2f:null 30:["$","title",null,{"children":"Final Project Part 1 - Inequality in Composite health across countries | Vy | Observable"}] 35:T483,across_countries = html`

`36:T483,within_countries = html`

`37:T465,box_quisker_global = html`

`38:[{"id":"31acbe9796c352b44204d9c6da8aa4ab2a9bf604754696f0e0ce7a97d1158f071a6c37c81c900551f92ec8e2de08f73a0685633d310378fa198451f31331f523","href":"https://static.observableusercontent.com/files/31acbe9796c352b44204d9c6da8aa4ab2a9bf604754696f0e0ce7a97d1158f071a6c37c81c900551f92ec8e2de08f73a0685633d310378fa198451f31331f523","name":"data_quintile.csv","type":"text/csv","lastModified":1651168675000.052,"size":103242},{"id":"0426659ea84415d85a41df3dc829ad9ce5b9c43b8e78cec2a2b79f708bc60efa0dd7ead6218290c000400fe158758f6e780894cd0d370f3c6738e2b915087ac1","href":"https://static.observableusercontent.com/files/0426659ea84415d85a41df3dc829ad9ce5b9c43b8e78cec2a2b79f708bc60efa0dd7ead6218290c000400fe158758f6e780894cd0d370f3c6738e2b915087ac1","name":"data_residence.csv","type":"text/csv","lastModified":1651168674910.994,"size":42170},{"id":"1edeb0f0d00744c165e296b53aeec5f031828c432adea366438bd95a7cd548a26db2a7b7dd7d5574a5bd9cf4ae5132e589706c5f25822c96358344a772a6fc8b","href":"https://static.observableusercontent.com/files/1edeb0f0d00744c165e296b53aeec5f031828c432adea366438bd95a7cd548a26db2a7b7dd7d5574a5bd9cf4ae5132e589706c5f25822c96358344a772a6fc8b","name":"data_education.csv","type":"text/csv","lastModified":1651168674945.858,"size":60604}] 33:{"id":"d00c686c2029ad8a","slug":"inequality-in-composite-health-across-countries","title":"Final Project Part 1 - Inequality in Composite health across countries","thumbnail":"5ff74e2eb9256e689a0a3a2f8915d207c256c8590f26f0c0a7fb5d1a37543980","access_level":"public","create_time":"$D2022-04-28T17:34:42.607Z","update_time":"$D2022-05-05T21:19:49.467Z","view_time":null,"first_public_time":"$D2022-04-30T01:10:06.001Z","has_unpublished_edits":null,"fork":{"id":"5a4f8779ce9bfcdd","slug":"change-in-global-health-inequities-over-time-project","title":"Final Project Part 2 - Change in global health inequities over time","owner":{"id":"70a28fc0926865c6","login":"incakoala","name":"Vy"}},"stars":0,"starred":false,"owner":{"type":"team","id":"70a28fc0926865c6","login":"incakoala","name":"Vy","avatar_url":"https://avatars-next.observableusercontent.com/5d008ae093fa8b024defa3485b161456bc6ba621b7c21ef65061c11f7ca7dd98","tier":"starter_2024"},"version":2744,"publish_version":2744,"role":null,"authored":false,"authors":[{"type":"individual","id":"ded1682d7fdbac61","login":"incakoala","name":"Vy","avatar_url":"https://avatars-next.observableusercontent.com/5d008ae093fa8b024defa3485b161456bc6ba621b7c21ef65061c11f7ca7dd98","tier":null}],"members":[{"type":"individual","id":"ded1682d7fdbac61","login":"incakoala","name":"Vy","avatar_url":"https://avatars-next.observableusercontent.com/5d008ae093fa8b024defa3485b161456bc6ba621b7c21ef65061c11f7ca7dd98","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":{"id":"800f1f5954fc5c7e","slug":"data-visualization-spring-2022","title":"Data Visualization Spring 2022 – Final Project","owner":{"id":"70a28fc0926865c6","login":"incakoala","name":"Vy"}},"type":"notebook","body":{"title":"Final Project Part 1 - Inequality in Composite health across countries","cells":[{"id":0,"value":"# Final Project Part 1 - Inequality in Composite health across countries\n\n* *NEU's [Data Visualization course](https://johnguerra.co/classes/data_visualization_spring_2022/) by John Alexis Guerra Gómez*\n* Part of the final project: **[Part 1](https://observablehq.com/@incakoala/inequality-in-composite-health-across-countries)** | [Part 2](https://observablehq.com/@incakoala/change-in-global-health-inequities-over-time-project) | [Part 3](https://observablehq.com/@incakoala/measuring-progress-towards-universal-health-coverage) | [Final Demo](https://amdaccache9.wixsite.com/publichealthinequity)\n* [Tweet](https://twitter.com/anthuriumcryst/status/1522316076788322304) | [Video](https://www.youtube.com/watch?v=PSUtLatCjOo)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2287,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1763,"value":"## Hypotheses\n\n* **Possible insight 1:** Countries in Africa have on average lower composite health compared to elsewhere\n \n* **Possible insight 2:** Within countries around the world, income levels are the main indicator of health disparities, with the difference between the poorest and richest sub-groups being over 30%","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2196,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2276,"value":"## Tasks\n\n**T1.** Compare the Composite Coverage Index (CCI) % **across countries** (Compare, Extremes)\n\n**T2.** Compare the Composite Coverage Index (CCI) % between different sub-groups (based on income, education, residence) **within countries** (Compare, Distribution)\n\n**T3.** Compare the Composite Coverage Index (CCI) % between different sub-groups (based on income, education, residence) **in all study countries** (Compare, Distribution)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2200,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2690,"value":"### T1. Compare the Composite Coverage Index (CCI) % across countries\n\n**How?**\n* Marks: Lines/bars\n* Channels: Horizontal position to represent the values, Trendline to highlight median value\n \n**Insights:**\nOut of the surveyed countries, Thailand has the highest overall average composite health, while Chad is the lowest overall. Countries in the Americas and Europe also tend to score higher compared to other WHO regions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2695,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2698,"value":"{\n across_countries;\n var divElement = document.getElementById('viz1651280894598'); var vizElement = divElement.getElementsByTagName('object')[0]; if ( divElement.offsetWidth > 800 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else { vizElement.style.width='100%';vizElement.style.height='827px';} var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2689,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1769,"value":"### T2. Compare the Composite Coverage Index (CCI) % between different sub-groups (based on income, education, residence) within countries (Compare, Distribution)\n\n**How?