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I am passionate about understanding how policy decisions drive economic and social change. As an avid creative, I love to model and write poetry, and I play varsity baseball at MIT.","id":"f542d3ea-8f8f-58ea-8d10-cb0bf7b9a4cd","name":"Brian 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CARES about COVID-19?","author":"Brian Williams","date":"August 30th, 2020","dateForSEO":"2020-08-30T00:00:00.000Z","timeToRead":3,"excerpt":"Is Section 18115 really doing anything? On June 4th, the U.S. Department of Health and Human Services (HHS) released new guidance requiring…","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\": \"WHO CARES about COVID-19?\",\n  \"author\": \"Brian Williams\",\n  \"date\": \"2020-08-30T00:00:00.000Z\",\n  \"tags\": [\"Covid\", \"Data\", \"Health\", \"Race\", \"Visualization\"],\n  \"hero\": \"images/pic-for-2nd-article-cares.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, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Is Section 18115 really doing anything?\")), mdx(\"p\", null, \"On June 4th, the U.S. Department of Health and Human Services (HHS) released new guidance requiring laboratories to include relevant demographic data, such as age and race, on every COVID-19 test. As specified in the Coronavirus Aid, Relief, and Economic Security (CARES) ActSection 18115,these changes went into effect on August 1st. But did anythingreallyhappen?\"), mdx(\"p\", null, \"As a follow up to the\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://blog.civicdatadesignlab.mit.edu/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"previous article\"), \"on missing reported race data in COVID-19 tests nationwide, we wanted to see the significant effects (if any) of the new federal guidance passed in the CARES Act Section 18115. Previously, we found large gaps in the aggregate data across the United States. From this, we asked if anything could be done at individual laboratories or medical testing centers to compensate for the gap. Now, we seek to investigate the impact of the new federal regulation on reporting race data.\"), mdx(\"p\", null, \"Data Comparisons, Before and After 18115\"), mdx(\"p\", null, \"Section 18115 may actually be impacting the level of reported race data nationwide\\u2026 But is it enough?\"), mdx(\"p\", null, \"From July 26th to August 26th, positive COVID-19 cases increased by over 1.5 million but only about 1 million had associated race data.\"), mdx(\"p\", null, \"In the United States, the percent of cases with associated race data has varied over time:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"cumulative as of August 26th: 55.65%\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"only between July 26th and August 26th: 66.10%\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"cumulative as of July 26th: 51.75%\")), mdx(\"p\", null, \"[[\", \"insert bar graph]\", \"](\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://chart-studio.plotly.com/~brianwilliams2022/17.embed\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://chart-studio.plotly.com/~brianwilliams2022/17.embed\"), \")\"), mdx(\"p\", null, \"This visualization shows the difference of percents of cases where race data is known, per state. The number represented in each bar is the percent difference in cases with associated race between two different periods: fromJuly 26th to August 26thand from the beginning of data collection up until July 26th.\"), mdx(\"p\", null, \"For example, let\\u2019s say that a given state\\u2019s total cases rose from July to August by x, and of those cases, there is a subsection where race data is unknown. Let\\u2019s say this subsection of unknown race data increases by y. I calculated 1 - (y/x), and compared that same calculation instead with the cumulative period from toward the beginning of the pandemic (late March) to July 26th.\"), mdx(\"p\", null, \"Some of these values are negative because the total number of Other and Unknown cases actuallyincreasesfrom July to August at a greater percentage compared to the beginning of the pandemic. This is pretty alarming.\"), mdx(\"p\", null, \"Please note that North Dakota (ND) seems to have a very large value: 87%. This is due to the fact that North Dakota only very recently started reporting race data foranyof their cases.\"), mdx(\"p\", null, \"But why?\"), mdx(\"p\", null, \"This seems unusual but I think it can be explained by retroactive revisions to the data (either by individual laboratories or a state\\u2019s Department of Public Health) which would move cases from these categories to their accurate racial category.\"), mdx(\"p\", null, \"For example, if a scientist was able to confirm a case or a set of cases belonged to certain demographic after the total number of cases had already been reported, you would simply move cases over to their respective categories. Maybe in certain situations, backlogs of case data prevent labs from sorting cases by demographic data before state deadlines but as time goes along, they are able to update their reported case data. This doesn\\u2019t seem to happen in many states and the testing efficiency problem could easily be a bigger and more widespread problem than I am expressing/speculating here.\"), mdx(\"p\", null, \"Here\\u2019s a regional breakdown of the same data.