Anticholinergic burden: First comprehensive analysis using claims data shows large variation by age and sex.

PURPOSE: The cumulative effect of medication inhibiting acetylcholine activity-also known as anticholinergic burden (AB)-can lead to functional and cognitive decline, falls, and death. Given that studies on the population prevalence of AB are rare, we aimed to describe it in a large and unselected population sample.
METHODS: Using the German Pharmacoepidemiological Research Database (GePaRD) with claims data from ~20% of the German population we analyzed outpatient drug dispensations in 2016. Based on the Anticholinergic Cognitive Burden (ACB) scale, we classified persons into four categories and determined the cumulative AB as continuous variable.
RESULTS: Among 16,470,946 persons (54% female), the prevalence of clinically relevant AB (ACB≥3) was 10% (women) and 7% (men). Below age 40 it was highest in persons ≤18 years (6% both sexes). At older ages (50-59 vs. 90-99 years), prevalence of ACB≥3 increased from 7% to 26% (men) and from 10% to 32% (women). Medication classes contributing to the cumulative AB differed by age: antihistamines, antibiotics, glucocorticoids (≤19 years), antidepressants (20-49 years), antidepressants, cardiovascular medication, antidiabetics (50-64 years), and additionally medication for urinary incontinence/overactive bladder (≥65 years). Medication dispensed by general physicians contributed most to the cumulative AB.
CONCLUSION: Although a clinically relevant AB is particularly common in older persons, prevalence in younger age groups was up to 7%. Given the risks associated with AB in older persons, targeted interventions at the prescriber level are needed. Furthermore, risks associated with AB in younger persons should be explored.

Introduction

Medications with anticholinergic activity (MACs) inhibit the effect of the neurotransmitter acetylcholine [1]. They are used for the treatment of diseases such as depression, psychosis, cardiovascular diseases, asthma, overactive bladder, and COPD [1]. The cumulative effect of MACs, also called anticholinergic burden (AB), has been shown to be associated with adverse health outcomes such as functional [2, 3] and cognitive decline [2, 4, 5], delirium [6, 7], falls [3, 8], and death [9, 10].

Although the majority of studies on the adverse effects of AB focused on older adults, there are studies suggesting that younger populations might also be affected. In some of those studies, AB was associated with impaired cognitive ability and real-world functioning as well as a negative impact on the outcomes of psychosocial treatment programs in patients with schizophrenia or schizoaffective disorder [11]. Notably, many of the medications contributing to the AB had indications other than psychiatric diseases [11]. Some studies showed impairment of verbal learning and/or verbal memory associated with AB in persons with schizophrenia [12-14] and major depressive disorder [15]. Furthermore, studies have shown an association between AB and delirium in pediatric intensive care patients [16] and critically ill middle-aged adults [17]. These data suggest that already in younger patients, AB might be associated with adverse effects. So far, only a single study has provided a comprehensive overview of the prevalence of AB in all age groups of a population [18]. However, in this study, age categories were defined broadly and AB prevalences were not stratified by sex within age groups.

In our study, we aimed to characterize the prevalence of AB in a large and unselected sample of the German general population and to assess the classes of medication contributing to the total cumulative AB, stratified by age and sex.

Methods

Data source

We used the German Pharmacoepidemiological Research Database (GePaRD), which is based on claims data from four statutory health insurance providers in Germany and currently includes information on approximately 25 million persons who have been insured with one of the participating providers since 2004 or later. Per data year, there is information on approximately 20% of the general population and all geographical regions of Germany are represented. In Germany, about 90% of the general population are covered by statutory health insurance. The health care system is characterized by uniform access to all levels of care and free choice of providers.

In addition to demographic data, GePaRD contains information on outpatient drug dispensations as well as outpatient (i.e., from general practitioners and specialists) and inpatient services and diagnoses. Information on medication includes the anatomical-therapeutic-chemical (ATC) code, the prescription and dispensation date, the specialty of the prescriber as well as the number of defined daily doses (DDDs). Diagnoses are coded according to the German modification of the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10-GM).

Study design and study population

We conducted a cross-sectional study using data from the year 2016, the most recent data at the time of analysis, to assess the prevalence of AB. We included all persons with at least one day of insurance coverage during the observation period, i.e., between 1 January and 31 December 2016 preceded by at least 365 days of continuous insurance (pre-observation period). We excluded persons with a place of residence outside of Germany, without valid information on age and sex as well as persons with a hospitalization of ≥90 days, which overlapped into this person’s observation period. For all included persons, the available (continuous) observation period in 2016 was used to assess the use of MAC. For persons with a hospitalization starting in 2016 and with a duration of ≥90 days, MAC use was only assessed until the start of this hospitalization (Fig 1).

Fig 1

Graphical depiction of study design.

We identified morbidities and treatment with medication excluding MAC using sensitive identification algorithms: The coding of morbidities was assessed any time prior to observation period (starting from 2004) through records of ≥1 ICD-10-GM inpatient or outpatient diagnoses or records of ≥1 codes of relevant operations, procedures or outpatient services as well as participation in disease management plans. This approach, i.e. taking into account all information on morbidity available for a person before 2016, aims to compensate for the fact that with secondary data, a person cannot be asked if he or she ever had a certain disease, as it would be done in a study based on primary data. Treatment with medication excluding MAC was assessed within 365 days before start of observation period (excluding start of observation period) based on records of ≥1 outpatient dispensations.

Assessment of the anticholinergic burden

Exposure to MAC was assessed based on outpatient prescriptions dispensed during the observation period, i.e., in 2016. Treatment durations were estimated based on DDDs. In case MAC were dispensed before 1 January 2016 and the days of supply covered by this dispensation overlapped with the observation period, the DDDs overlapping with the observation period were also considered. We assumed lower DDDs for persons aged ≤18 and ≥65 years if recommended in the respective Summary of Product Characteristics. Moreover, we identified the specialty of the prescribing physician for each dispensation of MAC. To quantify the AB in individuals, we used a list of relevant MAC and a scoring system proposed by Kiesel et al. [19]. Kiesel et al. systematically reviewed published lists of MAC and corresponding scores, mainly developed in the US, UK or Australia, and adapted them to medications relevant for Germany [19]. Their categorization of AB [19] was based on the Anticholinergic Cognitive Burden (ACB) scale, which was developed by Boustani et al. to identify persons at risk for cognitive impairment [20]. Based on this scoring system, MACs dispensed during the observation period were scored according to their anticholinergic effects: ACB score 1 (evidence from in vitro data that chemical entity has antagonist activity at muscarinic receptor), ACB score 2 (evidence from literature, prescriber’s information, or expert opinion of clinical anticholinergic effect) or ACB score 3 (evidence from literature, expert opinion, or prescriber’s information that medication may cause delirium) [20, 21]. Boustani et al. considered dispensation of MAC with an ACB score 2 or 3 as well as a total ACB score of 3 or higher as clinically relevant [20]. For the interpretation of this study, we defined ACB≥3 as clinically relevant and additionally considered ACB categories ACB = 0, ACB = 1, ACB = 2, and ACB≥3 in order to assess borderline AB in the study population. For our study population, the AB was calculated for each person on a daily basis during the observation period by adding up the scores of all dispensed MACs. Prevalence of morbidities, treatment with medication other than MAC, and health care utilization were stratified by the highest category of AB reached during the observation period.

