Abstract
Objectives
To estimate prescribing tends of and correlates independently associated with high-risk anticholinergic prescriptions among adults aged 65 or older in office-based outpatient visits.
Design
Repeated cross-sectional analysis of the National Ambulatory Medical Care Survey (NAMCS).
Participants
A national sample of office-based physician visits by adults age 65 or older from 2006 to 2015 (n=96,996 unweighted).
Measurements
Prescriptions of high-risk anticholinergics, regardless of indication, were identified. Then, overall prescribing trends were estimated from 2006 to 2015. Stratified analyses of prescribing trends by physician specialty and anticholinergic drug class were also performed. Lastly, we used a multivariable logistic regression analysis to estimate the odds of high-risk anticholinergic prescriptions.
Results
Between 2006 and 2015, 5,876 (6.2%) out of 96,996 visits by older adults, representative of 14.6 million total visits nationally, listed a high-risk anticholinergic prescription. The most common drug classes were antidepressants, antimuscarinics, and antihistamines, which accounted for more than 70% of prescribed anticholinergics. Correlates independently associated with higher odds of receiving high-risk anticholinergic prescriptions included: female sex, the Southern geographic region, specific physician specialties (e.g., psychiatry and urology), receipt of six or more concomitantly prescribed medications, and related clinical diagnoses (e.g., urinary continence) (p<0.01 for all).
Conclusion
The prevalence of high-risk anticholinergic prescriptions was stable over time, but varied by physician specialty and drug class. Promoting quality prescribing is needed as safer alternatives are available.
Keywords: Anticholinergics, older adults, prescribing, outpatient care
INTRODUCTION
Despite a range of clinical indications, medications with anticholinergic properties pose a number of risks to older adults. These agents are commonly prescribed for allergies, obstructive pulmonary disease, and urge urinary incontinence;1,2 however, they are associated with risks of developing serious adverse events, including cognitive impairment, falls, dementia, and increased mortality in older adults.3–12 This vulnerability to adverse events is due in large part to a variety of aging-related physiological changes, such as reduced hepatic and renal functions, and increased blood-brain barrier permeability.13 While some of these medications rely directly on anticholinergic action for their therapeutic effect (e.g., antispasmodics for irritable bowel syndrome and antimuscarinics for urge urinary incontinence),14 others have anticholinergic side effects separate from their therapeutic action (e.g., antidepressants and antihistamines).
To promote safer prescribing, the recent Beers criteria identified 52 specific strong, high-risk anticholinergic agents in nine major drug classes as potentially inappropriate medications in older adults, and recommends avoidance of 35 of these 52 drugs regardless of indication.4 In particular, on the basis of a strong recommendation grounded on systematic reviews of clinical studies, these 35anticholinergic drugs are to be avoided regardless of indication.4 Previous studies investigated anticholinergic use at the national level, but relied on self-reported survey questionnaire from civilians, and used old data and older versions of Beers criteria.11,15 Furthermore, these studies were conducted cross-sectionally such that they did not investigate secular trends of prescribing over time. In light of updated Beers criteria and up-to-date nationally representative survey data collected from physicians from 2006 to 2015, we investigated whether prescribing patterns have changed over time, and whether these patterns vary by physician specialty and anticholinergic class. We also estimated demographic and clinical correlates independently associated with these high-risk anticholinergic medication prescriptions.
METHODS
Data source and study sample
We used data from the 2006–2015 National Ambulatory Medical Care Survey (NAMCS), an annual cross-sectional survey of office-based physician visits. The NAMCS nationally represents ambulatory medical care services, including prescription trends.16 We limited our sample to all visits by adults aged 65 or older (n=102,806 unweighted). We excluded observations with any missing covariates (5.7%), which were missing at random by Little’s test,17 leaving a final sample size of 96,996. Using publicly available de-identified data, this study was exempted from the Institutional Review Board (#2000021850) at Yale School of Medicine as no direct human subjects were involved. Further details of the survey, including descriptions, questionnaires, sampling methodology and datasets, are publicly available on the NAMCS website.16
Measures
High-risk anticholinergic medications
We examined the first eight medications listed as prescribed for each visit, ensuring consistency across years. Based on Beers criteria and Rudolph et al.’s anticholinergic risk scale,4,18 we characterized use of 35 high-risk anticholinergic medications with recommendations for complete avoidance. These medications included: 1) antidepressants (amitriptyline, amoxapine, clomipramine, desipramine, imipramine, nortriptyline, paroxetine, protriptyline, and trimipramine); 2) antimuscarinics (darifenacin, fesoterodine, flavoxate, oxybutynin, solifenacin, tolterodine, and trospium); 3) antihistamines (brompheniramine, carbinoxamine, chlorpheniramine, clemastine, cyproheptadine, dexbrompheniramine, dexchlorpheniramine, dimenhydrinate, doxylamine, hydroxyzine, meclizine, and triprolidine); 4) skeletal muscle relaxants (cyclobenzaprine and orphenadrine); and 5) antispasmodics (belladonna alkaloids, clidinium-chlordiazepoxide, dicyclomine, hyoscyamine, and propantheline).
