Anaysis of US Opioid Mortaity and ER Visit Data [CDC Wonder + AHRQ HCUP-US Databases] Richard A Lawhern, Ph.D + John Aen Tucker, Ph.D.* Apri 2018 + Richard A Lawhern, Ph.D Data interpretation and concusions * John Aen Tucker, Ph.D. - Data extraction, organization and Exce spreadsheet graphics
Objectives and Sources Assess trends, patterns in opioid prescriptions versus opioid-reated mortaity by US State Assess trends, patterns in Emergency Department admissions for opioid-reated causes Sources * CDC Wonder Database * CDC Prescribing Data * Agency for Heathcare Research Quaity HCUP-US Database Trends Checked in Organization for Economic Cooperation and Deveopment (OECD = 34 industriaized countries) Data Current Apri 2018
Take-Away Concusions There is no consistent causa reationship between rates of opioid prescription and rates of opioid-overdose-reated deaths by US State. Production restrictions on schedued opioid drugs either prescribed for patients or diverted wi not reduce opioid-overdose-reated deaths or opioid-reated hospita admissions.
Graphica Anaysis of Overdose Rates by US State
About Data Anaysis Exce spreadsheets offer regression anaysis capabiities, to examine how strongy one set of data may be reated to another. R-Squared is a mathematica measure of how we two groups of data fit with a mode of the reationship between them. In a strong reationship, R-Squared shoud be above 0.9. This means that the data fit cosey around a moving average ine. The smaer the vaue of R- Squared, the weaker is the fit and the weaker is the reationship. In charts which foow, data on opioid-reated overdoses from a sources (ega and iega) and data on hospita and ER visits invoving opioids of a kinds (ega and iega) are compared with State-by-State rates of medica opioid prescriptions. Computed R-squared for a of the data is so ow that no consistent reationship can be detected. Higher rates of prescription are NOT causing increased drug overdose deaths. Other factors must be at work.
Rxing Rate vs Deaths per 100,000 By State, 2006 25 20 R² = 0.13624 Deaths per 100,000 15 10 5 0 0 20 40 60 80 100 120 140 Rx's Per 100 Opioid prescriptions per 100 peope by US State vs. opioid-reated deaths per 100K. Weak upward trend with prescription rates, wide variation between States
Rxing Rate vs Deaths per 100,000 By State, 2010 30 25 R² = 0.36984 20 Deaths per 100,000 15 10 5 0 0 20 40 60 80 100 120 140 160 Rx's Per 100 Opioid prescriptions per 100 peope by US State vs. opioid-reated deaths per 100K. Weak upward trend with prescription rates, wide variation between States
Rxing Rate vs Deaths per 100,000 By State, 2016 60 50 R² = 0.00262 40 Deaths per 100,000 30 20 10 0 0 20 40 60 80 100 120 140 Rx's Per 100 Opioid prescription rates per 100 peope by US State versus opioid-reated deaths per 100,000. No consistent trends, wide variations, very poor data fit.
Graphica Anaysis of Hospita Visits vs Opioid Prescribing
Opioid-Reated ER Visits vs. Opioid Rxing by State, 2010 350 300 R² = 0.01641 250 ER Visits per 100,000 200 150 100 50 0 0 20 40 60 80 100 120 140 160 Rx's Per 100 Opioid prescription rates per 100 peope by US State vs. ER Visits per 100,000. Wide variations between states, no consistent trends.
Opioid-Reated ER Visits vs. Opioid Rxing by State, 2015 600 500 R² = 0.0065 ER Visits per 100,000 400 300 200 100 0 0 20 40 60 80 100 120 140 Rx's Per 100 Opioid prescriptions per 100 peope by US State versus opioid-reated ER visits. Wide variations between States. No consistent trends.
Change in Rxing vs. Change in ER Visits by State 2010 to 2015 350 R² = 0.14248 300 250 ER Visits per 100,000 200 150 100 50 0-40 -35-30 -25-20 -15-10 -5 0 5 10 Rx's Per 100 Change in prescribing vs. change in ER Visits by US State. Major variations between States. Reduced prescribing but increasing admissions.