We also calculated a measure which we called “cumulative AB”. We calculated this additional measure because it allowed us to assess the proportion of AB attributable to a certain class of MAC (e.g., antidepressants) or to a certain physician specialty. This measure was called “cumulative burden” because it takes into account all dispensations in the observation period (i.e. in 2016). This cumulative AB was calculated as follows for each person: We first multiplied the AB score of each MAC dispensed to the person during the observation period or overlapping the observation period with the length of supply (based on DDD) and then summed up the score points of all dispensations. Subsequently, these AB scores were summed up per person to calculate the cumulative AB. For example, a person receiving 200 DDDs of metformin (ACB score 1) and 30 DDDs of tramadol (ACB score 2) during the observation period had a cumulative AB of 260 (i.e., the result of 200 x 1 + 30 x 2). This method was proposed by Campbell et al. [5]. Campbell et al. further divided the cumulative AB by the number of days in the exposure period to transfer the total AB score into a mean score per person but this additional transformation was not relevant in the context of our study [5].

Data analysis

We calculated the period prevalence of AB for each of the four AB categories for the observation period. The prevalence was calculated as the number of persons in the respective AB category (numerator) divided by the number of included persons (denominator). Again, persons were allocated to the highest level of AB reached during the observation period.

In order to describe which proportion of the cumulative AB was attributable to a certain class of MAC (e.g., antidepressants) or physician specialty (e.g., general practitioners), the cumulative AB of a MAC class or physician specialty of each respective age and sex group was divided by the total cumulative AB in that age and sex group.

Data management and analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

Ethics and approvals

In Germany, the utilization of health insurance data for scientific research is regulated by the Code of Social Law. All involved health insurance providers as well as the German Federal Office for Social Security and the Senator for Health, Women and Consumer Protection in Bremen as their responsible authorities approved the use of GePaRD data for this study. Informed consent for studies based on claims data is required by law unless obtaining consent appears unacceptable and would bias results, which was the case in this study. According to the Ethics Committee of the University of Bremen studies based on GePaRD are exempt from institutional review board review.

Results

The study population included a total of 16,470,946 persons (53.6% female) with a median age of 45 years (Q1–Q3: 26–61 years) (Fig 2).

Fig 2

Flow chart illustrating the inclusion and exclusion of persons into the study.

For the majority of the study population, we observed no AB during the observation period, i.e., ACB = 0 in 68.5% of men and in 61.7% of women (Table 1). Prevalence of ACB = 1 was 17.6% in men and 19.7% in women, for ACB = 2, it was 6.7% in men and 8.2% in women, while a clinically relevant AB (ACB≥3) was observed in 7.2% of men and 10.4% of women (Fig 3).

Table 1

Number and period prevalence of persons with and without anticholinergic burden measured through the Anticholinergic Cognitive Burden (ACB) scale during the observation period (2016), by sex and age.

		ACB score
	Total	ACB = 0		ACB = 1		ACB = 2		ACB ≥ 3
Sex	Na	Nb	Prevalence (%)	Nb	Prevalence (%)	Nb	Prevalence (%)	Nb	Prevalence (%)
Men	7,635,507	5,232,064	68.5	1,342,836	17.6	507,767	6.7	552,840	7.2
Age
≤ 18	1,361,884	1,069,419	78.5	185,882	13.6	30,520	2.2	76,063	5.6
19 to 29	1,128,700	956,617	84.8	121,521	10.8	31,648	2.8	18,914	1.7
30 to 39	1,077,901	862,425	80.0	138,917	12.9	44,353	4.1	32,206	3.0
40 to 49	975,152	715,119	73.3	158,347	16.2	55,720	5.7	45,966	4.7
50 to 59	1,210,955	790,562	65.3	240,566	19.9	92,866	7.7	86,961	7.2
60 to 69	853,986	448,660	52.5	214,327	25.1	96,021	11.2	94,978	11.1
70 to 79	697,604	285,943	41.0	191,496	27.5	100,439	14.4	119,726	17.2
80 to 89	293,902	93,090	31.7	82,108	27.9	49,783	16.9	68,921	23.5
90 to 99	35,049	10,119	28.9	9,557	27.3	6,353	18.1	9,020	25.7
≥ 100	374	110	29.4	115	30.7	64	17.1	85	22.7
Women	8,835,439	5,447,201	61.7	1,741,731	19.7	728,329	8.2	918,178	10.4
Age
≤ 18	1,286,334	1,017,119	79.1	167,010	13.0	28,592	2.2	73,613	5.7
19 to 29	1,120,182	867,427	77.4	175,136	15.6	47,879	4.3	29,740	2.7
30 to 39	1,154,740	850,292	73.6	194,824	16.9	62,185	5.4	47,439	4.1
40 to 49	1,214,399	804,947	66.3	236,844	19.5	87,472	7.2	85,136	7.0
50 to 59	1,516,942	888,755	58.6	329,169	21.7	138,788	9.1	160,230	10.6
60 to 69	1,086,934	531,914	48.9	264,899	24.4	128,807	11.9	161,314	14.8
70 to 79	923,303	344,264	37.3	238,524	25.8	138,632	15.0	201,883	21.9
80 to 89	429,269	117,652	27.4	109,445	25.5	76,061	17.7	126,111	29.4
90 to 99	100,998	24,229	24.0	25,221	25.0	19,451	19.3	32,097	31.8
≥ 100	2,338	602	25.7	659	28.2	462	19.8	615	26.3

a Denominator of the prevalence are persons insured for ≥1 day within the observation period and with ≥1 year continuous insurance before.

b Numerator of the prevalence, calculated as the number of persons with ACB = 0, ACB = 1, ACB = 2, and ACB ≥ 3, respectively. Persons will be allocated in the highest level of the ACB score ever reached during the observation period.

Fig 3

Proportion of anticholinergic burden measured through the Anticholinergic Cognitive Burden (ACB) scale (2016), by sex and age.

Both in men and women, the prevalence of ACB≥3 was about 6% in persons aged ≤18 years and thus higher than in persons aged 19–49 years. At older ages, the prevalence of ACB≥3 steadily increased. In men, it increased from 7.2% (50–59 years) to 11.1% (60–69 years) and 17.2% (70–79 years). The same pattern was seen in women but the prevalences were about 3–4 percentage points higher (50–59 years: 10.6%, 60–69 years: 14.8%, 70–79 years: 21.9%).

For all morbidities and medications assessed prior to start of observation period, prevalences increased with increasing ACB score (S1 Table). For example, compared to persons with lower or no ACB, persons with ACB≥3, had higher prevalences of psychiatric and behavioral, musculoskeletal as well as endocrine and metabolic diseases. They were prescribed medications from a higher number of different prescribers and had higher prevalences of cardiovascular therapy, analgesics and psychiatric medication. Moreover, persons with ACB≥3 were, on average, more frequently hospitalized, remained hospitalized for longer periods and had a higher prevalence of nursing home residency and obesity.

