Exploratory Analysis

Somee Lee, Deborah Markos, Audrey Luey, Sungjun Cho

Spring 2023

Abstract

We are concerned with the role that substance abuse and certain drugs play in our world today. This is mainly because we have noticed that contrasting social health determinants create various challenges relating to drug use.

Keywords

Drug-related deaths, substance abuse, overdose, opioid, Connecticut

Introduction

The continuous evolution of the society we live in today poses different challenges for many, and the way in which we choose to face them is unique to each of our circumstances. More specifically, it is becoming increasingly important to bring attention to the way in which drugs are used and monitored. As we know, there are various medications designed to treat specific conditions, however their individual guidelines can still mislead people or be overlooked. While drugs are made with the intention of helping people, we want to explore how the opposite can occur. We acknowledge that the use of drugs is very open-ended and that people have their own, different reasons for engaging in them. In order to fully grasp the severity of drug abuse and accidents, reliable and transparent data must be evaluated.

This investigation seeks to answer some key questions that aim to provide context for accidental drug related deaths from 2012-2021. They also help us to better understand and prevent these occurences from happening:

What type of drug/substance has the highest accidental cause of death?
What age group experiences the most drug/substance abuse?
Do male or females have a higher accidental cause of death due to drug/substances?
What city in Connecticut has the highest drug-related accidental cause of death?

Related Work

Drug overdose deaths have become a major public health issue in recent years, posing a serious threat to society. While the loss of lives is tragic, the effects of these deaths go beyond the individuals who have passed away. Families and communities are also impacted, making it imperative to take action to address this problem. Effective prevention and treatment measures are needed to combat this issue and reduce the number of overdose deaths. According to the CDC, overdose deaths among adolescents aged 10-19 years have increased significantly since the end of 2019, with illicitly manufactured fentanyl and counterfeit pills being the major culprits. This data underscores the urgency of implementing preventive measures and intervention strategies to curb the epidemic.

The Connecticut Department of Public Health plays a vital role in providing comprehensive opioid and drug overdose statistics specific to the state. Unintentional drug overdoses have become a leading cause of death in Connecticut, surpassing even motor vehicle accidents. The department collects and analyzes data through the State Unintentional Drug Overdose Reporting System (SUDORS), incorporating information from death certificates, medical examiner reports, and toxicology results. These resources are essential in raising awareness, monitoring overdose trends, and implementing effective prevention and response measures.

Fortunately, addiction is a treatable condition, and the National Institutes of Health reaffirm that it can be effectively managed. Through awareness, prevention, and treatment, we can work towards reducing the number of overdose deaths and supporting those affected by addiction. Prevention programs can help individuals avoid addiction and the risks that come with it, while treatment programs can provide individuals with the tools they need to overcome addiction and lead healthy, productive lives. By investing in these programs, we can work together to make a positive impact on public health and ensure that those who are affected by addiction receive the support they need.

The Dataset

Where did you find the data? Please include a link to the data source

We found our data on Accidental Drugs from the data.gov website. Accidental Drugs is the link to the website.

Who collected the data?

The data was collected by the State of Connecticut Chief Medical Examiner, published by data.ct.gov.

How was the data collected or generated?

The data came from a study conducted by the Office of the Connecticut Chief Medical Examiner. The data was gathered using a variety of sources, including death certificates, lab tests, and investigation reports. The Connecticut Chief Medical Examiner gathers this information to examine sudden, unexpected, and violent deaths in Connecticut. The data set includes information on drug-related deaths in Connecticut residents as well as visitors who died in Connecticut.

Why was the data collected?

The dataset provides a list of accidental deaths related to drug overdose in Connecticut from 2012 to 2021, along with the substances detected. The purpose of the dataset is to provide information on drug-related deaths in the state, especially in Connecticut.

How many observations (rows) are in your data?

There are 9202 observations in our dataset

How many features (columns) are in the data?

There are 48 features in our dataset.

What, if any, ethical questions or questions of power do you need to consider when working with this data?

The data on accidental drug-related deaths raises several ethical questions and issues of power that need to be considered when working with this data. One of the primary ethical concerns is ensuring that the privacy and confidentiality of the individuals who have died are protected. Data should be handled with respect and sensitivity to the victims and their families.

