{"componentChunkName":"component---node-modules-narative-gatsby-theme-novela-src-templates-article-template-tsx","path":"/the-importance-of-the-\"starting-point\"-in-tracking-covid-by-region","result":{"data":{"allSite":{"edges":[{"node":{"siteMetadata":{"name":"MIT Civic Data Design Lab"}}}]}},"pageContext":{"article":{"id":"ab69d513-8612-52c7-a90e-a2fe11bafeb7","slug":"/the-importance-of-the-\"starting-point\"-in-tracking-covid-by-region","secret":false,"title":"The Importance of the \"Starting Point\" in Tracking COVID by Region","author":"Griffin Kantz","date":"June 15th, 2020","dateForSEO":"2020-06-15T00:00:00.000Z","timeToRead":5,"excerpt":"When comparing how different regions have been impacted by the coronavirus over time, it is important to define a \"starting point\": an early…","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"The Importance of the \\\"Starting Point\\\" in Tracking COVID by Region\",\n  \"author\": \"Griffin Kantz\",\n  \"date\": \"2020-06-15T00:00:00.000Z\",\n  \"excerpt\": \"\",\n  \"tags\": [\"Covid\", \"Data\", \"Visualization\"],\n  \"hero\": \"images/covid-19-critical-mass_graphic.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, \"When comparing how different regions have been impacted by the coronavirus over time, it is important to define a \\u201Cstarting point\\u201D: an early timepoint in the spread of the virus from which the timepoints of future observations can be measured. Although one may think that the natural place to start would be a region\\u2019s first recorded COVID case or the first COVID fatality, this can lead to improper or uninformative region-to-region comparisons. Testing at the beginning of a local COVID outbreak can be seriously unreliable, and the spread of the disease from the first handful of cases to the next is tied to the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6915e1.htm\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"idiosyncrasies of people\\u2019s daily behaviors\"), \".\"), mdx(\"p\", null, \"In New York state, where COVID \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"may have arrived more than one month before the first confirmed case\"), \" on March 1, 2020, confirmed fatalities soared from 1 death to nearly 500 in just two weeks. Conversely, the Norfolk-Newport News area of Virginia had still not witnessed ten deaths one month after its first. \"), mdx(\"p\", null, \"COVID metrics between different afflicted regions appear to begin behaving in a more predictable manner once the disease has reached a critical mass and begun spreading widely. COVID data analysts try to set the starting point for their measurements at some level where this critical mass has likely been reached and the initial random variation has dissipated.\"), mdx(\"p\", null, \"Different modelers will choose different thresholds. The \", mdx(\"em\", {\n    parentName: \"p\"\n  }, mdx(\"a\", _extends({\n    parentName: \"em\"\n  }, {\n    \"href\": \"https://ig.ft.com/coronavirus-chart/?areas=usa&areas=gbr&cumulative=0&logScale=1&perMillion=0&values=deaths\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Financial Times\")), \" measures new cases or deaths by country from the day 10 cases/day or 3 deaths/day was reached, and cumulative cases/deaths from the day of the 100th case or death. The \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://91-divoc.com/pages/covid-visualization/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"data visualization at 91-divoc.com\"), \" measures by nation from the day of the 100th case or the 10th death; for regions, it starts at 20 cases or 5 deaths.\"), mdx(\"h2\", {\n    \"id\": \"what-threshold-makes-the-most-sense\"\n  }, \"What threshold makes the most sense?\"), mdx(\"p\", null, \"In our analysis, we examined the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Johns Hopkins CSSE time series dataset\"), \" (up to 5/31/20) to determine how varying the choice of starting point affects the precision of forecasts. We chose to analyze counts of confirmed COVID fatalities, which are more reliable early-stage figures than confirmed COVID cases. (However, deaths always lag cases by up to two weeks.)\"), mdx(\"p\", null, \"First, we grouped U.S. county-level COVID death counts into the top 100 most populous Census metropolitan statistical areas (MSAs), which adhere to county boundaries. By population, the largest of these is New York-Newark-Jersey City and the smallest is Chattanooga.\"), mdx(\"p\", null, \"Next, we shifted the daily figures for the MSAs to synchronize the two weeks before and two weeks after the day \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" total deaths was reached, where \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1, 2, 5, 10, 20, 50, 100, 200, 500, 1,000, and 2,000.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"(Why these amounts? Contagions spread exponentially, and these numbers break the span from 1 to 2,000 into roughly equal logarithmic intervals. As of today, only three MSAs have reached 5,000 total deaths. And why two weeks? That is roughly the duration of a COVID infection.)