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Research ArticleOriginal Research

The Impact of Increased Hydrocodone Regulation on Opioid Prescribing in an Urban Safety-Net Health Care System

Thomas F. Northrup, Kelley Carroll, Robert Suchting, Yolanda R. Villarreal, Mohammad Zare and Angela L. Stotts
The Journal of the American Board of Family Medicine May 2019, 32 (3) 362-374; DOI: https://doi.org/10.3122/jabfm.2019.03.180356
Thomas F. Northrup
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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Kelley Carroll
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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Robert Suchting
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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Yolanda R. Villarreal
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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Mohammad Zare
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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Angela L. Stotts
From the Department of Family and Community Medicine, University of Texas Health Science Center at Houston (UTHealth) McGovern Medical School, Houston, TX (TFN, KC, YRV, MZ, ALS); Ambulatory Care Services, Grady Health System, Atlanta, GA (KC); Department of Psychiatry and Behavioral Sciences, UTHealth McGovern Medical School, Houston, TX (RS, ALS).
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    Figure 1.

    Probability of hydrocodone-combination prescriptions by rescheduling period (A) and by rescheduling period within medical specialty (B), patient diagnoses (C), patient sex (D), patient race/ethnicity (E), and chronic opioid therapy (COT) status (F). Note. For panel E, patients identified as “American Indian, Asian/Pacific Islander, and Middle Eastern” were combined with patients identified as “Other/Unknown” in the electronic-medical-record (EMR) to form “Other,” due to relatively low overall frequencies for these race/ethnicities. PCP, primary care physician.

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    Figure 2.

    Probability of hydrocodone-combination prescription by age in years (centered) for each rescheduling period.

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    Table 1.

    Patient and Opioid Prescription Characteristics for the Total Sample and by Chronic Opioid Therapy Status

