Body fatness charts based on BMI and waist circumference

Objective To present percent body fat (PBF) charts based on body mass index (BMI) and waist circumference (WC) which can supplement current public health guidelines for obesity. Methods Based on data from the National Health and Nutrition Examination Survey (NHANES) III for 18- to 65-year-olds, a semi-parametric spline approach was utilized, in which no specific functional forms for BMI and WC are assumed, to depict graphically the relationship between BMI, WC, and PBF. Four distinct PBF charts were created, categorized by gender and ethnicity which are based on data from 2,170 white females, 1,902 African American females, 1,905 white males, and 1,635 African American males. Results PBF prediction based on the semi-parametric spline model outperformed competing linear models. For men, BMI is largely inconsequential, and WC plays a primary role in determining PBF levels. For women, the interaction between BMI and WC is more complex. To have low body fat, women would need to watch both their BMI and WC measurements carefully. Conclusions PBF charts, which incorporate information from three dimensions that are as simple to read as a BMI chart to help determine a person's level of fatness, were proposed.

Age-specific changes in BMI and BMI distribution among Australian adults using cross-sectional surveys from 1980 to 2008

Background: Research efforts have focused mainly on trends in obesity among populations, or changes in mean body mass index (BMI), without consideration of changes in BMI across the BMI spectrum. Examination of age-specific changes in BMI distribution may reveal patterns that are relevant to targeting of interventions.

Methods: Using a synthetic cohort approach (which matches members of cross-sectional surveys by birth year) we estimated population representative annual BMI change across two time periods (1980 to 1989 and 1995 to 2008) by age, sex, socioeconomic position and quantiles of BMI. Our study population was a total of 27 349 participants from four nationally representative Australian health surveys; Risk Factor Prevalence Study surveys (1980 and 1989), the 1995 National Nutrition Survey and the 2007/8 National Health Survey.

Results: We found greater mean BMI increases in younger people, in those already overweight and in those with lower education. For men, age-specific mean annual BMI change was very similar in the 1980s and the early 2000s (P=0.39), but there was a recent slowing down of annual BMI gain for older women in the 2000s compared with their same-age counterparts in the 1980s (P<0.05). BMI change was not uniform across the BMI distribution, with different patterns by age and sex in different periods. Young adults had much greater BMI gain at higher BMI quantiles, thus adding to the increased right skew in BMI, whereas BMI gain for older populations was more even across the BMI distribution.

Conclusions: The synthetic cohort technique provided useful information from serial cross-sectional survey data. The quantification of annual BMI change has contributed to an understanding of the epidemiology of obesity progression and identified key target groups for policy attention—young adults, those who are already overweight and those of lower socioeconomic status.

Noncommunicable disease prevention and control in Mongolia: a policy analysis

BACKGROUND: Noncommunicable diseases (NCDs) are the major global cause of morbidity and mortality. In Mongolia, a number of health policies have been developed targeting the prevention and control of noncommunicable diseases. This paper aimed to evaluate the extent to which NCD-related policies introduced in Mongolia align with the World Health Organization (WHO) 2008-2013 Action Plan for the Global Strategy for the Prevention and Control of NCDs. METHODS: We conducted a review of policy documents introduced by the Government of Mongolia from 2000 to 2013. A literature review, internet-based search, and expert consultation identified the policy documents. Information was extracted from the documents using a matrix, mapping each document against the six objectives of the WHO 2008-2013 Action Plan for the Global Strategy for the Prevention and Control of NCDs and five dimensions: data source, aim and objectives of document, coverage of conditions, coverage of risk factors and implementation plan. 45 NCD-related policies were identified. RESULTS: Prevention and control of the common NCDs and their major risk factors as described by WHO were widely addressed, and policies aligned well with the objectives of the WHO 2008-2013 Action Plan for the Global Strategy for the Prevention and Control of NCDs. Many documents included explicit implementation or monitoring frameworks. It appears that each objective of the WHO 2008-2013 NCD Action Plan was well addressed. Specific areas less well and/or not addressed were chronic respiratory disease, physical activity guidelines and dietary standards. CONCLUSIONS: The Mongolian Government response to the emerging burden of NCDs is a population-based public health approach that includes a national multisectoral framework and integration of NCD prevention and control policies into national health policies. Our findings suggest gaps in addressing chronic respiratory disease, physical activity guidelines, specific food policy actions restricting sales advertising of food products, and a lack of funding specifically supporting NCD research. The neglect of these areas may hamper addressing the NCD burden, and needs immediate action. Future research should explore the effectiveness of national NCD policies and the extent to which the policies are implemented in practice.

