Abstract
Objective:
To investigate the time-course of anticholinergic drug use in nursing home residents and assess if any temporal change in anticholinergic use varied by nursing home quality rating.
Design:
Retrospective repeated cross-sectional analysis of Medicare enrollment, Parts A, B and D claims data linked to the Minimum Data Set from 2009 to 2017.
Setting:
Medicare-certified nursing homes
Participants:
Long-term residents 65 years or older with nursing home stay of at least 100 consecutive days within a given calendar year.
Measurements:
Estimates of anticholinergic drug prescription rates between 2009 and 2017 were based on a binary variable indicating whether a resident received a drug with high anticholinergic activity, as defined by the Anticholinergic Cognitive Burden scale, for at least one day during the initial 100 consecutive days of nursing home stay in a given calendar year. We used mixed effects logistic regression models to determine adjusted rates of anticholinergic use each year and test the interaction between nursing home quality rating and year, while adjusting for patient and nursing home characteristics.
Results:
The cohort included 786,858 100-day nursing home stays (299,354 unique residents) in 6,703 nursing homes for the years 2009 to 2017. Prescription rates were stable at approximately 34-35% between 2009 and 2011, then gradually decreased to 24.3% in 2017 (p < .0001), with the decline being more pronounced in nursing homes having high quality ratings (p < .0001). Rates for anticholinergic drugs in nursing homes with 4-5 star quality rating (33.7% in 2011 to 23.3% in 2017) showed a steeper decline over time relative to nursing homes with 1-2 star quality rating (34.2% in 2011 to 25.2% in 2017) (p < 0.0001).
Conclusions:
The use of drugs with high anticholinergic activity has declined from 2009 to 2017, with a greater decline in higher quality nursing homes.
Keywords: Anticholinergic drug use, antipsychotics, nursing homes
INTRODUCTION
The use of drugs with anticholinergic activity is associated with increased risk for adverse effects in older adults including falls, confusion, delirium, dementia and cognitive decline 1-5. Nursing home residents are vulnerable to the adverse effects of anticholinergic drugs due to age-related physiological changes such as increased permeability of the blood-brain barrier, deterioration of hepatic and renal function, multimorbidity, polypharmacy and decreased cognitive reserve 6-8. Anticholinergic medication includes drugs from several therapeutic classes prescribed for conditions such as vertigo, allergies, psychosis, Parkinson’s disease, depression, behavioral problems and bladder disorders 9,10.
A national cross-sectional study of community-dwelling older Americans found that 9.6% received anticholinergic medication in 2009 and 2010 11. A repeated cross-sectional study using data from office-based physician visits by older people from 2006 to 2015 estimated that the high activity anticholinergic drug prescription rate declined from 6.8% in 2008-2009 to 4.7% in 2014-2015 12. Anticholinergic drug use is higher in nursing home residents compared to the community-dwelling population. A cross-sectional study using a national sample of 2009-2010 Medicare Part D beneficiaries estimated that nearly one-third of nursing home residents used drugs with high anticholinergic burden 13.
Due to the growing evidence of harm from anticholinergic drugs in older adults, the American Geriatric Society’s (AGS) Beers Criteria in 2012 included for the first time drugs with strong anticholinergic activity as potentially inappropriate medication in older adults 14. This list was again updated by the AGS in 2015 15 and 2019 16. In addition, in 2012 the Centers for Medicare & Medicaid Services (CMS) specifically targeted antipsychotic drug use in nursing homes, via educational interventions and public reporting of data 17. Among drugs with strong anticholinergic properties, antipsychotics are the most commonly prescribed drugs in older nursing home residents 13. The effect of these initiatives on the use of anticholinergic drugs in nursing homes is unknown. Nursing home quality ratings and organizational culture are associated with outcomes 18. For example, residents in nursing homes with low quality ratings had more severe depressive symptoms after nursing home admission.