    Patient CharacteristicTotal SamplePatients Not on COTPatients on COT
    Across ReschedulingPre-ReschedulingPost-ReschedulingAcross ReschedulingPre-ReschedulingPost-ReschedulingAcross ReschedulingPre-ReschedulingPost-Rescheduling
    Unique patients (n)824324231152602651162973737814173161257414788
    Prescriptions per unique patient (M, SD)2.43 (3.57)2.04 (2.30)2.17 (2.56)1.16 (0.45)1.10 (0.30)1.14 (0.39)7.21 (5.56)4.27 (3.25)4.81 (3.64)
    Patients w/ days' supply available (n)*37328225822186223726128371156313602974510299
    Patient age in years (M, SD)47.54 (14.48)48.59 (14.35)48.24 (14.35)46.14 (14.67)46.46 (14.68)46.33 (14.65)52.80 (12.42)53.63 (12.12)53.13 (12.27)
    Women (n, %)46743 (56.70%)24015 (56.76%)30339 (57.68%)36789 (56.50%)16678 (56.09%)21681 (57.34%)9954 (57.48%)7337 (58.65%)8658 (58.55%)
    Race/ethnicity
    American Indian (n, %)131 (0.16%)53 (0.13%)94 (0.18%)106 (0.16%)36 (0.12%)76 (0.20%)25 (0.14%)17 (0.14%)18 (0.12%)
    Asian/Pacific Islander (n, %)2524 (3.06%)1253 (2.96%)1505 (2.86%)2189 (3.36%)1036 (3.48%)1227 (3.25%)335 (1.93%)217 (1.73%)278 (1.88%)
    Black/African American (n, %)28720 (34.84%)15582 (36.83%)18852 (35.84%)21187 (32.54%)9970 (33.53%)12251 (32.40%)7533 (43.50%)5612 (44.63%)6601 (44.64%)
    Hispanic/Latino (n, %)39930 (47.23%)18638 (44.05%)24449 (46.48%)33082 (50.81%)14566 (48.98%)19520 (51.62%)5848 (33.77%)4072 (32.38%)4929 (33.33%)
    Middle Eastern (n, %)930 (1.13%)421 (1.00%)599 (1.14%)810 (1.24%)340 (1.14%)499 (1.32%)120 (0.69%)81 (0.64%)100 (0.68%)
    White/Caucasian (n, %)10872 (13.19%)6213 (14.68%)6901 (13.12%)7455 (11.45%)3661 (12.31%)4075 (10.78%)3417 (19.73%)2552 (20.3%)2826 (19.11%)
    Other/Unknown (n, %)325 (0.39%)151 (0.36%)202 (0.38%)287 (0.44%)128 (0.43%)164 (0.43%)38 (0.22%)23 (0.18%)36 (0.24%)
    Primary diagnosis
    Headaches/nerve (n, %)1310 (1.59%)740 (1.75%)959 (1.82%)821 (1.26%)363 (1.22%)505 (1.34%)489 (2.82%)377 (3%)454 (3.07%)
    Neoplasms (n, %)2001 (2.43%)1143 (2.7%)1324 (2.52%)1199 (1.84%)564 (1.9%)696 (1.84%)802 (4.63%)579 (4.6%)628 (4.25%)
    Infections (n, %)3634 (4.41%)1731 (4.09%)2218 (4.22%)3196 (4.91%)1431 (4.81%)1842 (4.87%)438 (2.53%)300 (2.39%)376 (2.54%)
    Other (n, %)9002 (10.92%)4432 (10.47%)5533 (10.52%)7453 (11.45%)3313 (11.14%)4336 (11.47%)1549 (8.95%)1119 (8.9%)1197 (8.09%)
    Fever/general symptoms (n, %)10180 (12.35%)5070 (11.98%)6243 (11.87%)8554 (13.14%)3919 (13.18%)4911 (12.99%)1626 (9.39%)1151 (9.15%)1332 (9.01%)
    Dental (n, %)11609 (14.08%)5382 (12.72%)7181 (13.65%)10327 (15.86%)4570 (15.37%)6067 (16.04%)1282 (7.40%)812 (6.46%)1114 (7.53%)
    Injuries (n, %)12413 (15.06%)6160 (14.56%)7164 (13.62%)10355 (15.90%)4827 (16.23%)5717 (15.12%)2058 (11.88%)1333 (10.6%)1447 (9.78%)
    Musculoskeletal (n, %)32283 (39.16%)17653 (41.72%)21980 (41.79%)23211 (35.65%)10750 (36.15%)13740 (36.34%)9072 (52.39%)6903 (54.9%)8240 (55.72%)
    Unique patients with MME (n)37328225822186223726128371156313602974510299
    Highest MME/day/patient (M, SD)21.92 (15.83)21.74 (14.55)21.26 (16.59)19.92 (9.33)19.98 (9.73)19.64 (8.74)25.41 (22.72)24.06 (18.88)23.07 (22.19)
    Highest MME/day/patient Category
    <20 (n, %)20206 (54.13%)12410 (54.96%)12379 (56.62%)14941 (62.97%)8070 (62.87%)7423 (64.20%)5265 (38.71%)4340 (44.54%)4956 (48.12%)
    20 to <50 (n, %)16485 (44.16%)9796 (43.38%)9162 (41.91%)8640 (36.42%)4672 (36.39%)4088 (35.35%)7845 (57.68%)5124 (52.58%)5074 (49.27%)
    50 to <90 (n, %)553 (1.48%)336 (1.49%)272 (1.24%)127 (0.54%)85 (0.66%)44 (0.38%)426 (3.13%)251 (2.58%)228 (2.21%)
    >90 (n, %)84 (0.23%)40 (0.18%)49 (0.22%)18 (0.08%)10 (0.08%)8 (0.07%)66 (0.49%)30 (0.31%)41 (0.40%)
    Prescription characteristics
        Total opioid prescriptions (n)200695863431143327582432631431931248515371271139