\")), mdx(\"p\", null, \"To determine the efficacy of each threshold of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", we created regressions for the \\u201Cbefore\\u201D and \\u201Cafter\\u201D weeks, based on the linear formula and its exponential transformation shown below:\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"1375px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"14.399999999999999%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": \"url('data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAADCAYAAACTWi8uAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAaUlEQVQI12WOSwqAMAwF041oF/VXFcSVqAsP1d7/Fk4gBdHCMJS+vFSEk3MWc51SuvEFB+wwGBus9hahJeve8/Ipc9DARDDgQikMttBjb65e879CDZwK4RF3Wg4L9xkrvWWD/TiWjtLzABCJOGODeCV/AAAAAElFTkSuQmCC')\",\n      \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"picture\", {\n    parentName: \"span\"\n  }, \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/c6ac1b1d9779498361dbb02cc978e5f5/0fffa/covid-19-critical-mass_equations.webp 1375w\"],\n    \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png 1375w\"],\n    \"sizes\": \"(max-width: 1375px) 100vw, 1375px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/c6ac1b1d9779498361dbb02cc978e5f5/8ff9b/covid-19-critical-mass_equations.png\",\n    \"alt\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\n    \"title\": \"Equations: log(deaths) = alpha + beta*days + epsilon. Deaths = lambda*e^(beta*days) + epsilon, lambda = e^alpha.\",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"(Here,\"), \" \\u03B1 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"and\"), \" \\u03B2 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"are the y-intercept and slope of the regression and\"), \" \\u03B5 \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"is the residual error.)\")), mdx(\"p\", null, \"Below in Chart 1, see an interactive graph illustrating the data for \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1 death (the day of the first recorded COVID death in each MSA).\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/3.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Chart 1. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.\")), mdx(\"p\", null, \"When \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1, the trajectories on the right (\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \") side of the graph diverge widely, signifying that this observation point occurs too early in the spread of the virus to meaningfully predict or compare trajectories. Additionally, for this and the next few values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", any data that would appear on the left (\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \") side of the graph are inscrutable, since counts of zero deaths have an infinitesimal logarithmic value and must therefore be discarded.\"), mdx(\"p\", null, \"When moving to higher thresholds of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", we are able to see the trajectories on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" side begin to coalesce and the data on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" side start to grow. For the highest \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", few MSAs have yet reached those death counts, so the trajectories on both sides of the graph are much fewer in number. Here are selected values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", visualized:\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/5.embed\"\n  }), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/9.embed\"\n  }), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"400\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/15.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Charts 2-4. Rendered in R, ggplot2, and Plotly. Hover over points to see more information.\")), mdx(\"p\", null, \"Across all these test values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the mean square error (MSE) of the observations to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" regression lines is what attracts our attention, more so than the regressions themselves. The MSE allows us to understand how closely the trajectories coalesce around the regression line, and thus around each other. A low MSE implies that the observations fit well around the line, indicating better predictability. For the first few values of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the MSE is lower on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" side than on the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" side. For larger \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \", the opposite is true: the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE is lower and the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE is higher.