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. 2025 Jun 27;48(10):1141–1147. doi: 10.1007/s40264-025-01562-5

Temporal Trends of Anticholinergic Drug Exposure Among Older Adults: A 25-Year Population-Based Study

Amanda Evelo 1,2, Silvan Licher 3,4, Bruno H Stricker 3, Loes E Visser 1,2, Rikje Ruiter 3,5,✉
PMCID: PMC12423137  PMID: 40579658

Abstract

Background

Exposure to anticholinergic drugs is associated with adverse outcomes, particularly among older adults. Limiting the anticholinergic burden (ACB) among older patients has been advocated for decades, but reliable population-level data on temporal trends are lacking. Here, we estimated the cumulative incidence and incidence rates (IRs) of a cumulative ACB score of three or more (cACB ≥ 3) among older adults in a community-dwelling population and described the changes in IR over the past 25 years.

Methods

Within the population-based Rotterdam Study, pharmacy dispensing records were obtained from 11,038 individuals aged 65+ years from 1996 to 2020. The cACB score was calculated with the Anticholinergic Cognitive Burden Scale and supplemented with drugs on the ACB scale by the Expertisecentre PHarmacotherapy in OldeR people (EPHOR). Age- and sex-specific IRs were calculated, and non-overlapping 5-year episodes were defined to determine time trends in IRs.

Results

The cumulative incidence of a cACB ≥ 3 was 25.3% between 1996 and 2020. Compared with 1996–2000, the IR of cACB ≥ 3 had declined by 54% between the 2016–2022 episode (IR ratio: 0.46, 95% confidence interval (CI): 0.41–0.52). Participants aged 86–90 years had more than 1.5 times the rate of a cACB ≥ 3 compared with participants aged 66–70 years (IR ratio: 1.67, 95% CI 1.46–1.91).

Conclusions

Exposure to anticholinergic drugs has decreased by over 50% between 1996 and 2020 in this population of community-dwelling adults. However, the oldest old had and remained to have the highest risk of a cACB ≥ 3 during our study period. Thus, prescribers and pharmacists should continue to regularly review the prescription of drugs with an ACB, especially among those vulnerable to adverse outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40264-025-01562-5.

Key Points

A low cumulative anticholinergic burden (cACB) is advised in older adults to prevent adverse outcomes.
The incidence rate of a cACB ≥ 3 has decreased by over 50% from 2016–2020 compared with 1996–2000.
Although a decreasing trend of a cACB ≥ 3 is seen for all age groups, the oldest old (i.e., 86–90 years) remain at highest risk of cACB ≥ 3.
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Introduction

Drugs with anticholinergic activity are frequently prescribed for various conditions that are common in older patients. However, for most of these drugs, the anticholinergic activity is not the main intended effect but is considered an adverse effect. Moreover, exposure to drugs with anticholinergic activity in older persons is associated with a higher risk for falls, delirium, and cognitive impairment [1–3]. This led to drugs with anticholinergic activity to be included on lists of potentially inappropriate drugs, even for healthy older adults [4–7].

The cumulative effect of taking one or more drugs with anticholinergic activity is referred to as anticholinergic burden (ACB). Since the turn of the millennium, various ACB scales have emerged [8, 9], one of which is the Anticholinergic Cognitive Burden scale. This scale is a frequently validated expert-based anticholinergic burden scale for adverse outcomes [10]. The scale ranks the anticholinergic properties of a drug from zero to three, where the scores of multiple drugs are additive to the total [11–13]. A cumulative Anticholinergic Cognitive Burden score of three or more (cACB ≥ 3) is considered clinically relevant and may serve as a relative contraindication in older adults [11–13]. The common use of drugs with anticholinergic activity in the general population along with the growing body of evidence for adverse effects associated with the ACB reflects the necessity to quantify and manage the ACB in a community-dwelling population [14–17].

