The idea of uniform change: is it time to revisit a central tenet of Rose's "Strategy of Preventive Medicine"?

Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019

BACKGROUND

The widening of group-level socioeconomic differences in body mass index (BMI) has received considerable research attention. However, the predictive power of socioeconomic position (SEP) indicators at the individual level remains uncertain, as does the potential temporal variation in their predictive value. Examining this is important given the increasing incorporation of SEP indicators into predictive algorithms and calls to reduce social inequality to tackle the obesity epidemic. We thus investigated SEP differences in BMI over three decades of the obesity epidemic in England, comparing population-wide (SEP group differences in mean BMI) and individual-level (out-of-sample prediction of individuals' BMI) approaches to understanding social inequalities.

METHODS

We used repeated cross-sectional data from the Health Survey for England, 1991-2019. BMI (kg/m2) was measured objectively, and SEP was measured via educational attainment, occupational class, and neighbourhood index of deprivation. We ran random forest models for each survey year and measure of SEP adjusting for age and sex.

RESULTS

The mean and variance of BMI increased within each SEP group over the study period. Mean differences in BMI by SEP group also increased: differences between lowest and highest education groups were 1.0 kg/m2 (0.4, 1.6) in 1991 and 1.3 kg/m2 (0.7, 1.8) in 2019. At the individual level, the predictive capacity of SEP was low, though increased in later years: including education in models improved predictive accuracy (mean absolute error) by 0.14% (- 0.9, 1.08) in 1991 and 1.05% (0.18, 1.82) in 2019. Similar patterns were obtained for occupational class and neighbourhood deprivation and when analysing obesity as an outcome.

CONCLUSIONS

SEP has become increasingly important at the population (group difference) and individual (prediction) levels. However, predictive ability remains low, suggesting limited utility of including SEP in prediction algorithms. Assuming links are causal, abolishing SEP differences in BMI could have a large effect on population health but would neither reverse the obesity epidemic nor reduce much of the variation in BMI.

Epidemiological impact of public health interventions against diabetes in Qatar: mathematical modeling analyses

Aims

To predict the epidemiological impact of specific, and primarily structural public health interventions that address lifestyle, dietary, and commuting behaviors of Qataris as well as subsidies and legislation to reduce type 2 diabetes mellitus (T2DM) burden among Qataris.

Methods

A deterministic population-based mathematical model was used to investigate the impact of public health interventions on the epidemiology of T2DM among Qataris aged 20-79 years, which is the age range typically used by the International Diabetes Federation for adults. The study evaluated the impact of interventions up to 2050, a three-decade time horizon, to allow for the long-term effects of different types of interventions to materialize. The impact of each intervention was evaluated by comparing the predicted T2DM incidence and prevalence with the intervention to a counterfactual scenario without intervention. The model was parameterized using representative data and stratified by sex, age, T2DM risk factors, T2DM status, and intervention status.

Results

All intervention scenarios had an appreciable impact on reducing T2DM incidence and prevalence. A lifestyle management intervention approach, specifically applied to those who are categorized as obese and ≥35 years old, averted 9.5% of new T2DM cases by 2050. An active commuting intervention approach, specifically increasing cycling and walking, averted 8.5% of new T2DM cases by 2050. Enhancing consumption of healthy diets including fruits and vegetables, specifically a workplace intervention involving dietary modifications and an educational intervention, averted 23.2% of new T2DM cases by 2050. A subsidy and legislative intervention approach, implementing subsidies on fruits and vegetables and taxation on sugar-sweetened beverages, averted 7.4% of new T2DM cases by 2050. A least to most optimistic combination of interventions averted 22.8-46.9% of new T2DM cases by 2050, respectively.

Conclusions

Implementing a combination of individual-level and structural public health interventions is critical to prevent T2DM onset and to slow the growing T2DM epidemic in Qatar.

