2b:null 2c:["$","title",null,{"children":"Explore the Librela sADR Data | d8abased | Observable"}] 31:T5e8,Plot.plot({ width: width, height: 600, x: { round: true }, marginLeft: 0, marginRight: 35, marginBottom: 40, marginTop: 40, insetTop: -20, marks: [ Plot.frame({fill: "#dddddd"}), Plot.rectY(weeklyByCountry, {x: "Received date", y: "Animals affected", interval: "Week", fill: "#333333cc", inset: 0}), Plot.rectY(weeklyByCountry, {x: "Received date", y: "Animals died", fill: "red", interval: "Week", inset: 0}), Plot.axisY({label: "Animals affected (black), died (red)", anchor: "right"}), Plot.gridY({stroke: "#333333", strokeOpacity: 0.2, strokeWidth: 1}), Plot.ruleY([0]), selectCountry === "All" ? Plot.lineY(weeklyByCountry, Plot.windowY({k: 30, anchor: "middle"}, {x: "Received date", y: "Animals affected", stroke: "#fefefe", strokeWidth: 2, curve: "basis"})) : [], Plot.tip(weeklyByCountry, Plot.pointerX({ x: "Received date", y: "Animals affected", title: (d) => { const date = new Date(d["Received date"]); const dateFormat = date.toLocaleDateString('en-US', { timeZone: "UTC", month: 'short', day: 'numeric', year: 'numeric' }); return `${dateFormat}\n\nAffected: ${d["Animals affected"]}\nDied: ${d["Animals died"]}`; } })), selectCountry === "All" ? Plot.tip( [`This was likely a significant reporting delay by Zoetis or EVVet, and represents cases for the prior 6-9 months.`], {x: new Date("2022-02-06"), y: 600, dx: 40, anchor: "middle", background: "white"} ) : [], ] })32:Td01,mainTickChart = Plot.plot({ marginLeft: 70, marginTop: 40, marginBottom: 0, marginRight: 5, width: width, caption: "Exploring Across 4 Dimensions: Time (x-axis), Place (y-axis), Affected (black), Dead (red)", x: { label: null, domain: domain.adj_data_extent }, y: { domain: d3.groupSort( data_daily, (g) => d3.min(g, (d) => d["Received date"]), (d) => d.Country ), label: null, padding: 0.1 }, marks: [ Plot.ruleX([0]), Plot.tickX( data.filter(d => d["Animals affected"] > 0), { x: "Received date", y: "Country", stroke: "black", strokeWidth: 1, strokeOpacity: (d) => Math.min((d3.interpolateNumber(domain.affected_extent[0], domain.affected_extent[1])(d["Animals affected"])/domain.affected_extent[1]), 0.2) } ), Plot.tickX( data.filter(d => d["Animals died"] > 0), { x: "Received date", y: "Country", stroke: "red", strokeWidth: 1, strokeOpacity: (d) => d3.interpolateNumber(domain.died_extent[0], domain.died_extent[1])(d["Animals died"])/domain.died_extent[1], // mixBlendMode: "plus-lighter" } // {x: "Received date", y: "Country", stroke: "red", strokeWidth: 1, strokeOpacity: (d) => 0.5, mixBlendMode: "plus-lighter"} ), Plot.gridX({ interval: "month", strokeDasharray: "1,2", strokeOpacity: 0.2 }), Plot.axisX({ anchor: "top", tickRotate: -90, interval: "month", // tickFormat: d => d.toLocaleDateString('en-US', { month: 'long', year: 'numeric' }) }), Plot.gridY({ x: (y) => data.find((d) => d.Country === y)?.['Received date'] }), Plot.axisY({ x: (y) => data.find((d) => d.Country === y)?.['Received date'], tickSize: 6, fontWeight: 600 }), // Plot.crosshairX(data_daily, {x: "Received date", y: "Country", ruleStroke: "#E59313", ruleStrokeOpacity: 0.5, textFillOpacity: 0}), // Plot.text(data_daily, Plot.pointerX({px: "Received date", py: "Country", dy: -10, dx: -60, frameAnchor: "bottom-left", fontSize: "11", fontVariant: "tabular-nums", fontWeight: 600, text: (d) => [`Date: ${Plot.formatIsoDate(d["Received date"])}`, `Country: ${d["Country"]}`, `Affected: ${d["Animals affected"]}`, `Died: ${d["Animals died"]}`].join(" | "), maxRadius: 6})), // Plot.dotX( // data_daily, // Plot.pointerX({ // x: "Received date", // y: "Country", // fill: "#ffffffdd", // stroke: "#E59313", // r: 6, // maxRadius: 6 // }) // ), Plot.tip(data_daily, Plot.pointerX({ x: "Received date", y: "Country", title: (d) => `Date: ${new Date(d["Received date"]).toLocaleDateString('en-US', { timeZone: "UTC", month: 'short', day: 'numeric', year: 'numeric' })}\nCountry: ${d.Country}\nAffected: ${ d["Animals affected"] }\nDied: ${d["Animals died"]}` })), ] })33:T6cf,Plot.plot({ marginLeft: 10, marginTop: 40, marginBottom: 0, marginRight: 5, width: width, x: { label: null, domain: domain.adj_data_extent }, y: { domain: ["United States"], label: null, padding: 0.1 }, marks: [ Plot.ruleX([0]), Plot.tickX( data.filter(d => d["Animals affected"] > 0), { x: "Received date", y: "Country", stroke: "black", strokeWidth: 1, strokeOpacity: (d) => Math.min((d3.interpolateNumber(domain.affected_extent[0], domain.affected_extent[1])(d["Animals affected"])/domain.affected_extent[1]), 0.2) } ), Plot.tickX( data.filter(d => d["Animals died"] > 0), { x: "Received date", y: "Country", stroke: "red", strokeWidth: 1, strokeOpacity: (d) => d3.interpolateNumber(domain.died_extent[0], domain.died_extent[1])(d["Animals died"])/domain.died_extent[1], // mixBlendMode: "plus-lighter" } ), Plot.gridX({ interval: "month", strokeDasharray: "1,2", strokeOpacity: 0.2 }), Plot.axisX({ anchor: "top", tickRotate: -90, interval: "month", // tickFormat: d => d.toLocaleDateString('en-US', { month: 'long', year: 'numeric' }) }), Plot.gridY({ x: (y) => data.find((d) => d.Country === y)?.['Received date'] }), Plot.axisY({ x: (y) => data.find((d) => d.Country === y)?.['Received date'], tickSize: 6, fontWeight: 600 }) ] })34:T493,The first case could be a participant in the U.S. field study, a mistaken country attribution, or some other anomaly. The adverse event report downloaded from EVVet says the dog was a Chesapeake Bay Retriever of an unknown age that weighed 0-4,999 kg and received 10 mg of Librela. The second case seems to comprise the 32 beagles from the safety trial conducted by Zoetis, *[Laboratory safety evaluation of bedinvetmab, a canine anti-nerve growth factor monoclonal antibody, in dogs](https://www.sciencedirect.com/science/article/pii/S1090023321001283?via%3Dihub)*, and the 8 beagles from the concurrent use (+ NSAID) trial described in the [FDA FOI Summary for Librela](https://animaldrugsatfda.fda.gov/adafda/app/search/public/document/downloadFoi/13959). Thinking more broadly, should these cases even be a part of our dataset? The answer really depends on the kind of analysis you’re doing. This underscores another seemingly minor detail that actually carries significant implications in generating summary