To assess the ACB in the ageing population, various studies examined temporal trends of the ACB over time. So far, evidence has been mixed; a few studies found a decrease in the prevalence rate over time [18–20], while others found an increase in the prevalence rate over time [21, 22]. The prevalence varies greatly (ranging from 4.7 to 46%), likely according to the time and place of the studies. Moreover, different anticholinergic burden scoring scales were used, due to differences in the used criteria to determine the ACB exposure. To our knowledge, previous studies did not define exposure as a cACB ≥ 3, which is considered a relative contraindication in older adults. Furthermore, several studies examined only two time points or episodes for trend analysis and did not cover recent years [21, 22]. In conclusion, several studies reported the prevalence of exposure to a high anticholinergic burden at one point in time or at different ages, yet it is important to measure the incidence of cACB ≥ 3 over time as it gives insight into the number of new patients who are prescribed cACB ≥ 3 over calendar years. This will give insight into a possible time trend of prescribing cACB ≥ 3 over the past 25 calendar years, providing evidence of whether prescription of multiple anticholinergic drugs has changed, for instance owing to advocacy for limiting their prescription among older adults. With increased knowledge of the potential harm that can be caused by drugs with anticholinergic activity, and the limited knowledge about the temporal trends, it is our aim to uncover the magnitude and potential change over time of ACB in the community-dwelling population. We will determine the cumulative incidence of a cACB ≥ 3 in different age categories of older adults in the Netherlands. In addition, we will determine the incidence rate (IR) and establish whether the IR has changed over the past 25 years.

Methods

Data Source and Study Design

This study was embedded in the Rotterdam Study, a large, prospective, population-based cohort study conducted among middle-aged and older persons in Rotterdam, the Netherlands. The rationale and design of the Rotterdam Study have been described previously [23]. In short, all persons aged ≥ 55 years in the Ommoord district of Rotterdam, the Netherlands, were invited to participate. Of them, 7983 were enrolled between 1990 and 1993 (RS-I). In 2000, an extended cohort was enrolled (RS-II, n = 3011). A third cohort (RS-III, n = 3932) was formed in 2006, for which all persons aged ≥ 45 years were invited. In 2016, for the fourth cohort (RS-IV, n = 3005), all persons aged ≥ 40 years were invited. Participants were continuously monitored on morbidity and mortality via the records of their general practitioners and on the use of drugs via the seven regional pharmacies. Data on drug use were available from 1991 onward and include the Anatomical Therapeutic Chemical (ATC) code, the dispensing date, the total amount of drug units per prescription, the prescribed daily number of units, and the product name of the drug. The Rotterdam Study was approved by the Medical Ethics Committee of Erasmus MC University Medical Center Rotterdam.

Participants from the Rotterdam Study who were 65–90 years old between 1 January 1995 and 31 December 2020 were included in this study. We included patients from multiple study waves of the Rotterdam Study. Thus, our cohort expanded over time due to our inclusion of participants who turned 65 years during follow-up. We excluded participants without follow-up data and participants with a cACB ≥ 3 at baseline.

Study Variables and Data Collection

Between 1 January 1995 and 31 December 2020, pharmacy dispensing records were extracted for every participant between 65 years and 90 years once every year on the participants’ birthday. The medication was defined as in-use when the dispensing date was before, and the theoretical end date was after the annual assessment date. To determine the ACB score, we primarily used the anticholinergic cognitive burden scale [13] and added drugs from the Expertisecentre PHarmacotherapy in OldeR people (EPHOR), the Netherlands, ACB scale [24]. The Anticholinergic Cognitive Burden scale is the most validated expert-based anticholinergic scale [10]. The scale ranks the anticholinergic properties from 0 to 3. Drugs with in vitro activity are scored 1. Drugs with a clinically relevant effect are scored 2 or 3. A score of 3 is given when drugs are associated with delirium, and other medication is scored 0. The ACB scale of EPHOR, which also ranks the anticholinergic properties from 0 to 3, lists a few drugs not mentioned on the Anticholinergic Cognitive Burden scale. Therefore, we added these drugs to the Anticholinergic Cognitive Burden scale, Appendix Table 1. A cACB ≥ 3 is considered clinically relevant by both scoring scales, thus, an individual was considered exposed to a potentially inappropriate ACB when the sum of the ACB score of simultaneously dispensed drugs was equal to or more than three [11–13, 24]. As mentioned before, after exposure to cACB ≥ 3, a patient was censored. The baseline year for any patient was the year they were included in our study. Additional patient demographic data were extracted, namely sex and age.