\"), mdx(\"p\", null, \"As \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" increases, the point at which the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE becomes less than the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE is where the near future becomes more predictable than the near past. In our data, this point occurs just shy of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 50, as visualized in the graph below:\"), mdx(\"iframe\", {\n    width: \"500\",\n    height: \"300\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    src: \"//plotly.com/~GriffinK/19.embed\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Chart 5. Mean square error for each value of\"), \" X\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \".\")), mdx(\"p\", null, \"The data shown in this graphic imply that the fatality trajectories have lost most of their early-stage variability around the time of the 50th death. After reaching this threshold, the trajectories behave more consistently \\u2014 not totally in lockstep, in fact far from it, but more consistently than at any point before.\"), mdx(\"p\", null, \"The widening difference between the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"after\"), \" MSE curves beyond \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 50 indicates that 100, 200, or 500 might be even better thresholds, but we must bear in mind the passage of time. The higher we set \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" as the starting point, the more precision we gain in modeling the future, but the more data we are willingly discarding. We should not choose a starting point so late in the outbreak that we end up ignoring weeks of mid-phase growth in the fatality count for the sake of a more precise model.\"), mdx(\"p\", null, \"The steep decline in both MSE curves at \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \" = 1,000 and 2,000 is an artifact of the rarity of those high death counts as of this month (June); data is simply too scarce for these \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"X\"), \". If more regions across the U.S. were suffering COVID fatality rates that severe, we could expect to see the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"before\"), \" MSE curve trend further and further upwards.\"), mdx(\"h2\", {\n    \"id\": \"what-we-can-learn\"\n  }, \"What we can learn\"), mdx(\"p\", null, \"This analysis shows how the growth in COVID fatalities in U.S. urban regions reaches a \\u201Ccritical mass\\u201D and loses its early-stage variability at some time around the 50th death. Graphing MSE shows that setting a higher starting point for measurements enables greater precision, but this comes with the price of discarding informative data.\"), mdx(\"p\", null, \"This is an important finding for comparative analysis and future COVID time-series data visualizations. Yet, we must caution against overstating the rigor of this analysis. We are not epidemiologists and this is not a professional epidemiological study. We have regressed over the variable of time but not over variables of human behavior or system factors. Hopefully this analysis, albeit rough, can impart some mathematical basis to the assumptions underlying future analysis.\"), mdx(\"p\", null, \"Download our data tables for this post \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://github.com/civic-data-design-lab/COVID-critical-mass\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"here\"), \".\"));\n}\n;\nMDXContent.isMDXComponent = 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\"images/screen-shot-2020-05-22-at-10.08.27-am.png\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, \"The COVID-19 pandemic has highlighted the importance of collecting and reporting data on health outcomes by race and ethnicity in order to quantify the way in which the virus\\u2019s impacts fall along existing lines of health and structural inequity. Just this past week, the federal government \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.npr.org/sections/coronavirus-live-updates/2020/06/04/869815033/race-ethnicity-data-to-be-required-with-coronavirus-tests-in-u-s\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"announced\"), \" that it will require data on race and ethnicity to be collected for all COVID-19 tests.