29:null 2a:["$","title",null,{"children":"Mapping Medicaid Quality: A tale pre-covid and post-covid | William Pang | Observable"}] 2f:[] 2d:{"id":"e97b01cb22e0b7c8","slug":null,"title":"Mapping Medicaid Quality: A tale pre-covid and post-covid","thumbnail":"1dff66637d356d27563677930ba0db9749b4730fbfca0fa579863bbce14b10d1","access_level":"public","create_time":"$D2024-05-28T23:21:56.789Z","update_time":"$D2024-06-07T16:48:16.931Z","view_time":null,"first_public_time":"$D2024-05-29T13:51:17.283Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"88df4b53b2fce8d2","login":"will-pang","name":"William Pang","avatar_url":"https://avatars-next.observableusercontent.com/4e5603477ef111cf1fc74cb0aab0dd58ddab37219e1b2a4e5e9c3d082c5532a0","tier":"starter_2024"},"version":1125,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"30424cd7adf510bb","login":"williampangbest1","name":"William Pang","avatar_url":"https://avatars-next.observableusercontent.com/4e5603477ef111cf1fc74cb0aab0dd58ddab37219e1b2a4e5e9c3d082c5532a0","tier":null}],"members":[{"type":"individual","id":"30424cd7adf510bb","login":"williampangbest1","name":"William Pang","avatar_url":"https://avatars-next.observableusercontent.com/4e5603477ef111cf1fc74cb0aab0dd58ddab37219e1b2a4e5e9c3d082c5532a0","tier":null,"role":"editor"},{"type":"team","id":"88df4b53b2fce8d2","login":"will-pang","name":"William Pang","avatar_url":"https://avatars-next.observableusercontent.com/4e5603477ef111cf1fc74cb0aab0dd58ddab37219e1b2a4e5e9c3d082c5532a0","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Mapping Medicaid Quality: A tale pre-covid and post-covid","cells":[{"id":0,"value":"# Mapping Medicaid Quality: A tale pre-covid and post-covid","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":293,"value":"In Yubin's post titled [\"What did the pandemic do to Medicaid health quality?\"](https://observablehq.com/d/fe579b9560e64b78), Yubin observed that rates of breast cancer screening exhibited a downward trend after the pandemic, foreboding the long-lasting impact the pandemic might have on health quality. A useful extension to this observation might be to ask: which states were able to 'buck' the trend of deterioration in health quality, and why?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":306,"value":"To examine the trends in health quality before and after the pandemic for each state, we can define a simple linear regression model:\n\n${tex.block`y_{\\text{rate | state, measure}} = \\beta_0 + \\beta_1 \\times \\text{reporting date} + \\beta_2 \\times \\text{covid indicator} + \\beta_3 \\times \\text{reporting date} \\times \\text{covid indiciator}`}","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":489,"value":"where ${tex`\\beta_1`} represents the slope before the pandemic, ${tex`\\beta_2`} represents the difference in intercept after the pandemic and before, and ${tex`\\beta_3`} represents the change in slope after the pandemic compared to before the pandemic. To convince yourself of this, imagine setting the `covid indicator` variable as 0 and picking a reporting date before 2021, which will eliminate ${tex`\\beta_2`} and ${tex`\\beta_3`} and only give you the slope pre-pandemic.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":319,"value":"We then rank the states that have show the smallest change in quality as the *most improved* (i.e., ${tex`\\beta_3`}) and conversely states with the largest change in quality as the *least improved*.