We investigated the time-course of anticholinergic drug use in nursing home residents and whether temporal changes in prescription patterns coincided with the publication of the updated Beers criteria and CMS intervention in 2012. Furthermore, we assessed if any temporal change in anticholinergic drug use varied by nursing home quality rating. To this end, we used Medicare Parts A, B and D claims data linked to the Minimum Data Set (MDS) for the years 2009 to 2017.
METHODS
Study Design and Data Sources
This retrospective repeated cross-sectional study used Medicare enrollment, Parts A, B and D claims data linked to the MDS for 2009-2017. MDS v2.0 was used for 2009 and the first three quarters of 2010, and MDS v3.0 was used for the last quarter of 2010 and 2011 to 2017. Data originated from a 20% random sample of fee-for-service beneficiaries enrolled in Medicare Parts A, B and D. The MDS is a comprehensive database of health assessments of nursing home residents performed upon admission and at least every 90 days thereafter. The MDS served as the primary source of data to identify a cohort of nursing home residents. Medicare Part D prescription data provided us with the National Drug Code (NDC) identifier, drug name, date of dispensing and estimated days’ supply for all prescriptions covered by Part D. We used the CMS Provider of Services (POS) file to capture nursing home characteristics. The University of Texas Medical Branch Institutional Review Board (IRB) deemed this study exempt from IRB review and approval.
Cohort Derivation
Supplemental Figure 1 shows the derivation of the cohort. The study cohort included long-term care nursing home residents with at least 100 consecutive days of a nursing home stay in a year. We generated cohorts for each calendar year separately. We used a previously-validated algorithm to identify long-term care nursing home stays 19. First, we identified residents with at least one MDS assessment in a calendar year. Second, we included residents if they had at least one day of stay in a long-term care nursing home. Residents who stayed only in a skilled nursing facility were excluded. Third, we included residents who had at least 100 consecutive days of a nursing home stay within a given calendar year. Residents could have multiple stays of 100 days, but we selected the first 100 days of stay for study in any year. During each nursing home stay, any time intervals corresponding to skilled nursing home stays were identified using Medicare Part A data (admission date and discharge date) and excluded. Fourth, residents were included if they were 65 years or older and enrolled in Medicare Parts A, B and D, without health maintenance organization (HMO) coverage for at least 90 days prior to their nursing home admission and for the 100 days of their stay. Fifth, we excluded residents from nursing homes which were missing data on nursing home characteristics. Sixth, we included residents only from nursing homes that contributed at least 5 residents in all years of the study period. The final study cohort included 786,858 100-day nursing home stays (299,354 unique residents) in 6,703 nursing homes for the years 2009 to 2017, with a maximum of one stay per resident per year. Of the 786,858 100-day stays, 79.8% were stays from January to April of enrollees who had been residing in the nursing home the prior year and 20.2% of the stays started directly after admission to the nursing home. In a sensitivity analysis, we re-formulated the cohort so that each enrollee only contributed one 100 day stay. For enrollees with 100 day stays in more than one year, we randomly selected one year with a 100 day stay for the sensitivity cohort, which included 299,354 stays in 299,354 residents.
Independent Variables
Both patient and nursing home characteristics were included in the analysis. The main independent variable was the calendar year of nursing home admission. Patient characteristics such as age, sex and race were extracted from the Medicare enrollment files. Nursing home characteristics included ownership type (for-profit, non-profit, government), location (urban, rural), bed number and geographic region (northeast, midwest, west, and south). For most nursing homes (> 99%) ownership type, location and number of beds did not change over the study period. When this was not the case, the mode of ownership, location and mean number of beds was assigned to the nursing home for all years.