\"), mdx(\"p\", null, \"The push for these data sits within a broader conversation about the importance of collecting racial data that has been ongoing among those working toward \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://www.healthaffairs.org/do/10.1377/hblog20200507.469145/full/?utm_source%3DNewsletter%26utm_medium%3Demail%26utm_content%3DEye%2BOn%2BHealth%2BReform%253A%2BRisk%2BCorridors%252C%2BCOVID-19%252C%2BAnd%2BThe%2BACA%253B%2BCOVID-19%253A%2BFederal%2BFunding%2BFor%2BContact%2BTracing%253B%2BMedicaid%2BMCOs%2BAnd%2BPayment%2BReform%253B%2BInequity%26utm_campaign%3DHAT%2B5-11-20%26&sa=D&ust=1590158619227000&usg=AFQjCNGo8Ae6MN6_8xi21Iua4cbQU62_Yg\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"health equity\"), \" and to mitigate the negative impacts of the \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://patientengagementhit.com/news/social-determinants-of-health-comorbidities-sway-covid-19-severity&sa=D&ust=1590158619229000&usg=AFQjCNHUbDqYt4PiAPAbP4WYpvXRBwCHVg\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"\\u201Csocial determinants of health\\u201D\"), \" \\u2013 or the place-based conditions that impact the health of individuals and communities. \"), mdx(\"p\", null, \"Over the last month or so, \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://www.theatlantic.com/ideas/archive/2020/04/stop-looking-away-race-covid-19-victims/609250/&sa=D&ust=1590158619231000&usg=AFQjCNGvZda446O1AMbKQ8ZIARTVGGXPNw\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"more\"), \" and more articles have been published discussing the disparate impact of COVID-19 on \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://labblog.uofmhealth.org/rounds/racial-disparities-time-of-covid-19&sa=D&ust=1590158619228000&usg=AFQjCNHWT_KM3b_dszEG9VEUE-ItDnojJA\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"African American\"), \", \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.washingtonpost.com/national/coronavirus-navajo-nation-crisis/2020/05/11/b2a35c4e-91fe-11ea-a0bc-4e9ad4866d21_story.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Indigenous\"), \", and \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://www.nytimes.com/2020/05/07/us/coronavirus-latinos-disparity.html&sa=D&ust=1590158619219000&usg=AFQjCNGB63MElzGWkmKzA6k6VgNhgCAgtA\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Latinx\"), \" people. These articles bring to light the ways in which existing inequities \\u2013 both in terms of access to resources and healthcare, and resulting from historic processes of discrimination such as \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.npr.org/2017/05/03/526655831/a-forgotten-history-of-how-the-u-s-government-segregated-america\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"redlining\"), \" \\u2013 are manifesting in COVID-19 infection and mortality rates.\"), mdx(\"p\", null, \"This burgeoning discussion also has brought into clear focus the inconsistencies in how data on race and ethnicity are collected and shared at the state level. Some states have robust data on COVID-19 testing results and death rates that include granular information by race and ethnicity and have made these data easily accessible to the public. Other states are lagging behind. \"), mdx(\"p\", null, \"One of the clear principles among those working to mitigate health disparities is the need to collect and report race and ethnicity data in order to understand differences in health outcomes before we can determine how to intervene in order to mitigate these inequities.  \"), mdx(\"p\", null, \"Our lab decided to look into how states have been collecting and sharing data on COVID-19 testing and deaths by race. \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://covidtracking.com/race&sa=D&ust=1590158619219000&usg=AFQjCNESMUbMb9qT-r1q-f8XRMbXnNj73g\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"The COVID Tracking Project\"), \" recently teamed up with the Antiracist Research & Policy Center to aggregate race data by state and to highlight where the gaps are. (Check out their regularly updated racial data tracker \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://docs.google.com/spreadsheets/u/1/d/e/2PACX-1vTfUQPxkhP_CRcGmnnpUBihnTNZ9Z8pcizII4_sc2o2n3opOoAJdAM4CRTJBI339tou8LWnQrqbTMgH/pubhtml%23&sa=D&ust=1590158619220000&usg=AFQjCNEYWrNViMBob1DzXPfjotH7ablwGA\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"here\"), \".)\"), mdx(\"p\", null, \"Using their data, we created these maps that show the percent of missing data on race and ethnicity among those who have tested positive for COVID-19, by state. The darker colors indicate states where the race and ethnicity of those who have tested positive for COVID-19 is least known.\"), mdx(\"iframe\", {\n    id: \"carto\",\n    title: \"Carto Map\",\n    src: \"https://mit.carto.com/u/chenab/builder/760496a1-4886-40c3-be1c-73ae28cfb2b9/embed\",\n    width: \"100%\",\n    height: \"520\",\n    allow: \"\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"(Data pulled June 4, 2020 from \", mdx(\"a\", _extends({\n    parentName: \"em\"\n  }, {\n    \"href\": \"https://covidtracking.com/race\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://covidtracking.com/race\"), \")\")), mdx(\"iframe\", {\n    id: \"carto-2\",\n    title: \"Carto Map Ethnicity\",\n    src: \"https://mit.carto.com/u/chenab/builder/03122e0c-35fb-4950-be0c-00af2281175c/embed\",\n    width: \"100%\",\n    height: \"520\",\n    allow: \"\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"(Data pulled June 4, 2020 from \", mdx(\"a\", _extends({\n    parentName: \"em\"\n  }, {\n    \"href\": \"https://covidtracking.com/race\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://covidtracking.com/race\"), \")\")), mdx(\"p\", null, \"It\\u2019s important to note, though, that even where testing data and outcomes are reported by race and ethnicity, an outstanding question remains of who is and isn\\u2019t being tested. Even the most \\u201Ccomplete\\u201D datasets don\\u2019t answer this more fundamental question. Though the federal government\\u2019s new requirement is vital to filling gaps in the data, this question of who is and isn\\u2019t tested will persist until testing becomes more ubiquitous.