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. 2019 Nov 15;11:100234. doi: 10.1016/j.abrep.2019.100234

Table 2.

Median opioid prescribing rates, median opioid-overdose death rates, and differences by urban-rural county classification, Michigan, 2013–2017.

Year All counties (N = 83)
Median (IQR)
Rural counties (n = 57)
Median (IQR)
Urban counties (n = 26)
Median (IQR)
Test statistic p value
Opioid prescribing rates
2013 105,040 (39,151) 110,052 (37,507) 102,179 (35,949) U = 530.0 .038
2014 113,262 (38,463) 115,921 (35,581) 107,529 (40,398) U = 526.0 .035
2015 118,348 (35,063) 126,766 (37,961) 112,666 (45,600) U = 503.0 .019
2016 116,270 (34,606) 125,063 (34,299) 109,444 (45,696) U = 493.0 .015
2017 105,446 (34,176) 113,901 (32,640) 99,540 (43,470) U = 459.0 .006



Opioid-overdose death rates
2013 5.71 (8.54) 4.18 (7.38) 8.64 (8.12) U = 418.0 .001
2014 5.91 (11.40) 4.25 (9.19) 9.16 (11.21) U = 411.0 .001
2015 9.75 (11.88) 6.14 (13.26) 12.27 (12.08) U = 485.0 .012
2016 10.61 (11.43) 8.18 (8.56) 15.48 (13.15) U = 317.0 <.001
2017 12.97 (11.62) 11.37 (13.8) 17.09 (13.22) U = 451.0 .004

Note. Counties defined as urban (RUCC = 1–3) and rural (RUCC = 4–9). Interquartile Range (IQR). Mann-Whitey (U) tests conducted to address non-normality. Opioid prescribing data reflects the number of opioid agonists and partial agonist prescriptions dispensed in each county, per 100,000 people. Opioid-overdose death rates reflect the number of deaths in which an opioid was identified as a contributing cause in each county, per 100,000 people.

Note. Preliminary analyses (using Kruskal-Wallis tests) demonstrated similar geographic patterns for both opioid-related outcomes if categorizing counties as urban (RUCC = 1–3, n = 26), rural/micropolitan (RUCC = 4–7, n = 43), and rural/remote (RUCC = 8–9, n = 14). Specifically, lower opioid prescribing rates were consistently observed for urban compared to both rural county categories, whereas higher opioid-overdose deaths rates were consistently observed for urban compared to both rural county categories. The overlap of these findings with comparisons using the dichotomous urban-rural county classification status, alongside the potential for Type II error due to the limited number of rural/remote counties (Jaccard & Becker, 2009), made using the validated urban-rural county classification scheme (USDA, 2019b) the most appropriate approach for these data.