James Bentham

A century of trends in adult human height

Being taller is associated with enhanced longevity, and higher education and earnings. We reanalysed 1472 population-based studies, with measurement of height on more than 18.6 million participants to estimate mean height for people born between 1896 and 1996 in 200 countries. The largest gain in adult height over the past century has occurred in South Korean women and Iranian men, who became 20.2 cm (95% credible interval 17.5–22.7) and 16.5 cm (13.3–19.7) taller, respectively. In contrast, there was little change in adult height in some sub-Saharan African countries and in South Asia over the century of analysis. The tallest people over these 100 years are men born in the Netherlands in the last quarter of 20th century, whose average heights surpassed 182.5 cm, and the shortest were women born in Guatemala in 1896 (140.3 cm; 135.8–144.8). The height differential between the tallest and shortest populations was 19-20 cm a century ago, and has remained the same for women and increased for men a century later despite substantial changes in the ranking of countries. DOI: http://dx.doi.org/10.7554/eLife.13410.001

Worldwide burden of cancer attributable to diabetes and high body-mass index : a comparative risk assessment

Summary Background Diabetes and high body-mass index (BMI) are associated with increased risk of several cancers, and are increasing in prevalence in most countries. We estimated the cancer incidence attributable to diabetes and high BMI as individual risk factors and in combination, by country and sex. Methods We estimated population attributable fractions for 12 cancers by age and sex for 175 countries in 2012. We defined high BMI as a BMI greater than or equal to 25 kg/m2. We used comprehensive prevalence estimates of diabetes and BMI categories in 2002, assuming a 10-year lag between exposure to diabetes or high BMI and incidence of cancer, combined with relative risks from published estimates, to quantify contribution of diabetes and high BMI to site-specific cancers, individually and combined as independent risk factors and in a conservative scenario in which we assumed full overlap of risk of diabetes and high BMI. We then used GLOBOCAN cancer incidence data to estimate the number of cancer cases attributable to the two risk factors. We also estimated the number of cancer cases in 2012 that were attributable to increases in the prevalence of diabetes and high BMI from 1980 to 2002. All analyses were done at individual country level and grouped by region for reporting. Findings We estimated that 5·6% of all incident cancers in 2012 were attributable to the combined effects of diabetes and high BMI as independent risk factors, corresponding to 792 600 new cases. 187 600 (24·5%) of 766 000 cases of liver cancer and 121 700 (38·4%) of 317 000 cases of endometrial cancer were attributable to these risk factors. In the conservative scenario, about 4·5% (626 900 new cases) of all incident cancers assessed were attributable to diabetes and high BMI combined. Individually, high BMI (544 300 cases) was responsible for twice as many cancer cases as diabetes (280 100 cases). 26·1% of diabetes-related cancers (equating to 77 000 new cases) and 31·9% of high BMI-related cancers (174 040 new cases) were attributable to increases in the prevalence of these risk factors from 1980 to 2002. Interpretation A substantial number of cancer cases are attributable to diabetes and high BMI. As the prevalence of these cancer risk factors increases, clinical and public health efforts should focus on identifying optimal preventive and screening measures for whole populations and individual patients. Funding NIHR and Wellcome Trust.

Trends in obesity and diabetes across Africa from 1980 to 2014: an analysis of pooled population-based studies

Abstract Background The 2016 Dar Es Salaam Call to Action on Diabetes and Other non-communicable diseases (NCDs) advocates national multi-sectoral NCD strategies and action plans based on available data and information from countries of sub-Saharan Africa and beyond. We estimated trends from 1980 to 2014 in age-standardized mean body mass index (BMI) and diabetes prevalence in these countries, in order to assess the co-progression and assist policy formulation. Methods We pooled data from African and worldwide population-based studies which measured height, weight and biomarkers to assess diabetes status in adults aged ≥ 18 years. A Bayesian hierarchical model was used to estimate trends by sex for 200 countries and territories including 53 countries across five African regions (central, eastern, northern, southern and western), in mean BMI and diabetes prevalence (defined as either fasting plasma glucose of ≥ 7.0 mmol/l, history of diabetes diagnosis, or use of insulin or oral glucose control agents). Results African data came from 245 population-based surveys (1.2 million participants) for BMI and 76 surveys (182 000 participants) for diabetes prevalence estimates. Countries with the highest number of data sources for BMI were South Africa (n = 17), Nigeria (n = 15) and Egypt (n = 13); and for diabetes estimates, Tanzania (n = 8), Tunisia (n = 7), and Cameroon, Egypt and South Africa (all n = 6). The age-standardized mean BMI increased from 21.0 kg/m2 (95% credible interval: 20.3–21.7) to 23.0 kg/m2 (22.7–23.3) in men, and from 21.9 kg/m2 (21.3–22.5) to 24.9 kg/m2 (24.6–25.1) in women. The age-standardized prevalence of diabetes increased from 3.4% (1.5–6.3) to 8.5% (6.5–10.8) in men, and from 4.1% (2.0–7.5) to 8.9% (6.9–11.2) in women. Estimates in northern and southern regions were mostly higher than the global average; those in central, eastern and western regions were lower than global averages. A positive association (correlation coefficient ≃ 0.9) was observed between mean BMI and diabetes prevalence in both sexes in 1980 and 2014. Conclusions These estimates, based on limited data sources, confirm the rapidly increasing burden of diabetes in Africa. This rise is being driven, at least in part, by increasing adiposity, with regional variations in observed trends. African countries’ efforts to prevent and control diabetes and obesity should integrate the setting up of reliable monitoring systems, consistent with the World Health Organization’s Global Monitoring System Framework.

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys.

BACKGROUND Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years. METHODS Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40-64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. FINDINGS Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40-64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. INTERPRETATION Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. FUNDING National Institutes of Health.

Detecting New Kinds of Patient Safety Incidents

We present a novel approach to discovering small groups of anomalously similar pieces of free text. The UKs National Reporting and Learning System (NRLS) contains free text and categorical variables describing several million patient safety incidents that have occurred in the National Health Service. The groups of interest represent previously unknown incident types. The task is particularly challenging because the free text descriptions are of random lengths, from very short to quite extensive, and include arbitrary abbreviations and misspellings, as well as technical medical terms. Incidents of the same type may also be described in various different ways. The aim of the analysis is to produce a global, numerical model of the text, such that the relative positions of the incidents in the model space reflect their meanings. A high dimensional vector space model of the text passages is produced; TF-IDF term weighting is applied, reflecting the differing importance of particular words to a descriptions meaning. The dimensionality of the model space is reduced, using principal component and linear discriminant analysis. The supervised analysis uses categorical variables from the NRLS, and allows incidents of similar meaning to be positioned close to one another in the model space. Anomaly detection tools are then used to find small groups of descriptions that are more similar than one would expect. The results are evaluated by having the groups assessed qualitatively by domain experts to see whether they are of substantive interest.