The nursing home quality ratings were retrieved from the Nursing Home Compare web site. The Five-Star Quality Rating System was created by CMS to facilitate nursing home comparison by patients and stakeholders. For each nursing home ratings, are determined from, inspections aimed to check compliance with Medicaid and Medicare’s quality requirements, the average time spent providing care per resident, and a quality measure which has information on 15 different physical and clinical measures for nursing home residents. The quality ratings range from 1 to 5, where 5 indicates highest quality rating. We combined nursing homes with quality ratings 1 and 2 as low-quality group, quality rating 3 as middle group and quality ratings 4 and 5 as high-quality group. Every Medicare- and Medicaid-certified nursing home in the US is evaluated monthly. In contrast to other nursing home characteristics, which remained constant over the study period, quality rating showed high variability even within the same year. Therefore, we defined quality rating as the mode of the quality rating of the nursing home during the 100-day resident stay (100 days may span 4 or 5 calendar months).
Outcome Variable
We defined outcome as a binary variable indicating whether a resident received a drug with high anticholinergic activity for at least one day during the initial 100 consecutive days of nursing home stay in a year. The Anticholinergic Cognitive Burden (ACB) scale was used to identify drugs with high anticholinergic activity.5,20 The ACB scale measures anticholinergic activity on a scale from 0 (no activity) to 3 (high activity). The ACB scale identifies 40 drugs with high anticholinergic activity (Supplemental Table 1), categorized by therapeutic class into antidepressants, antipsychotics, antihistamines, antispasmodics, antivertigo, antimuscarinics and antiparkinsonian.4,21,22 We used the Micromedex RED BOOK to identify all NDC drug codes corresponding to the generic names of the 40 drugs and matched those to the NDCs in the Part D claims data. Similarly, for the secondary aim the outcome was defined based on the subset of drugs classed as antipsychotics.
Statistical Analysis
Descriptive statistics, including counts and percentages, were used to describe the study cohort. Bivariate analyses were performed to assess the effect of the independent variables and estimate the unadjusted rates of anticholinergic drug use. We used mixed effects logistic regression models to derive the adjusted rates of anticholinergic drug use. The outcome variable was anticholinergic drug use and the model controlled for all patient (age, race, sex, and year) and nursing home (quality rating, ownership, location, bed number, region) characteristics. While nursing home quality rating is a nursing home characteristic, in the multilevel model it was entered as a patient characteristic signifying the nursing home quality during the 100-day period that the patient resided in the nursing home. From the mixed effects logistic regression model, we examined the effect of year on anticholinergic prescription rate. We also performed joinpoint trend analysis to investigate whether there was a change in the slope in prescription rates during the study period 23. The time points included in this analysis were the adjusted rates for each year derived from the adjusted regression model described above. In all models, nursing home ID was used to control for the effect of nested observations within nursing homes. We did not account for repeated observations within resident because it was computationally not feasible. To address the issue, we performed a sensitivity analysis where we picked randomly one 100-day stay per resident, in cases where a resident contributed more than one 100-day stay in the sample, and we repeated the analysis. We tested the hypothesis that the association of year with anticholinergic prescription rate varied by nursing home quality rating by adding an interaction term between nursing home quality rating and year. All analyses were performed using SAS Enterprise version 7.12 (SAS Institute, Inc., Cary, NC).
RESULTS
The study cohort included 786,858 100-day nursing home stays (299,354 unique residents) in 6,703 nursing homes for the years 2009 to 2017 (Supplemental Figure 1). Table 1 summarizes descriptive characteristics of the study cohort. Fully 49.9% of residents were 85 years or older, 74.2% were female and 79.6% white. Most admissions took place in for-profit nursing homes (67.5%), in urban areas (72.1%) and in homes with fewer than 200 beds (81.3%).
Table 1.
Descriptive Characteristics and Prevalence of Anticholinergic Drug Prescriptions in Nursing Homes for the period 2009 – 2017 by Admission and Nursing Home Characteristics.