\"), mdx(\"p\", null, \"[[\", \"insert color map]\", \"](\", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://plotly.com/~brianwilliams2022/35.embed\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"http://plotly.com/~brianwilliams2022/35.embed\"), \")\"), mdx(\"p\", null, \"Discussion: Almost too little, too late\"), mdx(\"p\", null, \"So far, the national average of cases with race data increased by about 15% directly following the period Section 18115 went into effect, compared to reported race averages during the rest of the pandemic.\"), mdx(\"p\", null, \"Is this difference big enough? And how much of it can be directly attributed to Section 18115? To be quite honest\\u2026 I\\u2019m not sure.\"), mdx(\"p\", null, \"Though somewhat significant, the impact is almost too little too late. Just imagine what we\\u2019re not seeing, and the things we\\u2019ve \", \"*\", \"already\", \"*\", \" missed. Before August, there were more than 2 million Covid-19 tests that were unidentifiable by race. This type of federal guidance should have been in place since February and at the latest, the beginning of March.\"), mdx(\"p\", null, \"It doesn\\u2019t seem like we will have access to complete, accurate, and thorough data sets into the foreseeable future for many reasons starting at the laboratory level stretching all the way to the federal level. I just hope policymakers, publicly obligated to support our communities, are doing their best trying to walk in the dark during this pandemic.\"), mdx(\"p\", null, \"Sources\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"a\", _extends({\n    parentName: \"li\"\n  }, {\n    \"href\": \"https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf\")), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"a\", _extends({\n    parentName: \"li\"\n  }, {\n    \"href\": \"https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"https://www.cdc.gov/coronavirus/2019-ncov/lab/reporting-lab-data.html#what-to-include\"))));\n}\n;\nMDXContent.isMDXComponent = 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457px"},"seo":{"src":"/static/683011498507327484dfabdb8b7b7a17/6050d/pic-for-2nd-article-cares.png"}}},{"id":"05fc4a31-394b-5eed-9975-130225fcf0e1","slug":"/data-from-reported-covid-19-tests-are-telling-an-incomplete-story:-here's-what-you-need-to-know","secret":false,"title":"Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know","author":"Brian Williams","date":"July 31st, 2020","dateForSEO":"2020-07-31T00:00:00.000Z","timeToRead":12,"excerpt":"As of July 26th, there were 4.2 million positive COVID-19 tests in the United States, however, only about 2.2 million of those cases have race data associated. ...How did we get here?","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\": \"Data from reported COVID-19 tests are telling an incomplete story: Here's what you need to know\",\n  \"author\": \"Brian Williams\",\n  \"date\": \"2020-07-31T00:00:00.000Z\",\n  \"excerpt\": \"As of July 26th, there were 4.2 million positive COVID-19 tests in the United States, however, only about 2.2 million of those cases have race data associated. ...How did we get here?\",\n  \"tags\": [\"Covid\", \"Data\", \"Health\", \"Race\", \"Visualization\"],\n  \"hero\": \"images/newplot-1-.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, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Missing Racial Data in COVID-19 Reporting\")), mdx(\"p\", null, \"In the past six months, hospitals, clinics, and medical institutions across the United States have conducted millions of COVID-19 tests. The data from the tests has been made publicly available through multiple avenues for public use. \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://covidtracking.com/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"The Covid Tracking Project\"), \", a volunteer organization launched from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"The Atlantic\"), \", has collected and published metadata to accompany the testing data. One such piece of metadata: race.\"), mdx(\"p\", null, \"When analyzing the reported race data, I\\u2019ve noticed that some states report race data much more consistently and thoroughly than others. This prompted me to dig into why this is, why a testing center does or does not report race, and how the state, county, municipal, and lab policies vary with regards to collecting and reporting race information.\"), mdx(\"p\", null, \"As a part of the CDDL\\u2019s Missing Data Project, this investigation tries to tackle just that: the missing data that is inherent in reported health data. By trying to highlight this missing data, we may be able to better illuminate issues within this larger system.\"), mdx(\"h3\", {\n    \"id\": \"at-first-glance\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"At First glance.\")), mdx(\"p\", null, \"Below is a graphic representing the racial breakdown of positive cases in each U.S. state and territory over time, as data is made available.\"), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"600\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/13.embed?link=false\"\n  }), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notes:\")), mdx(\"p\", null, \"It\\u2019s an \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.plotly.com/~brianwilliams2022/13.