Table 2.
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.