What are possible limitations or problems with this data? (at least 200 words)

While the Accidental Drug-Related Deaths dataset can be a useful resource for researchers and elected officials, it is important to be aware of its limitations and possible issues. Some possible limitations and challenges include lacking data on demographics, misreporting, misrepresenting, and so on. The dataset, for example, only includes data from Connecticut, which may not be an indicator of other states or regions. Drug use trends and accessibility to drugs might vary greatly across states and regions, thus it is important to evaluate the data in the context of these gaps. Another disadvantage is the lack of information on demographics. While the dataset contains information on the deceased person’s age and gender, it lacks information on other important demographic factors such as race, ethnicity, and socioeconomic status. This can make understanding how drug use and drug-related fatalities vary among different categories in the population difficult. Last but not least, the dataset has a drawback in that it only includes data from 2012 to 2021. While this can provide insights into long-term trends, it may not account for recent shifts in patterns of drug use or the impact of recent changes in policy. In conclusion, while the Accidental Drug-Related Deaths dataset provides valuable information on drug-related deaths in Connecticut, we must be mindful of its limits and potential issues when analyzing and utilizing the data.

Implications

Technologists and policymakers can work at the same time to make bigger data-sharing relationships that permit greater collaboration and coordination amongst stakeholders. By sharing data for the duration of one of a variety agencies and organizations, they can find out dispositions and patterns in drug-related deaths, display the effectiveness of prevention and therapy programs, and make more educated alternatives about how to allocate resources. Additionally, prevention and remedy software that is associated to particular populations and materials can be developed the use of our research. If facts expose that a sure substance is related with a excessive variety of unintended deaths, policymakers can goal rules or training campaigns to tackle the issue. Also, they can boost equipment and applied sciences that assist stop unintentional overdoses or aid folks in recovery, they will be capable of gaining knowledge of algorithms to become aware of patterns in drug-related deaths and predict which populations are at the best possible risk. This may also additionally assist designers create extra straight forward and on hand assets that furnish data on drug-related deaths and substance abuse. It should encompass cellular apps, websites, or academic substances that are tailor-made to unique age businesses or demographics too.

Limitations & Challenges

These data may contain only parts of the main problem while it is difficult to analyze all the researches provided since it is too broad. Also, limitations and challenges with this records contain underreporting or misreporting of the elements worried in the overdose deaths, as properly as barriers in the accuracy checking out methods. There may additionally be boundaries in the demographic records available, such as lacking information on race and ethnicity or socioeconomic status. Challenges may consist of making sure the accuracy and completeness of the data, specially when dealing with statistics associated to drug use and overdose deaths. Also, the scope of this research is restricted to examining present data, and accurate go-overs can also be wanted to discover the underlying reasons and elements contributing to drug-related deaths. Finally, there may be challenges in successfully speaking the findings of this lookup to policymakers and the widespread public, and in making sure that the pointers are carried out effectively.

Summary Information

One of the data calculated was the age of the individuals who died as a result of unintentional drug usage. The data set was created by utilizing the slice_max function to avoid multiple variables of the same value from being pulled using the slice function while also determining the oldest age using the max function. Similarly, I used slice_min to determine the earliest age at which someone died from drug overdose. The results showed that 87 was the oldest age of death, and 14 was the youngest. Our team felt it was crucial to identify the youngest and oldest people to die from drug usage since the findings essentially highlighted the need for everybody to use safety while handling drugs, regardless of age.

Another value analyzed was whether males or females have a higher accidental cause of death because of drugs. To determine that, I first created a new data set using the count function that had only the ‘Sex’ variable. I then used the filter and max functions to determine which sex had the greatest number of drug-related fatalities. According to the findings, drug misuse was more frequently the cause of accidental death in Male. We found the findings intriguing because men were three times as likely to die from drug-related accidents than women.

Another value calculated was the year with the most accidental drug-related deaths. The least was what? In order to determine that, I first modified the main data set by simply removing the years from the date variable, and changing the variable “Date” to “year.” Then, using the count function, I created a new data set that contained only the variable “year.” In order to determine which year had the most and least unintentional drug-related deaths, I used the filter and max functions. The results showed that 2021 was the year with the greatest number of fatalities and 2012 was the year with the fewest. It was important to determine which years saw the most and least deaths because doing so may identify a trend regarding the rate of drug-related fatalities in Connecticut.

