The Metro: Wayne County finally sees drop in overdose deaths, but new drugs spark concern 12%

By Matt Williams0%

7/28/2026, 1:21:44 PM

BS Summary: This article contains 17 faulty reasoning types, including Framing Effect, Appeal to Authority, and Attempt to Sell a Product or Service, with Negativity Bias as the most egregious example at 13.1% saturation with 43 hits. Analysis detected 377 faulty-reasoning hits from 328 analyzed words, generating a BS Score of 20.7% and a BS Rank of 12% (27,645 of 31,201 articles). This article is better (less manipulative) than 88.60% of the article peer group.

Overdose deaths in Michigan have been declining since the height of the COVID-19 pandemic brought on concerning increase in drug-related fatalities. 
But, Wayne County’s numbers stayed high until 2024 when it saw overdose deaths decrease by 42%. 
Wayne County still leads the state in overdose deaths. 
Black Michiganders are disproportionately affected, with an overdose rate nearly double that of all other residents. 
Accessibility to life-saving Naloxone (aka Narcan) and peer recovery coaches have been cited as a few reasons behind the decreases. 
However, experts say keeping people from becoming addicted to opioids has gotten tougher. 
Avani Sheth, the chief medical officer for Wayne County, says drugs like carfentanil pose a challenge for continued overdose prevention. 
“The drug supply continues to change and evolve in increasingly dangerous ways.” 
Carfentanil, used to tranquilize large animals like elephants, is 100 times stronger than fentanyl and even small amounts of the drug can be deadly. 
Sheth spoke to The Metro’s Sam Corey about why certain communities experience more deaths, what helps patients navigate recovery, and how expanding resources can help local communities. 
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The post The Metro: Wayne County finally sees drop in overdose deaths, but new drugs spark concern appeared first on WDET 101.9 FM . 
Article reasoning-pattern comparisonThis article: 4.9%Matt Williams: 0.0%WDET: 1.2%Confirmation Bias4.9%This article: 0.0%Matt Williams: 3.2%WDET: 0.3%Anchoring Bias0.0%This article: 8.2%Matt Williams: 3.0%WDET: 1.6%Availability Heuristic8.2%This article: 4.9%Matt Williams: 0.0%WDET: 0.5%Representativeness Heuristic4.9%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Hindsight Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.6%Overconfidence Bias0.0%This article: 12.2%Matt Williams: 2.3%WDET: 3.4%Framing Effect12.2%This article: 0.0%Matt Williams: 0.0%WDET: 0.7%Loss Aversion0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.5%Status Quo Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Sunk Cost Effect0.0%This article: 0.0%Matt Williams: 2.4%WDET: 2.4%Optimism Bias0.0%This article: 4.0%Matt Williams: 2.0%WDET: 0.7%Pessimism Bias4.0%This article: 13.1%Matt Williams: 5.0%WDET: 2.5%Negativity Bias13.1%This article: 7.9%Matt Williams: 0.0%WDET: 1.0%Self-Serving Bias7.9%This article: 0.0%Matt Williams: 0.0%WDET: 0.2%Fundamental Attribution Error0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Actor-Observer Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.8%In-Group Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.3%Out-Group Homogeneity Bias0.0%This article: 0.9%Matt Williams: 0.0%WDET: 3.4%Halo Effect0.9%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Horn Effect0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.6%Recency Bias0.0%This article: 6.4%Matt Williams: 0.0%WDET: 0.2%Primacy Effect6.4%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Blind-Spot Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Ad Hominem0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Straw Man0.0%This article: 11.3%Matt Williams: 0.0%WDET: 1.8%Appeal to Authority11.3%This article: 3.7%Matt Williams: 0.0%WDET: 0.7%False Dilemma3.7%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Slippery Slope0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Circular Reasoning0.0%This article: 0.0%Matt Williams: 3.0%WDET: 2.4%Hasty Generalization0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Red Herring0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.3%Bandwagon0.0%This article: 0.0%Matt Williams: 0.0%WDET: 2.8%Appeal to Emotion0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.3%Begging the Question0.0%This article: 0.0%Matt Williams: 0.0%WDET: 1.1%Post Hoc (False Cause)0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Tu Quoque0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.2%Burden of Proof0.0%This article: 7.3%Matt Williams: 0.0%WDET: 0.1%Appeal to Nature7.3%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Composition/Division0.0%This article: 6.1%Matt Williams: 0.0%WDET: 1.2%Anecdotal6.1%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%No True Scotsman0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.8%Ambiguity (Equivocation)0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Middle Ground0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Personal Incredulity0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.1%Special Pleading0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Genetic Fallacy0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.8%Unattributed Quote0.0%This article: 3.7%Matt Williams: 0.0%WDET: 0.4%Quote-first Misdirection3.7%This article: 6.1%Matt Williams: 0.0%WDET: 3.0%Biased Writer Voice6.1%This article: 3.7%Matt Williams: 0.0%WDET: 1.8%Indoctrination3.7%This article: 0.0%Matt Williams: 0.0%WDET: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Matt Williams: 0.0%WDET: 0.0%Politically Right Leaning Bias0.0%This article: 10.7%Matt Williams: 2.7%WDET: 5.9%Attempt to Sell a Product or S…10.7%

328 words analyzed.

Speakers

2speakers13%attributed speech287writer words
Selected voice

Avani Sheth

100%flagged-word coverage
32 attributed words78% of attributed speech82% writer coverage
0%20.0%40.0%Quote-first Misdirection+37.5 ptsWriter: 0.0%Avani Sheth: 37.5%37.5%Indoctrination+37.5 ptsWriter: 0.0%Avani Sheth: 37.5%37.5%Attempt to Sell a Product -12.2 ptsWriter: 12.2%Avani Sheth: 0.0%0.0%Biased Writer Voice-7.0 ptsWriter: 7.0%Avani Sheth: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.