Persons with ACB≥3 were more frequently users of antidepressants (45.3% vs. 8.8%), antihistamines (17.7% vs. 7.1%), and antipsychotics (13.9% vs. 1.3%) (Table 2). Individuals who used medications for urinary incontinence/overactive bladder had ACB≥3 by default (13.0%), since all of these medications have an ACB score of 3.

Table 2

Prevalence of use of medications with anticholinergic activity (MACs) in persons with anticholinergic burden measured through the anticholinergic cognitive burden (ACB) scale during the observation period (2016).

	ACB scorea
MAC class	ACB = 1	ACB = 2	ACB ≥ 3
	N = 3,084,567b	N = 1,236,096b	N = 1,471,018b
Antidepressants	271,776 (8.8%)	272,488 (22.0%)	665,675 (45.3%)
Antihistamines	218,228 (7.1%)	63,216 (5.1%)	260,137 (17.7%)
Antipsychotics	40,943 (1.3%)	66,412 (5.4%)	204,098 (13.9%)
Benzodiazepines	66,391 (2.2%)	54,438 (4.4%)	143,152 (9.7%)
Cardiovascular medication	487,324 (15.8%)	276,345 (22.4%)	343,257 (23.3%)
Diuretics	58,088 (1.9%)	63,387 (5.1%)	111,202 (7.6%)
Gastrointestinal medication	8,208 (0.3%)	47,898 (3.9%)	90,185 (6.1%)
Opioids	200,374 (6.5%)	170,598 (13.8%)	275,654 (18.7%)
Medication for Parkinson’s disease	32,517 (1.1%)	31,349 (2.5%)	91,838 (6.2%)
Medication for urinary incontinence/overactive bladder	0 (0.0%)	0 (0.0%)	191,768 (13.0%)
Medication for respiratory diseases	125,958 (4.1%)	85,436 (6.9%)	135,733 (9.2%)
Glucocorticoids	697,422 (22.6%)	332,459 (26.9%)	414,559 (28.2%)
Tropane alkaloids	0 (0.0%)	0 (0.0%)	9,131 (0.6%)
Immunosuppressants	10,696 (0.3%)	11,859 (1.0%)	14,953 (1.0%)
Muscle relaxants	123,733 (4.0%)	44,098 (3.6%)	99,581 (6.8%)
Antiemetics	203,433 (6.6%)	72,146 (5.8%)	133,709 (9.1%)
Antibiotics	444,014 (14.4%)	311,271 (25.2%)	215,548 (14.7%)
Antiepileptics	16,259 (0.5%)	29,071 (2.4%)	58,048 (3.9%)
Non-opioid analgesics	126,367 (4.1%)	68,198 (5.5%)	95,586 (6.5%)
Antidiabetics	233,543 (7.6%)	161,959 (13.1%)	179,913 (12.2%)
Other MAC	41,977 (1.4%)	40,833 (3.3%)	48,087 (3.3%)

a Categorization based on the highest level of the ACB score ever reached during the observation period.

b Denominator is the number of included persons who had ≥1 dispensation of MAC for ≥1 day during the observation period.

Median total cumulative burden increased with higher age, was highest among the age group 80–94 years, and decreased slightly in age group ≥95 years (Fig 4).

Fig 4

Median (Q1-Q3) cumulative anticholinergic burden, by sex and age.

The contribution of the medication classes of MAC to the total cumulative AB differed between age groups (Table 3). In persons aged ≤19 years, antihistamines and antibiotics contributed most—with about 20–24% each—to the cumulative burden, followed by glucocorticoids with about 12–13%. In females, the contribution of antidepressants to the cumulative AB was twice as high as in males (16% vs. 8%). In persons aged 20–64 years, antidepressants contributed most to the cumulative AB, with proportions ranging between 25% in men aged 50–64 years to 48% in women aged 20–34 years. From age group 65–79 onwards, cardiovascular medication contributed to 24–26% of the AB in men and 21–23% in women. The proportion of diuretics increased particularly from age group 65–79 onwards and contributed to 6–19% of the cumulative AB in men and 6–17% in women. Also, the contribution of medication for urinary incontinence or overactive bladder increased with higher age to up to 14% (men aged 80–94 years). The contribution of antidiabetics to the cumulative AB was highest in men aged 50–79 years (17–19%). The contribution of medication for the treatment of respiratory diseases, gastrointestinal medications, and opioids increased slightly in persons aged ≥65 years, while the contribution of glucocorticoids to the AB decreased.

Table 3

Contribution of anticholinergic medication classes to cumulative anticholinergic burden in men and women, with at least one dispensation of medication with anticholinergic activity (MAC) during the observation period (2016), by age group.

	Age groupb
	≤ 19		20–34		35–49		50–64		65–79		80–94		≥ 95
Characteristics	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
	N = 306,894	N = 288,767	N = 260,776	N = 378,155	N = 372,387	N = 568,948	N = 627,682	N = 913,551	N = 609,698	N = 848,695	N = 221,985	N = 373,292	N = 4,021	N = 16,830
MAC class (%)c
Antidepressants	8.1%	16.3%	38.7%	47.9%	35.9%	45.3%	25.6%	36.1%	11.8%	21.4%	10.3%	19.4%	11.2%	18.9%
Antihistamines	24.0%	20.7%	3.6%	4.4%	2.0%	2.8%	1.2%	1.7%	0.9%	1.2%	1.3%	1.7%	2.7%	2.4%
Antipsychotics	3.9%	3.1%	18.1%	8.3%	14.6%	9.1%	6.4%	6.2%	2.1%	2.8%	1.6%	2.4%	2.4%	3.5%
Benzodiazepines	0.6%	0.6%	1.0%	0.7%	1.0%	0.9%	0.8%	0.9%	0.6%	1.0%	0.8%	1.3%	1.6%	2.3%
Cardiovascular medication	0.9%	0.8%	2.5%	2.4%	7.6%	5.8%	16.5%	12.2%	23.8%	20.8%	25.4%	23.3%	26.0%	22.5%
Diuretics	0.2%	0.2%	0.3%	0.4%	1.1%	1.3%	2.9%	2.7%	5.6%	5.5%	9.8%	9.8%	19.0%	16.7%
Gastrointestinal medication	0.7%	0.9%	1.0%	1.2%	1.5%	1.3%	1.9%	1.7%	2.1%	2.0%	1.9%	1.8%	1.8%	1.6%
Opioids	0.4%	0.5%	2.0%	1.6%	3.1%	2.5%	2.9%	2.8%	2.5%	3.3%	3.0%	5.3%	4.5%	8.2%
Medication for Parkinson’s disease	0.2%	0.1%	0.2%	0.3%	0.5%	0.5%	1.4%	1.0%	4.0%	2.5%	5.3%	2.9%	2.1%	1.7%
Medication for urinary incontinence/overactive bladder	8.9%	5.6%	2.8%	3.2%	2.4%	4.0%	3.4%	6.0%	8.9%	10.7%	13.5%	12.5%	12.1%	10.8%
Medication for respiratory diseases	2.9%	2.4%	1.6%	2.3%	2.9%	3.2%	5.0%	4.6%	6.4%	4.9%	6.2%	3.6%	4.9%	2.6%
Glucocorticoids	12.5%	12.3%	9.4%	10.7%	7.5%	8.5%	6.4%	7.1%	6.5%	6.8%	6.3%	5.4%	5.1%	3.6%
Tropane alkaloids	2.0%	1.9%	0.1%	0.1%	0.1%	0.0%	0.1%	0.0%	0.1%	0.0%	0.1%	0.0%	0.1%	0.0%
Immunosuppressants	1.4%	1.1%	1.8%	1.3%	1.1%	0.8%	0.6%	0.5%	0.3%	0.3%	0.2%	0.1%	0.1%	0.1%
Muscle relaxants	0.8%	0.7%	1.4%	1.3%	1.4%	1.5%	1.0%	1.1%	0.5%	0.5%	0.3%	0.3%	0.1%	0.2%
Antiemetics	0.8%	1.1%	0.8%	1.1%	0.4%	0.6%	0.3%	0.5%	0.3%	0.5%	0.4%	0.7%	0.6%	0.9%
Antibiotics	20.1%	21.4%	4.6%	5.7%	2.1%	2.2%	1.1%	1.3%	1.1%	1.2%	1.1%	0.9%	1.1%	0.9%
Antiepileptics	9.9%	7.1%	7.0%	3.3%	5.0%	2.7%	2.9%	2.0%	1.5%	1.1%	1.0%	0.7%	0.8%	0.3%
Non-opioid analgesics	0.2%	0.5%	1.1%	1.2%	1.6%	1.8%	1.6%	2.1%	1.1%	1.7%	0.9%	1.3%	0.9%	0.9%
Antidiabetics	0.1%	0.3%	1.1%	1.3%	7.1%	3.4%	16.6%	7.4%	18.6%	10.3%	10.1%	6.0%	2.8%	1.8%