A prospective population-based multicentre study on the impact of maternal body mass index on adverse pregnancy outcomes: Focus on normal weight

Background

Maternal body mass index (BMI) below or above the reference interval (18.5–24.9 kg/m²) is associated with adverse pregnancy outcomes. Whether BMI exerts an effect within the reference interval is unclear. Therefore, we assessed the association between adverse pregnancy outcomes and BMI, in particular within the reference interval, in a general unselected pregnant population.

Methods

Data was extracted from a prospective population-based multicentre cohort (Risk Estimation for PrEgnancy Complications to provide Tailored care (RESPECT) study) conducted between December 2012 to January 2014. BMI was studied in categories (I: <18.5, II: 18.5–19.9, III: 20.0–22.9, IV: 23.0–24.9, V: 25.0–27.4, VI: 27.5–29.9, VII: >30.0 kg/m²) and as a continuous variable within the reference interval. Adverse pregnancy outcomes were defined as composite endpoints for maternal, neonatal or any pregnancy complication, and for adverse pregnancy outcomes individually. Linear trends were assessed using linear-by-linear association analysis and (adjusted) relative risks by regression analysis.

Results

The median BMI of the 3671 included women was 23.2 kg/m² (IQR 21.1–26.2). Adverse pregnancy outcomes were reported in 1256 (34.2%). Linear associations were observed between BMI categories and all three composite endpoints, and individually for pregnancy-induced hypertension (PIH), preeclampsia, gestational diabetes mellitus (GDM), large-for-gestational-age (LGA) neonates; but not for small-for-gestational-age neonates and preterm birth. Within the reference interval, BMI was associated with the composite maternal endpoint, PIH, GDM and LGA, with adjusted relative risks of 1.15 (95%CI 1.06–1.26), 1.12 (95%CI 1.00–1.26), 1.31 (95%CI 1.11–1.55) and 1.09 (95%CI 1.01–1.17).

Conclusions

Graded increase in maternal BMI appears to be an indicator of risk for adverse pregnancy outcomes even among women with a BMI within the reference interval. The extent to which BMI directly contributes to the increased risk in this group should be evaluated in order to determine strategies most valuable for promoting safety and long-term health for mothers and their offspring.

Filling the weight gap: Estimating body weight and BMI using height, chest and upper arm circumference of Swiss conscripts in the first half of the 20th century

We estimate weight and BMI values based on height, chest circumference, and mid upper arm circumference measures of Swiss conscripts in the city of Zurich for each year between 1904 and 1932. Height, chest circumference, and mid upper arm circumference were measured each year from 1904 to 1951. Body weight is available from 1933 to 1951. We used prediction equations from the literature, and also developed our own equations, which we tested and validated on the dataset from 1933 to 1951. We used a representative sample of usually 19-year-old Swiss males (N = 88,792, coverage > 88 %). There was an increase in average height and chest circumference between 1904 and 1951. During both world wars, chest circumference, mid upper arm circumference, weight, and BMI decreased, while height stagnated. Overall mean weight and BMI increased from 1904 to 1951, but decreased during the Great Depression. After World War II, weight quickly returned to the pre-war and pre-Great Depression level, while BMI had not reached the 1933 level by 1951. Average weights of the lower and middle socioeconomic groups were catching up with average weight of the upper socioeconomic group from 1904 to 1951. The convergence in height is less pronounced. Finally, we show that it is possible to accurately predict mean weight and BMI from other anthropometric measurements. We suggest that our estimation approach could be replicated for other historical populations to obtain more information on how nutritional status changed over time.

Heterogeneity in adult anthropometry by socioeconomic factors: Indian National Family Health Survey 2006 and 2016

BACKGROUND/OBJECTIVES

Recent evidence suggests nonconstant nature of dispersion in adult women's body mass index (BMI) across sociodemographic groups. The overall variances in BMI and height are also shown to have substantially changed over time. We modeled complex variation in adults' anthropometry-BMI and height-by wealth and education, and assessed their differences over time in India.

SUBJECTS/METHODS

Data from a total of 768,130 women and 180,691 men from the Indian National Family Health Survey (NFHS) 2006 and 2016 were used for the analysis. The average association between wealth and education with anthropometry was assessed from linear regression models assuming constant variance. Individual heterogeneity was modeled to obtain separate variances in anthropometry for each wealth quintile and education level. All analyses were stratified by survey year and sex.

