3f:null 40:["$","title",null,{"children":"Social Vulnerability and Prevalence of Obesity: A Comparison across States | Anna D | Observable"}] 46:T401,### Introduction Social vulnerabilities and health outcomes go hand in hand. Pre-existing conditions, the ability to afford treatment, preventative measures are all informed by the environment we live in. [According to the CDC, in the United States, 42.4% of the population is obese.](https://www.cdc.gov/obesity/data/adult.html#:~:text=Obesity%20is%20a%20common%2C%20serious,from%2030.5%25%20to%2042.4%25) Obesity, a pre-existing condition, can be due to genetics, diet, comorbidity, etc. There are countless reasons individuals end up obese, many out of their control, but one thing that is true for all of them is that it impacts how they are treated in public, in healthcare settings, and as we have seen over the past few years, pre-existing conditions play a part in how an immune system may respond to a deadly virus. [Research has found that obesity is a "novel risk factor for hospitalization and death" due to the Coronavirus.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779330/pdf/12979_2020_Article_212.pdf) 47:T493,scatter = html`

Prevalence of Obesity across the United States

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Prevalence of Obesity across Male and Female populations

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Social Vulnerability and Prevalence of Obesity by Gender

` 4a:T4e5,ses_f = html`

Socioeconomic Scores and Obesity Prevalence in Women by State

` 4b:T4e3,ses_m = html`

Socioeconomic Scores and Obesity Prevalence in Men by State

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","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":233,"value":"$46","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":194,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"{\n scatter;\n var divElement = document.getElementById('viz1651747174138'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":257,"value":"* Marks: circles (points)\n* Channels: position on common scale for magnitude (horizontal & vertical position)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"Again we see Mississippi at the head of the pack with the highest prevalence of obesity in women and the second highest in men amongst the states. A general trend of note is that most states' female populations have higher prevalence of obesity compared to the male population of that state.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":188,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"{\n obese_bar;\n var divElement = document.getElementById('viz1651747324070'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"* Marks: bars (lines)\n* Channels: length for magnitude & color for identity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":244,"value":"We can take a closer look into how prevalence across gender matches up, as well as having the ability to compare multiple states. Here we can see, California's female population scores higher than its male population in terms of obesity prevalence.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":204,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":205,"value":"{\n scatter2;\n var divElement = document.getElementById('viz1651747254869'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"* Marks: circles (points)\n* Channels: position on common scale for magnitude & color for identity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":248,"value":"At the state level there does not appear to be any pattern or correlation between overall social vulnerability score and % of male/female population that is obese.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":212,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":213,"value":"{\n ses_f;\n var divElement = document.getElementById('viz1651747401732'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":273,"value":"* Marks: circles (points)\n* Channels: position on common scale for magnitude (horizontal & vertical position)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":252,"value":"We take a look at the socioeconomic metric, because a [CDC report](https://www.cdc.gov/nchs/products/databriefs/db50.htm) states that higher income women tend to be less likely to be obese compared to lower income women and that for men it prevalence of obesity stays generally the same across income levels. Amongst women there appears to be a more delineated upward trend in socioeconomic score as obesity prevalence increases, lining up with that report. This trend is not as distinct with men although there is a general upwards trend as well. However similar to population density and SVI, examining SVI and obesity feels too general at the state level. A deeper dive into the obesity prevalence and Social Vulnerability Index can be found [here](https://observablehq.com/@amdaccache/a-deep-dive-into-social-vulnerability-and-prevalence-of-obe?collection=@amdaccache/cs7295-project) where the metrics are explored within the state of California.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":218,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":219,"value":"{\n ses_m;\n var divElement = document.getElementById('viz1651747486699'); var vizElement = divElement.getElementsByTagName('object')[0]; vizElement.style.width='100%';vizElement.style.height=(divElement.offsetWidth*0.75)+'px'; var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement);}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 45:"$44:body"