statistics like _average animals affected per day by country_. If we include these cases, the U.S. timeline appears 3x to 4x times wider than it truly is.35:T70a,Plot.plot({ marginLeft: 40, marginTop: 40, marginBottom: 0, marginRight: 5, width: width, x: { label: null, domain: domain.adj_data_extent }, y: { domain: ["United States"], label: null, grid: false }, marks: [ Plot.ruleX([0]), Plot.tickX( data_usa_postlaunch.filter(d => d["Animals affected"] > 0), { x: "Received date", y: "Country", stroke: "black", strokeWidth: 1, strokeOpacity: (d) => Math.min((d3.interpolateNumber(domain.affected_extent[0], domain.affected_extent[1])(d["Animals affected"])/domain.affected_extent[1]), 0.2) } ), Plot.tickX( data_usa_postlaunch.filter(d => d["Animals died"] > 0), { x: "Received date", y: "Country", stroke: "red", strokeWidth: 1, strokeOpacity: (d) => d3.interpolateNumber(domain.died_extent[0], domain.died_extent[1])(d["Animals died"])/domain.died_extent[1], // mixBlendMode: "plus-lighter" } ), Plot.gridX({ interval: "month", strokeDasharray: "1,2", strokeOpacity: 0.2 }), Plot.axisX({ anchor: "top", tickRotate: -90, interval: "month", // tickFormat: d => d.toLocaleDateString('en-US', { month: 'long', year: 'numeric' }) }), Plot.gridY({ x: (y) => data_usa_postlaunch.find((d) => d.Country === y)?.['Received date'] }), Plot.axisY({ x: (y) => data_usa_postlaunch.find((d) => d.Country === y)?.['Received date'], tickSize: 0, fontWeight: 600 }) ] })36:T6af,Plot.plot({ marginLeft: 45, marginTop: 40, marginBottom: 0, marginRight: 5, width: width, x: { label: null, domain: domain.adj_data_extent }, y: { domain: ["United Kingdom"], label: null, grid: false }, marks: [ Plot.ruleX([0]), Plot.tickX( data_uk.filter(d => d["Animals affected"] > 0), { x: "Received date", y: "Country", stroke: "black", strokeWidth: 1, strokeOpacity: (d) => Math.min((d3.interpolateNumber(domain.affected_extent[0], domain.affected_extent[1])(d["Animals affected"])/domain.affected_extent[1]), 0.2) } ), Plot.tickX( data_uk.filter(d => d["Animals died"] > 0), { x: "Received date", y: "Country", stroke: "red", strokeWidth: 1, strokeOpacity: (d) => d3.interpolateNumber(domain.died_extent[0], domain.died_extent[1])(d["Animals died"])/domain.died_extent[1], } ), Plot.gridX({ interval: "month", strokeDasharray: "1,2", strokeOpacity: 0.2 }), Plot.axisX({ anchor: "top", tickRotate: -90, interval: "month", // tickFormat: d => d.toLocaleDateString('en-US', { month: 'long', year: 'numeric' }) }), Plot.gridY({ x: (y) => data_uk.find((d) => d.Country === y)?.['Received date'] }), Plot.axisY({ x: (y) => data_uk.find((d) => d.Country === y)?.['Received date'], tickSize: 0, fontWeight: 600 }) ] })37:T5ef,Indeed, the frequency of global sADRs grew exponentially after the U.S. launch due to its market size and Zoetis’s successful U.S. rollout. It is likely, in part, that this dramatic increase in sample size caused Librela sADRs to become more easily noticeable even by the mainstream media, and through the network effects of social media. _The Wall Street Journal_’s, [What Killed Their Pets? Owners Blame Meds, but Vets Aren’t Sure](https://www.wsj.com/health/pharma/dog-cat-arthritis-drugs-bcdddea6) and _The Washington Post_’s, [An arthritis drug helps old dogs, but some owners worry about side effects](https://www.washingtonpost.com/wellness/2024/04/20/librela-dog-arthritis-drug-side-effects/), were both published in April, 2024, just 6 months after Librela’s release in the U.S. Local news like _NewsChannel 21 (KTVZ)_, aired TV segments like: [‘I just wanted her to go in peace:’ Bend man’s dog passes after receiving Librela, a pain-blocker to treat osteoarthritis](https://ktvz.com/news/2024/06/25/i-just-wanted-her-to-go-in-peace-bend-mans-dog-passes-after-receiving-librela-a-pain-blocker-to-treat-osteoarthritis/) and [‘It’s horrible:’ Second C.O. dog owner tells NewsChannel 21 of tragic experience with prescribed drug Librela](https://ktvz.com/lifestyle/pets/2024/06/28/its-horrible-second-c-o-dog-owner-tells-newschannel-21-of-tragic-experience-with-prescribed-drug-librela/). This may be [The Law of Large Numbers](https://en.wikipedia.org/wiki/Law_of_large_numbers) at work. 38:T602,#### VeDDRA The _Veterinary Dictionary for Drug Related Affairs_, colloquially known as **VeDDRA**, is a harmonized system for categorizing reactions: a standardized way for veterinary health professionals worldwide to identify a reaction and the systemic disorder underlying that reaction. The European Medicines Agency provides [a document that outlines the principles of VeDDRA and how clinicians should use it](https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guidance-notes-use-veterinary-dictionary-drug-regulatory-activities-veddra-terminology-reporting-suspected-adverse-events-animals-and-humans_en.pdf). Coming back to our example of _ataxia_ from earlier, _section 7_ in the VeDDRA principles document addresses just how ambiguous diagnoses can get: > _Head tilt may be reported in association with adverse reactions which did not result from the administration of a product into the ear. The choice of this term at LLT level can be difficult as it may be due to either inner ear or neurological causes. The usual default would be to select the term ‘Head tilt – ear disorder’ , but in situations where the overall picture is one of a neurological disturbance, the alternative ‘Head tilt – neurological disorder’ should be selected. Conversely, balance problems may be vestibular in origin, so when they are reported in association with a possible ear disorder, a term from the SOC ‘Ear and labyrinth disorders’ should be included with terms from the SOC ‘Neurological disorders’._ 39:T445,Unfortunately, the EVVet data doesn’t automatically provide the VeDDRA categorizations as a part of their _Reactions_ data, and future work would need to map reactions per case to the various VeDDRA columns to determine the most common systemic failures leading to Librela sADRs. Librela, for instance, is not supposed to be prescribed to dogs with pre-existing neurological conditions, a notoriously difficult thing to determine without expensive screening protocols. Mapping reactions into VeDDRA categories would answer questions like: > _How many sADRs were neurological in nature?