RESULTS

On average, the positive socioeconomic gradient in adult's BMI and height persisted over time with slight attenuation. The residual variance in BMI ranged from 10.1 to 14.9 (7.2-11.3) by education level and 6.1 to 17.4 (5.0-13.0) by household wealth for women (men) in 2006, and they increased over time for the lower socioeconomic groups but remained the same or decreased for the higher socioeconomic groups. No significant pattern was observed for variation in height for both genders.

CONCLUSIONS

We found potential reversal in the socioeconomic patterning in BMI variability in India as suggested by the increasing dispersion among the least educated and poorest populations. For a comprehensive understanding of nutrition transition in developing countries, it is necessary to assess the changes in means and variances of anthropometry in tandem.

Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016

Background

The complex etiology of child growth failure and anemia—commonly used indicators of child undernutrition—involving proximate and distal risk factors at multiple levels is generally recognized. However, their independent and joint effects are often assessed with no clear conceptualization of inferential targets.

Methods

We utilized hierarchical linear modeling and a nationally representative sample of 139,116 children aged 6–59 months from India (2015–2016) to estimate the extent to which a comprehensive set of 27 covariates explained the within- and between-population variation in height-for-age, weight-for-age, weight-for-height, and hemoglobin level.

Results

Most of the variation in child anthropometry and hemoglobin measures was attributable to within-population differences (80–85%), whereas between-population differences (including communities, districts, and states) accounted for only 15–20%. The proximate and distal covariates explained 0.2–7.5% of within-population variation and 2.1–34.0% of between-population variation, depending on the indicator of interest. Substantial heterogeneity was observed in the magnitude of within-population variation, and the fraction explained, in child anthropometry and hemoglobin measures across the 36 states/union territories of India.

Conclusions

Policies and interventions aimed at reducing between-population inequalities in child undernutrition may require a different set of components than those concerned with within-population inequalities. Both are needed to promote the health of the general population, as well as that of high-risk children.

Association between population mean and distribution of deviance in demographic surveys from 65 countries: cross sectional study

OBJECTIVES

To examine whether conditions related to scarcity at the left side of the distribution (anaemia, severe chronic energy deficiency, and underweight) are as strongly related to population means as conditions of excess at the right side of the distribution (overweight and obesity).

DESIGN

Observational study.

SETTING

65 countries, with nationally representative cross sectional data from 1994 to 2014 obtained from the Demographic Health Surveys.

PARTICIPANTS

Non-pregnant women aged 20-49. Sample of 65 countries and n=524 380 for analysis of BMI; sample of 44 countries and n=316 465 for analysis of haemoglobin.

MAIN OUTCOME MEASURES

The association between mean and prevalence of each category. For BMI, prevalence of severe chronic energy deficiency (SCED, BMI <16.0), underweight (BMI <18.5), overweight (BMI >25) and obese (BMI >30.) were measured; for haemoglobin, prevalence of anaemia (haemoglobin <12.0 g/dL) and severe anaemia (haemoglobin <8.0 g/dL) were examined.

RESULTS

There was a strong association between mean BMI and prevalence of overweight (r²=0.98; r=0.99; β=8.3 (8.0 to 8.6)) and obesity (r²=0.93; r=0.97; β=4.2 (3.9 to 4.5)). For left sided conditions, a moderate to strong association was found between mean BMI and prevalence of underweight (r²=0.67; r=-0.82; β=-2.7 (-3.1 to -2.2)), and a weaker association for SCED (r²=0.38; r=-0.61; β=-0.32 (-0.43 to -0.22)). There was a moderate association between mean haemoglobin and prevalence of anaemia (r²=0.46; r=-0.68; β=-10.8 (-14.5 to -7.1)) and a weaker association with severe anaemia (r²=0.30; r=-0.55; β=-0.55 (-0.81 to -0.29)).

CONCLUSIONS

The associations between population means and prevalence of conditions of scarcity such as low BMI and anaemia were substantially weaker than the associations of mean BMI with conditions of excesses such as overweight and obesity.

Understanding the potential of state-based public health genomics programs to mitigate disparities in access to clinical genetic services

Purpose

State Health Agencies (SHAs) have developed public health genomics (PHG) programs that play an instrumental role in advancing precision public health, but there is limited research on their approaches. This study examines how PHG programs attempt to mitigate or forestall health disparities and inequities in the utilization of genomic medicine.

Methods

We compared PHG programs in three states: Connecticut, Michigan, and Utah. We analyzed 85 in-depth interviews with SHA internal and external collaborators and program documents. We employed a qualitative coding process to capture themes relating to health disparities and inequities.

Results

Each SHA implemented population-level approaches to identify individuals who carry genetic variants that increase risk of hereditary cancers. However, each SHA developed a unique strategy—which we label public health action repertoires—to reach specific subgroups who faced barriers in accessing genetic services. These strategies varied across states given demographics of the state population, state-level partnerships, and availability of healthcare services.

Conclusion

Our findings illustrate the imperative of tailoring PHG programs to local demographic characteristics and existing community resources. Furthermore, our study highlights how integrating genomics into precision public health will require multilevel, multisector collaboration to optimize efficacy and equity.

Population-level trends in the distribution of body mass index in Canada, 2000-2014

OBJECTIVE

Research studying population-level body mass index (BMI) trends document increases in mean or prevalence of overweight/obese but less consideration has been given to describing the changing distribution of BMI. The objective of this research was to perform a detailed analysis of changes in the BMI distribution in Canada.

METHODS

Using data from the CCHS (2000-2014), we analyzed distributional parameters of BMI for 492,886 adults aged 25-64 years. We further stratified these analyses for women and men, education level, and region of residence.

RESULTS

Mean BMI has increased for most subgroups of the Canadian population. Mean BMI values were higher for men, while standard deviation (SD) of the BMI distribution was systematically higher in women. Increases in mean BMI were accompanied with increases in SD of BMI across cycles. Across survey cycles, the 95th percentile increased more than 10 times more rapidly compared to the 5th percentile, showing a very unequal change between extreme values in the BMI distribution over time. There was a relationship between SD with BMI, but these relations were generally not different between educational categories and regions. This suggests that the growing inter-individual inequalities (i.e., dispersion) in BMI were not solely attributable to socioeconomic and demographic factors.

CONCLUSIONS

This study supports the hypothesis that the simultaneous increases in mean BMI and SD of the BMI distribution are occurring, and suggests the need to move beyond the mean-centric paradigm when studying a complex public health phenomenon such as population change in BMI.

Emerging Trends in Clinical Research: With Implications for Population Health and Health Policy

Policy Points: Significant advances in clinical medicine that have broader societal relevance may be less accessible to population health researchers and policymakers because of increased specialization within fields. We describe important recent clinical advances and discuss their broader societal impact. These advances include more expansive strategies for disease prevention, the rise of precision medicine, applications of human microbiome research, and new and highly successful treatments for hepatitis C infection. These recent developments in clinical research raise important issues surrounding health care costs and equitable resource allocation that necessitate an ongoing dialogue among the fields of clinical medicine, population health, and health policy.

CONTEXT

Developments in clinical medicine have important implications for population health, and there is a need for interdisciplinary engagement among clinical medicine, the social sciences, and public health research. The aim of this article is to help bridge the divide between these fields by exploring major recent advances in clinical medicine that have important implications for population health.

METHODS

We reviewed the most cited articles published from 2010 to 2015 in 5 high-impact clinical journals and selected 5 randomized controlled trials and 2 related clinical practice guidelines that are broadly relevant to population health and policy.