**\n* Marks: Dots/cicles\n* Channels:\n * Horizontal position to represent values, color hues to represent ordinal categories\n * Connected lines: the longer the line, the more prevalent health inequity is within a country\n \n**Insights**\n* There are noticeable inequities within countries, with the richer/more privileged sub-groups scoring higher in the composite health index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2656,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2658,"value":"{\n within_countries;\n var divElement = document.getElementById('viz1651273016022'); var vizElement = divElement.getElementsByTagName('object')[0]; if ( divElement.offsetWidth > 800 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else { vizElement.style.width='100%';vizElement.style.height='827px';} var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2192,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2714,"value":"### T3. Compare the Composite Coverage Index (CCI) % between different sub-groups (based on income, education, residence) in all study countries (Compare, Distribution)\n\n**How?**\n* Marks: Bars, lines, dots\n* Channels: Verticle position to represent the median values, data distribution through their quartiles, as well as outliers\n \n**Insights**\n* Comparing populations on the basis of economic status shows the most prominent pattern of inequities, followed by education and place of residence\n* There are even higher inequalities among the poorest/least privileged sub-groups globally, suggesting a deep pattern of inequity globally","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2704,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2706,"value":"{\n box_quisker_global;\n var divElement = document.getElementById('viz1651372877507'); var vizElement = divElement.getElementsByTagName('object')[0]; if ( divElement.offsetWidth > 800 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.width='1000px';vizElement.style.height='827px';} else { vizElement.style.width='100%';vizElement.style.height='977px';} var scriptElement = document.createElement('script'); scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js'; vizElement.parentNode.insertBefore(scriptElement, vizElement); \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2710,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1766,"value":"## Data Abstraction\n\n* Data source: [WHO – Health equity monitor: Reproductive, maternal, newborn and child health interventions (RMNCH), combined](https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/health-equity-monitor-reproductive-maternal-newborn-and-child-health-interventions-%28rmnch%29-combined)\n* Dataset type: Table - Static\n\n**Attributes**\n \n* **country**: Categorical\n* **region**: Categorical\n* **dimension**: Ordinal - Sequential\n * depending on the dataset:\n * quintile: Quintile 1 – 5\n * education: None, Primary, Secondary or Higher\n * residence: Rural, Urban\n* **cci**: Quantitative - Sequential\n* **year**: Temporal","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2188,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1798,"value":"## Data processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2627,"value":"Inputs.table(data_residence_cleaned)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2642,"value":"DOM.download(serialize(data_residence_cleaned), \"data_residence_cleaned\", \"Download JSON\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2619,"value":"data_residence_cleaned = data_residence\n.reduce((all, curr) => {\n let obj = {};\n obj.country = curr.Location;\n obj.region = curr.ParentLocation;\n obj.dimension = curr.Dim1;\n obj.cci = parseFloat(curr.FactValueNumeric);\n obj.year = parseInt(curr.Period);\n all.push(obj)\n return all;\n}, []).sort((a, b) => (a.country > b.country) ? 1 : -1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2605,"value":"data_residence = FileAttachment(\"data_residence.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2617,"value":"Inputs.table(data_education_cleaned)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2639,"value":"DOM.download(serialize(data_education_cleaned), \"data_education_cleaned\", \"Download JSON\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2611,"value":"data_education_cleaned = data_education\n.reduce((all, curr) => {\n let obj = {};\n obj.country = curr.Location;\n obj.region = curr.ParentLocation;\n obj.dimension = curr.Dim1;\n obj.cci = parseFloat(curr.FactValueNumeric);\n obj.year = parseInt(curr.Period);\n all.push(obj)\n return all;\n}, []).sort((a, b) => (a.country > b.country) ? 1 : -1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2602,"value":"data_education = FileAttachment(\"data_education.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2615,"value":"Inputs.table(data_quintile_cleaned)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2636,"value":"DOM.download(serialize(data_quintile_cleaned), \"data_quintile_cleaned\", \"Download JSON\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2608,"value":"data_quintile_cleaned = data_quintile\n.reduce((all, curr) => {\n let obj = {};\n obj.country = curr.Location;\n obj.region = curr.ParentLocation;\n obj.dimension = `Quintile ${curr.Dim1ValueCode[curr.Dim1ValueCode.length - 1]}`;\n obj.cci = parseFloat(curr.FactValueNumeric);\n obj.year = parseInt(curr.Period);\n all.push(obj)\n return all;\n}, []).sort((a, b) => (a.country > b.country) ? 1 : -1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2596,"value":"data_quintile = FileAttachment(\"data_quintile.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1793,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2632,"value":"import {serialize} from \"@palewire/saving-json\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 34:"$33:body"