| Characteristic | Nursing Home Admissions (%) |
Anticholinergic Prescriptions |
Unadjusted Rate (%) |
Adjusted Rate (%) |
|
|---|---|---|---|---|---|
| Total | 786858 | (100) | 245753 | 31.2 | 30.3 |
| Age (years) | |||||
| 65-74 | 136041 | (17.3) | 53282 | 39.2* | 39.1* |
| 75-84 | 258488 | (32.9) | 87667 | 33.9 | 32.9 |
| 85+ | 392329 | (49.9) | 104804 | 26.7 | 25.8 |
| Sex | |||||
| Female | 584218 | (74.2) | 184754 | 31.6* | 31.1* |
| Male | 202640 | (25.8) | 60999 | 30.1 | 27.6 |
| Race | |||||
| Black | 100793 | (12.8) | 25573 | 25.4* | 22.8* |
| Hispanic | 39124 | (5.0) | 12363 | 31.6 | 30.4 |
| Other | 20704 | (2.6) | 5355 | 25.9 | 27.8 |
| White | 626237 | (79.6) | 202462 | 32.3 | 31.6 |
| Year | |||||
| 2009 | 92858 | (11.8) | 33014 | 35.6* | 34.8* |
| 2010 | 88433 | (11.2) | 30953 | 35.0 | 34.3 |
| 2011 | 90752 | (11.5) | 31518 | 34.7 | 34.0 |
| 2012 | 90596 | (11.5) | 30381 | 33.5 | 32.7 |
| 2013 | 90216 | (11.5) | 28538 | 31.6 | 30.8 |
| 2014 | 88747 | (11.3) | 26120 | 29.4 | 28.5 |
| 2015 | 82814 | (10.5) | 23084 | 27.9 | 26.8 |
| 2016 | 82753 | (10.5) | 21805 | 26.3 | 25.2 |
| 2017 | 79689 | (10.1) | 20340 | 25.5 | 24.3 |
| Quality Rating | |||||
| 1-2 | 313112 | (39.8) | 101907 | 32.5* | 30.7* |
| 3 | 162602 | (20.7) | 51192 | 31.5 | 30.1 |
| 4-5 | 311144 | (39.5) | 92654 | 29.8 | 29.7 |
| Ownership | |||||
| For-Profit | 531469 | (67.5) | 171537 | 32.3* | 30.8* |
| Government | 67055 | (8.5) | 19756 | 29.5 | 28.8 |
| Non-Profit | 188334 | (23.9) | 54460 | 28.9 | 29.1 |
| Location | |||||
| Rural | 219626 | (27.9) | 74479 | 33.9* | 31.5* |
| Urban | 567232 | (72.1) | 171274 | 30.2 | 29.7 |
| Bed Number | |||||
| ≥200 | 147169 | (18.7) | 41720 | 28.3* | 29.5 |
| <200 | 639689 | (81.3) | 204033 | 31.9 | 30.3 |
| Region | |||||
| Midwest | 180632 | (23.0) | 55540 | 30.7* | 29.7* |
| Northeast | 218587 | (27.8) | 59080 | 27.0 | 26.7 |
| South | 322252 | (41.0) | 113207 | 35.1 | 33.9 |
| West | 65387 | (8.3) | 17926 | 27.4 | 26.2 |
Using mixed effects logistic regression, adjusted rates were derived by modeling the outcome as a function of the fixed effects year, race, sex, age, nursing home quality rating, ownership type, location, bed number and geographic region while controlling for the nesting of observations in nursing homes.
Statistically significant difference between groups (p < .0001).
Table 1 also reports unadjusted and adjusted rates of anticholinergic use by patient and nursing home characteristics. Overall, 31.2% of residents were prescribed anticholinergic drugs at least once during their first 100 consecutive days of nursing home stay. Both unadjusted and adjusted rates varied significantly across all patient and nursing home characteristics (p < .0001), except for number of beds, which lost significance in the adjusted model. Unadjusted rates of anticholinergics use were higher in younger residents (39.2% in 65-74 years vs 26.7% in ≥85 years), females (31.6% vs. 30.1% in males) and whites (32.3% vs. 25.4% in blacks). Anticholinergics use was higher in for-profit nursing homes (32.3% vs 28.9% in non-profit), nursing homes in rural areas (33.9 % vs. 30.2% in urban), in the South region (35.1% vs. 27.0% in the Northeast) and in those with low quality ratings (32.5% for rating 1 or 2 vs 29.8% in those with rating 4 or 5). Adjusted rates followed a similar pattern as the unadjusted rates.
