Differences in cardiovascular disease mortality between local authorities in England: an ecological analysis

BackgroundLarge inequalities in age-standardised mortality rates of cardiovascular disease (CVD) exist at the local authority level within England, with particular areas consistently having the highest rates. Higher deprivation is associated with higher CVD mortality, but we know little about how the demographics and environments of local areas contribute to variations in mortality rates. The aim of this study was to explore the extent to which demographic, health, and environmental factors explain differences in all ages and premature CVD mortality between local authorities in England.MethodsAll data were sourced for each local authority in England. Outcome variables were age-standardised CVD mortality for all ages and those under 75 years in 2012–14. Data obtained were prevalence of ethnic and socioeconomic groups from the UK 2011 census; Public Health England data on index of multiple deprivation (IMD) score; prevalence of smoking, physical activity, obesity and overweightness; and Ordnance Survey environmental data on percentage of food shops, eating out shops, green or blue space, sporting facilities, and health facilities. We used the Akaike Information Criterion to assess which types of variables provided the best statistical model to explain variation in CVD mortality between local authorities.FindingsInclusion of health, demographic, environment, and IMD variables provided the best fit for explaining variation in CVD mortality at all ages (adjusted r²=0·60). Indian and Pakistani ethnicity and the IMD score in local authorities remained significantly associated with the outcome, with corresponding p values all less than 0·01. CVD mortality was 44 per 100 000 population greater in areas with the highest proportions of Indian and Pakistani ethnicities and 110 per 100 000 greater in the most deprived local authorities than in the least deprived areas. For CVD mortality below age 75, exclusion of environmental data improved the fit of the model (adjusted r²=0·82). Overweight prevalence (p=0·0481), Indian (p=0·0111), Pakistani (p=0·0003), and Bangladeshi (p=0·003) ethnicity, and the IMD score (p<0·0001) all remained significantly associated with premature CVD mortality when the best fitting model was used. Premature mortality was 13 per 100 000 and 18–25 per 100 000 greater in local authorities where the proportion of overweight people and Asian ethnicities was highest. Premature mortality in the most deprived local authorities was 68 per 100 000 greater than in the least deprived authorities.InterpretationThese findings are valuable for understanding which factors might be most useful for local authorities to target to reduce CVD mortality. This study combined a large amount of existing data; however, it was conducted at an ecological level, so analyses using individual-level mortality outcomes are also needed.FundingThe authors' posts are funded by the British Heart Foundation.     

Social frailty dimensions and frailty models over time

IntroductionSocial frailty is a complex concept and there is still no consensus on the criteria that best define it, nor on the role that social dimensions play in well-established frailty models.AimTo analyse the predictive value of social frailty dimensions on distinct frailty models.MethodA non-probabilistic sample of 193 community-dwelling adults aged 65 years and over was recruited in 2016 and followed for three years. Frailty was assessed by the Tilburg Frailty Indicator, the Groningen Frailty Indicator, and the Fried Phenotype criteria. Questions about living alone, social network, social support, loneliness, and frequency of social activities engagement were used to assess social criteria. Bivariate correlations and sequential multiple hierarchical logistic regression analyses were performed.ResultsAt baseline, 22.2% older adults lived alone, 47.2% reported missing people around them, 21.1% reported lack of social support, 26.1% reported having reduced their participation in social activities recently and 52.2% reported loneliness. The percent of frail individuals varied across frailty measures, and social criteria showed significant correlations and increased the prediction of frailty status. Loneliness and social activities engagement were associated with frailty as assessed by the Tilburg frailty Indicator and by the Fried Phenotype criteria; the lack of social support is associated with frailty as assessed by the Groningen Frailty Indicator. Living alone and lack of social relationships did not predict frailty.ConclusionIncluding social dimensions in a frailty model needs a consensual theoretical basis as they have different roles in predicting frailty, varying over time and across assessment tools.     

In-hospital mortality after acute myocardial infarction in China: a nationwide cross-sectional analysis

Background

The prevalence of acute myocardial infarction (AMI) has increased in China within the past few decades and is now a major cause of mortality. Percutaneous coronary intervention (PCI) is an effective treatment for AMI. We aimed to investigate how geographical and hospital characteristics affect in-hospital mortality and PCI use for AMI admissions in tertiary hospitals.

Risk of type 2 diabetes and cumulative excess weight exposure in the Framingham Offspring Study

AimMid-life obesity is associated with T2D risk. However, less is known about the cumulative effect of obesity during adulthood.MethodsFramingham Offspring Study participants who had an examination at 35 ± 2 years and were initially free of T2D were included in this study (N = 1026). A cumulative excess weight (CEW) score (year*kg/m²) was calculated until T2D diagnostic or the end of follow-up.ResultsEighty-four individuals (8.2%) developed T2D over 20 ± 6 years. Mean CEW scores were 118.0 ± 114.6 year*kg/m² in individuals who developed T2D and 30.2 ± 91.4 year*kg/m² in those who did not develop T2D (P < 0.01). T2D risk was doubled for each standard deviation increase in the CEW score (OR = 1.99 [1.64-2.40]; P < 0.001). However, CEW score was only significantly associated with T2D incidence for participants with a baseline BMI < 25 kg/m² (OR = 2.13 [1.36–3.36]; P < 0.001).ConclusionsAccumulating weight between the mid-thirties to the mid-fifties increases the risk of developing T2D. However, BMI in mid-thirties remains a stronger predictor of T2D risk.     

