Adiposity has differing associations with incident coronary heart disease and mortality in the Scottish population: cross-sectional surveys with follow-up

Objective: Investigation of the association of excess adiposity with three different outcomes: all-cause mortality, coronary heart disease (CHD) mortality and incident CHD. Design: Cross-sectional surveys linked to hospital admissions and death records. Subjects: 19 329 adults (aged 18–86 years) from a representative sample of the Scottish population. Measurements: Gender-stratified Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause mortality, CHD mortality and incident CHD. Separate models incorporating the anthropometric measurements body mass index (BMI), waist circumference (WC) or waist–hip ratio (WHR) were created adjusted for age, year of survey, smoking status and alcohol consumption. Results: For both genders, BMI-defined obesity (greater than or equal to30 kg m−2) was not associated with either an increased risk of all-cause mortality or CHD mortality. However, there was an increased risk of incident CHD among the obese men (hazard ratio (HR)=1.78; 95% confidence interval=1.37–2.31) and obese women (HR=1.93; 95% confidence interval=1.44–2.59). There was a similar pattern for WC with regard to the three outcomes; for incident CHD, the HR=1.70 (1.35–2.14) for men and 1.71 (1.28–2.29) for women in the highest WC category (men greater than or equal to102 cm, women greater than or equal to88 cm), synonymous with abdominal obesity. For men, the highest category of WHR (greater than or equal to1.0) was associated with an increased risk of all-cause mortality (1.29; 1.04–1.60) and incident CHD (1.55; 1.19–2.01). Among women with a high WHR (greater than or equal to0.85) there was an increased risk of all outcomes: all-cause mortality (1.56; 1.26–1.94), CHD mortality (2.49; 1.36–4.56) and incident CHD (1.76; 1.31–2.38). Conclusions: In this study excess adiposity was associated with an increased risk of incident CHD but not necessarily death. One possibility is that modern medical intervention has contributed to improved survival of first CHD events. The future health burden of increased obesity levels may manifest as an increase in the prevalence of individuals living with CHD and its consequences

Examining the BMI-mortality relationship using fractional polynomials

<p>Abstract</p> <p>Background</p> <p>Many previous studies estimating the relationship between body mass index (BMI) and mortality impose assumptions regarding the functional form for BMI and result in conflicting findings. This study investigated a flexible data driven modelling approach to determine the nonlinear and asymmetric functional form for BMI used to examine the relationship between mortality and obesity. This approach was then compared against other commonly used regression models.</p> <p>Methods</p> <p>This study used data from the National Health Interview Survey, between 1997 and 2000. Respondents were linked to the National Death Index with mortality follow-up through 2005. We estimated 5-year all-cause mortality for adults over age 18 using the logistic regression model adjusting for BMI, age and smoking status. All analyses were stratified by sex. The multivariable fractional polynomials (MFP) procedure was employed to determine the best fitting functional form for BMI and evaluated against the model that includes linear and quadratic terms for BMI and the model that groups BMI into standard weight status categories using a deviance difference test. Estimated BMI-mortality curves across models were then compared graphically.</p> <p>Results</p> <p>The best fitting adjustment model contained the powers -1 and -2 for BMI. The relationship between 5-year mortality and BMI when estimated using the MFP approach exhibited a J-shaped pattern for women and a U-shaped pattern for men. A deviance difference test showed a statistically significant improvement in model fit compared to other BMI functions. We found important differences between the MFP model and other commonly used models with regard to the shape and nadir of the BMI-mortality curve and mortality estimates.</p> <p>Conclusions</p> <p>The MFP approach provides a robust alternative to categorization or conventional linear-quadratic models for BMI, which limit the number of curve shapes. The approach is potentially useful in estimating the relationship between the full spectrum of BMI values and other health outcomes, or costs.</p

The relationship between body size and mortality in the linked Scottish Health Surveys: cross-sectional surveys with follow-up

Objective: To investigate the relationship between body mass index (BMI), waist circumference (WC) or waist–hip ratio (WHR) and all-cause mortality or cause-specific mortality. Design: Cross-sectional surveys linked to hospital admissions and death records. Subjects: In total, 20 117 adults (aged 18–86 years) from a nationally representative sample of the Scottish population. Measurements: Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause, or cause-specific, mortality. The three anthropometric measurements BMI, WC and WHR were the main variables of interest. The following were adjustment variables: age, gender, smoking status, alcohol consumption, survey year, social class and area of deprivation. Results: BMI-defined obesity (greater than or equal to30 kg m−2) was not associated with increased risk of mortality (HR=0.93; 95% confidence interval=0.80–1.08), whereas the overweight category (25–&#60;30 kg m−2) was associated with a decreased risk (0.80; 0.70–0.91). In contrast, the HR for a high WC (mengreater than or equal to102 cm, womengreater than or equal to88 cm) was 1.17 (1.02–1.34) and a high WHR (mengreater than or equal to1, women&#8805;0.85) was 1.34 (1.16–1.55). There was an increased risk of cardiovascular disease (CVD) mortality associated with BMI-defined obesity, a high WC and a high WHR categories; the HR estimates for these were 1.36 (1.05–1.77), 1.41 (1.11–1.79) and 1.44 (1.12–1.85), respectively. A low BMI (&#60;18.5 kg m−2) was associated with elevated HR for all-cause mortality (2.66; 1.97–3.60), for chronic respiratory disease mortality (3.17; 1.39–7.21) and for acute respiratory disease mortality (11.68; 5.01–27.21). This pattern was repeated for WC but not for WHR. Conclusions: It might be prudent not to use BMI as the sole measure to summarize body size. The alternatives WC and WHR may more clearly define the health risks associated with excess body fat accumulation. The lack of association between elevated BMI and mortality may reflect the secular decline in CVD mortality.</p

Examining Alternative Measures of Social Disadvantage Among Asian Americans: The Relevance of Economic Opportunity, Subjective Social Status, and Financial Strain for Health

Socioeconomic position is often operationalized as education, occupation, and income. However, these measures may not fully capture the process of socioeconomic disadvantage that may be related to morbidity. Economic opportunity, subjective social status, and financial strain may also place individuals at risk for poor health outcomes. Data come from the Asian subsample of the 2003 National Latino and Asian American Study (n = 2095). Regression models were used to examine the associations between economic opportunity, subjective social status, and financial strain and the outcomes of self-rated health, body mass index, and smoking status. Education, occupation, and income were also investigated as correlates of these outcomes. Low correlations were observed between all measures of socioeconomic status. Economic opportunity was robustly negatively associated with poor self-rated health, higher body mass index, and smoking, followed by financial strain, then subjective social status. Findings show that markers of socioeconomic position beyond education, occupation, and income are related to morbidity among Asian Americans. This suggests that potential contributions of social disadvantage to poor health may be understated if only conventional measures are considered among immigrant and minority populations

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe