Why is obesity occurring in medcs

Second, the measurement of burden inequities can inform understanding of the changing distribution of cardiometabolic diseases. Third, the mix of available and effective interventions to prevent overweight and treat overweight-related diseases may vary based on the personal wealth of those in need, indicating important planning and targeting needs for national health programs.

In this analysis, we aim to identify a wealth-overweight transition zone, which is a term we will use to denote the range of gross domestic product GDP per capita where overweight burden shifts within countries from the wealthy to the poor. This work contributes to the extant literature by characterizing the wealth-overweight transition zone, with potential implications for mitigation strategies.

The analysis has three aims: to 1 characterize overweight and obesity wealth gradients as countries develop economically; 2 identify the range of GDP per capita where the transition occurs; and 3 project overweight and obesity burden transitions to We extracted data from two series of surveys: the Demographic and Health Surveys and the World Health Surveys [ 15 , 16 ].

These are household, cross-sectional surveys that employ a multistage sampling design. The Demographic and Health Surveys collect anthropometrically measured height and weight from all respondents, as well as a series of asset indicators meant to capture personal wealth. The World Health Surveys collect mostly self-reported height and weight measurements, along with many asset indicators.

From these sources, we compiled surveys, representing countries across different time points from to A total of 2. For survey respondents aged 15—17, classification was made according to the International Obesity Task Force's growth curve [ 17 ]. Adults with missing or implausible BMI measurements less than 12 or greater than 60 were excluded more information is available in S1 Appendix Section 2.

Height and weight data in the World Health Surveys are self-reported, and some studies have shown bias in self-reporting of height and weight [ 18 ]. We used GDP per capita collected from the Institute for Health Metrics and Evaluation as a measure of national income in constant purchasing power parity adjusted dollars [ 19 ]. This data source also projects GDP for all countries in our study up to , which we use to project overweight and obesity distribution trends.

We linked GDP per capita to surveys by country and year identifiers. To capture personal wealth, we constructed a household-specific wealth index based on asset ownership. First, we collected survey-specific wealth ranks, meaning relative rank order of personal wealth within a country at the time of the survey based on asset inventories.

We use the relative personal wealth rank because we can obtain it directly from each of our surveys using a premade Demographic and Health Surveys or easily-derived World Health Surveys index from principal components analyses of wealth questionnaire responses.

To calculate the first principal component, we use all available asset indicators for each country, and perform country-specific principal component analyses. As a robustness check, we also calculate a second personal wealth index that is comparable across countries and years. In order to obtain this second wealth index, we perform principal components analysis on nine asset measures widely available in the Demographic and Health Surveys: water access, sanitation, floor material, electricity, refrigerator, motorcycle, car, phone, and rooms per person.

The personal wealth index is the first principal component, which captures the maximal variance amongst these assets. We then use the index scores across the entire study population across all surveys to calculate wealth deciles.

No prospective analysis plan was specified for this study. This work solely uses secondary, de-identified data and did not require ethics approval. As the study question concerns within-country changes, we started with a fixed-effects regression specification for both our wealth-overweight transition estimation and projections; additional sensitivity analyses and a meta-analytic technique are described below. We conducted multivariate regression analyses using the individual survey respondent as the unit of observation.

The purpose of these models is to estimate how the wealth-overweight gradient changes with economic development. We do this by estimating the following regression equation, with a binomial outcome distribution and logit link function: 1 where individuals are indexed by i , country by c , and year by t.

Individual controls include age in years and sex indicator for female. Year fixed effects control for common time trends shared among all countries, in effect allowing us to estimate our relationship of interest after removing global trends in overweight. We estimate logistic models for the binary outcome variable of individual overweight and include findings with obesity and BMI as the dependent variables in Tables G—L in S1 Appendix.

We also estimate the same model with country-specific age and sex trends in Fig E and wealth quintiles in Fig F in S1 Appendix. After estimating the basic shift in overweight and obesity trends between the poor and wealthy, we projected this shift to We project the overweight and obesity prevalence to of each sex and 5-year age groups, from age 15 to 49, using Eq 1 with a linear time trend.

We use GDP per capita projections from the Institute for Health Metrics and Evaluation to estimate the effect of economic development to [ 19 ]. We incorporated variance in the GDP per capita series and parameter uncertainty from our regression model to quantify an uncertainty interval for our projections.

After estimation of Eq 1 , we took 1, draws from the multivariate normal distribution defined by the model parameter estimates and the variance-covariance matrix of the model.

To create our predictions, those were coupled with 1, draws provided by the Institute for Health Metrics and Evaluation for their GDP per capita series. These draws incorporate model, data, and parameter uncertainty by using ensemble modeling techniques, drawing from the sub-model variance-covariance matrices, and adding a random walk of statistical noise to each forecast with variance based on the residual of the observed data for each sub-model.

We weight the predicted age—sex-specific overweight and obesity rates by the United Nations World Population Prospects age—sex-specific population projections to aggregate overweight and obesity rates to the national level by personal wealth decile [ 20 ].

Thus, these projections also capture the effects of aging on overweight and obesity rates. In Table U in S1 Appendix , we report results for out-of-sample validation of these projections.

We collected data from nationally representative surveys with individual-level data for 2. Fig 1 stratifies the overweight 1A and obesity 1B prevalence by level of economic development and ranked quintile of personal wealth. While Fig 2 adjusted for confounders, the shifting pattern was seen from the raw data.

Unadjusted overweight 1A and obesity 1B prevalence obtained directly from survey data, stratified by GDP per capita and within-survey personal wealth decile.

The columns represent GDP per capita categories, and the rows represent deciles of within-country wealth. Within each GDP per capita category, deciles with the lowest prevalence are coded in green, and deciles with the highest prevalence are coded in red. All prevalence estimates were obtained using survey weights. GDP, gross domestic product. Each point represents the probability of being overweight 2A or obese 2B relative to the richest decile 90th—th percentile at different GDP per capita cutoffs.

The lines are color coded by wealth decile. The wealth-overweight and wealth-obesity transition zones are denoted by the vertical lines. The first line marks where the richest decile was no longer the most likely to be overweight or obese. The second line marks where the richest decile was less likely than the poorest to be overweight or obese. We evaluate the change in the gradient based on where the other percentiles are statistically significantly greater than zero which means the wealth group has a higher chance of obesity than the 90th—th percentile personal wealth income group.

Fig 2 reports the adjusted probability of being overweight 2A and obese 2B in each individual-level wealth decile relative to the richest decile with increasing GDP per capita. At the GDP per capita of low-income countries, there was an increasing probability of overweight and obesity with personal wealth relative to individuals in the poorest decile.

However, developing countries and international organizations are beginning to respond to the rise in NCDs among poor populations. This network is working on solutions that involve multiple sectors, including a focus on promoting health through schools and an annual Move for Health Initiative to increase physical activity. Research is also beginning to suggest that obesity itself can be used to identify without medical diagnosis those most at risk for several NCDs. If this research is successful at pointing the way to low-cost identification of the most at-risk patients, broad-brush solutions such as eliminating salt in all processed foods could be avoided.

Nonetheless, some population-level interventions promise a multitude of benefits to countries that are either at high or low risk of rising obesity rates. Resource Library. Article Details Date October 1, In contrast, Thailand has experienced an increase in the per-head consumption of starchy roots and pulses as well as fruit, which Thais consume more than animal products.

This variety in diets carries certain implications, the report argues. Globalisation will not — in the medium term — place massive restrictions on the scope for policy action, and policy needs to start where people are, in terms of their preferences and traditions.

Yet, Wiggins acknowledges that governments have been timid in staking out positions on diet. This is not to conclude that diet policy must be timid, says the report, even if that is, apparently, the public mood. It contrasts government reluctance to act on diets with strong action to limit smoking. Although diet is a more diverse issue than smoking, says the report, there may be scope for governments to take more incremental measures that could pave the way for the public to accept something needs to be done if future health costs are to be contained.

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