Figure 1 displays the rates of anticholinergic drug use in nursing homes from 2009 to 2017 adjusted for resident and nursing home characteristics. From 2009 to 2011, anticholinergic drug use rates were stable at approximately 34-35%, followed by a decline over the period from 2011 to 2017. A joinpoint analysis indicated that the slope from 2009 to 2011 was not significantly different from zero (p ≈ 0.6), but it then declined by 1.7% per year (p < 0.0001) from 2011 to 2017.
Figure 1.
Adjusted Prevalence of Anticholinergic Prescriptions in Nursing Homes by Year and Joinpoint Analysis for the Years 2009 to 2017. The adjusted rates for the calendar year were estimated using Mixed Effects Logistic Regression modeling, while adjusting for race, sex, age, quality rating, location, bed number, ownership type and geographic region. Error bars are 95% confidence intervals. The model-derived rates were used in subsequent joinpoint analysis, which indicated a statistically significant change of slope with Joinpoint estimate for the year 2011 (95% Confidence Interval 2011 – 2013). For the period 2009 to 2011, the slope was not statistically different from zero (p ≈ 0.6), and for the years 2011 to 2017 the estimated rate of decline in the prevalence of anticholinergic prescriptions was 1.7% per year (p < 0.0001).
Figure 2 shows the change over time in the adjusted rates of anticholinergic drug use stratified by nursing home quality ratings. Rates for anticholinergic drug use in nursing homes with a high-quality rating showed a steeper decline over time relative to nursing homes with a low-quality rating (p < 0.0001). We repeated this analysis limited to antipsychotic drugs (Supplemental Table 2) and found a somewhat stronger relationship (Supplemental Figure 2).
Figure 2.
Adjusted Prevalence of Anticholinergic Prescriptions per Year by Nursing Home Quality Rating. The moderating effect of nursing home quality rating on the decline in anticholinergic prescription rate over time was investigated using Mixed Effects Logistic Regression modeling, controlling for the nesting of observations in nursing homes while adjusting for the fixed effects race, sex, age, location, bed number, ownership type and geographic region. Error bars are 95% confidence intervals. Prescribing rate in nursing homes with a quality rating “4-5” declined from 33.7% in 2011 to 23.3% in 2017, whereas it declined from 34.2% in 2011 to 25.2% in 2017 in nursing homes with a quality rating “1-2”. Prescribing rate in nursing homes with a quality rating “3” declined from 34.3% in 2011 to 24.5% in 2017.
Figure 3 displays the time trends for the rates of specific classes of anticholinergic drugs. All drugs had at least a 25% relative decline in use between 2009 and 2017, but the pattern and timings of the changes varied. For example, the decline in antipsychotic use roughly paralleled the decline in overall use of anticholinergics shown in Figure 1, while the decline in antispasmodics started earlier and that in antiparkinsonian drugs somewhat later.
Figure 3.
Prescription Rates of Specific Anticholinergic Drug Classes Over the Period 2009 - 2017. Please note that the y-axes denoting rates of drug use are different in each plot. The choice of y-axis' scale on each plot was made based on are intention to focus on the relative changes of rates among years. The absolute and relative changes between 2009 and 2017 are reported on the upper-right corner of each plot. The adjusted rates for each drug class were estimated using Mixed Effects Logistic Regression modeling, controlling for the nesting of observations in nursing homes while adjusting for the fixed effects race, sex, age, quality rating, location, bed number, ownership type and geographic region. In all drug classes the effect of year was statistically significant (p < .0001). Error bars are 95% confidence intervals.
In a sensitivity analysis we repeated the analyses shown in Table 1, and Figures 1 and 2, using a cohort where Medicare enrollees who were in nursing homes over multiple years contributed only one 100-day stay, selected at random, to the analysis. This had no noticeable effect on the results (Supplemental Table 3).
