Analysis of US Opioid Mortality and ER Visit Data

Transcription

1 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 Richard A Lawhern, Ph.D Data interpretation and concusions * John Aen Tucker, Ph.D. - Data extraction, organization and Exce spreadsheet graphics

2 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

3 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.

4 Graphica Anaysis of Overdose Rates by US State

5 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.

6 Rxing Rate vs Deaths per 100,000 By State, R² = Deaths per 100, 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

7 Rxing Rate vs Deaths per 100,000 By State, R² = Deaths per 100, 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

8 Rxing Rate vs Deaths per 100,000 By State, R² = Deaths per 100, 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.

9 Graphica Anaysis of Hospita Visits vs Opioid Prescribing

10 Opioid-Reated ER Visits vs. Opioid Rxing by State, R² = ER Visits per 100, 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.

11 Opioid-Reated ER Visits vs. Opioid Rxing by State, R² = ER Visits per 100, Rx's Per 100 Opioid prescriptions per 100 peope by US State versus opioid-reated ER visits. Wide variations between States. No consistent trends.

12 Change in Rxing vs. Change in ER Visits by State 2010 to R² = ER Visits per 100, Rx's Per 100 Change in prescribing vs. change in ER Visits by US State. Major variations between States. Reduced prescribing but increasing admissions.

13 OverdoseDeathsPer10K Longitudina Anaysis of Opioid ODs by Age to Year Who died of opioid-reated overdose by age group and year?

14 OECD Countries - Overdose Deaths Vs. Opioid Defined Daiy Doses 8 7 Overdoses per 100,000 Popuation R² = 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.

15 Observations US Opioid-reated deaths/100k popuation doubed from 2006 to 2016 Deaths/100K increase weaky with prescription rates for but not for Decreasing prescriptions in 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.

16 Observations (2) Maximum US mortaity rate (2016) attributed to opioid overdose is.06% - Compared to.007% in other deveoped countries. US mortaity rate increase 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; 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.

17 Observations (3) * Prescribing rates are not a significant driver in either US overdose deaths or ER admission rates.

18 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 year age categories. Popuation data unavaiabe for the odest age category beyond Organization for Economic Cooperation and Deveopment (34 Nations) - Opioid Consumption data from

19 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 Rx and Admissions data are aggregated by drug type and medica diagnosis code. Adverse outcomes are not reiaby tracked to diverted versus therapeutic use.

20 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.