Clinical diagnoses
Participating physicians were asked to report patient’s reason for visit, physician’s diagnosis, and cause of injury and procedures. These indications were then independently coded by SRA International Inc. (Durham, North Carolina)19 specifically for NAMCS data collection and not for medical billing. The NAMCS provides up to three clinical diagnoses at each visit using the International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnostic codes. We constructed several indicator variables for indications potentially relevant to anticholinergic use. These indications consisted of: 1) allergic conditions (287.0, 472.0, 473.0, 473.1, 473.2, 473.3, 473.8, 473.9, 477.0, 477.1, 477.8, 477.9, 536.8, 558.3, 558.9, 579, 691.8, 692.9, 693.1, 708.0, 789.00, 995.0, and 995.3); 2) chronic respiratory diseases (491–496); 3) irritable bowel syndromes (556, except 556.7); 4) overactive bladder (596.51, 788.41, 788.42, and 788.63) or urinary incontinence (388.30–388.36, and 788.91); 5) neuropathic pain (053.1, 250.6, 338.4, 346, 350, 357.2, 729.1, 729.2, and 784.0); 6) parkinsonism, dyskinesia or dystonia (333.71, 333.72, 333.79, 333.81–333.85, 333.89, 333.90, and 333.99); 7) vertigo (386 and 780.4) or motion sickness (994.6); 8) cognitive impairment (331.83) or dementia (290, 294.10, 294.11, 294.20, 294.21, 331.19, 331.82, and 331.0); 9) other psychiatric disorders (290–319, except cognitive impairment and dementia); 10) insomnia (327.00, 327.01, 327.02, 327.09, 780.51, and 780.52); 11) dry mouth (527.7), blurry vision (368.8), fatigue (780.71 and 780.79), hypersomnia (780.54) or tachycardia (785.0); and 12) constipation (564.00–564.02, and 564.09)..
Covariates
Similar to prior work on potentially inappropriate prescriptions, we selected covariates based on the potential for clinically relevant confounding.20,21 We included the following demographic variables: age (65–74, 74–85, or 85+), gender, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, or other), geographic region (Northeast, Midwest, South, or West), primary source of payment (Private, Medicare, Medicaid, or other), reason for visit (acute problem, routine chronic problem, preventive care, or pre- or post-surgery care), and number of repeat visits within the past 12 months (none, 1–2, 3–5, or 6+). For clinical characteristics, variables included:20,21 physician specialty (primary care, psychiatry, neurology, urology, or other), time spent with a doctor (in minutes), number of chronic conditions (<2 or ≥2), and number of concomitant medications prescribed (<6 or ≥6).
Data analysis
We estimated if demographic and clinical characteristics differed by anticholinergic prescription status using Bonferroni-adjusted bivariate analyses. Second, we assessed overall trends across the 10-year period in the percentage of visits with anticholinergic prescriptions using logistic regression models. Survey year was the main predictor variable, and we combined years into 2-year intervals, assigning values ranging from 1 to 5 (1=2005–2006, 2=2007–2008, etc.). We transformed this variable by subtracting 1 and dividing by 4, resulting in values between 0 and 1, which allowed us to interpret the odds ratio as the change in the odds of anticholinergic prescriptions across the 10-year period. We repeated these analyses, stratified by physician specialty, and anticholinergic class. Lastly, we used a multivariable-adjusted logistic regression analysis to identify demographic and clinical correlates independently associated with high-risk anticholinergic medication prescriptions. We performed all analyses using Stata MP/6-Core version 15.1 (College Station, TX), accounting for the survey sampling design (e.g., unequal probability of selection, clustering, and stratification).22
RESULTS
Selected characteristics of the sample
Between 2006 and 2015, 5,876 (6.2%) out of 96,996 visits by adults 65 and older, representative of 14.6 million visits nationally, listed a high-risk anticholinergic prescription. Patient visits in which anticholinergics were prescribed were significantly more likely to be made by women, those with ≥2 chronic conditions, those prescribed ≥6 concomitant medications, and those with related clinical diagnoses, such as overactive bladder or urinary incontinence, neuropathic pain, or vertigo/motion sickness (see Table 1). Among visits in which anticholinergics were prescribed, 53.1% were to primary care, whereas 11.6% were to psychiatry, neurology, or urology.