DISCUSSION
This pharmacoepidemiologic study investigated the time-course of anticholinergic drug use in a sample derived from nationally representative datasets of long-term nursing home residents from 2009 to 2017. We found that prescription rates were stable at approximately 34-35% between 2009 and 2011, then gradually decreased to 24.3% in 2017, with the decline being more pronounced in nursing homes with high quality ratings.
The declining use of anticholinergic drugs is consistent with results found in prior studies. Rhee et al. studied 35 drugs with high anticholinergic activity and found a reduction in use from 6.1% in 2006-2007 to 4.7% in 2014-2015 in outpatient visits 12. In nursing homes in Finland, the mean anticholinergic risk scale score of all drugs prescribed declined from 1.1 in 2003 to 0.8 in 2017, suggesting lower use of anticholinergic drugs 24. Our study included 40 drugs with high anticholinergic activity and found that drug use was stable at approximately 34-35% between 2009 and 2011 and then gradually decreased to 24.3% in 2017. The inclusion of anticholinergic drugs in the AGS Beers criteria in 2012 may in part explain the reduction in anticholinergic drug use. In addition, the CMS’s initiatives to reduce antipsychotic drug use in nursing homes may have some spillover effect in reducing the use of high-risk anticholinergic drugs.
The decline in anticholinergic drug prescription rates could represent less drug use for certain indications, such as the use of antipsychotics for behavioral problems. It could also represent therapeutic substitution of drugs with similar indications but with lower anticholinergic activity. An example might be the substitution of selective serotonin reuptake inhibitors for tricyclic antidepressants.
A nursing home’s prescribing culture influences the prescription of drugs 25. The greater decline in anticholinergic use in nursing homes with higher quality ratings may be explained by their use of alternative medications or of non-pharmacological approaches. Nursing homes with better quality ratings may have greater access to registered nurses, pharmacists and geriatricians which may encourage lower use of drugs with anticholinergic activity. Future studies can identify factors and processes employed by high-quality rated nursing homes which can be translated to low-quality nursing homes to further reduce anticholinergic drug use.
The most common anticholinergic drugs prescribed in nursing homes are antipsychotics. Maust et al. found that antipsychotic drug use fell from 21.3% in 2009, quarter 1 to 11.5% in 2014, quarter 4 26. The most recent CMS analysis reported that antipsychotic use in long-term care fell from 23.9% in 2011 to 14.3% in 2019 27. Our study found that the unadjusted rates of use of antipsychotics with high anticholinergic activity fell from 16.4% in 2011 to 12.7% in 2017. We included only seven antipsychotic medications with high anticholinergic activity, whereas the CMS includes all antipsychotic drugs. This difference may explain the lower rate of antipsychotic use in our study.
In long term care, it is unclear what should be the optimal rate of use for anticholinergic drugs. Certain clinical situations may require the use of drugs with high anticholinergic activity. For example, a nursing home resident with behavioral and psychotic symptoms may require the use of antipsychotics with high anticholinergic activity to control hallucinations and aggression, and a drug with high anticholinergic activity may be the best option in some patient with Parkinson’s disease or other conditions. However, the decline in these high-risk drugs in long-term care is encouraging.
Our study has several limitations. First, enrollment in Part D Medicare data increased over time in this population; thus, the denominator of nursing home residents used to calculate the rate of anticholinergic drug use may have changed in characteristics over time. Second, to standardize the outcome, we examined anticholinergic use for each patient only during the first 100 days of nursing home stay in any year, which may have underestimated the rate of prescribing. Third, we did not control for the effect of repeated observations of the same residents throughout the years. However, we performed a sensitivity analysis, by picking randomly one 100-day stay per resident, and the results agree with the main findings (Supplemental Table 3). Fourth, there are several different scales for measuring anticholinergic drug use. The anticholinergic drug scale chosen may influence the estimates of anticholinergic drug use. We choose the ACB 20 scale because it is well suited for the quantification of anticholinergic drug exposure 22 and shows a good dose-response relationship with several adverse outcomes in older adults 21. Fifth, Part D Medicare does not cover over the counter medication therefore the absence of information about over the counter antihistamine claims in our data lead to underestimation of overall antihistamines prescription rates. Sixth, we did not perform formal time series analysis because the policy interventions were not directly targeted to reduce anticholinergic drug use and we did not form a specific hypothesis to test the effect of interventions. Despite these limitations, our study has significant strengths such as the large representative cohort size, the extensive observation period and the adjustment for both patient and nursing home characteristics.