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"interactive visualization\"), \"! \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\"), \" (1) Use the options in the top right to start/pause the animation, or even select states to compare percentages over time. You can highlight one racial category (like Asian or white) and see those individual trends in the states over time. This is a very useful feature!\"), mdx(\"p\", null, \"I urge you to pay attention to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category as percentages move over time. \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notice these states and territories in particular:\"), \" North Dakota (ND), New York (NY), Puerto Rico (PR), Texas (TX), Northern Marianas (MP), and Virgin Islands (VI). These regions do a particularly poor job in reporting race in their testing results.\"), mdx(\"p\", null, \"When investigating, I wondered \\u201CWhat\\u2019s the functional difference between the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" designation and the\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \"designation?\\u201D In the context of the other racial and ethnic categories: \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"American Indian or Alaska Native, Asian, Black or African American, Latinx, Native Hawaiian or Other Pacific Islander\"), \", and \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"White\"), \", the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category doesn\\u2019t give any description of value more than the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category. And in some states, reported race data seems to switch categories - from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" to \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" - after a certain date. Therefore, I decided \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" will be treated as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" for the purposes of this project. Regardless, it speaks to how \\u201Cmissing data\\u201D is pervasive throughout the healthcare system.\"), mdx(\"h3\", {\n    \"id\": \"the-bigger-picture\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"The Bigger Picture\")), mdx(\"p\", null, \"As of July 26th, there were \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"4.2 million\"), \" positive COVID-19 tests in the United States, however, only about \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"2.2 million\"), \" of those cases have race data associated. The \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category is purposefully \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"not\"), \" considered to be a race designation in this calculation because of how it differs descriptively from \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Multiracial\"), \". (2)\"), mdx(\"p\", null, \"In other words, for any given COVID test, you could flip a coin to determine whether the patient\\u2019s race is known. With that level of (un)certainty, I ask: what aren\\u2019t we seeing? What could all this missing racial data mean for real COVID testing results? And how is it impacting our communities?\"), mdx(\"p\", null, \"From various \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"sources\"), \", we know that the pandemic impacts varying demographics differently, notably, disproportionately impacting Black and Latinx communities. To that end, how accurately can we create solutions or policy decisions to alleviate and support these communities with only 50% certainty of data?\"), mdx(\"p\", null, \"In the below visualization, the known percentages of cases with reported race are plotted against each state\\u2019s testing per capita. (3) The plot is animated to show how each state has progressed over time.\"), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"800\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/11.embed?link=false\"\n  }), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Notes:\")), mdx(\"p\", null, \"Another \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://plotly.com/~brianwilliams2022/11.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"interactive visualization\"), \"! \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\"), \" Use the options in the top right to pan around the graph and select states to highlight their path over time. You can also select multiple states with Shift+Select for easy comparisons. Hover over each state bubble for more relevant data. You can press each category name to only view states of that category as well.\"), mdx(\"p\", null, \"The size of each state bubble represents its total positive tests. So a state with more cases will be represented as a larger bubble than a state with fewer cases in this visualization.\"), mdx(\"h3\", {\n    \"id\": \"categories\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Categories\")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"[\", \"Highly Impacted States, Increasing, Decreasing, and Constant]\")), mdx(\"p\", null, \"States are categorized by whether this \\u201Cknown percentage\\u201D factor has increased, decreased, or remained relatively constant from the first time data is available for that state to the latest data available. In order for a state to be considered \\u201Cincreasing,\\u201D it would need to increase by 5 or more percentage points. Similarly, \\u201Cdecreasing\\u201D states are those that decreased by 5 or more percentage points. \\u201CConstant\\u201D states are those that lie in the middle.