Table 1.
Selected characteristics (weighted column %) of adults ages 65 and older by anticholinergic prescription status in office-based outpatient visits, 2006–2015.
| Anticholinergic prescription (%) | Total | P-value† | ||
|---|---|---|---|---|
|
| ||||
| No | Yes | |||
| Sample size | ||||
| Unweighted sample | 91,120 | 5,876 | 96,996 | |
| Weighted visits | 221,095,517 | 14,602,357 | 235,697,874 | |
|
| ||||
| Age | ||||
| 65–74 | 51.2 | 49.7 | 51.1 | 0.171 |
| 75–84 | 35.8 | 36.1 | 35.9 | |
| 85+ | 13.0 | 14.2 | 13.0 | |
| Gender | ||||
| Female | 56.2 | 70.1 | 57.0 | <0.001 |
| Male | 43.8 | 29.9 | 43.0 | |
| Race/ethnicity | ||||
| Non-Hispanic White | 79.1 | 79.3 | 79.1 | 0.639 |
| Non-Hispanic Black | 7.9 | 7.8 | 7.9 | |
| Hispanic | 8.2 | 8.9 | 8.3 | |
| Other a) | 4.8 | 4.1 | 4.7 | |
| Region | ||||
| Northeast | 19.8 | 17.3 | 19.6 | 0.030 |
| Midwest | 19.3 | 20.2 | 19.3 | |
| South | 38.4 | 41.5 | 38.5 | |
| West | 22.6 | 21.1 | 22.5 | |
| Primary source of payment | ||||
| Private | 15.9 | 14.0 | 15.8 | 0.116 |
| Medicare | 79.4 | 81.0 | 79.5 | |
| Medicaid | 2.6 | 2.9 | 2.6 | |
| Other b) | 2.1 | 2.0 | 2.1 | |
| Reason for visit | ||||
| Acute problem | 25.7 | 26.7 | 25.7 | <0.001 |
| Routine chronic problem | 53.0 | 56.1 | 53.2 | |
| Preventive care | 12.6 | 11.5 | 12.6 | |
| Pre- or post-surgery | 8.7 | 5.7 | 8.5 | |
| Physician specialty | ||||
| Primary care | 39.1 | 53.1 | 39.9 | <0.001 |
| Psychiatry | 1.3 | 2.5 | 1.4 | |
| Neurology | 1.6 | 2.7 | 1.7 | |
| Urology | 3.7 | 6.4 | 3.9 | |
| Other c) | 54.4 | 35.4 | 53.2 | |
| Repeat of visits in the past 12 months | ||||
| 0 visit | 6.3 | 4.6 | 6.2 | <0.001 |
| 1–2 visits | 35.4 | 32.5 | 35.2 | |
| 3–5 visits | 33.6 | 33.3 | 33.6 | |
| 6+ visits | 24.7 | 29.5 | 25.0 | |
| Time spent with doctor | ||||
| < 15 min. | 18.2 | 15.9 | 18.1 | 0.062 |
| 15–20 min. | 49.1 | 49.5 | 49.1 | |
| 21–30 min. | 21.0 | 22.3 | 21.1 | |
| > 30 min. | 11.7 | 12.2 | 11.7 | |
| ≥2 chronic conditions d) | 57.7 | 68.2 | 58.4 | <0.001 |
| ≥6 medications | 29.5 | 67.5 | 31.9 | <0.001 |
| Visit diagnosis | ||||
| Allergic conditions | 3.6 | 3.6 | 3.6 | 0.920 |
| Chronic respiratory diseases | 4.1 | 4.3 | 4.1 | 0.659 |
| Irritable bowel syndromes | 0.1 | 0.0 | 0.1 | 0.282 |
| Overactive bladder or urinary incontinence | 0.5 | 2.9 | 0.6 | <0.001 |
| Neuropathic pain | 1.5 | 3.3 | 1.6 | <0.001 |
| Parkinsonism, dyskinesia, or dystonia | 0.5 | 0.9 | 0.5 | 0.010 |
| Vertigo or motion sickness | 0.7 | 3.7 | 0.9 | <0.001 |
| Cognitive impairment or dementia | 0.9 | 0.9 | 0.9 | 0.916 |
| Other psychiatric disorders | 3.3 | 8.3 | 3.6 | <0.001 |
| Other psychiatric disorders (with potential anticholinergic burden) | 1.7 | 2.8 | 1.8 | <0.001 |
| Insomnia | 0.5 | 1.5 | 0.6 | <0.001 |
| Dry mouth, blurry vision, fatigue, hypersomnia, or tachycardia | 1.1 | 1.5 | 1.1 | 0.147 |
| Constipation | 0.6 | 0.8 | 0.6 | 0.140 |
Note:
compares proportion differences by anticholinergic prescription using a weight-corrected, Bonferroni-adjusted chi-squared statistic.