_ Consequently, these sort of answers could shed much light on whether screening procedures need to be enforced and black box warnings need to be put in place. While this is out of scope for this particular exercise, it’s certainly something future work should address. In the meantime, here is a lookup table that lets you search across VeDDRA (also available in the 📘 [`Search & Download the Data`](https://observablehq.com/@d8abased/searchanddownload) notebook alongside the EVVet sADR search).3a:T5ae,Arguably, _incidence rate_ is the most important metric in any sADR conversation, as it is the key determinant of _risk_ and the key metric considered by regulatory bodies like the FDA and EMA in approving or rejecting a new therapeutic. Would you give your dog Librela if the sADR incidence rate is `1 in 10,000`? How about `1 in 1,000` or `1 in 100`? The omission of _incidence rate_ in this conversation is by design, as we do not have an accurate estimate for the denominator. Ideally Zoetis would publicly share an accurate estimate from their sales and customer data, but barring that, a forensic calculation would be required. As we know, Librela is administered monthly, and has been in some markets for over 3 years and less than a few months in others. Estimating a denominator would need to account for many factors like (not a comprehensive list, and in no particular order): * Number of vials sold * Average number of vials per dose, _e.g. bigger dogs get 2 vials per dose_ * Average number of doses administered per dog, _i.e. how many months does the average dog stay on Librela?_ * Average percent of vials typically kept in storage * Times in market across markets * Under-reporting and over-reporting adjustments * ... Future work can and should address this, but it remains out of scope for the current exercise. If you’d like to contribute to this effort or have ideas that you’d like to share, be sure to reach out below.3b:T4e1,function reactionStats(data) { // Flatten all reactions into a single array and trim each reaction const allReactions = data.flatMap(d => d.Reaction.split(",").map(r => r.trim())); // Count each unique reaction using d3.rollup const reactionCounts = d3.rollup( allReactions, v => v.length, r => r ); // Calculate total unique reactions const total_unique_reactions = reactionCounts.size; // Calculate average reactions per case, using Math.ceil for ceiling const avg_reactions_per_case = Math.ceil(d3.mean(data, d => d.Reaction.split(",").length)); // Calculate max reactions in any single row const max_reactions = d3.max(data, d => d.Reaction.split(",").length); // Determine the top n reactions by count const top_50_reactions = Array.from(reactionCounts) .sort((a, b) => b[1] - a[1]) .slice(0, 50) .map(d => ({ "Reaction": d[0], "Animals affected": d[1] })); const next_100_reactions = Array.from(reactionCounts) .sort((a, b) => b[1] - a[1]) .slice(51, 150) .map(d => ({ "Reaction": d[0], "Animals affected": d[1] })); return { allReactions, total_unique_reactions, avg_reactions_per_case, max_reactions, top_50_reactions, next_100_reactions }; }3c:T5ec,// Plot.plot({ // width: width, // height: 600, // x: { round: true }, // marginLeft: 35, // marginRight: 35, // marginBottom: 40, // marginTop: 40, // insetTop: -20, // marks: [ // Plot.frame({fill: "#dddddd"}), // Plot.rectY(weekly, {x: "Received date", y: "Animals affected", interval: "Week", fill: "#333333", inset: 0}), // Plot.rectY(weekly, {x: "Received date", y: "Animals died", fill: "red", interval: "Week", inset: 0}), // Plot.axisY({label: "Frequency"}), // Plot.axisY({label: null, anchor: "right"}), // Plot.gridY({stroke: "#333333", strokeOpacity: 0.2, strokeWidth: 1}), // Plot.ruleY([0]), // Plot.lineY(weekly, Plot.windowY({k: 30, anchor: "middle"}, {x: "Received date", y: "Animals affected", stroke: "#fefefe", strokeWidth: 2, curve: "basis"})), // Plot.tip(weekly, Plot.pointerX({ // x: "Received date", // y: "Animals affected", // title: (d) => { // const date = new Date(d["Received date"]); // const dateFormat = date.toLocaleDateString('en-US', { timeZone: "UTC", month: 'short', day: 'numeric', year: 'numeric' }); // return `${dateFormat}\n\nAffected: ${d["Animals affected"]}\nDied: ${d["Animals died"]}`; // } // })), // Plot.tip( // [`This was likely a significant reporting delay by Zoetis or EVVet, and represents cases for the prior 6-9 months.`], // {x: new Date("2022-02-06"), y: 600, dx: 40, anchor: "middle", background: "white"} // ), // ] // })3d:[{"id":"3448b0912d098483a82b14ec5f9d9b890453749b0333efa055d4b9c1ea5a7a77cd90f138ca6b26d8dafcd7031be6cc1f71b09b213e4814988d56dfedb17e3b38","href":"https://static.observableusercontent.com/files/3448b0912d098483a82b14ec5f9d9b890453749b0333efa055d4b9c1ea5a7a77cd90f138ca6b26d8dafcd7031be6cc1f71b09b213e4814988d56dfedb17e3b38","name":"image.png","type":"image/png","lastModified":1720923889887.65,"size":108871}] 2f:{"id":"2749aeac0d7d5e05","slug":"explore","title":"Explore the Librela sADR Data","thumbnail":"6287dc19774016856c579e33e12932ded32a9f611bbee8c9137c13d68a0b0c02","access_level":"public","create_time":"$D2024-07-12T00:21:59.079Z","update_time":"$D2024-12-27T21:24:43.330Z","view_time":null,"first_public_time":"$D2024-07-12T01:17:10.037Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"387bada7be81c746","login":"d8abased","name":"d8abased","avatar_url":"https://avatars-next.observableusercontent.com/5680b5bdb4c31365df3342e07cb358027698748c24682dbd4476d6e8cc3e9e82","tier":"starter_2024"},"version":5479,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"ad011ae0dc79cf45","login":"d8abased","name":"the d8a team","avatar_url":"https://avatars-next.observableusercontent.com/70386b934552f1ef82ce5a323c14a14824b8e72ac7d241df9ecb75c85c27608b","tier":null}],"members":[{"type":"team","id":"387bada7be81c746","login":"d8abased","name":"d8abased","avatar_url":"https://avatars-next.observableusercontent.com/5680b5bdb4c31365df3342e07cb358027698748c24682dbd4476d6e8cc3e9e82","tier":"starter_2024","role":"editor"},{"type":"individual","id":"ad011ae0dc79cf45","login":"d8abased","name":"the d8a team","avatar_url":"https://avatars-next.observableusercontent.com/70386b934552f1ef82ce5a323c14a14824b8e72ac7d241df9ecb75c85c27608b","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"subscribed":null,"forks":0,"collection":{"id":"8e2a9ac146d4c315","slug":"librela","title":"Librela","owner":{"id":"387bada7be81c746","login":"d8abased","name":"d8abased"}},"type":"notebook","body":{"title":"Explore the Librela sADR Data","cells":[{"id":0,"value":"# Explore the Librela sADR Data\n## A Visual Tour of the Official Librela Suspected Adverse Drug Reaction Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":407,"value":"

Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing “look over there”.
— Randall Patrick Munroe, Creator of xkcd

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":4209,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4233,"value":"renderMenu()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4376,"value":"If you haven’t already, be sure to read the brief 📘 [`Introduction`](https://librela.d8abased.com) notebook first. It will acquaint you with the source of this data—European Medicines Agency’s [EudraVigilance Veterinary Database of Suspected Adverse Drug Reaction Reports](https://www.adrreports.eu/vet/en/index.html), colloquially known as *EVVet*—and offer some thoughts on how to reason about it. \n\n___","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4509,"value":"

📝 Note
All the `metrics` and visualizations on this page reflect the data from when it was last updated: **${d3.timeFormat(\"%B %d, %Y, %H:%M:%S UTC\")(new Date(meta.csvs[0].timestamp))}**. They will be refreshed within a day or so of EVVet releasing new data. So, check back often for the latest data.

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1539,"value":"\n\nRight off the bat, the dataset provides an insight that serves as a great baseline for our imminent exploration: **`${percent_vet_health_pro}%`** of sADR cases were reported by domain experts like veterinarians, physicians, and health care professionals. This is not to say that pet owners are not credible reporters — that’s a _[false dichotomy](https://xkcd.com/2592/)_; after all, it is the pet owners who usually witness the sADRs in the first place. The point here is that veterinarians reporting what pet owners witness adds meaningful confirmation to these signals.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":3095,"value":"vetPlot = Plot.plot({\n width: width,\n x: {round: true, axis: null},\n y: {label: \" \"},\n marginLeft: 0,\n marginRight: 0,\n marginTop: 0,\n marginBottom: 4,\n caption: percent_vet_health_pro + \"% of the official \" + total_cases.toLocaleString('en-US') + \" cases have been reported by veterinary professionals\",\n color: { legend: true, range: [\"#E59313\", \"#000000ff\", \"#FFC66D\", \"pink\"], domain: [\"Veterinarian\", \"Physician\", \"Other health care professional\", \"Animal owner\"]},\n marks: [\n Plot.barX(data, {\n x: \"Received date\",\n fill: \"Reporter\"\n }),\n Plot.ruleX([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1628,"value":"As of the last fetch of these reports on **${d3.timeFormat(\"%B %d, %Y, %H:%M:%S UTC\")(new Date(meta.csvs[0].timestamp))}**: \n\n> **`${total_cases.toLocaleString('en-US')}`** Librela sADR cases have been reported,
\n**`${animals_affected.toLocaleString('en-US')}`** dogs have been affected (a case can include multiple dogs), _and_
\n**`${animals_died.toLocaleString('en-US')}`** **`(${Math.round((animals_died / animals_affected) * 100)}%)`** died before the report was filed.\n\nThe trend is easy to see in a trusty stacked bar chart. But even such a naive visualization highlights something odd: the outlier bar in February 2022. This was likely caused by a significant reporting delay either by [Zoetis, Inc.](https://www.zoetis.com), Librela’s manufacturer, to EVVet, or by EVVet itself. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":5096,"value":"viewof selectCountry = Inputs.select(all_plus_countries, {label: \"Select a country\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5112,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5448,"value":"We’re just a minute into the data and it has provided us with a great example of an _adjustment_ that future work must undertake to proportionally redistribute the delayed data earlier in time (a standard best practice in data science). Failing to do so _distorts_ the true story the data is trying to tell us as the time periods before February 2022 likely had far more affected and dead dogs than what appears in the distorted version below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2242,"value":"### Exploring Across 4 Dimensions: Time, Place, Affected, Dead ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1553,"value":"A different, perhaps more interesting, visualization is this tick chart that takes you on a 4-dimensional journey across the `${total_countries}` countries where Zoetis markets this drug. \n\nIt shows the sADRs across countries (*y-axis*), over time (*x-axis*). The ticks represent batches of sADR cases submitted for the corresponding country at the corresponding points in time. The opacity (darkness) of the ticks represents the magnitude of the impact: **`black`** represents total dogs affected, and **`red`**, dogs who died before the report was filed. The y-axis is sorted chronologically by first reported sADR which roughly (we’ll see why this is often misleading) aligns with availability of Librela in that country.\n\nTake a moment with this chart. It tells some interesting stories, and teaches us some interesting lessons, both, about Librela, but also about the tricky world of data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":796,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1631,"value":"As you can clearly see, Librela was first launched in a majority of EEA countries as early as March, 2021, but it didn’t really hit some of its largest markets like Australia, Canada, Brazil, and most notably, the United States, until mid-late 2023. \n\nSince the U.S. launch has driven so much recent media coverage, let’s look at that data in isolation for a second.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2246,"value":"### Zooming into United States","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2047,"value":"Librela became commercially available in the U.S. only in [October 2023](https://news.zoetis.com/press-releases/press-release-details/2023/Zoetis-Demonstrates-Canine-Innovation-Leadership-with-Commercial-Launch-of-Librela-bedinvetmab-injection-in-the-U.S.