Table 1.

Characteristics per episode and per cohort

Episode stratum, years
1996–2000 2001–2005 2006–2010 2011–2015 2016–2020
Characteristics per cohort
No. at risk 5456 5678 5446 5252 4656
RS-I 5456 4071 2746 1797 1018
RS-II 1607 2337 1911 1475
RS-III 363 1544 2163
Women, (%) 60.7 59.2 58.6 58.5 58.4
RS-I 60.7 60.3 61.1 61.9 63.2
RS-II 56.4 55.5 55.9 56.8
RS-III 59.5 57.7 57.2
Age at start of episode, (mean, years) 73.7 73.8 74.1 74.1 74.6
RS-I 73.7 74.7 77.7 80.7 83.9
RS-II 71.5 70 72.8 76.7
RS-III 74 67.9 68.7
Number of cases 872 672 463 423 361
RS-I 872 489 253 176 75
RS-II 183 185 149 130
RS-III 25 98 156
Person-years at risk 20,730 21,771 21,153 20,994 18,156
RS-I 20,730 17,114 11,768 7357 3669
RS-II 4657 8723 8703 6341
RS-III 662 4934 8146
Follow-up per patient, (mean, years) 3.8 3.8 3.9 4.0 3.9
RS-I 3.8 4.2 4.3 4.1 3.6
RS-II 2.9 3.7 4.6 4.3
RS-III 1.8 3.2 3.8
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RS Rotterdam study cohort

Statistical Analysis

To avert variation per year and obtain a clearer temporal trend, analysis was conducted on a 5-year calendar basis, using episodes constructed for every 5 years. In total, five episodes (1996–2000, 2001–2005, 2006–2010, 2011–2015, 2016–2020) were created.

Participants were censored after they had obtained a cACB ≥ 3. The cumulative incidence over the study period from 1996 to 2020 was described as percentage of participants with a cACB ≥ 3 for the first time. Stratified analyses on the cumulative incidence were performed for 5-year age groups, sex, and 5-year episodes.

To calculate IRs per person-year, person-time at risk for a cACB ≥ 3 ended at the first of the following: a cACB ≥ 3, death, end of episode, or end of follow-up, whichever came first. We calculated person-years at risk per 5-year episode by adding each participant’s contribution of follow-up time within that episode. A single participant was able to contribute to multiple age groups and multiple episodes if that person did not have a cACB ≥ 3 at the start of the age group or episode. We stratified for age group, episode, and sex. Poisson regression was used to calculate IRs and 95% confidence intervals (95% CIs) by episode. All models were adjusted for age group, episode, and sex. The log of follow-up time was used as an offset. In addition, Poisson regression was used to calculate the incidence rate ratio (IRR) for every episode compared with the first.

In addition, we identified the five most frequently used drugs with an ACB score equal to three in 2018 and grouped them by drug class. Thereafter, we calculated the cumulative incidence per drug class stratified by episode.

Results

In total, 11,038 participants were included in this study between 1 January 1996 and 31 December 2020. Among our full cohort, the mean age at index was 70.9 years, and women accounted for 59.0% of the participants. The mean follow-up was 9.3 years per participant. Characteristics of data per episode and per cohort of the Rotterdam Study are presented in Table 1. A total of 2791 incident cases of cACB ≥ 3 were recorded. Thus, the cumulative incidence of cACB ≥ 3 was approximately 25% between 1996 and 2020.

The IR of a cACB ≥ 3 decreased over time, Appendix Table 2. Between 1996 and 2020, the IR decreased from 42 per 1000 person-years in 1996–2000 to 20 per 1000 person-years in 2016–2020. After stratification by age groups, participants who were relatively older generally showed higher IRs of cACB ≥ 3 across the episodes (Fig. 1). The oldest age group (86–90 years) had IRs between 23.0 and 57.0 per 1000 person-years, while the youngest age group (66–70 years) had IRs between 17.4 and 30.5 per 1000 person-years dependent on the episode. Moreover, the IR decreased over time for all age groups. Our sex-stratified results did not show statistically significant different IRs for men and women (Appendix Table 2).

Fig. 1.