In conclusion, the use of drugs with high anticholinergic activity has declined from 2009 to 2017, with a greater decline in higher quality nursing homes.
Supplementary Material
Supplemental Table 1. Drugs with Strong Anticholinergic Activity: Anticholinergic Cognitive Burden Scale Score of 3.
Supplemental Table 2. Descriptive Characteristics and Prevalence of Antipsychotics Drug Prescriptions in Nursing Homes for the period 2009 – 2017 by Patient and Nursing Home Characteristics.
Supplemental Table 3. Sensitivity Analysis – Accounting for multiple patient observations within nursing home by randomly picking one patient observation per nursing home. Descriptive Characteristics and Prevalence of Anticholinergic Drug Prescriptions in Nursing Homes for the period 2009 – 2017 by Patient and Nursing Home Characteristics.
Supplemental Figure 1. Cohort Derivation
Supplemental Figure 2. Adjusted Prevalence of Antipsychotic Prescriptions per Year by Nursing Home Quality Rating. The moderating effect of nursing home quality rating on the decline in antipsychotic prescription rate over time was investigated using Mixed Effects Logistic Regression modeling, controlling for the nesting of observations in nursing homes while adjusting for the fixed effects race, sex, age, location, bed number, ownership type and geographic region. Error bars are 95% confidence intervals. The rates in nursing homes with a quality rating “1 −2” was 15.1% in 2009 vs. 11.9% in 2017; for those with a quality rating “3,” it was 15.0% in 2009 vs. 11.6% in 2017; and for those with a quality rating of “4 −5,” it was 15.0% in 2009 vs. 10.3% in 2017.
ACKNOWLEDGEMENTS
Financial Disclosure:
This work was supported by the National Cancer Institute (K05-CA134923) and the Claude D. Pepper Older Americans Independence Center Award (P30-AG024832-12).
Footnotes
Conflict of Interest: The authors have no conflicts of interest to disclose.
Sponsor’s Role: The funding sources had no role in the study design, collection management and analysis of data, preparation or review of the manuscript or the decision to submit the it for publication.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Table 1. Drugs with Strong Anticholinergic Activity: Anticholinergic Cognitive Burden Scale Score of 3.
Supplemental Table 2. Descriptive Characteristics and Prevalence of Antipsychotics Drug Prescriptions in Nursing Homes for the period 2009 – 2017 by Patient and Nursing Home Characteristics.
Supplemental Table 3. Sensitivity Analysis – Accounting for multiple patient observations within nursing home by randomly picking one patient observation per nursing home. Descriptive Characteristics and Prevalence of Anticholinergic Drug Prescriptions in Nursing Homes for the period 2009 – 2017 by Patient and Nursing Home Characteristics.
Supplemental Figure 1. Cohort Derivation
Supplemental Figure 2. Adjusted Prevalence of Antipsychotic Prescriptions per Year by Nursing Home Quality Rating. The moderating effect of nursing home quality rating on the decline in antipsychotic prescription rate over time was investigated using Mixed Effects Logistic Regression modeling, controlling for the nesting of observations in nursing homes while adjusting for the fixed effects race, sex, age, location, bed number, ownership type and geographic region. Error bars are 95% confidence intervals. The rates in nursing homes with a quality rating “1 −2” was 15.1% in 2009 vs. 11.9% in 2017; for those with a quality rating “3,” it was 15.0% in 2009 vs. 11.6% in 2017; and for those with a quality rating of “4 −5,” it was 15.0% in 2009 vs. 10.3% in 2017.