includes Asians, American Indian/Alaska Natives (AIANs), Native Hawaiian or Other Pacific Islanders (NHOPI), or 2+ reported racial/ethnic groups;
includes worker’s compensation, self-pay, no charge, and others;
includes obstetrics/gynecology, cardiovascular diseases, dermatology, urology, neurology, ophthalmology, otolaryngology, and others; and
was based 14 chronic conditions (yes/no) collected by the NAMCS (e.g., arthritis, congestive heart failure, and diabetes).
National trends of high-risk anticholinergic prescriptions
Table 2 shows national trends of anticholinergic prescriptions from 2006 to 2015. Overall, the anticholinergic prescription rate increased from 6.1% in 2006–2007 to 6.8% in 2008–2009, then decreased to 4.7% in 2014–2015. In primary care visits, the rate decreased over time from 8.7% in 2006–2007 to 6.4% in 2014–2015 (OR across 10-year period trends=0.94; 95% CI=0.89, 0.98). The most common drug classes were antidepressants, antimuscarinics, and antihistamines, which accounted for more than 70% of prescribed anticholinergics in any given time interval.
Table 2.
Prevalence of anticholinergic prescriptions (weighted %) in office-based outpatient visits by physician specialty and medication class among US adults ages 65 and older, 2006–2015 NAMCS.
| Years (%) | Overall | Trend (2006–2015) | |||||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
||||||||
| 2006–2007 | 2008–2009 | 2010–2011 | 2012–2013 | 2014–2015 | OR† | 95% CI | P-value | ||
| Visits in which any strong anticholinergics prescribed | 6.1% | 6.8% | 6.7% | 6.5% | 4.7% | 6.2% | 0.95 | 0.91 – 0.98 | 0.004 |
| Stratified by physician specialty | |||||||||
| Primary care | 8.7% | 8.7% | 8.8% | 8.2% | 6.4% | 8.2% | 0.94 | 0.89 – 0.98 | 0.013 |
| Psychiatry | 15.9% | 11.9% | 10.1% | 13.0% | 7.6% | 11.3% | 0.85 | 0.72 – 1.01 | 0.058 |
| Neurology | 7.9% | 10.2% | 9.4% | 9.0% | 13.4% | 9.9% | 1.11 | 0.82 – 1.51 | 0.490 |
| Urology | 10.1% | 10.0% | 11.5% | 10.8% | 8.8% | 10.3% | 0.98 | 0.90 – 1.07 | 0.670 |
| Other a) | 3.6% | 4.8% | 4.6% | 4.5% | 3.0% | 4.1% | 0.96 | 0.90 – 1.02 | 0.212 |
| Stratified by medication class | |||||||||
| Antidepressants | 35.3% | 28.7% | 31.0% | 31.8% | 39.0% | 32.6% | 1.04 | 0.96 – 1.13 | 0.340 |
| Antimuscarinics | 29.5% | 38.6% | 30.0% | 27.2% | 29.6% | 31.3% | 0.94 | 0.67 – 1.02 | 0.117 |
| Antihistamines | 22.7% | 16.1% | 19.2% | 17.8% | 17.8% | 18.6% | 0.95 | 0.87 – 1.03 | 0.237 |
| Skeletal muscle relaxants | 10.5% | 13.7% | 16.5% | 19.0% | 11.6% | 14.6% | 1.09 | 0.99 – 1.19 | 0.069 |
| Antispasmodics | 7.0% | 8.4% | 9.1% | 9.1% | 7.1% | 8.3% | 1.02 | 0.93 – 1.12 | 0.673 |
|
| |||||||||
| Sample Size | Total | ||||||||
|
|
|||||||||
| Unweighted sample | 899 | 1,011 | 1,041 | 2,063 | 862 | 5,876 | |||
| Weighted visits | 2,759,559 | 3,461,389 | 3,242,283 | 3,064,665 | 2,074,460 | 14,602,357 | |||
Note:
across the 10-year period trends.
includes obstetrics/gynecology, cardiovascular diseases, dermatology, urology, neurology, ophthalmology, otolaryngology, and others.