Rationale and design of the National Program of Cancer Registries' breast, colon, and prostate cancer patterns of care study

Background: Investigators from the Centers for Disease Control and Prevention (CDC), National Program of Cancer Registries (NPCR), are collaborating with public health professionals from seven states and the District of Columbia to conduct the Patterns of Care study to assess the quality of cancer data and to determine whether stage-specific treatments are being carried out. Methods: To assess the quality and completeness of cancer care data in the United States, trained staff from the Patterns of Care study are abstracting medical records to obtain detailed clinical data on treatment, tumor characteristics, stage at diagnosis, and demographics of representative samples of patients diagnosed with breast, colon, and prostate cancer. Altogether staff from each of the eight participating cancer registries will abstract 500 cases of breast, prostate, and colon/rectum/anus cancer for the CONCORD study and an additional 150 cases of localized breast cancer, 100 cases of stage III colon cancer, and 100 cases of localized prostate cancer for the Patterns of Care study. Chi-square tests will be used to compare routine registry data with re-abstracted data. The investigators will use logistic regression techniques to describe the characteristics of patients with localized breast and prostate cancer and stage III colon cancer. Age, race, sex, type of insurance, and comorbidity will be examined as predictors of the use of those treatments that are consistent with consensus guidelines. The investigators plan to use data from the CONCORD study to determine whether treatment factors are the reason for the reported differences between relative survival rates in the United States and Europe. Conclusions Results from the methodology used in the Patterns of Care study will provide, for the first time, detailed information about the quality and completeness of stage and treatment data that are routinely collected by states participating in the NPCR. It will add significantly to our understanding of factors that determine receipt of treatment in compliance with established guidelines. As part of the CONCORD study, it will also examine differences in survival among cancer patients with breast, prostate, and colon/rectum/anus cancers in the United States and Europe.     

新型冠状病毒肺炎疫情预测建模、数据融合与防控策略分析

自2019年12月以来,武汉暴发的COVID-19疫情由于春节人口流动快速蔓延,自2020年1月23日起全国大范围实施围堵缓疫策略,并不断提高检测和检出率,有效地抑制了疫情快速蔓延的趋势。在COVID-19爆发的早期,如何利用数学模型并结合少量和实时更新的多源数据,对疫情进行风险分析,评估防控策略的有效性和时效性等具有非常重要的现实意义。本研究将结合前期研究基础,系统介绍如何依据疫情发展的不同阶段和数据的完善,逐步建立符合我国防控策略的COVID-19传播动力学模型,给出模型由自治到非自治,风险评估指标由基本再生数到有效再生数,疫情发展与评估由早期的SEIHR传播动力学决定到最终取决于隔离人群和疑似人群规模的演变等的重要研究思路。     

Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy

Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. In this paper, we propose a method for the semi-automatic detection of inconsistencies in guidelines using preference learning, and we apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a knowledge base describing the domain.We successfully built a generic model suitable for all infectious diseases and patient profiles. This model includes both preferences and necessary features. It allowed the detection of 106 candidate inconsistencies which were analyzed by a medical expert. 55 inconsistencies were validated. We showed that therapeutic strategies of guidelines in antibiotherapy can be formalized by a preference model. In conclusion, we proposed an original approach, based on preferences, for modeling clinical guidelines. This model could be used in future clinical decision support systems for helping physicians to prescribe antibiotics.     

National,regional, and global trends in adult overweight and obesity prevalences

Background

Overweight and obesity prevalence are commonly used for public and policy communication of the extent of the obesity epidemic, yet comparable estimates of trends in overweight and obesity prevalence by country are not available.

Methods

We estimated trends between 1980 and 2008 in overweight and obesity prevalence and their uncertainty for adults 20 years of age and older in 199 countries and territories. Data were from a previous study, which used a Bayesian hierarchical model to estimate mean body mass index (BMI) based on published and unpublished health examination surveys and epidemiologic studies. Here, we used the estimated mean BMIs in a regression model to predict overweight and obesity prevalence by age, country, year, and sex. The uncertainty of the estimates included both those of the Bayesian hierarchical model and the uncertainty due to cross-walking from mean BMI to overweight and obesity prevalence.

Results

The global age-standardized prevalence of obesity nearly doubled from 6.4% (95% uncertainty interval 5.7-7.2%) in 1980 to 12.0% (11.5-12.5%) in 2008. Half of this rise occurred in the 20 years between 1980 and 2000, and half occurred in the 8 years between 2000 and 2008. The age-standardized prevalence of overweight increased from 24.6% (22.7-26.7%) to 34.4% (33.2-35.5%) during the same 28-year period. In 2008, female obesity prevalence ranged from 1.4% (0.7-2.2%) in Bangladesh and 1.5% (0.9-2.4%) in Madagascar to 70.4% (61.9-78.9%) in Tonga and 74.8% (66.7-82.1%) in Nauru. Male obesity was below 1% in Bangladesh, Democratic Republic of the Congo, and Ethiopia, and was highest in Cook Islands (60.1%, 52.6-67.6%) and Nauru (67.9%, 60.5-75.0%).

Conclusions

Globally, the prevalence of overweight and obesity has increased since 1980, and the increase has accelerated. Although obesity increased in most countries, levels and trends varied substantially. These data on trends in overweight and obesity may be used to set targets for obesity prevalence as requested at the United Nations high-level meeting on Prevention and Control of NCDs.