Another variable calculated was the location of the vast majority of people who died as a result of unintentional drug use. I first created another data set using the count function that contained only the ‘Residence City’ variable to determine that. I then used the filter and max functions to figure out which city had the most number of residents who had died from drug-related accidents. According to the findings, the city where the persons who died from unintentional drug use resided was HARTFORD. It was interesting to see which city has the most residents because I learned on How Diverse is Connecticut that HARTFORD is one of Connecticut’s most diverse cities. It raises the question, what can be deduced from the data found?

Last but not least, we calculated which city had the greatest and least drug-related fatalities. I used the count function to create a new data set containing only the variable “Death City” in order to identify that. After that, I used the filter and max functions to determine which city experienced the highest number of fatal drug overdoses. The results showed that the city with the most drug-related unintentional deaths was designated as HARTFORD while the variable ‘least_deaths_city’ produced a list of 39 cities with each having just one death. Our team was interested in which city had the most drug-related fatalities because we wanted to discover whether there was any connection between the cities with the most and the fewest fatal drug overdoses.

Table

year	Age	Sex	Death City
2014	14	Female	HARTFORD
2021	14	Female	BRISTOL
2018	15	Male	BRISTOL
2020	15	Male	PUTNAM
2020	16	Male	TORRINGTON
2012	16	Male	WATERFORD
2021	16	Male	MANCHESTER
2018	17	Female	MERIDEN
2012	17	Female	NEW MILFORD
2012	17	Male	ENFIELD
2020	17	Female	FAIRFIELD
2014	17	Male	DANBURY
2017	17	Male	NEW PRESTON
2019	17	Female	NEW HAVEN
2021	17	Male	ENFIELD
2018	17	Female	NEW BRITAIN
2015	17	Female	MERIDEN
2016	17	Female	NEW LONDON
2015	17	Female	WATERTOWN
2018	17	Male	NEW LONDON
2016	18	Male	EAST HADDAM
2012	18	Male	NEW LONDON
2014	18	Male	CHAPLIN
2020	18	Female	EAST HARTFORD
2015	18	Male	GLASTONBURY

Our table displays the year, age, and city of death for each individual. The table lists ages in chronological order, with the youngest listed as 14 and the oldest listed as 87 at the bottom. This table compares the cities where individuals have died and the year to show the trend of the ages among those who have died from drug use unintentionally. One can learn more about trends and patterns in unintentional drug-related death over time by looking at these aggregate metrics. To show the variations in unintentional drug-related death rates, the table allows comparisons across various years, ages, and cities.

Chart 1

We chose this chart to analyze drug trends and determine which types of drugs/substances have the highest and lowest rates of accidental cause of death. Based on the data, we found that Fentanyl had the highest number of accidental deaths, while Morphine (not heroin) had the lowest. To visualize this trend effectively, we decided to create a scatter plot spanning from 2012 to 2021.

The graph reveals important information about the drugs’ impact on accidental deaths. Fentanyl shows a consistent increase in accidental deaths over the entire period from 2012 to 2021. Notably, there was a sharp rise in deaths during 2015 to 2016, indicating a significant increase in Fentanyl-related fatalities during that time. This suggests that 2015 was likely the year when Fentanyl started to gain popularity and became more prevalent as a cause of accidental deaths.

On the other hand, Morphine (not heroin) only appears prominently around 2016 and steadily declines afterward. The graph illustrates a relatively lower number of accidental deaths attributed to Morphine (not heroin) compared to Fentanyl.

Overall, this chart provides valuable insights into the drug-related accidental deaths, indicating the increasing dominance of Fentanyl over the years, while highlighting the limited impact of Morphine (not heroin) in comparison.

Chart 2

Rationale for choosing this chart:

The purpose of creating this visualization was to compare the two gender categories and their drug use as identified in our data set. A bar chart allows for categorical data to be easily interpreted and ranked by their numerical values accordingly.

Insights from the chart:

This bar chart illustrates that from 2012-2021, males in Connecticut had a higher number of accidental drug related deaths than women. Given that the gap between these values is quite large, it leads us to question why this may be. While there could be many explanations, some may include that men are more likely to take risks, have easier access to drinking or the use of drugs, and that certain stigmas surrounding women and drug/substance use can discourage their participation. Regardless, it is important to keep in mind that substances do not discriminate based on gender, and both men and women can experience drug abuse and their effects.

Chart 3