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":406,"value":"I ended up selecting four measures which I thought would be representative of preventive care (and had reasonable data completeness/cleanliness):\n- BCS-AD: Breast Cancer Screening\n- CCS-AD: Cervical Cancer Screening\n- CHL-AD: Chlamydia Screening in Women\n- PPC-AD: Prenatal and Postpartum Care: Postpartum Care","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":545,"value":"viewof measures = Inputs.select(measure_names, {value: \"BCS-AD\", label: \"Measure Name\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":530,"value":"viewof state = Inputs.select(state_names, {value: \"Alabama\", label: \"State\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":551,"value":"Plot.plot({\n grid: true,\n marks: [\n Plot.dot(filter(measures, state), {\n x: \"reporting_date\",\n y: \"state_rate\",\n fill: \"state\",\n tip: true\n }),\n Plot.linearRegressionY(filter(measures, state), {\n x: \"reporting_date\",\n y: \"state_rate\",\n stroke: \"postcovid\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":366,"value":"The results, ran in R, are produced below (*Note: Not all states report the entire set of quality measures. States with more than one measure missing out of the four selected were removed.*). A **higher ranking** corresponds to a larger ${tex`\\beta_3`}, indicating **more improvement** compared to pre-pandemic.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":374,"value":"multiple_measures_rank1 = selectedMeasures","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":377,"value":"Plot.plot({\n projection: \"identity\",\n width: 975,\n height: 610,\n color: {label: \"ranking (larger value = more improved)\", legend: true, scheme: \"Greens\", type: \"quantize\", n: 10, domain: [d3.min(selectedMeasures.map(d => d.final_ranking)), d3.max(selectedMeasures.map(d => d.final_ranking))]},\n marks: [\n Plot.geo(topojson.feature(us, us.objects.states), Plot.centroid({\n fill: d => selectedMeasuresFinalScore.get(d.properties.name),\n title: d => `${d.properties.name}\\nFinal Ranking: ${selectedMeasuresFinalScore.get(d.properties.name)}`,\n tip: true\n })),\n Plot.geo(topojson.feature(us, us.objects.nation)),\n Plot.geo(topojson.feature(us, us.objects.states, { strokeOpacity: 0.2 }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":155,"value":"*Medicaid Spending by State per enrollee (FY2022)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":713,"value":"viewof medicaid_spending_view = Inputs.radio([\"All\", \"Selected Measures\"], {value: \"Selected Measures\", label: \"Map View\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5,"value":"Plot.plot({\n projection: \"identity\",\n width: 975,\n height: 610,\n color: {scheme: \"RdPu\", type: \"quantize\", n: 20, domain: [d3.min(Medicaid_Spending_2022.map(d => d.PMPY)), d3.max(Medicaid_Spending_2022.map(d => d.PMPY))]},\n marks: [\n Plot.text([\"A higher ranking/darker color indicates greater spending\"], {frameAnchor: \"top-left\", fontWeight: \"bold\"}),\n Plot.geo(topojson.feature(us, us.objects.states), Plot.centroid({\n fill: d => Medicaid_Spending_2022_PMPY.get(d.properties.name),\n title: d => `${d.properties.name}\\n$${Medicaid_Spending_2022_PMPY.get(d.properties.name)} PMPY\\nRanking: ${Medicaid_Spending_2022_PMPY_Ranking.get(d.properties.name)}`,\n tip: true\n })),\n Plot.geo(topojson.feature(us, us.objects.nation)),\n Plot.geo(topojson.feature(us, us.objects.states, { strokeOpacity: 0.2 }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":460,"value":"try_it(htl.html`The Spearman Correlation between improvement rankings and Medicaid spending is 0.34`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":926,"value":"

Bearing the inconsistencies in data reporting for various quality measures in mind, we can draw two broad conclusions:
\n \n
(1) health quality measures, especially for measures related to preventative care, seem to exhibit a downward deteriorating trend post-pandemic as compared to before
(2) improvement in quality compared to pre-pandemic seems to be weakly correlated with Medicaid Spending by state.
