We are trying to investigate and answer the question, “What are the relationships between drug overdose death rates and other social and economic factors, such as poverty, unemployment, and education levels, in the United States?” We believe that drug overdoses are a real world problem that affect millions of people. “However, broader social and contextual domains are also essential contributors to the opioid crisis such as interpersonal relationships and the conditions of the community and society that people live in. Despite efforts to tackle the issue, the rates of opioid misuse and non-fatal and fatal overdose remain high.”(BMC) To make progress on this real-world problem, we need to explore the relationships between drug overdose death rates and various social and economic factors in the United States. To do this, we will need to collect data on these factors, such as poverty rates, unemployment rates, and education levels, and combine it with the data on drug overdose death rates that we already have. By analyzing this data, we hope to identify patterns and correlations between drug overdose death rates and these social and economic factors. This could help us understand the root causes of the opioid crisis and develop more effective interventions and policies to address it. Overall, the goal is to use data-driven insights to make progress in the fight against drug overdoses and to improve the health and well-being of individuals and communities affected by this crisis.
https://health-policy-systems.biomedcentral.com/articles/10.1186/s12961-020-00596-8
We can use the data on drug overdose death rates, by drug type and selected population characteristics, to examine the correlations between drug overdose deaths and various social and economic factors. By clustering the data, we can explore if there are any natural groupings of populations that are more susceptible to drug overdoses based on these factors, such as poverty, education, and unemployment rates. This can help identify patterns and trends that may not be immediately obvious, and inform targeted interventions and prevention strategies to address the root causes of drug overdoses in these vulnerable populations.
import pandas as pd
# Load the CSV file into a DataFrame
df = pd.read_csv('Drug.csv')
# Display the first few rows of the DataFrame
print(df.head(20))
data_dict = {
"INDICATOR": "Drug overdose death rates",
"PANEL": "All drug overdose deaths",
"PANEL_NUM": 0,
"UNIT": "Deaths per 100,000 resident population, age-adjusted",
"UNIT_NUM": 1,
"STUB_NAME": "Total",
"STUB_NAME_NUM": 0,
"STUB_LABEL": "All persons",
"STUB_LABEL_NUM": 0.1,
"YEAR": 1999,
"YEAR_NUM": 1,
"AGE": "All ages",
"AGE_NUM": 1.1,
"ESTIMATE": 6.1,
"FLAG": ""
}
data_dict_list = [data_dict]
INDICATOR PANEL PANEL_NUM \
0 Drug overdose death rates All drug overdose deaths 0
1 Drug overdose death rates All drug overdose deaths 0
2 Drug overdose death rates All drug overdose deaths 0
3 Drug overdose death rates All drug overdose deaths 0
4 Drug overdose death rates All drug overdose deaths 0
5 Drug overdose death rates All drug overdose deaths 0
6 Drug overdose death rates All drug overdose deaths 0
7 Drug overdose death rates All drug overdose deaths 0
8 Drug overdose death rates All drug overdose deaths 0
9 Drug overdose death rates All drug overdose deaths 0
10 Drug overdose death rates All drug overdose deaths 0
11 Drug overdose death rates All drug overdose deaths 0
12 Drug overdose death rates All drug overdose deaths 0
13 Drug overdose death rates All drug overdose deaths 0
14 Drug overdose death rates All drug overdose deaths 0
15 Drug overdose death rates All drug overdose deaths 0
16 Drug overdose death rates All drug overdose deaths 0
17 Drug overdose death rates All drug overdose deaths 0
18 Drug overdose death rates All drug overdose deaths 0
19 Drug overdose death rates All drug overdose deaths 0
UNIT UNIT_NUM STUB_NAME \
0 Deaths per 100,000 resident population, age-ad... 1 Total
1 Deaths per 100,000 resident population, age-ad... 1 Total
2 Deaths per 100,000 resident population, age-ad... 1 Total
3 Deaths per 100,000 resident population, age-ad... 1 Total
4 Deaths per 100,000 resident population, age-ad... 1 Total
5 Deaths per 100,000 resident population, age-ad... 1 Total
6 Deaths per 100,000 resident population, age-ad... 1 Total
7 Deaths per 100,000 resident population, age-ad... 1 Total
8 Deaths per 100,000 resident population, age-ad... 1 Total
9 Deaths per 100,000 resident population, age-ad... 1 Total
10 Deaths per 100,000 resident population, age-ad... 1 Total
11 Deaths per 100,000 resident population, age-ad... 1 Total
12 Deaths per 100,000 resident population, age-ad... 1 Total
13 Deaths per 100,000 resident population, age-ad... 1 Total
14 Deaths per 100,000 resident population, age-ad... 1 Total
15 Deaths per 100,000 resident population, age-ad... 1 Total
16 Deaths per 100,000 resident population, age-ad... 1 Total
17 Deaths per 100,000 resident population, age-ad... 1 Total
18 Deaths per 100,000 resident population, age-ad... 1 Total
19 Deaths per 100,000 resident population, age-ad... 1 Sex
STUB_NAME_NUM STUB_LABEL STUB_LABEL_NUM YEAR YEAR_NUM AGE \
0 0 All persons 0.1 1999 1 All ages
1 0 All persons 0.1 2000 2 All ages
2 0 All persons 0.1 2001 3 All ages
3 0 All persons 0.1 2002 4 All ages
4 0 All persons 0.1 2003 5 All ages
5 0 All persons 0.1 2004 6 All ages
6 0 All persons 0.1 2005 7 All ages
7 0 All persons 0.1 2006 8 All ages
8 0 All persons 0.1 2007 9 All ages
9 0 All persons 0.1 2008 10 All ages
10 0 All persons 0.1 2009 11 All ages
11 0 All persons 0.1 2010 12 All ages
12 0 All persons 0.1 2011 13 All ages
13 0 All persons 0.1 2012 14 All ages
14 0 All persons 0.1 2013 15 All ages
15 0 All persons 0.1 2014 16 All ages
16 0 All persons 0.1 2015 17 All ages
17 0 All persons 0.1 2016 18 All ages
18 0 All persons 0.1 2017 19 All ages
19 2 Male 2.1 1999 1 All ages
AGE_NUM ESTIMATE FLAG
0 1.1 6.1 NaN
1 1.1 6.2 NaN
2 1.1 6.8 NaN
3 1.1 8.2 NaN
4 1.1 8.9 NaN
5 1.1 9.4 NaN
6 1.1 10.1 NaN
7 1.1 11.5 NaN
8 1.1 11.9 NaN
9 1.1 11.9 NaN
10 1.1 11.9 NaN
11 1.1 12.3 NaN
12 1.1 13.2 NaN
13 1.1 13.1 NaN
14 1.1 13.8 NaN
15 1.1 14.7 NaN
16 1.1 16.3 NaN
17 1.1 19.8 NaN
18 1.1 21.7 NaN
19 1.1 8.2 NaN