\"), mdx(\"p\", null, \"Understanding where these data are missing is, of course, just one piece of the larger conversation about how the structural features of our society have left some communities more vulnerable to the pandemic than others. If you\\u2019re interested, \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.google.com/url?q=https://www.healthaffairs.org/do/10.1377/hblog20200507.469145/full/?utm_source%3DNewsletter%26utm_medium%3Demail%26utm_content%3DEye%2BOn%2BHealth%2BReform%253A%2BRisk%2BCorridors%252C%2BCOVID-19%252C%2BAnd%2BThe%2BACA%253B%2BCOVID-19%253A%2BFederal%2BFunding%2BFor%2BContact%2BTracing%253B%2BMedicaid%2BMCOs%2BAnd%2BPayment%2BReform%253B%2BInequity%26utm_campaign%3DHAT%2B5-11-20%26&sa=D&ust=1590158619230000&usg=AFQjCNE3I8JdtgTHBTX5Z4m1lHlh8LGnBQ\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"here\"), \" is a great piece that delves into how inequity is playing a role as a \\u201Cpre-existing condition\\u201D in our current healthcare system.\"));\n}\n;\nMDXContent.isMDXComponent = 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The graph above shows the changes of test per capita and percentage of positive tests returned. The size of the circle indicate the…","canonical_url":null,"subscription":true,"body":"function _extends() { _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\n/* @jsx mdx */\nvar _frontmatter = {\n  \"title\": \"Covid-19 Tests and Inequality\",\n  \"author\": \"Zhuangyuan (Yuan) Fan\",\n  \"date\": \"2020-05-29T00:00:00.000Z\",\n  \"tags\": [\"General\", \"Covid\"],\n  \"hero\": \"images/image1-01.jpg\"\n};\n\nvar makeShortcode = function makeShortcode(name) {\n  return function MDXDefaultShortcode(props) {\n    console.warn(\"Component \" + name + \" was not imported, exported, or provided by MDXProvider as global scope\");\n    return mdx(\"div\", props);\n  };\n};\n\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n      props = _objectWithoutProperties(_ref, [\"components\"]);\n\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Chart 1. The graph above shows the changes of test per capita and percentage of positive tests returned. The size of the circle indicate the median household income\")), mdx(\"h2\", {\n    \"id\": \"introduction\"\n  }, \"Introduction\"), mdx(\"p\", null, mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.newyorker.com/magazine/2020/05/04/seattles-leaders-let-scientists-take-the-lead-new-yorks-did-not\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"New York City was slow to respond to COVID-19.\"), \" It took the city officials over a month to shift adequate testing resources to areas that were suffering most: New York\\u2019s low-income and disproportionately minority neighborhoods and households. \"), mdx(\"p\", null, \"Many journalists and civil society actors have begun to raise alarm at what is proving to be an economic and social disparity in our governments\\u2019 response. For the few state and local governments that have released details data on COVID19 fatalities, researchers on our team begin to observe a clear uneven pattern: low-income communities have been much harder hit by COVID19 than high-income communities.  We also know that widespread testing is essential to both helping infected individuals and also containing the virus at a regional level. However, income should not decide whether or not one has access to adequate testing and care.  \"), mdx(\"p\", null, \"Several recent studies have attempted to analyze if state and local testing strategies are disbursed and utilized in more high-income than low-income regions.  Two studies, by Borjas, G. J. (2020) and Schmitt-Groh\\xE9 (2020) respectively, used zip code level data in New York City. Using data from April 5th, Borjas finds that people residing in poor neighborhoods were less likely to be tested than people residing in rich neighborhoods, while, with data from April 2nd to April 13th, Schmitt-Groh\\xE9 finds that the distribution of Covid-19 tests was equal across income brackets. \"), mdx(\"p\", null, \"\\u2018in comparing these two results, it become more evident that a key contributing factor in the differences is \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, mdx(\"em\", {\n    parentName: \"strong\"\n  }, \"time\")), \".\"), mdx(\"h2\", {\n    \"id\": \"timing-is-everything\"\n  }, \"Timing is everything.