-2023-HbWY2ZuORk/default.aspx) (with select veterinary practices offering _“early experience”_ use in September 2023), but you can see some case data as early as February 2022. Let’s zoom into those cases.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1821,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1850,"value":"Inputs.table(data_usa_prelaunch, {\n // rows: 30.7,\n columns: [\n \"Received date\",\n \"Case number\",\n \"Reporter\",\n \"Country\",\n \"Reaction\",\n \"Gender\",\n \"Breed\", \n \"Animals affected\",\n \"Animals treated\",\n \"Animals died\",\n \"Species\", \n \"Drug\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1859,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1834,"value":"When we adjust the U.S. data to filter out these temporal anomalies to capture Librela’s true **_time in market_** in the U.S., a very clear picture emerges: it shows that there’s a disproportionate relationship between the density and color of the U.S. data _and_ time, especially when compared to other countries.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1872,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2100,"value":"For reference, here is the U.K. data. The U.K. is currently in second place as measured by the total sADR impact.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2095,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2079,"value":"And now when we run the summary stats to compare one with the other, we see the true scale of disproportionality of impact against time.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1891,"value":"Plot.plot({\n width: width,\n marginLeft: 80,\n marginRight: 12,\n marginTop: 0,\n y: {\n label: null\n },\n x: {\n label: \"Animals died | affected\"\n },\n marks: [\n Plot.rectX(\n data_daily,\n Plot.groupY(\n { x: \"sum\" },\n {\n x: \"Animals affected\",\n y: \"Country\",\n fill: \"#333333\",\n sort: { y: \"x\", reverse: true, limit: 2 }\n }\n )\n ),\n Plot.rectX(\n data_daily,\n Plot.groupY(\n { x: \"sum\" },\n {\n x: \"Animals died\",\n y: \"Country\",\n fill: \"#f00000\",\n sort: { y: \"x\", reverse: true, limit: 2 }\n }\n )\n ),\n Plot.ruleX([0]),\n ]\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2104,"value":"Librela has been on the U.K. market for **`${time_in_market_uk.toLocaleString('en-US')}`** days, and on the U.S. market for **`${time_in_market_us.toLocaleString('en-US')}`** days. In other words, it’s been in the U.K. **`${Math.round((time_in_market_uk / time_in_market_us) * 10) / 10}x`** longer than it’s been in the U.S. But the U.S. already accounts for **`${Math.round((us_affected / uk_affected) * 10) / 10}x`** more dogs affected by sADRs, and **`${Math.round((us_died / uk_died) * 10) / 10}x`** more deaths as compared to the U.K.\n\nThe U.S. now accounts for **`${Math.round((us_affected / animals_affected) * 100)}%`** of _all_ Librela sADR affected dogs…","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2715,"value":"Plot.plot({\n height: 140,\n width: width,\n marginLeft: 10,\n marginRight: 10,\n x: {percent: true, label: \"% of dogs affected\"},\n color: {legend: false, range: ({length}) => d3.quantize(d3.interpolateSpectral, length)},\n marks: [\n Plot.barX(\n data.filter(d => d[\"Animals affected\"]),\n Plot.stackX(\n {order: \"x\", reverse: true},\n Plot.groupZ(\n {x: \"proportion\"},\n {z: \"Country\", inset: 0.5, fill: \"Country\", tip: true}\n )\n )\n ),\n Plot.text(\n data.filter(d => d[\"Animals affected\"]),\n Plot.filter(\n (d) => d.length > 100,\n Plot.stackX(\n {order: \"x\", reverse: true},\n Plot.groupZ(\n {x: \"proportion\", text: \"first\"},\n {z: \"Country\", text: \"Country\", fill: \"white\", fontSize: 11, fontWeight: 600, rotate: 90}\n )\n )\n )\n ),\n Plot.ruleX([0, 1])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4410,"value":"… and **`${Math.round((us_died / animals_died) * 100)}%`** of _all_ Librela sADR deaths!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4399,"value":"Plot.plot({\n height: 140,\n width: width,\n marginLeft: 10,\n marginRight: 10,\n x: {percent: true, label: \"% of dogs died\"},\n color: {legend: false, range: ({length}) => d3.quantize(d3.interpolateSinebow, length)},\n marks: [\n Plot.barX(\n data.filter(d => d[\"Animals died\"]),\n Plot.stackX(\n {order: \"x\", reverse: true},\n Plot.groupZ(\n {x: \"proportion\"},\n {z: \"Country\", inset: 0.5, fill: \"Country\", tip: true}\n )\n )\n ),\n Plot.text(\n data.filter(d => d[\"Animals died\"]),\n Plot.filter(\n (d) => d.length > 10,\n Plot.stackX(\n {order: \"x\", reverse: true},\n Plot.groupZ(\n {x: \"proportion\", text: \"first\"},\n {z: \"Country\", text: \"Country\", fill: \"white\", fontSize: 11, fontWeight: 600, rotate: 90}\n )\n )\n )\n ),\n Plot.ruleX([0, 1])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2077,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2353,"value":"### The Nature of the Reactions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1614,"value":"Switching gears to a different dimension of the data, arguably the most important one: **Reactions**, i.e. the array of side-effects and conditions experienced by dogs experiencing sADRs.\n\nA total of **`${reaction_stats.total_unique_reactions}`** unique reactions have been reported to EVVet, with an average of **`${reaction_stats.avg_reactions_per_case}`** reactions per case, a maximum of **`${reaction_stats.max_reactions}`** reactions for a single case, and a grand total of **`${reaction_stats.allReactions.length.toLocaleString('en-US')}`** reactions, and counting.\n\nZooming into the top **`50`** reported reactions paints a picture of a diversity of multi-systemic symptoms, conditions, and disorders ranging from mild in severity, to disabling, to fatal.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1659,"value":"Plot.plot({\n width: width,\n marginLeft: 0,\n insetRight: 20,\n insetLeft: 5,\n x: { grid: true},\n color: { legend: true, scheme: \"Turbo\" },\n marks: [\n Plot.barX(reaction_stats.top_50_reactions, {\n x: \"Animals affected\", y: \"Reaction\", sort: {y: \"-x\"}, fill: \"Animals affected\", tip: true,\n }),\n Plot.axisY({\n label: null,\n anchor: \"right\",\n x: (y) => reaction_stats.top_50_reactions.find((d) => d.Reaction === y)?.