise equates to an 84% increase in the number of people with multimorbidity: from 19·2 million in 2019 to 35·3 million in 2049 (35·3 million to 35·4 million). This projected increase is greatest in the most deprived quintile, with an excess 1·07 million (1·04 million to 1·10 million) cumulative incident basic multimorbidity cases and 0·70 million (0·67 million to 0·74 million) complex multimorbidity cases over and above the projected cases for the least deprived quintile, largely driven by inequalities in those younger than 65 years. The median expected number of years to be lived without multimorbidity at age 30 years in 2019 is 15·12 years (14·62-16·01) in the least deprived IMD quintile and 12·15 years (11·61-12·60) in the most deprived IMD quintile. INTERPRETATION: The number of people living with multimorbidity will probably increase substantially in the next 30 years, a continuation of past observed increases partly driven by changing population size and age structure. Inequalities in the multimorbidity burden increase at each stage of disease accumulation, and are projected to widen, particularly among the working-age population. Substantial action is needed now to address population health and to prepare health-care and social-care systems for coming decades. FUNDING: University of Liverpool and National Institute for Health and Care Research School for Public Health Research.

Investigating the impact of undiagnosed anxiety and depression on health and social care costs and quality of life: cross-sectional study using household health survey data.

BACKGROUND: There is uncertainty around the costs and health impacts of undiagnosed mental health problems. AIMS: Using survey data, we aim to understand the costs and health-related quality-of-life decrements from undiagnosed anxiety/depression. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

Association between gender social norms and cardiovascular disease mortality and life expectancy: an ecological study.

OBJECTIVE: Examine 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. DESIGN: Ecological study with the country as the unit of analysis. SETTING: Global, country-level data. PARTICIPANTS: Global 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 MEASURES: Country-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. RESULTS: Higher 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 (ß 4.86, 95% CIs 3.18 to 6.54; ß 5.28, 95% CIs 3.42 to 7.15; ß 4.89, 95% CIs 3.18 to 6.60), and lower female and male life expectancy (ß -0.07, 95% CIs -0.11 to -0.03; ß -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. CONCLUSIONS: Our 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.

Identifying best modelling practices for tobacco control policy simulations: a systematic review and a novel quality assessment framework.

BACKGROUND: Policy simulation models (PSMs) have been used extensively to shape health policies before real-world implementation and evaluate post-implementation impact. This systematic review aimed to examine best practices, identify common pitfalls in tobacco control PSMs and propose a modelling quality assessment framework. METHODS: We searched five databases to identify eligible publications from July 2013 to August 2019. We additionally included papers from Feirman et al for studies before July 2013. Tobacco control PSMs that project tobacco use and tobacco-related outcomes from smoking policies were included. We extracted model inputs, structure and outputs data for models used in two or more included papers. Using our proposed quality assessment framework, we scored these models on population representativeness, policy effectiveness evidence, simulated smoking histories, included smoking-related diseases, exposure-outcome lag time, transparency, sensitivity analysis, validation and equity. FINDINGS: We found 146 eligible papers and 25 distinct models. Most models used population data from public or administrative registries, and all performed sensitivity analysis. However, smoking behaviour was commonly modelled into crude categories of smoking status. Eight models only presented overall changes in mortality rather than explicitly considering smoking-related diseases. Only four models reported impacts on health inequalities, and none offered the source code. Overall, the higher scored models achieved higher citation rates. CONCLUSIONS: While fragments of good practices were widespread across the reviewed PSMs, only a few included a 'critical mass' of the good practices specified in our quality assessment framework. This framework might, therefore, potentially serve as a benchmark and support sharing of good modelling practices.

Tobacco Control Policy Simulation Models: Protocol for a Systematic Methodological Review.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: Data collection is in progress. Results are expected to be completed and submitted for publication by April 2021. CONCLUSIONS: 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. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020178146; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020178146. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26854.

Inequalities in incident and prevalent multimorbidity in England, 2004-19: a population-based, descriptive study.

BACKGROUND: The increasing burden of multimorbidity and its socioeconomic gradient poses unique challenges to the provision and structure of health care. We aimed to describe inequalities and trends over time in multimorbidity prevalence, incidence, and case fatality among adults of all ages in England using primary care electronic health records. METHODS: We used a random sample of 991 243 individuals from the Clinical Practice Research Datalink Aurum database registered at participating general practices within England between Jan 1, 2004, and Dec 31, 2019, linked to the 2015 English Index of Multiple Deprivation (IMD). We used the following two outcome measures: basic multimorbidity, comprising two or more chronic conditions; and complex multimorbidity, comprising at least three chronic conditions affecting at least three body systems. We calculated crude, age-standardised, and age-sex-standardised annual incidence, prevalence, and case fatality rates, along with median age of onset for both multimorbidity types. We calculated absolute and relative inequalities for each outcome. FINDINGS: In 2004, 30·8% of our study population had basic multimorbidity and 15·1% had complex multimorbidity. This increased to 52·8% and 32·7%, respectively, in 2019. Although the overall incidence of basic multimorbidity remained stable over the 16-year study period, the incidence among people of working age and the incidence of complex multimorbidity increased gradually. Socioeconomic deprivation was associated with an increased incidence of both multimorbidity types in working-age adults. The median age at onset of complex multimorbidity was 7 years younger for the most deprived quintile of the IMD compared with the least deprived quintile. INTERPRETATION: The burden of multimorbidity in England has increased substantially over the past 16 years with persistent inequalities, which are worse in working-age adults and for complex multimorbidity. Prevention efforts to reduce the onset and slow the progression of multimorbidity are essential to reduce the increasing impact on patients and health systems alike. FUNDING: University of Liverpool and UK National Institute for Health Research School for Public Health Research.

Multimorbidity: the case for prevention.

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.