Multivariable logistic regression analysis
Table 3 presents the results of multivariable-adjusted logistic regression analysis, which estimated the odds of receiving a anticholinergic medication independent of other factors. Older women had 1.88 times greater odds of receiving high-risk anticholinergic prescriptions than older men (p<0.001; 95% CI, 1.71–2.06). When compared to visits in Northeast region, visits in the South regions had 1.21 times greater odds of receiving high-risk anticholinergic prescriptions (p=0.007; 95% CI, 1.05–1.39). Visits to psychiatry and urology specialists had greater odds of receiving high-risk anticholinergic prescriptions, respectively, compared with primary care visits (p<0.001).
Table 3.
Adjusted odds ratios (AOR) of receiving strong anticholinergic prescriptions among adults ages 65 or older in office-based outpatient settings, 2006–2015 NAMCS.
| (Reference group in a parenthesis) | AOR | 95% CI | P-value |
|---|---|---|---|
| Age (65–74) | |||
| 75–84 | 0.96 | 0.87 – 1.06 | 0.427 |
| 85+ | 0.95 | 0.84 – 1.08 | 0.452 |
| Gender (Male) | |||
| Female | 1.88 | 1.71 – 2.06 | <0.001 |
| Race/ethnicity (Non-Hispanic White) | |||
| Non-Hispanic Black | 0.92 | 0.76 – 1.11 | 0.392 |
| Hispanic | 1.07 | 0.90 – 1.26 | 0.441 |
| Other a) | 0.97 | 0.72 – 1.31 | 0.848 |
| Region (Northeast) | |||
| Midwest | 1.02 | 0.89 – 1.18 | 0.748 |
| South | 1.21 | 1.05 – 1.39 | 0.007 |
| West | 1.02 | 0.88 – 1.19 | 0.774 |
| Primary source of payment (Private) | |||
| Medicare | 1.03 | 0.90 – 1.17 | 0.668 |
| Medicaid | 1.16 | 0.87 – 1.53 | 0.306 |
| Other b) | 1.11 | 0.79 – 1.54 | 0.547 |
| Reason for visit (Acute problem) | |||
| Routine chronic problem | 0.96 | 0.87 – 1.06 | 0.401 |
| Preventive care | 0.87 | 0.73 – 1.03 | 0.103 |
| Pre- or post-surgery | 0.91 | 0.74 – 1.11 | 0.337 |
| Physician specialty (Primary care) | |||
| Psychiatry | 1.69 | 1.12 – 2.55 | 0.012 |
| Neurology | 1.29 | 0.88 – 1.90 | 0.193 |
| Urology | 2.06 | 1.75 – 2.44 | <0.001 |
| Other c) | 0.62 | 0.55 – 0.70 | <0.001 |
| Repeat of visits in the past 12 months (Never) | |||
| 1–2 visits | 1.10 | 0.91 – 1.33 | 0.345 |
| 3–5 visits | 1.01 | 0.83 – 1.23 | 0.94 |
| 6+ visits | 1.11 | 0.90 – 1.37 | 0.322 |
| Time spent with a doctor (< 15 min.) | |||
| 15–20 min. | 0.98 | 0.86 – 1.12 | 0.777 |
| 21–30 min. | 0.99 | 0.86 – 1.15 | 0.928 |
| > 30 min. | 1.00 | 0.82 – 1.15 | 0.713 |
| ≥2 chronic conditions (<2) | 1.00 | 0.91 – 1.10 | 0.987 |
| ≥6 medications (<6) | 5.14 | 4.66 – 5.68 | <0.001 |
| Visit diagnosis | |||
| Allergic conditions (No) | 0.91 | 0.69 – 1.22 | 0.538 |
| Chronic respiratory diseases (No) | 0.83 | 0.67 – 1.02 | 0.077 |
| Irritable bowel syndromes (No) | 0.36 | 0.05 – 2.71 | 0.319 |
| Overactive bladder or urinary incontinence (No) | 5.28 | 3.81 – 7.31 | <0.001 |
| Neuropathic pain (No) | 1.66 | 1.26 – 2.18 | <0.001 |
| Parkinsonism, dyskinesia, or dystonia (No) | 1.37 | 0.83 – 2.26 | 0.215 |
| Vertigo or motion sickness (No) | 4.96 | 3.80 – 6.47 | <0.001 |
| Cognitive impairment or dementia (No) | 0.70 | 0.47 – 1.04 | 0.074 |
| Other psychiatric disorders (No) | 1.90 | 1.45 – 2.50 | <0.001 |
| Insomnia (No) | 2.01 | 1.28 – 3.15 | 0.002 |
| Dry mouth, blurry vision, fatigue, hypersomnia, or tachycardia (No) | 1.00 | 0.70 – 1.43 | 1.000 |
| Constipation (No) | 1.29 | 0.84 – 1.96 | 0.245 |
|
| |||
| Sample size | |||
| Unweighted sample | 96,996 | ||
| Weighted visits | 235,697,874 | ||
|
| |||
| F-statistic | 58.75 (p<0.001) | ||