[\"Animals affected\"], \n tickSize: 6,\n tickFormat: (d, i, _) => d\n }),\n Plot.axisX({anchor: \"top\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2591,"value":"And here are the next **`100`** (on their own x-scale for clarity).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2589,"value":"Plot.plot({\n width: width,\n marginLeft: 0,\n insetRight: 200,\n insetLeft: 5,\n x: { grid: true},\n color: { legend: true, scheme: \"Turbo\" },\n marks: [\n Plot.barX(reaction_stats.next_100_reactions, {x: \"Animals affected\", y: \"Reaction\", sort: {y: \"-x\"}, fill: \"Animals affected\", tip: true}),\n Plot.axisY({\n label: null,\n anchor: \"right\",\n x: (y) => reaction_stats.next_100_reactions.find((d) => d.Reaction === y)?.[\"Animals affected\"], \n tickSize: 6\n }),\n Plot.axisX({anchor: \"top\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2356,"value":"If you have been catching on, then you may have noticed a new issue. \n\nDue to the lack of _normalization_ in the EVVet reaction set, several independently listed reactions — e.g. ataxia, lameness, hind limb ataxia, head tilt, balance problems — could intuitively be referring to one and the same reaction. If these similar/synonymous groups were collapsed, reactions like _ataxia_ (for example) could end up being one of the most suffered sADRs, irrespective of its organ system underpinnings. In the meantime, we can still make use of this reaction data in meaningful ways as we’ll see a little later in the 📘 [` Search & Download the Data`](https://observablehq.com/@d8abased/searchanddownload) notebook.\n\nFuture work would need to address these sort of entity resolution issues via K-means clustering, etc. But in doing so, we must be careful to not violate the principles of *VeDDRA* terminology. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4246,"value":"renderMenu()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4019,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4073,"value":"It also underscores the various organ systems and types of disorders that could be at the root of an sADR. This is just a truncated view (level 1 and level 2 of 4 levels) of how the various types of reaction classes are categorized against organ systems. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4046,"value":"Plot.plot({\n axis: null,\n width: width,\n marginTop: 20,\n marginBottom: 20,\n marginLeft: 50,\n marginRight: 215,\n height: 3000,\n marks: [\n Plot.tree(veddra_tree, {path: \"name\", delimiter: \".\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4095,"value":"$39","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4115,"value":"viewof search_veddra = Inputs.search(veddra, {\n placeholder: \"Search VeDDRA...\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4113,"value":"Inputs.table(search_veddra, {\n rows: 10.7,\n columns: [\n \"Current Low Level Term (LLT)\",\n \"Current System Organ Class (SOC) Term\",\n \"Current High Level Term (HLT)\",\n \"Current Preferred Term (PT)\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2817,"value":"
\n### The Drug Column","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2857,"value":"The _Drug_ column provides a comma-separated list of all the drugs the dog was receiving at the time the sADR occurred. It’s a fairly noisy column, and one that relies on the time and thoroughness of reporters (mostly veterinary health professionals) doing the data entry. Nonetheless, let’s see if we can make some use of it. \n\nThe first question that immediately comes to mind:\n\n> *How many dogs received **only** Librela?*\n\nFirst we need to determine the strings that represent Librela in the _Drug_ column:\n\n~~~js\nconst unique_librela_strings = uniqueDrugs.filter(drug => drug.startsWith(\"Librela\"));\n~~~\n\n> Librela 10 mg - Solution for injection
\n> Librela 15 mg - Solution for injection
\n> Librela 20 mg - Solution for injection
\n> Librela 30 mg - Solution for injection
\n> Librela 5 mg - Solution for injection","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":3886,"value":"Here’s what the data looks like with only those rows (we’re displaying only the _Received date_ and _Drug_ columns for clarity):","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2811,"value":"Inputs.table(drug_stats.only_librela_given, {\n rows: 7.2,\n columns: [\n \"Received date\",\n \"Drug\"\n ],\n width: {\n \"Received date\": 100\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2873,"value":"**`${drug_stats.only_librela_given.length.toLocaleString('en-US')}`** of the **`${total_cases.toLocaleString('en-US')}`** total sADR cases list only Librela as the drug the dog(s) in question were receiving. That’s **`${Math.round((drug_stats.only_librela_given.length / total_cases) * 100)}%`**.\n\nOver time that looks something like this (the `red` represents _“Librela only”_ cases while `black` represents all cases):","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2822,"value":"Plot.plot({\n marginLeft: 10,\n marginTop: 40,\n marginBottom: 0,\n marginRight: 0,\n width: width,\n x: {\n label: null,\n domain: domain.adj_data_extent\n },\n y: {\n label: null,\n grid: false\n },\n marks: [\n Plot.ruleX([0]),\n Plot.tickX(\n data,\n {x: \"Received date\", stroke: \"black\", strokeWidth: 1, strokeOpacity: 0.1}\n ),\n Plot.tickX(\n drug_stats.only_librela_given,\n {x: \"Received date\", stroke: \"red\", strokeWidth: 1, strokeOpacity: 0.1}\n ),\n Plot.axisX({\n anchor: \"top\",\n tickRotate: -90,\n interval: \"month\"\n }),\n ]\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2918,"value":"While it’s prudent to not get too carried away with this particular cross-section of the data journey, it is certainly interesting that such a large proportion of cases fall into the _Librela only_ category.\n\nFuture work would attempt to identify some sort of confirming indicator — perhaps a poll of clinics to determine the level of data-entry discipline — to adjust for the accuracy of this particular aspect of the data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2359,"value":"## What About Incidence Rate?