a The cumulative anticholinergic burden of a MAC is calculated by multiplying each MAC’s anticholinergic cognitive burden (ACB) score by its duration (prescribed number of DDDs).

b The total anticholinergic burden per age group is calculated by summing up each person’s cumulative anticholinergic burden during the observation period in the respective age group.

c Cumulative anticholinergic burden stratified by MAC class.

Prescriptions from general practitioners were the main contributors to the cumulative AB (Table 4). The proportion ranged between 40 and 41% in persons aged 20–34 years and increased to over 70% and more in persons aged 65 or older. In the age groups 20–49 years, prescriptions from physicians specializing in psychology and psychiatry contributed to about one fourth of the total cumulative AB. The number of different physician specialties that contributed 5% or more to the cumulative AB was five in persons aged ≤19 years, 3–4 in persons aged 20–64 years, and 2–3 in persons aged ≥65 years.

Table 4

Contribution of prescriber specialty to cumulative anticholinergic burden in men and women, with at least one dispensation of medication with anticholinergic activity (MAC) during the observation period (2016), by sex and age group.

	Age groupb
	≤ 19		20–34		35–49		50–64		65–79		80–94		≥ 95
Characteristics	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
	N = 306,894	N = 288,767	N = 260,776	N = 378,155	N = 372,387	N = 568,948	N = 627,682	N = 913,551	N = 609,698	N = 848,695	N = 221,985	N = 373,292	N = 4,021	N = 16,830
Prescribers of MAC (%)c
General practitioner	58.3%	54.1%	39.2%	41.0%	48.5%	47.0%	63.5%	56.5%	71.2%	68.5%	73.3%	78.2%	85.0%	86.3%
Anesthesiology	0.1%	0.1%	0.3%	0.4%	0.5%	0.8%	0.6%	1.0%	0.3%	0.6%	0.2%	0.3%	0.1%	0.1%
Ophthalmology	5.0%	5.5%	2.3%	2.5%	1.6%	1.5%	1.2%	1.3%	1.9%	2.2%	2.0%	1.6%	1.1%	0.6%
Surgery	0.8%	0.7%	1.0%	1.0%	1.2%	1.4%	1.0%	1.3%	0.6%	0.9%	0.4%	0.5%	0.2%	0.2%
Gynecology	0.0%	0.3%	0.0%	1.1%	0.0%	1.0%	0.0%	1.3%	0.0%	1.8%	0.0%	0.9%	0.0%	0.2%
Otorhinolaryngology	2.0%	1.7%	1.3%	1.4%	0.9%	0.8%	0.5%	0.4%	0.2%	0.2%	0.1%	0.1%	0.0%	0.0%
Dermatology	7.5%	8.7%	2.3%	3.2%	0.9%	1.1%	0.5%	0.6%	0.3%	0.3%	0.3%	0.2%	0.3%	0.3%
Internal medicine	1.6%	1.7%	4.2%	4.5%	4.5%	4.6%	6.1%	5.8%	7.3%	6.1%	5.7%	3.5%	2.5%	1.5%
Pediatrics	5.6%	4.8%	0.1%	0.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
Psychology and psychiatry	5.9%	9.7%	27.7%	26.7%	25.3%	26.0%	15.9%	19.9%	6.8%	9.0%	5.2%	6.0%	2.9%	5.3%
Neurosurgery	0.0%	0.0%	0.1%	0.1%	0.2%	0.2%	0.1%	0.1%	0.1%	0.1%	0.0%	0.0%	0.0%	0.0%
Neurology	0.6%	0.9%	5.1%	5.3%	4.8%	5.2%	3.5%	4.0%	3.1%	2.9%	2.8%	2.2%	1.8%	1.6%
Radiology	0.0%	0.0%	0.0%	0.0%	0.0%	0.1%	0.0%	0.1%	0.0%	0.0%	0.0%	0.0%	0.1%	0.0%
Physical medicine and rehabilitation	0.0%	0.0%	0.1%	0.1%	0.1%	0.1%	0.1%	0.1%	0.0%	0.1%	0.0%	0.1%	0.0%	0.0%
Urology	3.3%	2.1%	1.6%	2.0%	1.5%	2.2%	2.4%	3.0%	6.4%	5.1%	8.7%	4.7%	5.1%	2.1%
Unknown specialty	9.8%	10.1%	15.6%	11.4%	11.1%	9.1%	5.6%	5.7%	2.4%	2.7%	1.6%	1.9%	1.2%	2.0%

a The cumulative anticholinergic burden of a MAC is calculated by multiplying each MAC’s anticholinergic cognitive burden (ACB) score by its duration (prescribed number of DDDs).

b The total anticholinergic burden per age group is calculated by summing up each person’s cumulative anticholinergic burden during the observation period in the respective age group.

c Cumulative anticholinergic burden stratified by prescribing physician’s specialty.

Discussion

In our study, which included an unselected sample of 16 million persons of the German general population, about 7% of men and 10% of women had a clinically relevant AB (ACB≥3) based on prescriptions in 2016. The prevalence of ACB≥3 was higher in women than in men across all age groups and—even though increasing with age—already reached levels of 2–7% (men) and 3–11% (women) in persons younger than 60 years. The classes of medication contributing to the total cumulative AB differed greatly between sex and age groups: While antidepressants had a dominant share in age groups <60 years, their relative proportion decreased among persons aged ≥60 years due to the increased prescribing of cardiovascular medication and antidiabetics with anticholinergic activity.