\"), mdx(\"p\", null, \"For example, if a state\\u2019s \\u201Cknown percentage\\u201D is 69.7% on it\\u2019s earliest date and on it\\u2019s latest date it\\u2019s 85.4%, the state will be placed in the \\u201CIncreasing\\u201D category which is labeled green in the visualization.\"), mdx(\"h3\", {\n    \"id\": \"discussion\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Discussion\")), mdx(\"p\", null, \"The motivation to use testing per capita as a metric rather than absolute state population was because we thought the rate of reporting race could be affected by the total number of tests, which correlates with a state\\u2019s population. Rather, we were interested in comparing states with similar tests per capita as that could correlate better with similar testing practices.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Highly Impacted States\")), mdx(\"p\", null, \"We expected states with large amounts of total positive tests (states like Arizona, California, Florida, Texas, and New York) to have very low amounts of race data reported compared to other states. We suspected that as people flooded into testing locations and the states\\u2019 testing per capita increased, some aspects of the testing process would give way. As testing locations made operational decisions to keep the testing process optimized and safe for patients and medical staff, we thought patient identification would be overlooked from the strain of this increased testing density.\"), mdx(\"p\", null, \"Looking back at this, we weren\\u2019t necessarily wrong! But considering how the state bubbles fluctuate over time and how scattered they are (suggesting little correlation between reported race data and testing density), we understand there are other variables contributing to discrepancies in reported race data other than just the impact of rising testing density.\"), mdx(\"p\", null, \"Nonetheless, here are some interesting numbers:\"), mdx(\"p\", null, \"As of July 26th,\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in those \", mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"five\"), \" highly impacted states: 32.81%\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in every state and territory: 51.75%\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The percent of cases that have race data in the \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"non-vulnerable\"), \" states: 66.29%\")), mdx(\"p\", null, \"In Arizona, California, Florida, Texas, and New York, for every 3 cases, only 1 case has race data reported, but in every other region in the United States combined, 2 out of 3 cases have race data reported. Yet, when you combine these two sets, the national average moves to 1 out of 2 cases having race data reported. This means that around \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"half of all COVID-19 cases in the United States\"), \" are in these \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"five, most impacted\"), \" states.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Increasing - Green\")), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Arizona (AZ)\"), \", Connecticut (CT), Delaware (DE), Georgia (GA), Illinois (IL), Louisiana (LA), Massachusetts (MA), Maryland (MD), Maine (ME), Michigan (MI), Missouri (MO), Nebraska (NE), New Hampshire (NH), New Jersey (NJ), Nevada (NV), Pennsylvania (PA), Virginia (VA), Vermont (VT), and Washington (WA) fall into this category.\"), mdx(\"p\", null, \"Generally, green should represent positive trends as these states are reporting more race data over time.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Decreasing - Red\")), mdx(\"p\", null, \"Alaska (AK), Alabama (AL), Arkansas (AR), Colorado (CO), District of Columbia (DC), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Florida (FL)\"), \", Hawaii (HI), Iowa (IA), Idaho (ID), Indiana (IN), Minnesota (MN), Mississippi (MS), Montana (MT), North Carolina (NC), Oklahoma (OK), Oregon (OR), Rhode Island (RI), South Carolina (SC), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Texas (TX)\"), \", Utah (UT), and Wyoming (WY) fall into this category.\"), mdx(\"p\", null, \"This is a red flag category and some concern should be shown toward data management and reporting around race from these areas as time goes on during the pandemic.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Category: Constant - Neutral\")), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"California (CA)\"), \", Guam (GU), Kansas (KS), Kentucky (KY), Northern Mariana Islands (MP), North Dakota (ND), New Mexico (NM), \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"New York (NY)\"), \", Ohio (OH), Puerto Rico (PR), South Dakota (SD), Tennessee (TN), Virgin Islands (VI), Wisconsin (WI), and West Virginia (WV) fall into this category.\"), mdx(\"p\", null, \"Notably, North Dakota, New York, and Puerto Rico do not report \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"any\"), \" race data for their reported cases. This is alarming, and we need better data from these regions.\"), mdx(\"p\", null, \"Here\\u2019s a clear \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"http://www.plotly.com/~brianwilliams2022/15.embed?link=false\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"visualization\"), \" of what these categories look like on a map. \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Please click the link to view in proper dimensions.\")), mdx(\"iframe\", {\n    width: \"1200\",\n    height: \"800\",\n    frameBorder: \"0\",\n    scrolling: \"no\",\n    style: {\n      \"border\": \"none\"\n    },\n    seamless: \"seamless\",\n    src: \"//plotly.com/~brianwilliams2022/15.embed?link=false\"\n  }), mdx(\"h3\", {\n    \"id\": \"interview-insights\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Interview Insights\")), mdx(\"p\", null, \"To get a more qualitative explanation of what is happening in the data, I sought experts and health officials to help me understand why so much race data could be missing and where in the process - from patient arrival to lab collection to data reporting - the missing link could occur. Additionally, I wanted to understand what the process of race designation is and how it is reported accurately.\"), mdx(\"p\", null, \"First, I consulted with MIT Medical Associate Medical Director & Chief of Student Health Shawn Ferullo. He explained that policies and procedures could vary greatly not only from state to state but from institution to institution. For example, Massachusetts General Hospital may have a completely different reporting protocol than MIT Medical, associated with a higher education institution, which has a much more rigidly defined community. But at the end of the day, Ferullo says, \\u201C\", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"what is being reported is what the State mandates to be reported.\"), \"\\u201D Let\\u2019s keep this in mind.\"), mdx(\"p\", null, \"From what I gathered, this is the testing methodology at MIT Medical, broken down into three key areas:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"Clinical Testing: Someone is sick or has symptoms and needs to be monitored, typically done in the office\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, \"Contact Tracing Testing: a separate waiting area for people who may have been potentially exposed to COVID-19 but may still be healthy\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Asymptomatic Testing:\"), \" Outside testing booths for people who don\\u2019t have symptoms but want testing, low-risk large volume testing is performed here, most similar to the concept of \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"drive-thru\"), \" testing (4)\")), mdx(\"p\", null, \"I\\u2019m most interested in how demographic data is being organized at these pop-up sites.\"), mdx(\"p\", null, \"As Ferullo explained, since MIT Medical uses an online health record system, with demographic data already on file, race data is automatically associated with a patient\\u2019s test, positive or negative. And in practice, this system minimizes the risk of losses of qualitative data even in the presence of high testing stress.\"), mdx(\"p\", null, \"He leaves this closing remark:\"), mdx(\"p\", null, \"\\u201CAs a clinician, so much of the day to day work is so patient-focused, as you can imagine\\u2026 The lab has whatever requirements it has to submit, so a lot of clinicians on the front lines may not even know what data is reported\\u2026 because they\\u2019re tasked with seeing the patient, collecting the test, and all of that. More of these bigger, drive up, population-based testing sites, I wonder how many are scrambling, quite honestly, if some states are just scrambling to get testing that they\\u2019re not as thoughtful with how they are setting up their systems\", \"[\", \"for data collecting]. I hate to think about how many states just don\\u2019t want to know or are intentionally not asking the question\\u201D\", \"[\", \"about race].\"), mdx(\"p\", null, \"Next, an excerpt from an interview with Sarita Shah, who is an epidemiologist at Emory University, studying racial disparities in areas with high rates of COVID-19 and volunteering with Fulton County\\u2019s health department. Notably, Shah sees the data collection problem firsthand. After doing nasal swabs at a drive-up testing site, she later calls those who test positive to fill in personal information, including race. \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"[\", \"self-identification]\"), \" However, even after multiple attempts, the team reaches only about half of these people. Shah says she\\u2019d love to note a person\\u2019s race when they\\u2019re sitting in front of her at the test site, but so far, the forms provided by labs that process the samples don\\u2019t have a place to note it. \\u201CI wish it was something more complex than that,\\u201D she says, \\u201Cbut it\\u2019s not.\\u201D \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"[\", \"the researcher also lacks an opportunity to identify patients and race data is lost]\"), \" \", \"[\", \"3]\"), mdx(\"p\", null, \"Her experience is not in isolation. Shortly after the interview with Ferullo, I followed up with MIT Medical Lab Director Jonathan Pelletier who confirmed that the template form used at MIT Medical to report testing results - sent directly from the Massachusetts Department of Public Health - has no column or option for reporting race designations. This is especially confusing because the previous data suggests Massachusetts is one of the leading states in which race data is being reported. It raises the question of how this is even possible? And where are they getting their data from if it\\u2019s not required, as Massachusetts historically has done a good job with their race data reporting. But these questions and many more, I do not have the answers to and as I leave them unexplored, I assume other laboratories across the nation are facing similar data issues.\"), mdx(\"p\", null, \"But maybe there\\u2019s hope; an amendment to the Coronavirus Aid, Relief, and Economic Security (CARES) Act passed back on June 4th will require laboratories to include relevant demographic data, such as age and race, on every test. \", \"[\", \"5] However, this is scheduled to go into effect on August 1st, leaving many passionate researchers and relief organizations in the dark about this crucial piece of metadata. I guess we\\u2019ll have to wait and see if this makes a difference in the data.\"), mdx(\"h3\", {\n    \"id\": \"conclusions\"\n  }, mdx(\"strong\", {\n    parentName: \"h3\"\n  }, \"Conclusions\")), mdx(\"p\", null, \"I believe the lack of normalized and uniform systems of reporting demographic data at the local level - individual laboratories, medical institutions, testing locations, communities, and counties - results in these large discrepancies that we see at the state and national level when researchers try to draw conclusions from data aggregates. This needs to change, and fast.\"), mdx(\"p\", null, \"\\u201COne problem that epidemiologists, in particular, have seen with all of this new lab testing sites data (pharmacies, drive-throughs, non-traditional lab settings) is incomplete data,\\u201D Scott Becker of the Association of Public Health Laboratories wrote in an email to NPR. And public health experts say what\\u2019s been needed are detailed breakdowns on how the virus is affecting Black and other marginalized communities. These groups have been hit especially hard, suffering higher case numbers per capita, serious illness, hospitalizations, and death.\\u201D \", \"[\", \"2]\"), mdx(\"p\", null, \"Lastly, accurate information on race is critical in making policy decisions, particularly for civil rights and federal programs like unemployment stimulus packages and eviction moratoriums. Race data is also used to promote equal employment opportunities and to assess racial disparities in health and environmental risks. \", \"[\", \"1] And in this case, laboratory testing data, in conjunction with case reports and other demographic data, provide vital guidance for mitigation and control activities, as well as properly allocating resources for relief. \", \"[\", \"5]\"), mdx(\"p\", null, \"As the country begins to reopen its doors, access to clear and accurate data is essential to communities and leadership as they make data-driven decisions for a phased reopening. For individuals, access to clear representations of real-world data improves feelings of safety, security, and awareness, and even empowers them to take action to support themselves, their families, and their communities. I hope we are able to see real change soon.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Project Notes - Methodology\")), mdx(\"p\", null, \"(1) On some days, a state\\u2019s percentages may add up to more than 100%, we understand these to be inaccuracies in the original data set of which the visualization was made. This could be attributed to either compiling errors or reporting errors from the individual medical institutions\", \"[\", \"labs].\"), mdx(\"p\", null, \"(2) For the purposes of this project, we considered the race designation \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" to be functionally the same as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" and therefore we included all cases reported having a race of \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" as unknown. We carry over this adjustment in all calculations made for unknown visualizations.\"), mdx(\"p\", null, \"This first suggestion came about after some manually screening of the CSV file used to generate the visualizations showed some interesting patterns in data management which in shorter words, looked like some cases originally designated as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" were swapped over to the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category. As the running totals continued in the spreadsheet, it seemed suspicious and we think this is a better way to account for that uncertainty.\"), mdx(\"p\", null, \"For instance, on May 13th in Iowa, 2878 cases are reported as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" and 232 cases as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \", then on May 17th, 3144 cases are now reported as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" and 0 cases as \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \", and from this moment forward, case numbers increase in the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Other\"), \" category but remain stagnant in the \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Unknown\"), \" category until June 17th. This is only noticeable because of how large a change it is but would be virtually undetectable if an individual laboratory chose to reinterpret data in this way. This seems more like a shift in data management rather than real testing results, and it\\u2019s these shifts that are most interesting to us as we try to interpret the gaps in the data.\"), mdx(\"p\", null, \"(3) What does testing per capita mean?\"), mdx(\"p\", null, \"After much deliberation, we choose to map the \\u201Cknown percentages\\u201D - the percent of positive cases in which race data exists - to testing per capita instead of positive tests because of some confounding variables. Testing per capita represents the total amount of tests performed in that state by the date specified divided by the state\\u2019s population. In short, we were looking for a good way to visualize magnitudes of uncertainty as states increase their number of tests. Do testing strategies change (per state, per district, per county?) as testing density increases, especially looking at race and ethnicity data?\"), mdx(\"p\", null, \"Generally speaking, the rate of infection of a disease multiplied by the total number of tests, should result in the total number of positive tests. Therefore, inherent differences in the rate of infection in separate states may impact positive test results without increasing the total number of tests - a confounding variable that made using total positive tests undesirable.\"), mdx(\"p\", null, \"And by using raw positive tests as an axis label, we would be assuming that increases in testing density are the only cause of increases in positive tests, when this may not be valid; the rate of infection also matters. There are many social regulations and restrictions in states, such as mask mandates or general climate patterns, which can impact the rate of infection. Especially considering the timing of this legislation varies greatly from state to state. Therefore, we thought testing per capita would be a more normalized metric off which to make conclusions.\"), mdx(\"p\", null, \"(4) Drive-thru refers to making tests more available in communities in an easier way, which would increase the volume of testing. With drive-thru testing, it is easier to keep patients social distancing by staying in their vehicles and to preserve clinician PPE.\"), mdx(\"p\", null, mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Sources\")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Non-Data\")), mdx(\"p\", null, \"[\", \"1] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.census.gov/topics/population/race/about.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"US Census - About Race\")), mdx(\"p\", null, \"[\", \"2] \", 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  }), \"Race, Ethnicity Data To Be Required With Coronavirus Tests In U.S.\")), mdx(\"p\", null, \"[\", \"3] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.sciencemag.org/news/2020/07/huge-hole-covid-19-testing-data-makes-it-harder-study-racial-disparities\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"\\u2018Huge hole\\u2019 in COVID-19 testing data makes it harder to study racial disparities\")), mdx(\"p\", null, \"[\", \"4] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.nytimes.com/interactive/2020/07/05/us/coronavirus-latinos-african-americans-cdc-data.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"The Fullest Look Yet at the Racial Inequity of Coronavirus\")), mdx(\"p\", null, \"[\", \"5] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.hhs.gov/sites/default/files/covid-19-laboratory-data-reporting-guidance.pdf\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"CARES Act Section 18115\")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Data\")), mdx(\"p\", null, \"[\", \"6] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://covidtracking.com/\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"The COVID Tracking Project | The COVID Tracking Project\")), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"For raw case data.\")), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"For region populations\")), mdx(\"p\", null, \"[\", \"7] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.census.gov/newsroom/press-kits/2019/national-state-estimates.html\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"2019 National and State Population Estimates\")), mdx(\"p\", null, \"[\", \"8] \", mdx(\"a\", _extends({\n    parentName: \"p\"\n  }, {\n    \"href\": \"https://www.doi.gov/oia/islands\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }), \"Islands We Serve | US Department of the Interior\")), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"American Samoa, Guam, Northern Mariana Islands, Virgin Islands.\")));\n}\n;\nMDXContent.isMDXComponent = 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I’m an aspiring protein engineer, who's actively looking for ways to uplift marginalized communities everywhere. I am a rising junior at MIT, majoring in Biological Engineering and minoring in Black Studies, and am looking forward to working in the Civic Data Design Lab this summer! So far, I have been analyzing and visualizing data reports on racial and ethnic data tied to COVID-19 test results and related deaths. In this project, I’m hoping to explain the confusing (and not so surprising) trends in the data and shed light on the reasons why discrepancies exist between states when reporting race in COVID-19 tests and deaths. Why is there missing data here, and what does this tell us? About testing strategies? About practicality? About policy?  I am passionate about understanding how policy decisions drive economic and social change. 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