FINDINGS

We discuss the following themes: (1) expanding indications for drug therapy and the inherent medicalization of the population as highlighted by studies and clinical guidelines supporting lower blood pressure targets or widespread statin use; (2) the tension in nutritional research between quantifying the impact of isolated nutrients and studying specific foods and dietary patterns, for example, the role of the Mediterranean diet in the primary prevention of cardiovascular disease; (3) the issue of high medication costs and the challenge of providing equitable access raised by the development of new and effective treatments for hepatitis C infection; (4) emerging clinical applications of research on the human microbiome as illustrated by fecal transplant to treat Clostridium difficile infections; and (5) the promise and limitations of precision medicine as demonstrated by the rise of novel targeted therapies in oncology.

CONCLUSIONS

These developments in clinical science hold promise for improving individual and population health and raise important questions about resource allocation, the role of prevention, and health disparities.

Early childhood linear growth faltering in low-income and middle-income countries as a whole-population condition: analysis of 179 Demographic and Health Surveys from 64 countries (1993-2015)

BACKGROUND

The causes of early childhood linear growth faltering (known as stunting) in low-income and middle-income countries remain inadequately understood. We aimed to determine if the progressive postnatal decline in mean height-for-age Z score (HAZ) in low-income and middle-income countries is driven by relatively slow growth of certain high-risk children versus faltering of the entire population.

METHODS

Distributions of HAZ (based on WHO growth standards) were analysed in 3-month age intervals from 0 to 36 months of age in 179 Demographic and Health Surveys from 64 low-income and middle-income countries (1993-2015). Mean, standard deviation (SD), fifth percentiles, and 95th percentiles of the HAZ distribution were estimated for each age interval in each survey. Associations between mean HAZ and SD, fifth percentile, and 95th percentile were estimated using multilevel linear models. Stratified analyses were performed in consideration of potential modifiers (world region, national income, sample size, year, or mean HAZ in the 0-3 month age band). We also used Monte Carlo simulations to model the effects of subgroup versus whole-population faltering on the HAZ distribution.

FINDINGS

Declines in mean HAZ from birth to 3 years of age were accompanied by declines in both the fifth and 95th percentiles, leading to nearly symmetrical narrowing of the HAZ distributions. Thus, children with relatively low HAZ were not more likely to have faltered than taller same-age peers. Inferences were unchanged in surveys regardless of world region, national income, sample size, year, or mean HAZ in the 0-3 month age band. Simulations showed that the narrowing of the HAZ distribution as mean HAZ declined could not be explained by faltering limited to a growth-restricted subgroup of children.

INTERPRETATION

In low-income and middle-income countries, declines in mean HAZ with age are due to a downward shift in the entire HAZ distribution, revealing that children across the HAZ spectrum experience slower growth compared to the international standard. Efforts to mitigate postnatal linear growth faltering in low-income and middle-income countries should prioritise action on community-level determinants of childhood HAZ trajectories.

FUNDING

Bill & Melinda Gates Foundation.

Variation in Anthropometric Status and Growth Failure in Low- and Middle-Income Countries

BACKGROUND

Addressing anthropometric failure in low- and middle-income countries can have 2 targets of inference: addressing differences between individuals within populations (W_pop) or differences between populations (B_pop). We present a multilevel framework to apply both targets of inference simultaneously and quantify the extent to which variation in anthropometric status and growth failure is reflective of undernourished children or undernourished populations.

METHODS

Cross-sectional data originated from the Demographic and Health Surveys program, covering children under age 5 from 57 countries surveyed between 2001 and 2015.

RESULTS

A majority of variation in child anthropometric status and growth failure was attributable to W_pop-associated differences, accounting for 89%, 83%, and 85% of the variability in z scores for height for age, weight for age, and weight for height. B_pop-associated differences (communities, regions, and countries combined) were associated with 11%, 17%, and 15% of the variation in height-for-age z score, weight-for-age z score, and weight-for-height z score. Prevalence of anthropometric failure was closely correlated with mean levels of height and weight. Approximately 1% of W_pop variability, compared with 30% to 50% of the B_pop variability, was explained by mean values of maternal correlates of anthropometric status and failure. Although there is greater explanatory power B_pop, this varied because of modifiability of what constitutes population.