Note:
includes Asians, American Indian/Alaska Natives (AIANs), Native Hawaiian or Other Pacific Islanders (NHOPI), or 2+ reported racial/ethnic groups;
includes worker’s compensation, self-pay, no charge, and others;
includes obstetrics/gynecology, cardiovascular diseases, dermatology, urology, neurology, ophthalmology, otolaryngology, and others; and
was based 14 chronic conditions (yes/no) collected by the NAMCS (e.g., arthritis, congestive heart failure, and diabetes).
Older patients with six or more concomitant medications prescribed had 5.14 times greater odds of receiving high-risk anticholinergic prescriptions when compared to those with <6 medications concomitantly prescribed (p<0.001; 95% CI, 4.66–5.68). Among clinical diagnoses, overactive bladder or urinary incontinence, neuropathic pain, vertigo/motion sickness, and insomnia were independently associated with greater odds of having high-risk anticholinergic prescriptions (p<0.01).
DISCUSSION
This pharmaco-epidemiologic study investigated predictors of and national prescribing trends in high-risk anticholinergic medications among older adults in a nationally representative sample of office-based outpatient visits from 2006 to 2015. The prevalence of visits with any anticholinergics prescribed was stable over time, increasing minimally from 6.1% in 2006–2007 to 6.8% in 2008–2009, and then decreasing to 4.7% in 2014–2015. These rates are slightly lower than that of the previous study using 2009–2010 Medical Expenditure Panel Survey (MEPS), which reported a prevalence rate of 9.6%.15 These marginal differences may be due to differences in data collection (e.g., individual- versus visit-level) or which medications were specifically included.
Among the selected medication classes, antidepressants were the most prevalent. This is corroborated by a recent study, which demonstrated a nearly twofold increase in antidepressant prescriptions 2002 to 2012.21 Furthermore, because safer pharmacologic alternatives with less anticholinergic activity are available for treatments of depression, anxiety, and insomnia, for example, safer prescribing practices should be encouraged. The multivariable logistic regression analysis shows demographic and clinical correlates independently associated with the odds of having strong, high-risk anticholinergic prescriptions. Consistent with previous studies,15,23 women and patients from the South were more likely to receive high-risk anticholinergic prescriptions. The regional variation may be due to differences in physician practices, patient preferences, and/or formulary structure.
Patients with six or more concomitant medications prescribed at the sampled visit also had a higher likelihood of having high-risk anticholinergic prescriptions. This may pose serious polypharmacy-related adverse drug events in older adults. Future research is needed to determine if high-risk anticholinergics moderate the relationship between polypharmacy and patient outcomes. Such a finding would further underscore the need for reduction in high-risk anticholinergic prescribing in older adults. In general, visits that had diagnoses often treated by anticholinergics (e.g., urinary incontinence) showed increased likelihoods of receiving high-risk anticholinergic prescriptions. The severity of these indications may justify prescribing anticholinergics in individual patients; however, the heightened risk of these medications merits informed shared decision-making.