Fig. 1

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cACB ≥ 3 per 1000 person-years, stratified by episode and age group

After we performed Poisson regression analysis to calculate IRRs for age groups, episodes, and sex, we found that participants aged between 86 and 90 years had more than 1.5 times the rate of having a cACB ≥ 3 compared with participants aged between 66 and 70 years (IRR: 1.67, 95% CI 1.46–1.91; Fig. 2). The IR between 2016 and 2020 declined to 54% of the IR from 1996 to 2000 (IRR 0.46, 95% CI 0.41–0.52). Women did not have a significantly higher IR than men (IRR: 1.005, 95% CI 0.93–1.09).

Fig. 2.

Fig. 2

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The forest plots represent the incidence trends for ACB ≥ 3 expressed as incidence rate ratio (IRR). In A, the IIR is estimated using the incidence rate of the referent first episode (1996–2000), adjusted for sex and age group. In B, the IIR is estimated using the incidence rate of the referent fist age group (66–70 years), adjusted for sex and episode.

Focusing on the most frequently used drugs with a single ACB score equal to three, we found that for amitriptyline and paroxetine, the decline in cumulative incidence across time was similar to the trend seen for the overall cumulative incidence of cACB ≥ 3. However, for the spasmolytics oxybutynin, solifenacin, and tolterodine, we found an increase up until 2006–2010, after which the cumulative incidence relatively decreased (Fig. 3).

Fig. 3.

Fig. 3

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Incidence of tricyclic antidepressants amitriptyline and paroxetine, and incidence of spasmolytics oxybutynine, solifenacin, and tolterodine

Discussion

Drugs with anticholinergic activity are frequently prescribed to older persons, with a cumulative incidence of cACB ≥ 3 of 25% between 1996 and 2020. Comparing the community-dwelling participants over time and by age, the IR of a cACB ≥ 3 decreased significantly between 1996 and 2020 for all age groups. However, over time, participants aged 86–90 years remained to have the highest IR compared with participants aged 65–70 years. Participants aged 86–90 years had a 1.5 times higher rate of a cACB ≥ 3, even after adjusting for the episode. As these are patients with increased risk of falls, delirium, and cognitive impairment, this might therefore be of concern.

Several studies have described changes in the prevalence rates of the ACB. However, to our knowledge, this is the first study to describe the cumulative incidence and IRs of a cACB ≥ 3 according to the Anticholinergic Cognitive Burden scale. A few studies have described changes over time. So far, evidence has been mixed; three studies found a decrease in the prevalence [18–20], while two others found an increase in the prevalence [21, 22]. However, a possible peak of exposure to anticholinergics may have been around the year 2010 [18, 21, 22] and could explain these inconsistent results when studies only measured two time points. In addition, the prevalence in the studies varies greatly, ranging from 4.7–46%, perhaps due to differences in the used scoring scales and criteria for the anticholinergic drug burden exposure. Although some studies used the Anticholinergic Cognitive Burden scale, they only evaluated drugs with an ACB score of three. However, we assessed a total score of three or more, as this total score is considered clinically relevant [24].

To gain insight into the drugs that caused the high ACB, we identified the five most frequently used drugs with an ACB score equal to three. For the combined incidence of an amitriptyline or paroxetine prescription, the decline in incidence across time is similar to the trend seen for the incidence of cACB ≥ 3. Prescribers should consider nortriptyline as an alternative to amitriptyline and paroxetine. In vitro, nortriptyline has a lower anticholinergic activity than amitriptyline [25]. This could be the reason why nortriptyline has a lower score on some other anticholinergic score scales [26]. Thus, nortriptyline may be preferred over other tricyclic antidepressants to lower the risk of adverse effects due to anticholinergics [27].

A major problem in older people is urinary incontinence, and anticholinergics can help reduce the symptoms of it. A stationary trend was found for the spasmolytics oxybutynin, solifenacin, and tolterodine. Solifenacin has been on the Dutch market since 2004, which could explain the increase until 2010. To reduce the ACB of a patient, mirabegron, a spasmolytic without anticholinergic drug activity, can be considered [28, 29].

Generally, for both the treatment of depression and urine incontinence, nonpharmacological treatments are preferred. However, when nonpharmacological treatments are insufficient, pharmacotherapy can be prescribed. When pharmacotherapy is prescribed and drugs with similar efficacy are considered, drugs with the lowest anticholinergic burden should, in our opinion, be preferred.