\n\n
Surprisingly, Alabama appears to perform the best using our methodology. However, note that Alabama earliest reported state rate is often below the mean, and the reason why Alabama has shown the best improvement overall might simply be due to a regression back to the mean. \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":969,"value":"*COVID-19 Mortality by State*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":976,"value":"viewof covid_year = Inputs.select([\"2020\", \"2021\", \"2022\"], {value: \"2020\", label: \"Year\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":971,"value":"viewof covid_mortality_view = Inputs.radio([\"All\", \"Selected Measures\"], {value: \"Selected Measures\", label: \"Map View\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":984,"value":"Plot.plot({\n projection: \"identity\",\n width: 975,\n height: 610,\n color: {scheme: \"PuBu\", type: \"quantize\", n: 20, domain: [d3.min(covid_19_mortality.map(d => d.Rank)), d3.max(covid_19_mortality.map(d => d.Rank))]},\n marks: [\n Plot.text([\"A higher ranking/darker color indicates higher age-adjusted mortality rate (per 100,000 total population)\"], {frameAnchor: \"top-left\", fontWeight: \"bold\"}),\n Plot.geo(topojson.feature(us, us.objects.states), Plot.centroid({\n fill: d => covid_19_mortality_rate_rank.get(d.properties.name),\n title: d => `${d.properties.name}\\nMortality Rate: ${covid_19_mortality_rate.get(d.properties.name)}\\nRank:${covid_19_mortality_rate_rank.get(d.properties.name)}`,\n tip: true\n })),\n Plot.geo(topojson.feature(us, us.objects.nation)),\n Plot.geo(topojson.feature(us, us.objects.states, { strokeOpacity: 0.2 }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1078,"value":"try_it(htl.html`The Spearman Correlation between improvement rankings and COVID-19 Mortality is ${covid_corr}`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":212,"value":"*Medicare Advantage Penetration Rate (Dec 2022)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":853,"value":"viewof ma_penetration_view = Inputs.radio([\"All\", \"Selected Measures\"], {value: \"Selected Measures\", label: \"Map View\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":245,"value":"Plot.plot({\n projection: \"identity\",\n width: 975,\n height: 610,\n color: {scheme: \"YlGn\", type: \"quantize\", n: 20, domain: [d3.min(MA_Penetration_2022_12.map(d => d.Rank)), d3.max(MA_Penetration_2022_12.map(d => d.Rank))]},\n marks: [\n Plot.text([\"A higher ranking/darker color indicates greater MA Penetration\"], {frameAnchor: \"top-left\", fontWeight: \"bold\"}),\n Plot.geo(topojson.feature(us, us.objects.states), Plot.centroid({\n fill: d => MA_Penetration_2022_12_Ranking.get(d.properties.name),\n title: d => `${d.properties.name}\\nPenetration: ${MA_Penetration_2022_12_Penetration.get(d.properties.name)}%\\nRank:${MA_Penetration_2022_12_Ranking.get(d.properties.name)}`,\n tip: true\n })),\n Plot.geo(topojson.feature(us, us.objects.nation)),\n Plot.geo(topojson.feature(us, us.objects.states, { strokeOpacity: 0.2 }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":479,"value":"try_it(htl.html`The Spearman Correlation between improvement rankings and MA Penetration is -0.14`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":272,"value":"*Medicaid Renewal Rate (May 23, 2024)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":857,"value":"viewof medicaid_renewal_view = Inputs.radio([\"All\", \"Selected Measures\"], {value: \"Selected Measures\", label: \"Map View\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":278,"value":"Plot.plot({\n projection: \"identity\",\n width: 975,\n height: 610,\n color: {scheme: \"OrRd\", type: \"quantize\", n: 20, domain: [d3.min(Medicaid_Renewal_05232024.map(d => d.Rank)), d3.max(Medicaid_Renewal_05232024.map(d => d.Rank))]},\n marks: [\n Plot.text([\"A higher ranking/darker color indicates greater Medicaid Renewal Rate\"], {frameAnchor: \"top-left\", fontWeight: \"bold\"}),\n Plot.geo(topojson.feature(us, us.objects.states), Plot.centroid({\n fill: d => Medicaid_Renewal_05232024_RenewalRank.get(d.properties.name),\n title: d => `${d.properties.name}\\nRenewal Rate: ${Math.round(Medicaid_Renewal_05232024_RenewalRate.get(d.properties.name))}%\\nRank: ${Medicaid_Renewal_05232024_RenewalRank.get(d.properties.name)}`,\n tip: true\n })),\n Plot.geo(topojson.feature(us, us.objects.nation)),\n Plot.geo(topojson.feature(us, us.objects.states, { strokeOpacity: 0.2 }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":482,"value":"try_it(htl.html`The Spearman Correlation between improvement rankings and Medicaid Renewal Rate is 0.06`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":445,"value":"## Caveats","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":448,"value":"To better ascertain whether the pandemic truly had an effect on the quality of healthcare, it might be more helpful to adopt a [differnece-in-difference-esque logic](https://www.publichealth.columbia.edu/research/population-health-methods/difference-difference-estimation) where we make sure to only compare states (or an ensemble of states) that are similar (for example: New York and Connecticut vs California and Oregon) and also exhibited similar trends prior to the pandemic. Still, designing a natural experiment will be quite tricky here, as both the control group and treatment group here will experience the \"intervention\" (i.e., COVID). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":518,"value":"## Datasets and Scripts","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":520,"value":"The raw datasets, as well as R scripts to pre-process the datasets, can be found [here](https://github.com/williampangbest1/innovation_team_interview/tree/main/medicaid_analysis).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":7,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":379,"value":"selectedMeasures = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/quality_measures_prepostcovid_rank.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":708,"value":"selectedMeasuresStates = selectedMeasures.map(d => d.state)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":383,"value":"selectedMeasuresFinalScore = new Map(selectedMeasures.map(({state, final_ranking}) => [state, final_ranking]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":553,"value":"
\n*Adult Quality Measures
\nData Sources: [Medicaid.gov](https://www.medicaid.gov/medicaid/quality-of-care/performance-measurement/adult-and-child-health-care-quality-measures/adult-health-care-quality-measures/index.html)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":560,"value":"adult_quality_measures = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/2014_to_2022_Child_and_Adult_Health_Care_Quality_Measures_Quality.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":607,"value":"function filter(measures, state){\n if (state == \"All\") {\n var adult_quality_measures_filtered = adult_quality_measures.filter(l => measures === l.measure_abbreviation) \n }\n else {\n var adult_quality_measures_filtered = adult_quality_measures.filter(l => measures === l.measure_abbreviation && state === l.state) \n }\n\n return adult_quality_measures_filtered\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":989,"value":"
\n*COVID-19 Mortality by State
\nData Sources: [CDC](https://www.cdc.gov/nchs/pressroom/sosmap/covid19_mortality_final/COVID19.htm)*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":992,"value":"covid_19_mortality_raw = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/covid_19_mortality_rate_by_state_2020_to_2022.csv\", \n({YEAR, STATE, RATE}) => ({year: YEAR, State: STATE, rate: +RATE}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1004,"value":"function filter_covid_mortality(){\n var covid_19_mortality_filtered = covid_19_mortality_raw.filter(l => l.year === covid_year)\n \n if (covid_mortality_view == \"All\"){\n return covid_19_mortality_filtered\n }\n else {\n return covid_19_mortality_filtered.filter(l => selectedMeasuresStates.includes(l.State)) \n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1012,"value":"covid_19_mortality = filter_covid_mortality().map((element, index) => {\n // Append city to each person object\n return { ...element, Rank: d3.rank(filter_covid_mortality().map(d => d.rate))[index]+1 };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1049,"value":"covid_19_mortality_rate = new Map(covid_19_mortality.map(({State, rate}) => [State, rate]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1057,"value":"covid_19_mortality_rate_rank = new Map(covid_19_mortality.map(({State, Rank}) => [State, Rank]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1115,"value":"covid_corr = (covid_year == \"2020\") ? -0.15: (covid_year == \"2021\") ? -0.13: -0.01","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":194,"value":"
\n*Medicaid Spending by State per enrollee
\nData Sources: [Total Monthly Medicaid & CHIP Enrollment and Pre-ACA Enrollment (Dec 2022)](https://www.kff.org/affordable-care-act/state-indicator/total-monthly-medicaid-and-chip-enrollment/?currentTimeframe=13&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D) from KFF and [Total Medicaid Spending (FY2022)](https://www.kff.org/medicaid/state-indicator/total-medicaid-spending/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D) from KFF*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":501,"value":"Medicaid_Spending_2022_Raw = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/Total_Medicaid_Spending_FY2022.