        Oncology (n, %)5668 (2.82%)2805 (3.25%)2863 (2.50%)937 (1.24%)434 (1.33%)503 (1.16%)4731 (3.79%)2371 (4.41%)2360 (3.32%)
        Dental (n, %)11477 (5.72%)4703 (5.45%)6774 (5.92%)7247 (9.56%)3007 (9.22%)4240 (9.82%)4230 (3.39%)1696 (3.16%)2534 (3.56%)
        Surgical (n, %)22344 (11.12%)9313 (10.79%)13011 (11.38%)10117 (13.34%)4074 (12.49%)6043 (13.99%)12207 (9.78%)5239 (9.75%)6968 (9.79%)
        Medicine (n, %)18618 (9.28%)7875 (9.12%)10743 (9.40%)3087 (4.07%)1326 (4.06%)1761 (4.08%)15531 (12.44%)6549 (12.19%)8982 (12.63%)
        Emergency (n, %)40187 (20.03%)18547 (21.48%)21640 (18.93%)27520 (36.29%)12511 (38.34%)15009 (34.75%)12667 (10.15%)6036 (11.24%)6631 (9.32%)
        Primary care (n, %)102401 (51.03%)43100 (10.79%)59301 (51.87%)26916 (35.50%)11279 (34.57%)15637 (36.20%)75485 (60.46%)31821 (59.24%)43664 (61.38%)
        Opioid prescribed
        Hydrocodone combination (n, %)63353 (31.57%)45289 (52.45%)18064 (15.80%)18701 (24.66%)16489 (50.53%)2212 (5.12%)44652 (35.76%)28800 (53.62%)15852 (22.28%)
        Tramadol (n, %)101101 (50.38%)35549 (41.17%)65552 (57.33%)41449 (54.66%)13827 (42.37%)27622 (63.95%)59652 (47.78%)21722 (40.44%)37930 (53.32%)
        Codeine combination (n, %)36221 (18.05%)5505 (6.38%)30716 (26.87%)15674 (20.67%)2315 (7.09%)13359 (30.93%)20547 (16.46%)3190 (5.94%)17357 (24.40%)
        Total prescriptions with days' supply (n)951264933645790262901284512445688363549133345
        Days' supply (M, SD)*12.55 (8.50)13.22 (9.02)13.44 (8.94)10.48 (7.79)10.66 (8.09)10.49 (7.66)16.16 (8.50)16.61 (9.06)16.75 (9.10)
        MME/day (M, SD)21.13 (15.71)21.53 (15.21)20.69 (16.22)19.70 (9.49)19.87 (10.22)19.50 (8.60)21.68 (17.48)22.18 (16.71)21.13 (18.25)
        MME/prescription (M, SD)316.89 (308.80)321.19 (299.44)312.26 (318.51)190.97 (160.85)196.33 (158.43)185.00 (163.29)364.98 (336.93)369.90 (326.19)359.75 (347.94)
        MME/day category (prescription level)
        <20 (n, %)54738 (57.54%)27677 (56.10%)27061 (59.10%)16889 (64.24%)8797 (63.54%)8092 (65.02%)37849 (54.98%)18880 (53.20%)18969 (56.89%)
        20 to < 0 (n, %)38906 (40.90%)20810 (42.18%)18096 (39.52%)9250 (35.18%)4950 (35.75%)4300 (34.55%)29656 (43.08%)15860 (44.69%)13796 (41.37%)
        50 to <90 (n, %)1308 (1.38%)753 (1.53%)555 (1.21%)132 (0.50%)87 (0.63%)45 (0.36%)1176 (1.71%)666 (1.88%)510 (1.53%)
        >90 (n, %)174 (0.18%)96 (0.19%)78 (0.17%)19 (0.07%)11 (0.08%)8 (0.06%)155 (0.23%)85 (0.24%)70 (0.21%)
    • COT, chronic opioid therapy; M, mean; MME, morphine milligram equivalency; SD, standard deviation.

    • ↵* Patients had at least one non-missing value for days' supply for an average to be calculated at the prescription level.

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The Journal of the American Board of Family     Medicine: 32 (3)
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The Impact of Increased Hydrocodone Regulation on Opioid Prescribing in an Urban Safety-Net Health Care System
Thomas F. Northrup, Kelley Carroll, Robert Suchting, Yolanda R. Villarreal, Mohammad Zare, Angela L. Stotts
The Journal of the American Board of Family Medicine May 2019, 32 (3) 362-374; DOI: 10.3122/jabfm.2019.03.180356

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The Impact of Increased Hydrocodone Regulation on Opioid Prescribing in an Urban Safety-Net Health Care System
Thomas F. Northrup, Kelley Carroll, Robert Suchting, Yolanda R. Villarreal, Mohammad Zare, Angela L. Stotts
The Journal of the American Board of Family Medicine May 2019, 32 (3) 362-374; DOI: 10.3122/jabfm.2019.03.180356
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Keywords

  • Codeine-Combination
  • Hydrocodone
  • Hydrocodone-Combination
  • MME
  • Opioid Prescription
  • Rescheduling
  • Tramadol

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