There are several limitations to this study. First, we extracted pharmacy dispensing records only once every year. Thus, when a patient does not obtain the medication consistently, we could have an over- or underestimation of the magnitude of ACB, albeit this would lead to nondifferential misclassification. Second, we could not include over the counter (OTC) medication. Therefore, our findings may underestimate the incidence of cACB ≥ 3. Albeit, OTC medication is generally indicated for a short amount of time. Third, one of the major challenges lies in defining the anticholinergic medications and their burden. There is a lack of consensus on which is the best scale for scientific research or clinical practice. We chose the Anticholinergic Cognitive Burden Score scale as it is the scale that is modified by EPHOR and used in clinical practice in the Netherlands. Recently, a new ACB scale has been developed that is based on drug properties to determine the ACB, instead of expert-based opinions. However, it has not been clinically validated [30]. Contrary, the Anticholinergic Cognitive Burden Score scale is the most frequently validated expert-based anticholinergic scale for adverse effects [10]. We chose a cutoff value of three or more because the Anticholinergic Cognitive Burden Score scale considers it clinically relevant [11, 13]. In addition, we chose to supplement this scoring scale with the ACB scale by EPHOR because it is the scale that is used by clinicians in the Netherlands. Lastly, we note that we examined the incidence of a cACB ≥ 3. Therefore, we did not measure or account for the period of time a patient had a cACB ≥ 3. A frequent or less frequent user were both counted as a case and consequently censored. Thus, no conclusion can be drawn in regard to the length of use of anticholinergic drugs.

There are several strengths of this study. This study is one of a few to explore temporal trends of the ACB with data collected over a long follow-up period of 25 years and is the first to explore the incidence of the ACB over this timeframe. Furthermore, this is the first study to present data on the exposure of cACB ≥ 3. A cACB ≥ 3 is considered clinically relevant and is advised to be avoided when possible in the oldest of older adults, as these are the persons with increased risk of falls, delirium, and cognitive impairment [3, 11, 24]. Thus, our results give insight into the proportion of people who might have a relative contraindication for cACB ≥ 3.

Conclusions

Exposure to a cACB ≥ 3 in the general population has decreased by over 50% in recent years compared with 1996–2000. Although this decreased IR is also seen among adults aged 86–90 years, this oldest age group remains at the highest risk of exposure to cACB ≥ 3 and, thus, their increased risk for falls, delirium, and cognitive impairment also remains elevated. Therefore, prescribers and pharmacists should continue to regularly review the benefits and harms of prescribing and continuing drugs with an ACB, especially among the oldest old.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 254 KB) (253.5KB, pdf)

Acknowledgements

The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists.

Declarations

Funding

This study is part of the ongoing population-based Rotterdam Study. The Rotterdam Study is funded by the Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam.

Conflicts of interest

The authors have declared that no conflicts of interest exist.

Availability of data and material

Datasets generated and/or analyzed during the current study are not publicly available owing to legal and ethical restraints but are available upon reasonable request. Requests can be directed to secretariat.epi@erasmusmc.nl, or visit the following website for more information http://www.ergo-onderzoek.nl/wp/contact. Sharing of individual participant data was not included in the informed consent of the study, and there is a potential risk of revealing participants’ identities as it is not possible to completely anonymize the data.

Ethics statement

The Rotterdam Study has been approved by the Medical Ethics Committee of the Erasmus MC (registration no. MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport (Population Screening Act WBO, license no. 1071272-159521-PG). The Rotterdam Study Personal Registration Data collection is filed with the Erasmus MC Data Protection Officer under registration no. EMC1712001. The Rotterdam Study has been entered into the Dutch Trial Register (NTR; https://onderzoekmetmensen.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP https://www.who.int/clinical-trials-registry-platform, search portal https://trialsearch.who.int/) under shared cat. no. NL6645/NTR6831. The Rotterdam Study project persistent identifier is https://ror.org/02ac58f22.

Consent for publication

Not applicable.

Code availability

Not applicable.

