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Across the country, communities are struggling with heroin and opioid addictions. According to Vox, there 47,000 overdose deaths in 2014. Two-thirds of these overdoses were linked to opioids and heroin. The opioid epidemic has been gaining attention nationwide. This is something I saw first hand in my hometown of Appleton, WI, where classmates struggled with heroin and opioid addiction. It is a serious problem with residents 11 and older, more specifically reaching the demographic of high school students and residents in their young twenties. These circumstances prompt two overarching for my final assignment. How much are opioid overdose death rates increasing? And, why are opioid overdose death rates increasing? My overall goal was to answer these questions regarding the state of Wisconsin specifically because there has been a rise in heroin deaths throughout the state. In attempt to answer my research questions, I collected various sets of relevant data for both the United Stated and the state of Wisconsin to find a correlation between the two. Asking about opioid overdose increasing is an important question to be asked because there has been a nationwide increase in heroin use of thirty-four percent from 2013-2014 (WI DHS). During that same time frame, the amount of heroin overdoses in Wisconsin increased by eighteen percent. In fact, drugs and medications account for ninety-seven percent of all poisoning deaths in the Wisconsin. In 2013, more Wisconsin residents died from drug poisoning than from suicide, breast cancer, colon cancer, firearms, influenza or HIV (WI DHS).

This visualization helps answer why opioid overdose death rates are increasing. According to WI DHS, heroin deaths are increasing due to the rising use and misuse of opioid pain relievers. Although opioids can be a beneficial medication by relieving pain by reducing the intensity of pain signals reaching the brain and affecting brain areas that control emotion in order to diminish the effects of painful stimulus, opioids can be dangerous due do dependency and addiction (NIH). When opioids are abused, a single dose can cause severe respiratory depression and death. Long term use can create dependency, addiction and various forms of brain damage that effect decision making abilities, the ability to regulate behavior and the ability to regulate responses to stressful situations (NIH). The line graph depicts the amount of grams of common opioid prescriptions that are being dispersed throughout Wisconsin. From 2008-2012, the grams of oxycodone (OxyContin) have increased tremendously. OxyContin is one of the most misused opioid prescriptions in the United States. Other commonly abused opioids such as Methadone (typically prescribed to help manage opioid addiction), hydromorphone (a commonly misused opioid pain medication and narcotic), hydorocodone (Vicodin) have also increased in distribution throughout this time span. The rise in grams of these drugs distributed in Wisconsin is important because the more grams that are prescribed leads to a higher likelihood of addiction and/or misuse for patients and/or drug abusers. Because both have risen tremendously from 2008-2012, there is a strong correlation with the distribution of opioid and overdose deaths.

The data that I used for this visualization came from the ARCOS Retail Drug Summary Reports. This is located on the U.S. Department of Justice website. In order to get the data I had to go through drug summary reports from each year, 2008-2012. Each drug summary consisted of over 600 pages of data. I manually extracted the data for grams of popular opioid drugs per zip code for the state of Wisconsin. This was a good use of the data coming from the Retail Drug Summary Reports because I only extracted data that was relevant to the use of opioids in Wisconsin. I didn’t focus on other drugs that could be misused because opioid abuse is the number one thing that can lead to heroin addiction. The only oversights or flaws that I could see in my visualization would be not accounting for the change in Wisconsin’s population over the five years the data was collected in and not mentioning how many grams are typical per dosage of each prescription. For example, a typical prescription of oxycodone is 10 mg for every 12 hours. Each gram distributed is equal to 100 doses of oxycodone. In 2012 there were over 1.2 million grams distributed in Wisconsin alone, this means over 1.2 billion doses of oxycodone distributed within that year. Does Wisconsin need over 1.2 billion doses of oxycodone prescribed in a year when the state only has a population of 5.725 million people? This drug is over prescribed and it is one of few opioids that are being misused every day. According to Vox, there are enough opioids prescribed in the U.S. for every adult to have a prescription.

Similar to the previous graph, this visualization helps answer why opioid overdose death rates are increasing. The filled map depicts the amount of grams of common opioid prescriptions that are being distributed throughout Wisconsin. Rather than seeing the grams per retail drug visualized in a line graph, this visualization lets the viewer see how many grams of each drug is being distributed for each 3-digit zip code within Wisconsin. From 2008-2012, the grams of oxycodone (OxyContin) have increased tremendously. OxyContin is one of the most misused opioid prescriptions in the United States. Other commonly abused opioids such as Methadone, hydromorphone, hydorocodone have also increased in distribution throughout this time span. The rise in grams of these drugs distributed in Wisconsin is important because the more amounts of grams that are prescribed leads to a higher likelihood of addiction and/or misuse for patients and/or drug abusers. Seeing the distribution of these prescriptions on a map of Wisconsin can help predict which areas will have the most opioid prescription and heroin overdoses. Overall, there is a strong correlation with the distribution of opioid and overdose deaths because both have risen tremendously from 2008-2012.

The data that I used for this visualization came from the ARCOS Retail Drug Summary Reports. This is located on the U.S. Department of Justice website. In order to get the data I had to go through drug summary reports from each year, 2008-2012. Each drug summary consisted of over 600 pages of data. I manually extracted the data for grams of popular opioid drugs per zip code for the state of Wisconsin. This was a good use of the data coming from the Retail Drug Summary Reports because I only extracted data that was relevant to the use of opioids in Wisconsin. I didn’t focus on other drugs that could be misused because opioid abuse is the number one thing that can lead to heroin addiction. The only oversights or flaws that I could see in my visualization would be not accounting for the change in Wisconsin’s population over the five years the data was collected in and not mentioning how many grams are typical per dosage of each prescription. I believe population wouldn’t have a strong significance, but the dosage of a medication would be good for the viewer to know in order to display more measurable/tangible values for the viewer. In this visualization it would be important for the viewer to know the population for each 3-digit zip code as well. A limitation I had was not being able to get access for 2013-2015. I think this would be useful information in trying to show the rise of opioid distribution and my information would seem more relevant and up-to-date if I were able to add data from those years.

