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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

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URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

About

URL: https://drexel-uhc.shinyapps.io/MS85/

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Extreme temperatures and mortality in 326 Latin American cities: a longitudinal ecological study

This folder contains all the code files needed to reproduce the findings in the paper. The files cannot be run without the mortality data, however. The mortality data cannot be made available due to data use agreements with the various countries.

Files

  • 00_read_data.R: Reads in and formats the data on the Drexel servers. Creates two dataframes, mort_temp.rds, for use in the city-specific analysis, and metadata.rds, for use in the meta-analysis.
  • 01_city_specific_models.R: Runs the analysis for each city and cause of death. The resulting coefficients are used in the meta-analysis.
  • 02_meta_analysis.R: Runs the meta-analysis on the coefficients of all cities. The resulting curves are used when estimating the risk ratios and attributable fractions.
  • 03_RRs.R: Calculated the risk ratios at the 5th and 95th temperature percentile centered at the MMT. Also calculates the increase in RR per increase in 1° C of extreme heat and decrease for extreme cold.
  • 04_attributable_fractions.R: Calculates the attributable fractions for exposure non-optimal temperature, heat, cold, extreme heat, and extreme cold.
  • 05_tables.R: Recreates all tables in MS85.
  • 06_figures.R: Recreates all figures in MS85.

Additional Resources

Additional city-specific results and summary information can be found in an interactive web application here: https://drexel-uhc.shinyapps.io/MS85/

The data and code repository for estimating daily temperatures is here: https://github.com/Drexel-UHC/salurbal_heat

All the code used here is heavily based on the code used for the analysis in the paper "Mortality risk attributable to high and low ambient temperature: a multi-country study" by Antonio Gasparrini and collaborators (The Lancet, 2015)

We downloaded their code from here: https://github.com/gasparrini/2015_gasparrini_Lancet_Rcodedata and edited it as needed for our analysis.

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URL: https://drexel-uhc.shinyapps.io/MS85/

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