Author contributions

A.E.—methodology, data analysis, data interpretation, writing (original draft), writing (review and editing), and visualization. S.L.—methodology, data interpretation, and writing (review and editing). B.S.—methodology, acquisition of data, interpretation of data, and writing (review and editing). L.V.—conceptualization, methodology, interpretation of data, and writing (review and editing). R.R.—conceptualization, methodology, interpretation of data, and writing (review and editing). All authors read and approved the final version.

Consent to participate

All participants provided written informed consent to participate in the study and to have their data utilized for research activities.

References

  • 1.Naja M, Zmudka J, Hannat S, Liabeuf S, Serot J-M, Jouanny P. In geriatric patients, delirium symptoms are related to the anticholinergic burden. Geriatr Gerontol Int. 2016;16(4):424–31. [DOI] [PubMed] [Google Scholar]
  • 2.Crispo JA, Willis AW, Thibault DP, Fortin Y, Hays HD, McNair DS, et al. Associations between anticholinergic burden and adverse health outcomes in Parkinson disease. PLoS ONE. 2016;11(3): e0150621. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Green AR, Reifler LM, Bayliss EA, Weffald LA, Boyd CM. Drugs contributing to anticholinergic burden and risk of fall or fall-related injury among older adults with mild cognitive impairment, dementia and multiple chronic conditions: a retrospective cohort study. Drugs Aging. 2019;36(3):289–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.The American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2023 updated AGS Beers Criteria® for potentially inappropriate medication use in older adults. J Am Geriatr Soc. 2023;71(7):2052–81. [DOI] [PMC free article] [PubMed]
  • 5.O’Mahony D, Cherubini A, Guiteras AR, Denkinger M, Beuscart JB, Onder G, et al. STOPP/START criteria for potentially inappropriate prescribing in older people: version 3. Eur Geriatr Med. 2023;14(4):625–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Mann NK, Mathes T, Sönnichsen A, Pieper D, Klager E, Moussa M, Thürmann PA. Potentially inadequate medications in the elderly: PRISCUS 2.0. Dtsch Arztebl Int. 2023;120(1–2):3–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Renom-Guiteras A, Meyer G, Thürmann PA. The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries. Eur J Clin Pharmacol. 2015;71(7):861–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Welsh TJ, van der Wardt V, Ojo G, Gordon AL, Gladman JRF. Anticholinergic drug burden tools/scales and adverse outcomes in different clinical settings: a systematic review of reviews. Drugs Aging. 2018;35(6):523–38. [DOI] [PubMed] [Google Scholar]
  • 9.Lisibach A, Benelli V, Ceppi MG, Waldner-Knogler K, Csajka C, Lutters M. Quality of anticholinergic burden scales and their impact on clinical outcomes: a systematic review. Eur J Clin Pharmacol. 2021;77(2):147–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Salahudeen MS, Duffull SB, Nishtala PS. Anticholinergic burden quantified by anticholinergic risk scales and adverse outcomes in older people: a systematic review. BMC Geriatr. 2015;15:31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Boustani M, Campbell NL, Munger S, Maidment I, Fox C. Impact of anticholinergics on the aging brain: a review and practical application. Aging Health. 2008;4(3):311–20. [Google Scholar]
  • 12.Campbell NL, Boustani M, Limbil T, Ott C, Fox C, Maidment I, et al. The cognitive impact of anticholinergics: a clinical review. Clin Interv Aging. 2009;4:225–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Campbell N, Maidment I, Fox C, Khan B, Boustani M. The 2012 update to the anticholinergic cognitive burden scale. J Am Geriatr Soc. 2013;61(No. S1 C46):S142–3. [Google Scholar]