There are several limitations to this study. First, NAMCS does not capture visits at hospital-affiliated clinics and emergency departments (about 8.5% of all outpatient visits), and excludes prescription by phone. Second, NAMCS selected information based on a randomly selected visit, which may result in incomplete prescription documentation. For instance, NAMCS does not capture anticholinergic prescriptions given by other clinicians other than the physician at the sampled visit. Third, since only generic names of prescribed medications are provided in data, we excluded some drugs from the analysis (e.g., doxepin) as dosing information is needed to estimate their indications precisely. For these reasons, our findings may underestimate the magnitude of anticholinergic prescribing at all time points.
Despite the limitations, the strengths of our study include generalizability due to the use of nationally representative data. In particular, the NAMCS is one of few datasets, which represents physicians’ practice and prescribing patterns in office-based care at the national level. Despite previous studies that divulge the use of strong, high-risk anticholinergic use and negative patient outcomes (e.g., developing cognitive impairment and dementia, or increased mortality) in older adults, the overall prescribing trends of these medications remain stable over time between 2006 and 2015. Our findings highlight that minimizing prescribing of high-risk anticholinergic medications is needed across physician specialty and drug class because safer alternatives are available. The findings from our study lay the foundation for future research to develop and implement better quality of care practices to reduce high-risk anticholinergic prescriptions in older adults.
Acknowledgments
Author Contributions: Study concept and design: Rhee and Ross; Data acquisition and statistical analyses: Rhee; Interpretation of data: All authors; Drafting of manuscript: Rhee; Critical revision of manuscript for important intellectual content: All authors; Supervision: Ross.
Data access and responsibility: Dr. Rhee had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Obtained funding: Rhee and Ouellet received funding support from the National Institutes of Health (NIH) (#T32AG019134).
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none were reported. All research procedures performed in this study are in accordance with the ethical standards of the Institutional Review Board at Yale University School of Medicine (#2000021850). In the past 36 months, Dr. Ross received research support through Yale University from Medtronic, Inc. and the Food and Drug Administration (FDA) to study issues in post-market medical device surveillance, from the Food and Drug Administration (FDA) to establish the Yale-Mayo Clinic Center for Excellence in Regulatory Science and Innovation (CERSI), from Johnson & Johnson to develop methods for clinical trial data sharing, from the Centers of Medicare and Medicaid Services (CMS) to develop measures for public reporting of hospital and physician quality, from the Blue Cross-Blue Shield Association (BCBSA) to advance pre-market evidence generation for medical products, from the Agency for Healthcare Research and Quality, and from the Laura and John Arnold Foundation to support the Collaboration on Research Integrity and Transparency (CRIT) at Yale.
Role of the Funder/Sponsor: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript, and decision to submit the manuscript for publication.
Footnotes
Impact Statement: We certify that this work is novel, as we sought to investigate national prescribing trends of high-risk anticholinergic medications in older adults.
References
- 1.Gray SL, Hanlon JT. Anticholinergic medication use and dementia: latest evidence and clinical implications. Ther Adv Drug Saf. 2016;7(5):217–224. doi: 10.1177/2042098616658399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Doraiswamy PM, Husain MM. Anticholinergic drugs and elderly people: a no brainer? Lancet Neurol. 2006;5(5):379–380. doi: 10.1016/S1474-4422(06)70421-5. [DOI] [PubMed] [Google Scholar]
- 3.Gray SL, Anderson ML, Dublin S, et al. Cumulative use of strong anticholinergics and incident dementia: a prospective cohort study. JAMA Intern Med. 2015;175(3):401–407. doi: 10.1001/jamainternmed.2014.7663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.American Geriatrics Society Beers Criteria Update Expert Panel. American Geriatrics Society 2015 Updated Beers Criteria for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc. 2015;63(11):2227–2246. doi: 10.1111/jgs.13702. [DOI] [PubMed] [Google Scholar]