As of now, only one other study has assessed the prevalence of AB without limitations on age or certain patient groups. The study of Cebron Lipovec et al. [18] was based on Slovenian outpatient prescriptions in 2018 and used the ACB scale for the assessment of AB. Results were stratified by the age groups children (≤18 years), adults (19–64 years), and older adults (≥65 years) but not by sex within these groups. The overall prevalence of ACB≥3 in the Slovenian population was 7.6%, similar to our results (7.2% in men and 10.4% in women). Prevalence of use of at least one MAC in Slovenian children was 20.7% which was similar to our study (21.5% in boys and 20.9% in girls). However, prevalence of ACB≥3 was much lower in Slovenian children (1.2% vs. 5.6% in boys and 5.7% in girls). The prevalence of use of at least one MAC among adults in Slovenia was in the lower ranges of the German results (25.8% vs. 15.2%–47.5% in men and 22.6%–62.7% in women). However, the prevalence of ACB≥3 for adults was similar (7.3% vs. 1.7%–11.1% in men and 2.7%–14.8% in women). Interestingly, the prevalence of use of at least one MAC in Slovenian older adults was much lower than in Germany with 43.1% vs. 59.0%–71.1% in men and 62.7%–76.0% in women as was the prevalence of ACB≥3 with 12.1% vs. 17.2%–22.7% in men and 21.9%–26.3% in women. As the list of MACs used in our study is more extensive than the one used by Cebron Lipovec et al. it is not clear whether the differences in prevalence of AB are due to the prescription behavior regarding MACs or the definition of MACs. However, the much lower prevalence of ACB≥3 in Slovenian older adults compared to German older adults is notable.

Among studies conducted in Germany, the comparison to our findings is hampered given that they were typically restricted to older adults or patients with a certain indication: Pfistermeister et al. [22] conducted a study in a population of hospitalized geriatric patients (median age 82 years), Ivchenko et al. [23] in older adults with overactive bladder (median age 75 years), Lippert et al. [24] in patients with dementia (mean age 84.7 years), Mayer et al. [25] in community-dwelling older German adults (median age 72 years), Phillips et al. [26] in community-dwelling older adults aged 65 years and older (mean age 73.8), and Mueller et al. [7] in patients undergoing cancer surgery (mean age 71.8 years). In the studies of Pfistermeister et al. [22] and Ivchenko et al. [23], where AB was assessed through ACB scale and categorized in the same way as in our study, the AB was similar, ACB≥3 27% and 25%, respectively, to the results of our study where the prevalence of an ACB≥3 was above 20% from age 70 in women and from age 80 in men. The studies of Lippert et al. [24] and Mayer et al. [25] also used the ACB scale but assessed AB as use of ≥1 MAC. The AB prevalence in their populations, 50% and 46%, respectively, was slightly lower than in our study (59%–71% in men, 63%–76% in women in the age groups 70 to ≥100 years). The studies of Phillips et al. [26] and Mueller et al. [7] reported much lower prevalences of AB, 19% and 16%, respectively, than our study. However, comparisons with the results of our study are difficult as Phillips et al. [26] used the Drug Burden Index (DBI) [27] and Mueller et al. [7] the Anticholinergic Drug Scale (ADS) [28] for the assessment of AB, which use different lists of MACs (e.g., unlike the ACB scale, the DBI does not consider inhaled MAC) and calculate AB differently (the DBI also includes the prescribed dose). Furthermore, in the study of Phillips et al. [26], there might have been a selection of healthier patients into the study population as suggested by their non-responder analysis.

Our study provides information on the use of MAC and AB across all age groups. This analysis showed that use of MAC in Germany can roughly be divided into four phases: (i) persons aged ≤19 years with a low cumulative AB mainly due to use of antihistamines, antibiotics, and glucocorticoids; (ii) persons aged 20–49 years with a low but steadily increasing cumulative AB with antidepressants as the main contributor to the cumulative AB; (iii) a transitional phase in persons aged 50–64 where the contribution of cardiovascular medication and antidiabetics starts to increase, which is higher in men than in women; and (iv) persons aged ≥65 years where the relative contribution of antidepressants decreases due to the increased contribution of medication for the treatment of cardiovascular disease, diabetes, and urinary incontinence/overactive bladder. The increased burden of chronic diseases is reflected in the high cumulative AB, which peaks in the age group 80–94 years.

MAC prescribed by general practitioners accounted for 39–86% of the total cumulative AB and thus had the highest share. In health systems with the general physician in the role of gatekeeper, this proportion might be even higher. In Germany, persons are free to choose which physician to see. There is no requirement of a referral from a general practitioner to access specialist care. In our study, there was an age gradient regarding the diversity of physician specialties contributing to the cumulative AB. In the oldest age groups, MACs were almost exclusively prescribed by general practitioners. In Germany, patients in these age groups are also treated by specialists but refills of medication are often prescribed by general practitioners. Therefore, this result is to be expected. These aspects are relevant if interventions to reduce the AB in specific patient groups or to increase the awareness of AB in general were to be designed. Our results suggest that general practitioners would be an important target group, particularly for older age groups but involvement of specialists, who often initiate prescriptions of a certain medication, may also be required.

Our study showed that there are persons with an AB considered to be clinically relevant in all age groups. This demonstrates the need to conduct studies on potentially harmful effects not only in older adults but also in children, adolescents, and the entire adult population. However, it has to be kept in mind that there are a lot of unanswered questions in regards to how AB can cause or contribute to clinically relevant adverse effects. For example, the time period over which the cumulative effects of anticholinergic burden may accrue and possibly produce harms are unclear. Also the role of type and dosage of single MACs and their overlap are not well understood. When planning a study on the risk of AB, this means that classifying persons as exposed or unexposed bears a high level of uncertainty, so robustness of findings would need to be assessed by comprehensive sensitivity analyses. Also in many other regards, studies on the risk of outcomes associated with AB are challenging, e.g. regarding issues such as confounding by indication, unmeasured confounding and time-varying exposure.

To our knowledge this is the first study in Germany providing a detailed description of the AB in an unselected population sample, i.e., without restrictions to a certain age or patient group. The large sample size allowed us to precisely estimate the prevalence of the AB stratified by age and sex. AB was estimated using the ACB scale—a widely used and validated tool—and a list of MACs created specifically for the German health care system. There are many scales for the assessment of AB and they have been shown to differ [29, 30]. Thus, direct comparisons with studies using other AB scales are difficult. Moreover, medications classified with an ACB score of 1 only have a possible anticholinergic effect based on in vitro affinity to muscarinic receptors without clinically relevant negative cognitive effects. It is not clear whether the cumulative use of several medications with a possible anticholinergic effect is equivalent to the AB induced through the use of medications with established and clinically relevant cognitive anticholinergic effects (ACB scores 2 or 3). However, some studies have shown increased risks of adverse effects already for an ACB score of 1 [22, 31].