\"), mdx(\"p\", null, \"These confounding conclusions motivated researchers in our lab to explore the relationship of Covid-19 testing, income levels, and time. First, we compare the zip code level testing and confirmed cases data from April 1st to May 4th in New York City\", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \".\"), \" Low-income zip codes show a higher fraction of positive testing results across time. Although the data from early April show a higher concentration of testing in high-income zip codes, this trend reversed itself over time in the last month.\"), mdx(\"p\", null, mdx(\"span\", _extends({\n    parentName: \"p\"\n  }, {\n    \"className\": \"gatsby-resp-image-wrapper\",\n    \"style\": {\n      \"position\": \"relative\",\n      \"display\": \"block\",\n      \"marginLeft\": \"auto\",\n      \"marginRight\": \"auto\",\n      \"maxWidth\": \"3000px\"\n    }\n  }), \"\\n      \", mdx(\"span\", _extends({\n    parentName: \"span\"\n  }, {\n    \"className\": \"gatsby-resp-image-background-image\",\n    \"style\": {\n      \"paddingBottom\": \"55.00000000000001%\",\n      \"position\": \"relative\",\n      \"bottom\": \"0\",\n      \"left\": \"0\",\n      \"backgroundImage\": 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We assigned each quintile a score from 1 to 5: 1 representing the lowest quintile and 5 representing the highest quintile. We then add the testing rate per capita score with the household income score created a map that illuminates where there is disparity between income and testing. A score of 10 represents the highest income and  testing rates, while a score of 2 represents lowest income and testing rates. In the map (Graph 4.1) we can see the extreme color of blue and red diminishing between early April and early May. However, there are still places that remain at score 2.\"), mdx(\"p\", null, \"If we plot positive returns per test rate (at zip code level) by quintile against the test per capita by quintile (Graph 4.2), we show a map with dark red indicating very high likelihood of getting a positive return per test but the overall tests per capita remain low. The blue color indicates the zip code has a very positive per test rate but high test per capita rate. These maps uncover the fact that even with the trend reversing, we still have many zip codes that are left behind. For example, as of May 4th, Flushing in Queens (11355) has a positive cases per test rate as high as 46.7% but their overall test per capita is less than 3%. 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     \"backgroundSize\": \"cover\",\n      \"display\": \"block\"\n    }\n  })), \"\\n  \", mdx(\"picture\", {\n    parentName: \"span\"\n  }, \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/8f267b3f473b3a575e179b2b67d2afff/62d33/mapforweb2.webp 1574w\"],\n    \"sizes\": \"(max-width: 1574px) 100vw, 1574px\",\n    \"type\": \"image/webp\"\n  })), \"\\n        \", mdx(\"source\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"srcSet\": [\"/static/8f267b3f473b3a575e179b2b67d2afff/2c1f1/mapforweb2.png 1574w\"],\n    \"sizes\": \"(max-width: 1574px) 100vw, 1574px\",\n    \"type\": \"image/png\"\n  })), \"\\n        \", mdx(\"img\", _extends({\n    parentName: \"picture\"\n  }, {\n    \"className\": \"gatsby-resp-image-image\",\n    \"src\": \"/static/8f267b3f473b3a575e179b2b67d2afff/2c1f1/mapforweb2.png\",\n    \"alt\": \"mapforweb2\",\n    \"title\": \"Positive per test rate vs. Test per capita\",\n    \"loading\": \"lazy\",\n    \"style\": {\n      \"width\": \"100%\",\n      \"height\": \"100%\",\n      \"margin\": \"0\",\n      \"verticalAlign\": \"middle\",\n      \"position\": \"absolute\",\n      \"top\": \"0\",\n      \"left\": \"0\"\n    }\n  })), \"\\n      \"), \"\\n    \")), mdx(\"p\", null, \"There are potentially many factors that contributed to New York\\u2019s decisions on where to allocation COVID19 resources. It could be because her healthcare system is just simply responding to the number of confirmed cases. Whatever the reason, we all have an obligation to understand and learn from these failings in order to better inform more equitable designs and plans. Many of us don\\u2019t have the luxury of time. So it is left to our planners, policy-makers, and public health officials to ensure that we make the very best of it. To know and never doubt that income should never dictate the acquisition of something so priceless - a life well lived\"), mdx(\"h2\", {\n    \"id\": \"reference\"\n  }, \"Reference:\"), mdx(\"p\", null, \"[\", \"1] Borjas, G. J. (2020).\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Demographic determinants of testing incidence and COVID-19 infections in New York City neighborhoods\"), \"(No. w26952). National Bureau of Economic Research.\"), mdx(\"p\", null, \"[\", \"2] Schmitt-Groh\\xE9, S., Teoh, K., & Uribe, M. (2020).\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Covid-19: Testing Inequality in New York City\"), \"(No. w27019). National Bureau of Economic Research.\"));\n}\n;\nMDXContent.isMDXComponent = 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