- 5.Risacher SL, McDonald BC, Tallman EF, et al. Association Between Anticholinergic Medication Use and Cognition, Brain Metabolism, and Brain Atrophy in Cognitively Normal Older Adults. JAMA Neurol. 2016;73(6):721–732. doi: 10.1001/jamaneurol.2016.0580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Campbell NL, Perkins AJ, Bradt P, et al. Association of Anticholinergic Burden with Cognitive Impairment and Health Care Utilization Among a Diverse Ambulatory Older Adult Population. Pharmacotherapy. 2016;36(11):1123–1131. doi: 10.1002/phar.1843. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Cossette B, Bagna M, Sene M, et al. Association Between Anticholinergic Drug Use and Health-Related Quality of Life in Community-Dwelling Older Adults. Drugs Aging. 2017;34(10):785–792. doi: 10.1007/s40266-017-0486-2. [DOI] [PubMed] [Google Scholar]
- 8.Green AR, Oh E, Hilson L, Tian J, Boyd CM. Anticholinergic Burden in Older Adults with Mild Cognitive Impairment. J Am Geriatr Soc. 2016;64(12):e313–e314. doi: 10.1111/jgs.14554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Papenberg G, Backman L, Fratiglioni L, Laukka EJ, Fastbom J, Johnell K. Anticholinergic drug use is associated with episodic memory decline in older adults without dementia. Neurobiol Aging. 2017;55:27–32. doi: 10.1016/j.neurobiolaging.2017.03.009. [DOI] [PubMed] [Google Scholar]
- 10.Sumukadas D, McMurdo ME, Mangoni AA, Guthrie B. Temporal trends in anticholinergic medication prescription in older people: repeated cross-sectional analysis of population prescribing data. Age Ageing. 2014;43(4):515–521. doi: 10.1093/ageing/aft199. [DOI] [PubMed] [Google Scholar]
- 11.Kachru N, Carnahan RM, Johnson ML, Aparasu RR. Potentially inappropriate anticholinergic medication use in older adults with dementia. J Am Pharm Assoc (2003) 2015;55(6):603–612. doi: 10.1331/JAPhA.2015.14288. [DOI] [PubMed] [Google Scholar]
- 12.Fox C, Richardson K, Maidment ID, et al. Anticholinergic medication use and cognitive impairment in the older population: the medical research council cognitive function and ageing study. J Am Geriatr Soc. 2011;59(8):1477–1483. doi: 10.1111/j.1532-5415.2011.03491.x. [DOI] [PubMed] [Google Scholar]
- 13.Klotz U. Pharmacokinetics and drug metabolism in the elderly. Drug Metab Rev. 2009;41(2):67–76. doi: 10.1080/03602530902722679. [DOI] [PubMed] [Google Scholar]
- 14.Tune LE. Anticholinergic effects of medication in elderly patients. J Clin Psychiatry. 2001;62(Suppl 21):11–14. [PubMed] [Google Scholar]
- 15.Kachru N, Carnahan RM, Johnson ML, Aparasu RR. Potentially inappropriate anticholinergic medication use in community-dwelling older adults: a national cross-sectional study. Drugs Aging. 2015;32(5):379–389. doi: 10.1007/s40266-015-0257-x. [DOI] [PubMed] [Google Scholar]
- 16.National Center for Health Statistics. Ambulatory Health Care Data: Questionnaires, Datasets, and Related Documentation. 2017 http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm.
- 17.Li C. Little’s test of missing completely at random. Stata J. 2013;13(4):795–809. [Google Scholar]
- 18.Rudolph JL, Salow MJ, Angelini MC, McGlinchey RE. The anticholinergic risk scale and anticholinergic adverse effects in older persons. Arch Intern Med. 2008;168(5):508–513. doi: 10.1001/archinternmed.2007.106. [DOI] [PubMed] [Google Scholar]
- 19.National Center for Health Statistics. 2015 NAMCS Micro-data file documentation. 2018 ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2015.pdf.
- 20.Rhee TG, Capistrant BD, Schommer JC, Hadsall RS, Uden DL. Effects of depression screening on diagnosing and treating mood disorders among older adults in office-based primary care outpatient settings: An instrumental variable analysis. Prev Med. 2017;100:101–111. doi: 10.1016/j.ypmed.2017.04.015. [DOI] [PubMed] [Google Scholar]
- 21.Rhee TG, Schommer JC, Capistrant BD, Hadsall RL, Uden DL. Potentially Inappropriate Antidepressant Prescriptions Among Older Adults in Office-Based Outpatient Settings: National Trends from 2002 to 2012. Adm Policy Ment Health. 2017 doi: 10.1007/s10488-10017-10817-y. [DOI] [PubMed] [Google Scholar]
- 22.Stata Statistical Software: Release 15 [computer program] College Station, TX: StataCorp LP; 2017. [Google Scholar]
- 23.Hong IS, Bishop JR. Anticholinergic use in children and adolescents after initiation of antipsychotic therapy. Ann Pharmacother. 2010;44(7–8):1171–1180. doi: 10.1345/aph.1M643. [DOI] [PMC free article] [PubMed] [Google Scholar]