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":737,"value":"function filter_medicaid_spending(){\n \n if (medicaid_spending_view == \"All\"){\n return Medicaid_Spending_2022_Raw\n }\n else {\n return Medicaid_Spending_2022_Raw.filter(l => selectedMeasuresStates.includes(l.State)) \n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":805,"value":"Medicaid_Spending_2022 = filter_medicaid_spending().map((element, index) => {\n // Append city to each person object\n return { ...element, Rank: d3.rank(filter_medicaid_spending().map(d => d.PMPY))[index]+1 };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":745,"value":"Medicaid_Spending_2022_PMPY = new Map(Medicaid_Spending_2022.map(({State, PMPY}) => [State, +PMPY]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":71,"value":"Medicaid_Spending_2022_Total = new Map(Medicaid_Spending_2022.map(({State, Spending}) => [State, +Spending]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":819,"value":"Medicaid_Spending_2022_PMPY_Ranking = new Map(Medicaid_Spending_2022.map(({State, Rank}) => [State, +Rank]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":204,"value":"
\n*Medicaid Penetration by State
\nData Sources: [MA State/County Penetration](https://www.cms.gov/data-research/statistics-trends-and-reports/medicare-advantagepart-d-contract-and-enrollment-data/ma-state/county-penetration) from CMS*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":206,"value":"MA_Penetration_2022_12_Raw = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/MA_Penetration_2022_12_by_state.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":862,"value":"function filter_ma_penetration_spending(){\n \n if (ma_penetration_view == \"All\"){\n return MA_Penetration_2022_12_Raw\n }\n else {\n return MA_Penetration_2022_12_Raw.filter(l => selectedMeasuresStates.includes(l.State)) \n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":870,"value":"MA_Penetration_2022_12 = filter_ma_penetration_spending().map((element, index) => {\n // Append city to each person object\n return { ...element, Rank: d3.rank(filter_ma_penetration_spending().map(d => d.penetration))[index]+1 };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":233,"value":"MA_Penetration_2022_12_Penetration = new Map(MA_Penetration_2022_12.map(({State, penetration}) => [State, penetration]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":237,"value":"MA_Penetration_2022_12_Eligible = new Map(MA_Penetration_2022_12.map(({State, total_eligible}) => [State, total_eligible]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"MA_Penetration_2022_12_Enrolled = new Map(MA_Penetration_2022_12.map(({State, total_enrolled}) => [State, total_enrolled]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":876,"value":"MA_Penetration_2022_12_Ranking = new Map(MA_Penetration_2022_12.map(({State, Rank}) => [State, Rank]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":288,"value":"
\n*Medicaid Renewal Rate\nData Sources: [Medicaid Enrollment and Unwinding Tracker](https://www.kff.org/report-section/medicaid-enrollment-and-unwinding-tracker-overview/) from KFF*\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":258,"value":"Medicaid_Renewal_05232024_Raw = d3.csv(\"https://raw.githubusercontent.com/williampangbest1/innovation_team_interview/main/medicaid_analysis/Medicaid_Renewal_Rate_05232024.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":892,"value":"function filter_medicaid_renewal_rate(){\n \n if (medicaid_renewal_view == \"All\"){\n return Medicaid_Renewal_05232024_Raw\n }\n else {\n return Medicaid_Renewal_05232024_Raw.filter(l => selectedMeasuresStates.includes(l.State)) \n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":261,"value":"Medicaid_Renewal_05232024 = filter_medicaid_renewal_rate().map((element, index) => {\n // Append city to each person object\n return { ...element, Rank: d3.rank(filter_medicaid_renewal_rate().map(d => d.RenewalRate))[index]+1 };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":905,"value":"Medicaid_Renewal_05232024_RenewalRate = new Map(Medicaid_Renewal_05232024.map(({State, RenewalRate}) => [State, RenewalRate]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":909,"value":"Medicaid_Renewal_05232024_RenewalRank = new Map(Medicaid_Renewal_05232024.map(({State, Rank}) => [State, Rank]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":60,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":532,"value":"state_names = ['All', 'Alabama', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":542,"value":"measure_names = ['AAB-AD', 'ABA-AD', 'AMM-AD', 'AMR-AD', 'BCS-AD', 'CBP-AD', 'CCP-AD', 'CCS-AD', 'CCW-AD', 'CHL-AD', 'COB-AD', 'FUA-AD', 'FUA/FUM-AD', 'FUH-AD', 'FUM-AD', 'FVA-AD', 'HA1C-AD', 'HPC-AD', 'IET-AD', 'LDL-AD', 'MPM-AD', 'MSC-AD', 'NCIDDS-AD', 'OHD-AD', 'OUD-AD', 'PCR-AD', 'PPC-AD', 'PQI01-AD', 'PQI05-AD', 'PQI08-AD', 'PQI15-AD', 'SAA-AD', 'SSD-AD']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":536,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":11,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":24,"value":"import {us} from \"@d3/us-state-choropleth/2\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":457,"value":"import {try_it} from \"@observablehq/notes\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W2f","stdlib":"1","titling":"auto"}} 2e:"$2d:body"