Statistical Analysis of County-Level Contributing Factors to Opioid-Related Overdose Deaths in the United State
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Pennsylvania State University
The model is designed to predict the overdose deaths from all types of opioids including prescription(e.g. oxycodone and hydrocodone) and illicit opioids (e.g. heroin and fentanyl) and to investigate eneral trends, as well as separate models for heroin and fentanyl.
The factors were categorized into three groups: demographic, socio-economic, and health care environmental group. These features were used as predictors to model the overdose deaths from all types of opioids including prescription (e.g., oxycodone and hydrocodone) and illicit opioids (e.g., heroin and fentanyl) to investigate general trend,as well as separate models for heroin and fentanyl. Multilevel mixed-effect regression was adopted to adequately model grouping effect across counties.
Input variables : Demographic,Ssocio-economic, Healthcare environmental infomarkers
Output Variables : Drug overdose Fatality
Metrics to Monitor
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Statistical
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Somers D |
Accuracy |
Precision and Recall |
Confusion Matrix |
F1 Score |
Roc and Auc |
Prevalence |
Detection Rate |
Balanced Accuracy |
Cohen's Kappa |
Concordance |
Gini Coefficent |
KS Statistic |
Youden's J Index
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Infrastructure
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Log Bytes |
Logging/User/IAMPolicy |
Logging/User/VPN |
CPU Utilization |
Memory Usage |
Error Count |
Prediction Count |
Prediction Latencies |
Private Endpoint Prediction Latencies |
Private Endpoint Response Count
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Visit Model :
github.com