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1727,"value":"Conspicuously absent so far in our exploration is any mention of suspected *incidence rate* based on these real-world sADRs. \n\nThe incidence rate is the probability that a dog administered Librela could be affected by an adverse drug reaction, as captured by this formula:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4947,"value":"sADRFormula()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4897,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2920,"value":"## Before We Move On","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2922,"value":"Hopefully this exploration acquainted you with the Librela sADR landscape. As we had stated at the beginning of this journey, sADR data is tricky business. That said, tricky is simply tricky; it's not _impossible_. And it is certainly still worthy of our attention. \n\nThe FDA said it best in their [Librela FOIA Response](https://www.fda.gov/media/178955/download?attachment):\n\n> _The primary purpose for maintaining the CVM ADE database is to provide an early warning or\nsignaling system to CVM for adverse effects not detected during pre-market testing of FDA-\napproved animal drugs and for monitoring the performance of drugs not approved for use in\nanimals._\n\nThis data exists precisely to catch an early signal of divergence between our back-testing data, i.e. sADRs in the clinical trial, and our forward-testing data, i.e. sADRs in the real world. \n\nThe sooner such a divergence is detected, the sooner we can minimize potential harm to living beings (in this case, arguably the best kind). 🐾","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":4283,"value":"renderMenu()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4172,"value":"cellFooter","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4191,"value":"cellDataLoaders","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4339,"value":"import {globals} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4224,"value":"import {renderMenu} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4168,"value":"import {cellFooter} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4185,"value":"import {cellDataLoaders} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4217,"value":"import {cellStyles} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4295,"value":"import {cfa} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4373,"value":"cfa()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4383,"value":"import {openSomeLinksInNewTab} from \"@d8abased/introduction\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4370,"value":"openSomeLinksInNewTab","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4220,"value":"cellStyles","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4936,"value":"function sADRFormula (){\n return md`\n${tex.block`\nP(\\text{L}_{\\text{sADR}}) = \\left( \\frac{N_{\\text{dogs\\_affected}}}{N_{\\text{dogs\\_treated}}} \\right)\n`}\n\n_where_,
\n> ${tex`P(\\text{L}_{\\text{sADR}})`} denotes the probability of suffering a Librela sADR
\n> ${tex`N_{\\text{dogs\\_affected}}`} denotes the total number of dogs who suffered a Librela sADR
\n> ${tex`N_{\\text{dogs\\_treated}}`} denotes the total number of dogs treated with Librela\n `\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":768,"value":"data = await d3.csv(\n `https://cors.openadverseevents.workers.dev/?destination=${globals.librela}`,\n (d) => {\n return {\n \"Received date\": new Date(d[\"Received date\"]),\n \"Case number\": d[\"Case number\"],\n \"Reporter\": d[\"Reporter\"],\n \"Country\": d[\"Country\"],\n \"Gender\": d[\"Gender\"],\n \"Species\": d[\"Species\"],\n \"Breed\": d[\"Breed\"],\n \"Reaction\": d[\"Reaction\"],\n \"Animals affected\": +d[\"Animals affected\"],\n \"Animals died\": +d[\"Animals died\"],\n \"Animals treated\": +d[\"Animals treated\"],\n \"AER form\": d[\"AER form\"],\n \"Drug\": d[\"Drug\"],\n };\n }\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4354,"value":"meta = await d3.json(\n `https://cors.openadverseevents.workers.dev/?destination=${globals.librela_meta}`,\n (d) => d\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3721,"value":"data_daily = Array.from(d3.rollup(data, \n (v) => ({\n AnimalsAffectedSum: d3.sum(v, d => d[\"Animals affected\"]),\n AnimalsDiedSum: d3.sum(v, d => d[\"Animals died\"]) // Adding if you also need this for consistency\n }), \n d => d[\"Received date\"], \n d => d.Country\n)).flatMap(([date, countries]) => Array.from(countries, ([country, values]) => ({\n \"Received date\": date,\n \"Country\": country,\n \"Animals affected\": values.AnimalsAffectedSum,\n \"Animals died\": values.AnimalsDiedSum\n})));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5120,"value":"function resample(data, country = \"All\") {\n const filteredData = country === \"All\" ? data : data.filter(d => d.Country === country);\n \n const dataByWeek = d3.rollup(\n filteredData, \n v => ({ \n \"Animals affected\": d3.sum(v, d => d[\"Animals affected\"]),\n \"Animals died\": d3.sum(v, d => d[\"Animals died\"])\n }), \n d => +d3.timeWeek(d[\"Received date\"])\n )\n\n const result = Array.from(dataByWeek, ([week, values]) => ({\n \"Received date\": new Date(week),\n \"Animals affected\": values[\"Animals affected\"],\n \"Animals died\": values[\"Animals died\"]\n }));\n \n return result\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5099,"value":"all_plus_countries = [\"All\", ...Array.from(new Set(data.map(d => d.Country))).sort()];","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5123,"value":"weeklyByCountry = resample(data, selectCountry)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4010,"value":"veddra = await d3.csv(\n `https://cors.openadverseevents.workers.dev/?destination=${globals.veddra}`,\n (d) => {\n return {\n \"New Line No.\": +d[\"New Line No. \"],\n \"New line no.\": +d[\"New line no.\"],\n \"Current System Organ Class (SOC) Term\": d[\"Current System Organ Class (SOC) Term\"],\n \"Current SOC Key\": +d[\"Current SOC Key\"],\n \"Current High Level Term (HLT)\": d[\"Current High Level Term (HLT)\"],\n \"Current HLT Key\": +d[\"Current HLT Key\"],\n \"Current Preferred Term (PT)\": d[\"Current Preferred Term (PT)\"],\n \"Current PT Key\": +d[\"Current PT Key\"],\n \"Current Low Level Term (LLT)\": d[\"Current Low Level Term (LLT)\"],\n \"Current LLT Key\": +d[\"Current LLT Key\"],\n \"Current Term Type\": d[\"Current Term Type\"]\n };\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4048,"value":"function tree(veddra) {\n const uniqueValues = new Set();\n \n veddra.forEach(d => {\n const soc = d[\"Current System Organ Class (SOC) Term\"];\n const hlt = d[\"Current High Level Term (HLT)\"];\n uniqueValues.add(`VeDDRA.