Our study was based on German claims data. Due to the nature of the data the study is not affected by recall or volunteer bias. Moreover, the study population was fairly stable: 91% of included persons were observable for the whole year of 2016, 98% were observable for 90 days or more and only 3.3% exited the study before the end of the observation period due to end of continuous insurance. Limitations of the data source include lack of information regarding the use of medication during hospitalization as well as lack of information on adherence—no information is available on whether dispensed medication was actually used by the patient. Furthermore, over-the-counter medication is not captured, thus dispensations of MACs, particularly of antihistamines, might have been underestimated. Treatment durations of MACs were estimated using DDDs as the prescribed dose is not available. However, for each MAC we reviewed summaries of product characteristics and, if applicable, adapted lower DDDs for persons aged <18 and ≥65 years. Nonetheless, this approach is not equivalent to other studies that had more information on dosage and used more sophisticated methods to take it into account. Finally, in our study we have not assessed AB in a longitudinal manner, which–in view of the aforementioned unanswered questions about clinically relevant AB levels–would be essential in a subsequent risk study to understand the potential link between AB exposure and negative health outcomes. Such risk studies are particularly needed in the younger population where it is even less clear if such a link exists at all.

In conclusion, this comprehensive overview showed that a clinically relevant AB is common in the German general population. This holds particularly true for older persons but there are also younger age groups with a prevalence of up to 7%. Among adults, prevalence of clinically relevant AB was consistently higher in women than in men. Given the known risks associated with AB in older persons, targeted interventions at the prescriber level are needed. Furthermore, studies exploring possible risks associated with AB in children, adolescents and the entire adult population are warranted.

Description of study population stratified by anticholinergic burden measured through Anticholinergic Cognitive Burden (ACB) score.

(DOCX)

Click here for additional data file.

12 Feb 2021

PONE-D-20-37319

Anticholinergic burden: first comprehensive analysis using claims data shows large variation by age and sex

PLOS ONE

Dear Dr. Reinold,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I would like to thank you for your patience while your manuscript was under review. As I'm sure you are aware, the on-going pandemic is impacting everyone's availability for activities like manuscript reviews. We now have 2 reviews in of your manuscript. You will see that the reviewers have identified the potential contribution of this research, in particular the inclusion of younger populations in the assessment of anticholinergic burden. Both reviewers, also raise some addition and important points for you to consider in both the analysis and the presentation of your work (please see what Reviewer #1 suggests). In particular, you will note that Reviewer #2 has raised some points about incorporating dose and co-use in the assessment of anticholinergic burden. If you chose to resubmit, I would suggest that you pay careful attention to these points in revisions to the analysis, your response, or both.

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Reviewer #2: Partly

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Reviewer #1: The authors present a descriptive cohort study to describe the anticholinergic burden among a general population from German. The authors found identified that anticholinergic burden (ACB) increased in females and older patients, which is well known from other studies. However, the main contribution to the literature are the findings in younger populations, which are often excluded in studies of ACB. Additionally, the analyses stratifying by medication class and prescriber are interesting, and can be important to help identify target groups for interventions to minimize ACB in practice.

Major comments

1. In the write-up of the study design and study population, it would be helpful if the authors specified the study design used as per the STROBE guidelines.

2. It may also be helpful if the authors were to provide a graphical depiction of the study design, particularly relating to the inclusion and exclusion criteria (For examples, see Schneeweiss S. et al. Ann Int Med 2019. PMID: 30856654)

3. A study flow diagram of patient inclusion would be useful (as per STROBE guidelines).

4. There seems to be a disconnect between the description in text and choice for supplementary vs. in-text tables. For example, the information presented in the appendix table contributes to a substantial portion of the text, and even one of the main conclusions in the first paragraph of the discussion. Conversely, very little attention is paid to Table 1 in the manuscript. I would consider to switch these tables, or consider combining both in one larger table?

5. Similar to the above, perhaps the authors clarify the purpose of Table 1?

6. How the medications were identified in Table 2? Here, I believe these are only the medications that were dispensed in the observation period in 2016? This does or does not include prescriptions dispensed prior to baseline? Perhaps a study design diagram could help clarify this, as recommended above.

7. The proportions presented in Table 3 are not clear to me. I believe this is described on page 3 lines 71-74 in the methods section. However, it would be useful if the authors provided an example of the calculation (or the written algorithm). Additionally, is this a validated approach or one developed by the authors?

Specific Comments

(Note the line numbers changed throughout the document, making it challenging to identify exact location of comments)

1. Page 2 Line 43 (Methods): What do OPS and EBM stand for? I could not find these acronyms defined previously.

2. Page 1 Line 1 (Results): For consistency with the following sentence, I would recommend changing “prevalences decreased with decreasing ACB score” to “prevalence increased with increasing ACB score”. Please also specify that these are prior morbidities and medications.

3. Page 1 line 10 (Discussion): Previous the acronym “AB” was used for “anticholinergic burden”, however, here it is spelled out. This should likely be switched to “AB”.

4. Page 1 line 19 (discussion): I believe there should be a comma after “interestingly”.

Reviewer #2: Thank you for the opportunity to review this interesting manuscript; which describes the assessment of anticholinergic burden among a large sample of Germans. The data source and sample are well described.

My main concern is with the measure of exposure - which is essentially supposed to be cumulative anticholinergic burden.

o Why limit to just one year of follow-up? There is a risk of underascertainment of exposure, particularly for anticholinergic medications dispensed just prior to the start of 2016.

o As I read the manuscript, the minimum follow up was only one day; this also could contribute to bias in the measure of exposure. Provide some justification for this; also perform sensitivity analyses on limiting to individual with a reasonable amount of follow up.

o When assessing cumulative anticholinergic exposure it is important to understand all of which medications are used, the dosage at which they are taken, and the patterns/overlap in which different medications are taken --> and all of these, over the long term. Aside from the already-identified issues with minimum and maximum follow up time for the cohort, dosage is inadequately addressed using the current methodology. A variety of publications exist that describe methods to estimate cumulative anticholinergic burden more precisely and these could be employed here.

After reviewing the methods and results, I am left with the feeling that there is room for inaccuracy -- and likely, underestimation -- in cumulative anticholinergic assessment. I think the authors could benefit from considering those other methods and also using sensitivity analyses to explore the impact on results.

With respect to outcomes, the prevalence of anticholinergic burden is interesting -- but really only as an intermediate on the pathway to the negative health outcomes that are the end result of high cumulative anticholinergic burden. The time period of the study is insufficient to truly understand the risk of these long term effects; and why such a short time period was selected was not expanded upon in the manuscript. Nonetheless, if the rationale for this study is that anticholinergic burden in young people is under-studied, demonstrating that this is important (because it is associated with negative health outcomes in young people) would be an important step. I think that whether anticholinergic exposure is an issue among young people remains to be established and this manuscript could delve more in to that

Minor points

-The manuscript could benefit from some proofreading/English language editing.

-The discussion is wordy and could be streamlined; particularly the paragraphs that repeat the results in comparing to the published literature.

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Reviewer #2: No

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25 Mar 2021

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--> We thank the editor for this suggestion. We have added the suggested phrase to the COI statement “Moreover, this does not alter our adherence to PLOS ONE policies on sharing data and materials.”

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--> We thank the editor for this suggestion. We modified the section “Availability of data and material” as follows and included it in the updated version of the cover letter.