CONCLUSIONS

Our results suggest that universal strategies to prevent future anthropometric failure in populations combined with targeted strategies to address both the impending and existing burden among children are needed.

Patterning of individual heterogeneity in body mass index: evidence from 57 low- and middle-income countries

Modeling variation at population level has become increasingly valued, but no clear application exists for modeling differential variation in health between individuals within a given population. We applied Goldstein's method (in: Everrit, Howell (eds) Encyclopedia of statistics in behavioral science, Wiley, Hoboken, 2005) to model individual heterogeneity in body mass index (BMI) as a function of basic sociodemographic characteristics, each independently and jointly. Our analytic sample consisted of 643,315 non-pregnant women aged 15-49 years pooled from the latest Demographic Health Surveys (rounds V, VI, or VII; years 2005-2014) across 57 low- and middle-income countries. Individual variability in BMI ranged from 9.8 (95% CI: 9.8, 9.9) for the youngest to 23.2 (95% CI: 22.9, 23.5) for the oldest age group; 14.2 (95% CI: 14.1, 14.3) for those with no formal education to 19.7 (95% CI: 19.5, 19.9) for those who have completed higher education; and 13.6 (95% CI: 13.5, 13.7) for the poorest quintile to 20.1 (95% CI: 20.0, 20.2) for the wealthiest quintile group. Moreover, variability in BMI by age was also different for different socioeconomic groups. Empirically testing the fundamental assumption of constant variance and identifying groups with systematically large differentials in health experiences have important implications for reducing health disparity.

The Epigenomic Analysis of Human Obesity

OBJECTIVE

Analysis of the epigenome-the chemical modifications and packaging of the genome that can influence or indicate its activity-enables molecular insight into cell type-specific machinery. It can, therefore, reveal the pathophysiological mechanisms at work in disease. Detected changes can also represent physiological responses to adverse environmental exposures, thus enabling the epigenetic mark of DNA methylation to act as an epidemiological biomarker, even in surrogate tissue. This makes epigenomic analysis an attractive prospect to further understand the pathobiology and epidemiological aspects of obesity. Furthermore, integrating epigenomic data with known obesity-associated common genetic variation can aid in deciphering their molecular mechanisms.

METHODS AND CONCLUSIONS

This review primarily examines epidemiological or population-based studies of epigenetic modifications in relation to adiposity traits, as opposed to animal or cell models. It discusses recent work exploring the epigenome with respect to human obesity, which to date has predominately consisted of array-based studies of DNA methylation in peripheral blood. It is of note that highly replicated BMI DNA methylation associations are not causal, but strongly driven by coassociations for more precisely measured intertwined outcomes and factors, such as hyperlipidemia, hyperglycemia, and inflammation. Finally, the potential for the future exploration of the epigenome in obesity and related disorders is considered.

Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches

Intersectionality theory can contribute to epidemiology and public health by furthering understanding of power dynamics driving production of health disparities, and increasing knowledge about heterogeneities within, and overlap between, social categories. Drawing on McCall, we relate the first of these potential contributions to categorical intersectionality and the second to anti-categorical intersectionality. Both approaches are used in study of risk of ischemic heart disease (IHD), based on register data on 3.6 million adults residing in Sweden by 2010, followed for three years. Categorical intersectionality is here coupled with between-group differences in average risk calculation, as we use intersectional categorizations while estimating odds ratios through logistic regressions. The anti-categorical approach is operationalized through measurement of discriminatory accuracy (DA), i.e., capacity to accurately categorize individuals with or without a certain outcome, through computation of the area under the curve (AUC). Our results show substantial differences in average risk between intersectional groupings. The DA of social categorizations is found to be low, however, due to outcome variability within and overlap between categories. We argue that measures of DA should be used for proper interpretation of differences in average risk between social (or any other) categories. Tension between average between-group risk and the DA of categorizations, which can be related to categorical and anti-categorical intersectional analyses, should be made explicit and discussed to a larger degree in epidemiology and public health.