OverdoseDeathsPer10K Longitudina Anaysis of Opioid ODs by Age - 1999 to 2016 40 0-20 21-35 36-50 51-60 61-70 35 30 25 20 15 10 5 0 1999 2004 2009 2014 Year Who died of opioid-reated overdose by age group and year?
OECD Countries - Overdose Deaths Vs. Opioid Defined Daiy Doses 8 7 Overdoses per 100,000 Popuation 6 5 4 3 2 R² = 0.00358 1 0 0 5000 10000 15000 20000 25000 30000 35000 Defined Daiy Doses per Miion Popuation Organization of Economic Cooperation and Deveopment 34 industriaized countries. Wide scatter, no consistent trends for overdose deaths vs. average daiy doses per miion popuation.
Observations US Opioid-reated deaths/100k popuation doubed from 2006 to 2016 Deaths/100K increase weaky with prescription rates for 2006-2015 -- but not for 2016. Decreasing prescriptions in 2010-2016 were accompanied by increasing deaths. Major statistica variation between US States, suggests mutipe factors and causes are operating. Decreasing prescriptions per 100K popuation from 2010 to 2015 BUT opioid-reated ER visits doubed. Something besides prescribing is going on iega street drugs.
Observations (2) Maximum US mortaity rate (2016) attributed to opioid overdose is.06% - Compared to.007% in other deveoped countries. US mortaity rate increase 1999-2016 is dominated by adoescents and aduts under 35. However, highest rates of opioid prescription are generay among aduts over 50. US Opioid mortaity over age 50 is stabe throughout; 35-50 rate initiay rises, then stabiizes from 2006 onward. In 34 industriaized countries, opioid overdose rates show no trend ine versus daiy opioid dose per miion popuation.
Observations (3) * Prescribing rates are not a significant driver in either US overdose deaths or ER admission rates.
Source Notes (1) Prescribing Data CDC Prescribing Data Page Mortaity and Popuation CDC Wonder Database - Data (deaths / 100,000) obtained by searching deaths by year and State using the imitation "Drug-induced causes" within the UCD - Drug/Acoho Induced Causes" modue of "Underying Cause of Death". A other search parameters were eft at their defauts. Mortaity rates are not age-adjusted. Where the death rate was described as "not reiabe" due to a ow death count, a nomina vaue was estimated by dividing number of deaths by popuation. Emergency Room Visits Agency for Heathcare Research Quaity - Data downoaded as Exce spreadsheet. ER visits per 100K popuation in 35 states for ER visits and in 46 states for inpatients. Correation of Prescribing With Mortaity and ER Visits Performed with Exce Spreadsheet Graphics Toos Longitudina Anaysis of Overdose Deaths by Age Cohort - Searched CDC wonder by age (1 year intervas) and State, using the Drug/Acoho Induced Causes seection in underying cause of death and choosing "drug reated." Compied into a tabe using Exce ookup functions and then grouped each year by 10-15 year age categories. Popuation data unavaiabe for the odest age category beyond 2013. Organization for Economic Cooperation and Deveopment (34 Nations) - Opioid Consumption data from https://www.incb.org/incb/en/narcoticdrugs/technica_reports/2016/narcotic-drugs-technica-report-2016.htm.
Source Notes (2) Opioid-Reated Hospita Use Estimated by Diagnostic Codes (CDC Wonder) - Hospita inpatient stays and ER visits incuding opioidreated hospita use are identified by any diagnosis from a range of codes in the Internationa Cassification of Diseases, reating to ega and iega opioids. ICD-9 prior to October 2015 ICD-10-CM after October 2015 - Rx and Admissions data are aggregated by drug type and medica diagnosis code. Adverse outcomes are not reiaby tracked to diverted versus therapeutic use.
Author Notes Richard A Lawhern, PhD is a technicay trained nonphysician heathcare writer and patient advocate, with 21 years experience in peer to peer socia media support groups and medica iterature anaysis. John Aan Tucker, PhD is a research chemist and business anayst for Fortune 1000 financia services firms. Neither author has a persona financia interest in the findings or data of this presentation.