-->In Germany, use of personal data is protected by the Federal Data Protection Act and particularly the use of claims data for research is regulated by the Code of Social Law. Researchers have to apply for a project-specific permit from the statutory health insurance providers which then need an approval from their governing authorities. The use of the data on which this publication is based was only allowed for BIPS employees within the framework of the specified project and limited to a pre-defined time span. Researchers who want to access the data on which this publication is based need to ask for new approval by the statutory health insurance providers DAK-Gesundheit (service@dak.de), die Techniker (service@tk.de), hkk Krankenkasse (info@hkk.de) and AOK Bremen/Bremerhaven (info@hb.aok.de) which upon granting approval would have to ask their respective authorities for approval. Please contact gepard@leibniz-bips.de for help with this process. The authors confirm that they had no special access privileges to the data and that other researchers will be able to access the data in the same manner as the authors by following the instructions described above.

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--> We thank the editor for pointing this out. The paragraph in question has been rewritten and the phrase “data not shown” is no longer in the manuscript.

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--> We thank the editor for this remark. We have moved the ethics statement to the methods section (Page 4, Line 109-115)

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5. Review Comments to the Author (Please note that all references to pages and lines refer to the clean version of the manuscript)

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors present a descriptive cohort study to describe the anticholinergic burden among a general population from German. The authors found identified that anticholinergic burden (ACB) increased in females and older patients, which is well known from other studies. However, the main contribution to the literature are the findings in younger populations, which are often excluded in studies of ACB. Additionally, the analyses stratifying by medication class and prescriber are interesting, and can be important to help identify target groups for interventions to minimize ACB in practice.

Major comments

1. In the write-up of the study design and study population, it would be helpful if the authors specified the study design used as per the STROBE guidelines.

--> We thank the reviewer for this remark. We now specify the study design in the section “study design and study population” (page 2, line 55):

--> Page 2, line 55: We conducted a cross-sectional study using data from the year 2016, the most recent data at the time of analysis.

2. It may also be helpful if the authors were to provide a graphical depiction of the study design, particularly relating to the inclusion and exclusion criteria (For examples, see Schneeweiss S. et al. Ann Int Med 2019. PMID: 30856654)

--> We thank the reviewer for this suggestion. We have created a graphical depiction of the study design (Figure 1). The example by Schneeweiss et al. is very clear and nicely illustrates the inclusion and exclusion criteria. As the example by Schneeweis et al. refers to a longitudinal study design whereas our study has a cross-sectional design, we adapted the template to make sure that there is no misunderstanding regarding the study design, while making sure that the inclusion and exclusion criteria are clearly depicted.

3. A study flow diagram of patient inclusion would be useful (as per STROBE guidelines).

--> We have followed the reviewer’s advice and have created a flow diagram to visualize the inclusion and exclusion process (Figure 2).

4. There seems to be a disconnect between the description in text and choice for supplementary vs. in-text tables. For example, the information presented in the appendix table contributes to a substantial portion of the text, and even one of the main conclusions in the first paragraph of the discussion. Conversely, very little attention is paid to Table 1 in the manuscript. I would consider to switch these tables, or consider combining both in one larger table?

5. Similar to the above, perhaps the authors clarify the purpose of Table 1?

--> We agree with the reviewer’s remarks. We have switched tables T1 and S1. The purpose of S1 (formerly T1) was to generally describe patients regarding health care utilization, morbidities and medication stratified by anticholinergic burden. As expected, patients with higher anticholinergic burden more often had chronic disease and more often received various types of pharmacological therapy.

6. How the medications were identified in Table 2? Here, I believe these are only the medications that were dispensed in the observation period in 2016? This does or does not include prescriptions dispensed prior to baseline? Perhaps a study design diagram could help clarify this, as recommended above.

--> We thank the reviewer for this comment. Table 2 does indeed contain only medications that were dispensed during the study period in 2016. In order to make this clearer we have followed the reviewer’s advice and created a graphical depiction of the study design (new Figure 1) and now also stress it in the methods section. Moreover, we have modified the title of Table 2 to “Prevalence of use of medications with anticholinergic activity (MACs) in persons with anticholinergic burden measured through the anticholinergic cognitive burden (ACB) scale during the observation period (2016)”

7. The proportions presented in Table 3 are not clear to me. I believe this is described on page 3 lines 71-74 in the methods section. However, it would be useful if the authors provided an example of the calculation (or the written algorithm). Additionally, is this a validated approach or one developed by the authors?

--> We thank the reviewer for this suggestion. We fully agree that the explanation how the cumulative AB score was calculated becomes clearer if it is illustrated by an example. Therefore, we have now inserted an example in the methods section. Furthermore, we now mention that our calculations follow the approach proposed by Campbell et al. and we explain that Campbell et al. additionally calculated a mean score per person whereas we focused on the total score and did not further transfer it into a mean (page 3, lines 91-99)

--> Page 3, lines 91-99: In order to describe the cumulative AB of each person during the observation period, we first multiplied the AB score of each MAC dispensed to the person during the observation period or overlapping the observation period with the length of supply (based on DDD) and then summed up the score points of all dispensations. Subsequently, these AB scores were summed up per person to calculate the cumulative AB. For example, a person receiving 200 DDDs of metformin (ACB score 1) and 30 DDDs of tramadol (ACB score 2) during the observation period had a cumulative AB of 260 (i.e., the result of 200 x 1 + 30 x 2). This method was proposed by Campbell et al. [1]. Campbell et al. further divided the cumulative AB by the number of days in the exposure period to transfer the total AB score into a mean score per person but this additional transformation was not relevant in the context of our study [1]..

Specific Comments

(Note the line numbers changed throughout the document, making it challenging to identify exact location of comments)

--> We thank the reviewer for pointing out this mistake. The line numbers were changed to a continuous enumeration

1. Page 2 Line 43 (Methods): What do OPS and EBM stand for? I could not find these acronyms defined previously.

--> We thank the reviewer for this remark. OPS stands for “Operationen- und Prozedurenschlüssel” which roughly translates to “Operations and procedure classification” and EBM-“Einheitlicher Bewertungsmaßstab” translates to “Uniform Value Scale”. We have chosen to remove the acronyms OPS and EBM from the text, as they do not provide any additional information to international readers (page 2, lines 64-66).

--> Page 2, lines 64-66: Morbidities were assessed any time prior to observation period (starting from 2004) through records of ≥1 ICD-10-GM inpatient or outpatient diagnoses or records of ≥1 codes of relevant operations, procedures or outpatient services as well as participation in disease management plans.

2. Page 1 Line 1 (Results): For consistency with the following sentence, I would recommend changing “prevalences decreased with decreasing ACB score” to “prevalence increased with increasing ACB score”. Please also specify that these are prior morbidities and medications.

--> We thank the reviewer for this suggestion. We have followed it and changed the sentence accordingly (page 7 lines 134-135).

Page 7 lines 134-135: For all morbidities and medications assessed prior to start of observation period, prevalences increased with increasing ACB score (S1 Table).

3. Page 1 line 10 (Discussion): Previous the acronym “AB” was used for “anticholinergic burden”, however, here it is spelled out. This should likely be switched to “AB”.

--> We thank the reviewer for pointing this out. We have abbreviated Anticholinergic burden to AB in the discussion

4. Page 1 line 19 (discussion): I believe there should be a comma after “interestingly”.

--> We thank the reviewer for spotting this mistake, we have corrected it accordingly.