  • 14.Phutietsile GO, Fotaki N, Jamieson HA, Nishtala PS. The association between anticholinergic burden and mobility: a systematic review and meta-analyses. BMC Geriatr. 2023;23(1):161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Taylor-Rowan M, Kraia O, Kolliopoulou C, Noel-Storr AH, Alharthi AA, Cross AJ, et al. Anticholinergic burden for prediction of cognitive decline or neuropsychiatric symptoms in older adults with mild cognitive impairment or dementia. Cochrane Database Syst Rev. 2022;8(8):CD015196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang K, Alan J, Page AT, Dimopoulos E, Etherton-Beer C. Anticholinergics and clinical outcomes amongst people with pre-existing dementia: a systematic review. Maturitas. 2021;151:1–14. [DOI] [PubMed] [Google Scholar]
  • 17.Stewart C, Taylor-Rowan M, Soiza RL, Quinn TJ, Loke YK, Myint PK. Anticholinergic burden measures and older people’s falls risk: a systematic prognostic review. Ther Adv Drug Saf. 2021;12:20420986211016644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Rhee TG, Choi YC, Ouellet GM, Ross JS. National prescribing trends for high-risk anticholinergic medications in older adults. J Am Geriatr Soc. 2018;66(7):1382–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Rinkinen M-O, Roitto H-M, Öhman HR, Kautiainen HJ, Tilvis RS, Strandberg TE, et al. Temporal trends over two decades in the use of anticholinergic drugs among older community-dwelling people in Helsinki, Finland. Drugs Aging. 2022;39(9):705–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.de Germay S, Rueter M, Montastruc F, Rousseau V, Lapeyre-Mestre M, Montastruc JL. Trends of atropinic (anticholinergic) exposure in the elderly: a 10-year analysis in the French EGB database. Fundam Clin Pharmacol. 2019;33(4):471–8. [DOI] [PubMed] [Google Scholar]
  • 21.Sumukadas D, McMurdo MET, Mangoni AA, Guthrie B. Temporal trends in anticholinergic medication prescription in older people: repeated cross-sectional analysis of population prescribing data. Age Ageing. 2013;43(4):515–21. [DOI] [PubMed] [Google Scholar]
  • 22.Grossi CM, Richardson K, Savva GM, Fox C, Arthur A, Loke YK, et al. Increasing prevalence of anticholinergic medication use in older people in England over 20 years: cognitive function and ageing study I and II. BMC Geriatr. 2020;20(1):267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Ikram MA, Kieboom BCT, Brouwer WP, Brusselle G, Chaker L, Ghanbari M, et al. The Rotterdam Study. Design update and major findings between 2020 and 2024. Eur J Epidemiol. 2024;39(2):183–206. [DOI] [PubMed] [Google Scholar]
  • 24.Expertisecentrum PHarmacotherapie bij OudeRen (Ephor). Drugs on the Anticholinergic Burden (ACB) scale 2018. Available from: https://ephor.nl/wp-content/uploads/2018/12/anticholinergic-drugs.pdf. Accessed 2 July 2024.
  • 25.Chew ML, Mulsant BH, Pollock BG, Lehman ME, Greenspan A, Mahmoud RA, et al. Anticholinergic activity of 107 medications commonly used by older adults. J Am Geriatr Soc. 2008;56(7):1333–41. [DOI] [PubMed] [Google Scholar]
  • 26.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):508–13. [DOI] [PubMed] [Google Scholar]
  • 27.Expertisecentrum PHarmacotherapie bij OudeRen (Ephor). Nortriptyline versus amitriptyline bij ouderen 2023 [updated 3 May 2023]. Available from: https://ephor.nl/nortriptyline-versus-amitriptyline-bij-ouderen/#:~:text=In%20de%20Ephor%20app%20geeft,en%20consensus%20van%20de%20expertgroep. Accessed 18 July 2024.
  • 28.Federatie Medische Specialisten. Urine-incontinentie 2e- en 3e-lijnszorg: Richtlijnendatabase; [updated 11 April 2024]. Available from: https://richtlijnendatabase.nl/richtlijn/urine-incontinentie_ui_2e-_en_3e-lijnszorg/ui_bij_de_kwestbare_ouderen/medicamenteuze_behandeling_ouderen_bij_ui_2024.html. Accessed 4 Aug 2024
  • 29.Farmacotherapeutisch Kompas. Mirabegron: Zorginstituut Nederland. Available from: https://www.farmacotherapeutischkompas.nl/bladeren/preparaatteksten/m/mirabegron. Accessed 27 Aug 2024.
  • 30.Phutietsile GO, Fotaki N, Nishtala PS. Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties. Br J Clin Pharmacol. 2024;90(9):2236–55. [DOI] [PubMed] [Google Scholar]

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