Dietary protein intake may reduce hospitalisation due to infection in Māori of advanced age: LiLACS NZ

OBJECTIVE: To investigate factors related to hospital admission for infection, specifically examining nutrient intakes of Māori in advanced age (80+ years). METHOD: Face-to-face interviews with 200 Māori (85 men) to obtain demographic, social and health information. Diagnoses were validated against medical records. Detailed nutritional assessment using the 24-hour multiple-pass recall method was collected on two separate days. FOODfiles was used to analyse nutrient intake. National Health Index (NHI) numbers were matched to hospitalisations over a two-year period (12 months prior and 12 months following dietary assessment). Selected International Classification of Disease (ICD) codes were used to identify admissions related to infection. RESULTS: A total of 18% of participants were hospitalised due to infection, most commonly lower respiratory tract infection. Controlling for age, gender, NZ deprivation index, diabetes, CVD and chronic lung disease, a lower energy-adjusted protein intake was independently associated with hospitalisation due to infection: OR (95%CI) 1.14 (1.00-1.29), p=0.046. CONCLUSIONS: Protein intake may have a protective effect on the nutrition-related morbidity of older Māori. Improving dietary protein intake is a simple strategy for dietary modification aiming to decrease the risk of infections that lead to hospitalisation and other morbidities.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

A qualitative study of the infant feeding beliefs and behaviours of mothers with low educational attainment

BACKGROUND: Infancy is an important period for the promotion of healthy eating, diet and weight. However little is known about how best to engage caregivers of infants in healthy eating programs. This is particularly true for caregivers, infants and children from socioeconomically disadvantaged backgrounds who experience greater rates of overweight and obesity yet are more challenging to reach in health programs. Behaviour change interventions targeting parent-infant feeding interactions are more likely to be effective if assumptions about what needs to change for the target behaviours to occur are identified. As such we explored the precursors of key obesity promoting infant feeding practices in mothers with low educational attainment.

METHODS: One-on-one semi-structured telephone interviews were developed around the Capability Opportunity Motivation Behaviour (COM-B) framework and applied to parental feeding practices associated with infant excess or healthy weight gain. The target behaviours and their competing alternatives were (a) initiating breastfeeding/formula feeding, (b) prolonging breastfeeding/replacing breast milk with formula, (c) best practice formula preparation/sub-optimal formula preparation, (d) delaying the introduction of solid foods until around six months of age/introducing solids earlier than four months of age, and (e) introducing healthy first foods/introducing unhealthy first foods, and (f) feeding to appetite/use of non-nutritive (i.e., feeding for reasons other than hunger) feeding. The participants' education level was used as the indicator of socioeconomic disadvantage. Two researchers independently undertook thematic analysis.

RESULTS: Participants were 29 mothers of infants aged 2-11 months. The COM-B elements of Social and Environmental Opportunity, Psychological Capability, and Reflective Motivation were the key elements identified as determinants of a mother's likelihood to adopt the healthy target behaviours although the relative importance of each of the COM-B factors varied with each of the target feeding behaviours.

CONCLUSIONS: Interventions targeting healthy infant feeding practices should be tailored to the unique factors that may influence mothers' various feeding practices, taking into account motivational and social influences.