Reviewer #2:

Thank you for the opportunity to review this interesting manuscript; which describes the assessment of anticholinergic burden among a large sample of Germans. The data source and sample are well described.

My main concern is with the measure of exposure - which is essentially supposed to be cumulative anticholinergic burden.

o Why limit to just one year of follow-up? There is a risk of underascertainment of exposure, particularly for anticholinergic medications dispensed just prior to the start of 2016.

--> We apologize for the fact that we have obviously created a misunderstanding regarding our study design. We think the word follow-up was misleading and we now specify in the methods section that this was a cross-sectional study aimed to describe the prevalence of anticholinergic burden in Germany stratified by age and sex group in 2016. In addition, we have now added a graphical depiction of the study design to avoid this misunderstanding (new Figure 1). We have taken different approaches to describe the prevalence of AB because we think this provides a more comprehensive picture of the situation. On the one hand, we described the persons regarding the highest category of AB reached during the study period (i.e. in 2016). In addition, we calculated a measure that allowed us to assess the proportion of AB attributable to a certain class of MAC (e.g., antidepressants) or physician specialty. We called this measure “cumulative burden” because it takes into account all dispensations in the study period (i.e. in 2016).

Regarding medication that was dispensed before the start of the study period, we included all anticholinergic medication that was dispensed in 2015 and whose treatment durations overlapped into the study period into calculation of the anticholinergic burden. In order to emphasize this point we modified the paragraph describing the assessment of exposure (pages 2-3, Lines 70-73):

--> Page 2-3, Line 70-73: Exposure to MAC was assessed based on outpatient prescriptions dispensed during the observation period, i.e., in 2016. Treatment durations were estimated based on DDDs. In case MAC were dispensed before 1 January 2016 and the days of supply covered by this dispensation overlapped with the observation period, the DDDs overlapping with the observation period were also considered.

o As I read the manuscript, the minimum follow up was only one day; this also could contribute to bias in the measure of exposure. Provide some justification for this; also perform sensitivity analyses on limiting to individual with a reasonable amount of follow up.

--> We hope our explanation above clarifies that the word follow-up was misleading here as we conducted a cross-sectional study. Our intention was to avoid inclusion criteria that resulted in a non-inclusive selection of the study population as we wanted to provide a comprehensive overview of AB in an unselected population sample. We fear that using a continuous observation period in 2016 as inclusion criterion would have led to excluding certain subgroups, e.g. those who died in 2016. It is important to note that there is little fluctuation in GePaRD. In our study 91% of included persons were observable for the whole year 2016. 98% were observable for 90 days or more. Moreover, only 3.3% of persons exited the study before the end of the study period due to end of a continuous insurance period. Therefore, we do not see the duration of follow-up as a source of bias requiring additional analyses. We now mention this important aspect in the discussion (page 15, lines 248-250).

--> Page 15, lines 248-250: Moreover, the study population was fairly stable: 91% of included persons were observable for the whole year of 2016, 98% were observable for 90 days or more and only 3.3% exited the study before the end of the observation period due to end of continuous insurance.

o When assessing cumulative anticholinergic exposure it is important to understand all of which medications are used, the dosage at which they are taken, and the patterns/overlap in which different medications are taken --> and all of these, over the long term. Aside from the already-identified issues with minimum and maximum follow up time for the cohort, dosage is inadequately addressed using the current methodology. A variety of publications exist that describe methods to estimate cumulative anticholinergic burden more precisely and these could be employed here.

--> As explained above we hope it is now clearer why we calculated the “cumulative anticholinergic burden” here. It was just used as a measure to describe some further aspects of the prevalence of AB. We fully agree that for a study aiming to determine risk associated with a high AB, a more precise approach would be needed to determine exposed and unexposed person-time, but this was not the aim of this study. We now address this point in the discussion (page 16, lines 256-259).

--> Page 16, lines 256-259: Finally, cumulative AB was used in this study as a measure to further describe certain aspects regarding the prevalence of MAC prescribing. It should be kept in mind, however, that this measure might not be suitable for studies assessing the risk of outcomes associated with AB where a precise classification of exposed and unexposed time windows is important.

After reviewing the methods and results, I am left with the feeling that there is room for inaccuracy -- and likely, underestimation -- in cumulative anticholinergic assessment. I think the authors could benefit from considering those other methods and also using sensitivity analyses to explore the impact on results.

With respect to outcomes, the prevalence of anticholinergic burden is interesting -- but really only as an intermediate on the pathway to the negative health outcomes that are the end result of high cumulative anticholinergic burden. The time period of the study is insufficient to truly understand the risk of these long term effects; and why such a short time period was selected was not expanded upon in the manuscript. Nonetheless, if the rationale for this study is that anticholinergic burden in young people is under-studied, demonstrating that this is important (because it is associated with negative health outcomes in young people) would be an important step. I think that whether anticholinergic exposure is an issue among young people remains to be established and this manuscript could delve more in to that

--> Thank you for this comment. As the reviewer states, the aim of this study was indeed to provide a comprehensive overview of the prevalence of AB by age and sex in an unselected population sample, including younger age groups which were understudied so far. We fully agree that risk studies would be needed in a next step to find out whether anticholinergic exposure is an issue among young people as our study only determined the prevalence of AB in this population. We now expand on this on page 14, Line 222-223

--> Page 14, Line 222-223: This demonstrates the need to conduct studies on potentially harmful effects not only in older adults but also in children, adolescents, and the entire adult population.

Minor points

-The manuscript could benefit from some proofreading/English language editing.

--> We thank the reviewer for this remark. The updated manuscript was proofread by a native speaker

-The discussion is wordy and could be streamlined; particularly the paragraphs that repeat the results in comparing to the published literature.

--> We thank the reviewer for this suggestion. We have shortened the discussion section accordingly.

________________________________________

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References

1. Campbell NL, Perkins AJ, Bradt P, Perk S, Wielage RC, Boustani MA, et al. Association of Anticholinergic Burden with Cognitive Impairment and Health Care Utilization Among a Diverse Ambulatory Older Adult Population. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2016;36(11):1123-31. doi: https://doi.org/10.1002/phar.1843.

Submitted filename: Reinold_et_al_2021_Response to Reviewers.docx

Click here for additional data file.

5 May 2021

PONE-D-20-37319R1

Anticholinergic burden: first comprehensive analysis using claims data shows large variation by age and sex

PLOS ONE

Dear Dr. Reinold,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

We now have 2 reviews back on your resubmitted manuscript. You will see that both reviewers stated that you have adequately addressed prior reviews but each have some remaining questions that they would like to see addressed (and I would agree). In particular, you will see that Reviewer #1 raises the issue of clarifying the study design including precise information on the timing of your measures; while Reviewer #2 asks for a more fulsome discussion of the cumulative measure and its use in younger adults. Reviewer #2 does acknowledge that many of their questions may not be answerable, it is clear that they are looking to see if a more thoughtful explanation of the utility of a cumulative measure (both the pros and the cons) as described in this study.

==============================

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing the majority of the comments raised during the initial review. I believe the authors have substantially improved the manuscript. However, I do have a couple of additional questions that I would appreciate if the authors could address further regarding the study design.

1. I do appreciate that the authors have tried to generate a figure for the study design, however, it is not quite clarifying the cross sectional design to me. