30:T554,md` ## Q1: How many participants has All-of-Us recruited to date?

As of ${enrollData.TOTAL[0].date} there are ${enrollData.TOTAL[0].metrics.TOTAL} participants reported as having sign-in accounts. Of those, ${enrollData.PRIMARY_CONSENT[0].metrics.Primary_Consent} are reported as having provided "primary consent". Amongst those, there is a smaller group of ${enrollData.ENROLLMENT_STATUS[0].metrics.core} "core enrolled participants", of which ${enrollData.ENROLLMENT_STATUS[0].metrics.registered} are reported as "registered" and ${enrollData.ENROLLMENT_STATUS[0].metrics.consented} are "consented".

The numbers for EHR are intriguing. They are higher than the enrollment numbers suggest, maybe a reflection of the Sync 4 Science component of the project: it reports ${enrollData.EHR_METRICS[0].metrics.EHR_CONSENTED} "EHR consented", with ${enrollData.EHR_METRICS[0].metrics.EHR_RECEIVED} "EHR received", but also only ${enrollData.EHR_METRICS[0].metrics.ORGANIZATIONS_ACTIVE} "active organizations" (unreported?).

`31:[] 2e:{"title":"AoU","cells":[{"id":0,"value":"md`# AoU\nMiscelaneous Q&A from AllofUS API emmerging at https://databrowser.researchallofus.org.\n\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"{\n let div=DOM.element('div')\n var trace1 = {\n x: [1, 2, 3, 4],\n y: [10, 15, 13, 17],\n mode: 'markers',\n type: 'scatter'\n }\n var x=enrollData.ENROLLMENT_STATUS_OVER_TIME.map(d=>d.date)\n var y_consented=enrollData.ENROLLMENT_STATUS_OVER_TIME.map(d=>d.metrics.consented)\n var y_core=enrollData.ENROLLMENT_STATUS_OVER_TIME.map(d=>d.metrics.core)\n var y_registered=enrollData.ENROLLMENT_STATUS_OVER_TIME.map(d=>d.metrics.registered)\n console.log({x,y_consented,y_core,y_registered})\n var traces=[\n {\n x:x,\n y:y_core,\n name:'core',\n mode:'line',\n type: 'scatter'\n },\n {\n x:x,\n y:y_registered,\n name:'registered',\n mode:'line',\n type: 'scatter'\n },\n {\n x:x,\n y:y_consented,\n name:'consented',\n mode:'line',\n type: 'scatter'\n }\n ]\n Plotly.newPlot(div,traces,{\n title:'Enrolement status over time'\n })\n \n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"{\n var div=DOM.element('div')\n var trace_age={x:[],y:[],type:'bar'}\n Object.keys(enrollData.AGE_RANGE[0].metrics).sort().forEach(k=>{\n trace_age.x.push(k)\n trace_age.y.push(enrollData.AGE_RANGE[0].metrics[k])\n })\n Plotly.newPlot(div,[trace_age],{\n title:`Age distribution as of ${enrollData.AGE_RANGE[0].date} (${trace_age.y.reduce((a,b)=>a+b)} people)`\n })\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":135,"value":"{\n var div = DOM.element('div')\n var codes=Object.keys(enrollData.GEO_STATE[0].metrics)\n var dt = {\n type: 'choropleth',\n locationmode: 'USA-states',\n locations: Object.keys(enrollData.GEO_STATE[0].metrics),\n z: Object.values(enrollData.GEO_STATE[0].metrics),\n //text: unpack(rows, 'state'),\n }\n Plotly.newPlot(div,[dt],{\n title: `NIH All-of-Us recruitment as of ${enrollData.GEO_STATE[0].date} (${dt.z.reduce((a,b)=>a+b)} people)`,\n geo:{\n scope: 'usa',\n showlakes: true,\n lakecolor: 'rgb(255,255,255)'\n }\n })\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"{\n var div = DOM.element('div')\n var codes=Object.keys(enrollData.RACE[0].metrics)\n var counts=Object.values(enrollData.RACE[0].metrics)\n var dt = {\n type: 'pie',\n labels:codes,\n values:counts\n }\n Plotly.newPlot(div,[dt],{\n title: `NIH All-of-Us recruitment RACE as of ${enrollData.GEO_STATE[0].date}`\n })\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":207,"value":"{\n var div = DOM.element('div')\n var codes=Object.keys(enrollData.GENDER_IDENTITY[0].metrics)\n var counts=Object.values(enrollData.GENDER_IDENTITY[0].metrics)\n var dt = {\n type: 'pie',\n labels:codes,\n values:counts\n }\n Plotly.newPlot(div,[dt],{\n title: `NIH All-of-Us recruitment GENDER_IDENTITY as of ${enrollData.GEO_STATE[0].date}
(click on labels in the legend to exclude/include identities:
clicking on Woman and Man will provide detail to remaining)`\n })\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":149,"value":"{\n var div=DOM.element('div')\n // sort labels by completed count\n var kk=Object.keys(enrollData.LIFECYCLE[0].metrics.completed).sort((a,b)=>((enrollData.LIFECYCLE[0].metrics.completed[a])<(enrollData.LIFECYCLE[0].metrics.completed[b]))? 1 : -1)\n var trace_not_completed={\n x:kk,\n y:kk.map(k=>enrollData.LIFECYCLE[0].metrics.not_completed[k]),\n type:'bar',\n name:'not completed'\n }\n var trace_completed={\n x:kk,\n y:kk.map(k=>enrollData.LIFECYCLE[0].metrics.completed[k]),\n type:'bar',\n name:'completed'\n }\n var trace_total={\n x:kk,\n y:kk.map(k=>enrollData.LIFECYCLE[0].metrics.completed[k]+enrollData.LIFECYCLE[0].metrics.not_completed[k]),\n type:'line',\n marker:{\n size:1\n },\n line:{\n dash:'dash',\n width:1,\n color:'gray'\n },\n name:'total'\n }\n \n Plotly.newPlot(div,[trace_completed,trace_not_completed,trace_total],{\n title:`Completion as of ${enrollData.LIFECYCLE[0].date}`\n })\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":222,"value":"md`## Q2: How many of these participants would qualify for Connect at each site?\nSo that'd be a table estimating the count of AoU participants between 40 and 65 in each state (the most granular locator in the summary data):`","pinned":false,"hidden":false,"mode":"ojs"},{"id":232,"value":"{\n const div = DOM.element('div')\n const n = Object.values(enrollData.AGE_RANGE[0].metrics).reduce((a,b)=>a+b)\n const n1 = enrollData.AGE_RANGE[0].metrics[\"40-49\"]+enrollData.AGE_RANGE[0].metrics[\"50-59\"]+enrollData.AGE_RANGE[0].metrics[\"60-69\"]/2\n const k = Math.round(n1/n*100)\n enrollData.AGE_RANGE[0].metrics \n //debugger\n div.innerHTML=`

As of ${enrollData.GEO_STATE[0].date}, ${k}% of all AoU participants are between age 40 and 65. This proportion suggests the following distribution of Connect eligible participants.

`\n const rep = DOM.element('pre')\n div.appendChild(rep)\n //rep.style.color=\"blue\"\n let h = 'State: Connect estimate'\n h += '
------------------------'\n const states = Object.keys(enrollData.GEO_STATE[0].metrics).sort()\n for (var i=0 ;i ${s} : ${Math.round(enrollData.GEO_STATE[0].metrics[s]*k/100)}`\n }\n rep.innerHTML=h\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"md`\n## Methods\nData and analytical methods.\n### Data\nEnrollment data if served at www.researchallofus.org/wp-json/research-hub-public-metrics/data.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"//enrollData = (await fetch('https://www.researchallofus.org/wp-json/research-hub-public-metrics/data')).json()\nenrollData = JSON.parse(JSON.parse(enrolDatatxt)[0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":384,"value":"enrolDatatxt = await (\n await fetch(\n \"https://script.google.com/macros/s/AKfycbwq7_At9wodB86ZLRP61lu82UwHe7SEbHwJgs43YM9TWlZH68SFOWCpgSIGjj2yPmKbFw/exec\"\n )\n).text()","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"x = (\n await fetch(\n \"https://public.api.researchallofus.org/v1/databrowser/survey-results?survey_concept_id=1586134\"\n )\n).json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"getConceptId = async function (id) {\n return (\n await fetch(\n `https://public.api.researchallofus.org/v1/databrowser/survey-results?survey_concept_id=${id}`\n )\n ).json();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":17,"value":"getConceptId('1586136')","pinned":false,"hidden":false,"mode":"ojs"},{"id":128,"value":"md`\n### Graphics\nCross platform Plotly was used to generate graphics.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":366,"value":"md`### Browser`","pinned":false,"hidden":false,"mode":"ojs"},{"id":369,"value":"root = {\n //(await fetch('https://www.researchallofus.org/wp-json')).json()\n var txt = await (\n await fetch(\n \"https://script.google.com/macros/s/AKfycbydrbDRTOp9qArIAqO34SzJJgszIYz5yGq5pdfQ7HZHHXQVYgjFTu7QR8wbaEnDIClyTw/exec\"\n )\n ).text();\n //).json()}\n return JSON.parse(JSON.parse(txt)[0]);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":373,"value":"search = {\n ///(\n ///await fetch('https://www.researchallofus.org/wp-json/wp/v2/search?\"')\n //var txt = await (\n // await fetch(\n // \"https://script.google.com/a/macros/mathbiol.org/s/AKfycbxs3nVqwCdzw41PnwC2TpHoSMlWBZ5xGjCZcLxLXUlOTGjc9L6x3ijSd-zlUWdNrBV6rQ/exec\"\n // )\n //).text();\n ///).json()}\n //return JSON.parse(JSON.parse(txt)[0]);\n return \"search root available\";\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}