This visualization offers the viewer a new way to interact with the drug retail summary compared to my first graph. This filled map allows users to see which areas in Wisconsin there are the most opioid prescriptions distributed. A filled map depicting overdose rates per 3-digit zip code would be the ideal compliment to this graph, but unfortunately I wasn’t able to receive that data due to privacy concerns. I saw this as a major limitation because it would help create a stronger correlation of opioid prescriptions and overdose because the viewer would be able to see these maps side by side. A USA Today data journalist, Erik Litke, was able to access this data and I would like to provide it as a supplement to my map. Here is his visualization for heroin deaths by county and here is another one for opioid prescription drug deaths. This provides a strong correlation to high opioid distribution in areas leading to higher overdose rates in those areas.

My visualization also allows the viewer to see how distribution in Wisconsin changes per year. The user is able to filter information by year and retail drug. If the user doesn’t want to filter out specific drugs they are able to click an area on the graph and that specific 3-digit zip code will be bold as the rest become less opaque. This is a way the viewer can isolate a variable more without filtering out other information. The information is presented in a way that is easy for the viewer to process and it saves time by condensing a lot of information into one map instead of showing data sets or writing about the increase of opioids distributed over time. A visualization drawback that I noticed is that if the viewer has multiple retail drugs selected on the filter, they are unable to see specific gram amounts per drug in any given zip code because Tableau sums the amounts for each checked retail drug.

This visualization helps answer how much opioid overdose death rates are increasing. This visualization shows dramatic increases of drug overdose deaths involving opioids, overdose deaths involving prescription opioids and overdose deaths involving heroin in Wisconsin from 2005-2014. This data was collected from Lauren J. Stockman, an epidemiologist from the Wisconsin Division of Public Health. Findings in this visualization are very important because if this information were released publicly, rather than by request, Wisconsin citizens would be able to see how serious of a problem opioid overdose related deaths are. Overall, drug overdose deaths involving opioids increase nearly 230% from 2005 to 2014. Overdose deaths involving prescriptions increased almost 165% from 2005 to 2014 and overdose deaths involving heroin increased almost 890%. This graph, next to the visualizations mention previously create a correlation with opioid drug distribution and opioid overdose related deaths in the state of Wisconsin.

The major limitation with this data set is that it did not offer more information about the overdoses. I wish this data set included death rates per 3-digit zip code or county. If this were an option, I would have also presented this visualization as a filled map to complement the WI Retail Drug Summary Deaths by 3-Digit Zip Code 2008-2012 visualization.

The purpose of this visualization is to answer how much opioid overdose death rates are increasing. This visualization displays the rise in opioid related drug poisoning deaths in the United States from 2000-2013. This is significant because it shows death rates for opioid analgesics and heroin. According to Web MD, “opioid analgesics are prescribed for moderate to severe pain, particularly of visceral origin, and are used in step two and step three of the analgesic ladder. Dependence and tolerance are well-known features with regular use although this should not inhibit prescribing in palliative care.” Comparing this visualization to the WI Opioid Overdose-related Death visualization lets the viewer compare the results from a state to national level. It is important for the viewer to realize this is an ongoing problem nationwide and this visualization shows the problem in a bigger context. This visualization proves the increase of opioid overdose deaths because from 2000 to 2013 there was a 369% increase of opioid analgesics deaths and a 448% increase of heroin deaths.

The data from this visualization was collected from the Centers for Disease Control and Prevention. Rather than only included opioid analgesics and heroin deaths, I also included various forms of demographics for the race and gender of the victims. By only having opioid analgesics and heroin deaths checked on my filter when the viewer first sees this visualization, I was hoping that it would invite the viewer to look at the demographics of who is being effected by this rather than confuse them. I think it is important to know which demographic this is affecting the most. This may be a flaw that could confuse my audience rather than help further inform them.

A limitation that I saw in this visualization was that I did not have the count of deaths per state for each of these variables. It would have been helpful to compare the death rates by state over a long span of time.

The purpose of this visualization is to answer how much opioid overdose death rates are increasing. This visualization displays the rise in opioid and drug related deaths in the United States from 2013-2014. This is significant because it shows death rates by state. Comparing this visualization to the WI Opioid Overdose-related Death visualization lets the viewer compare the results from state to state. It is important for the viewer to see which states are struggling the most with overdose deaths because there could be a strong correlation with the amounts of retail drugs distributed by each state. If correlations represented causation as well, there may be more strict retail drug distribution policies.. This visualization proves the increase of opioid overdose deaths because from 2013 to 2014 there were only ten states that didn’t show an increase in overdose death rates.

The data from this visualization was collected from the Centers for Disease Control and Prevention. Although I was also given the significance of death increase per state in this data set, I only displayed the number of deaths per state over the two-year span. I was limited by this data set because there was only data available for a two-year span and I was unable to get death per 100,000 people. The only thing I would be able to have as an additional variable would be the age-rate. This visualization would have been much more beneficial if I would have been able to get this data for a 5-10 year span.

This data was displayed in a filled map because it would be the most beneficial way for the viewer to see the difference between overdose deaths per state. If the viewer were to read this as a text it would take much longer to get the major points across and show the user which states are suffering from this the most.