${soc}`);\n uniqueValues.add(`VeDDRA.${soc}.${hlt}`);\n });\n\n return [{ name: \"VeDDRA\", size: null }, ...Array.from(uniqueValues).map(value => ({ name: value, size: null }))];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4052,"value":"veddra_tree = tree(veddra)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3677,"value":"function domains() {\n const data_extent = d3.extent(data, d => d[\"Received date\"]);\n const adj_data_extent = data_extent.map((date, index) => d3.timeDay.offset(new Date(date), index === 0 ? -14 : 14));\n \n return {\n data_extent: data_extent,\n adj_data_extent: adj_data_extent,\n affected_extent: d3.extent(d3.rollup(data, v => d3.sum(v, d => d[\"Animals affected\"]), d => d[\"Received date\"]).values()),\n died_extent: d3.extent(d3.rollup(data, v => d3.sum(v, d => d[\"Animals died\"]), d => d[\"Received date\"]).values())\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3687,"value":"domain = domains()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1817,"value":"data_usa = d3.filter(data, d => d.Country === \"United States\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1838,"value":"data_usa_prelaunch = d3.filter(data_usa, d => new Date(d[\"Received date\"]) >= new Date(\"2022-01-01\") && new Date(d[\"Received date\"]) <= new Date(\"2023-10-08\"));\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1865,"value":"data_usa_postlaunch = d3.filter(data_usa, d => new Date(d[\"Received date\"]) >= new Date(\"2023-10-08\"));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2088,"value":"time_in_market_us = (new Date(data_usa_postlaunch[data_usa_postlaunch.length - 1][\"Received date\"]) - new Date(data_usa_postlaunch[0][\"Received date\"])) / (1000 * 60 * 60 * 24);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2081,"value":"data_uk = d3.filter(data, d => d.Country === \"United Kingdom\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2085,"value":"time_in_market_uk = (new Date(data_uk[data_uk.length - 1][\"Received date\"]) - new Date(data_uk[0][\"Received date\"])) / (1000 * 60 * 60 * 24);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2137,"value":"us_died = d3.sum(data_usa, (d) => d[\"Animals died\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2117,"value":"us_affected = d3.sum(data_usa, (d) => d[\"Animals affected\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2139,"value":"uk_affected = d3.sum(data_uk, (d) => d[\"Animals affected\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2142,"value":"uk_died = d3.sum(data_uk, (d) => d[\"Animals died\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2877,"value":"total_cases = data.length","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":889,"value":"animals_died = d3.sum(data, (d) => d[\"Animals died\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":876,"value":"animals_affected = d3.sum(data, (d) => d[\"Animals affected\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":976,"value":"total_countries = new Set(data.map(d => d.Country)).size - 1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1283,"value":"percent_vet_health_pro = Math.round((d3.rollup(data, \n v => v.length, \n d => d.Reporter === \"Veterinarian\" || d.Reporter === \"Other health care professional\" || d.Reporter === \"Physician\"\n).get(true) / data.length) * 100);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1605,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1602,"value":"reaction_stats = reactionStats(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2789,"value":"function drugStats(data) {\n // Flatten all drugs and get unique\n const allDrugs = data.flatMap(d => d.Drug.split(\",\").map(r => r.trim()));\n const uniqueDrugs = [...new Set(allDrugs)];\n\n // Filter for unique drugs starting with \"Librela\"\n const unique_librela_strings = uniqueDrugs.filter(drug => drug.startsWith(\"Librela\"));\n\n // Filter data for rows where the Drug column contains only unique_librela_strings\n const only_librela_given = data.filter(d => {\n const drugsInRow = d.Drug.split(\",\").map(r => r.trim());\n return drugsInRow.every(drug => unique_librela_strings.includes(drug)); // Every drug in the row must be a Librela drug\n });\n\n return {\n unique_librela_strings,\n only_librela_given\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2793,"value":"drug_stats = drugStats(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":988,"value":"// function resample(data, timeframe) {\n// const startOfPeriod = (date) => {\n// switch (timeframe) {\n// case 'Week':\n// return d3.timeWeek.floor(date);\n// case 'Month':\n// return d3.timeMonth.floor(date);\n// default:\n// throw new Error(\"Unsupported timeframe: \" + timeframe);\n// }\n// };\n\n// const aggregated = d3.rollups(data, \n// v => ({\n// 'Animals affected': d3.sum(v, d => d['Animals affected']),\n// 'Animals died': d3.sum(v, d => d['Animals died'])\n// }), \n// d => d3.timeFormat(\"%Y-%m-%d\")(startOfPeriod(new Date(d['Received date']))) // Ensure date is formatted correctly\n// ).map(([date, values]) => ({\n// 'Received date': new Date(date + \"T00:00:00Z\"), // Parse back into a Date object without time\n// ...values\n// }));\n\n// return aggregated;\n// }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4996,"value":"// weekly = resample(data, \"Week\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4977,"value":"// test = d3.filter(resample(data, \"Week\"), d => new Date(d[\"Received date\"]) >= new Date(\"2024-04-01\") && new Date(d[\"Received date\"]) <= new Date(\"2024-05-31\"));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1357,"value":"// viewof affected_died_period = Inputs.radio([\"Week\", \"Month\"], {label: null, value: \"Month\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5126,"value":"// us = d3.sum(weeklyByCountry, (d) => d[\"Animals affected\"])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1311,"value":"$3c","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W3d","stdlib":"1","titling":"auto"}} 30:"$2f:body"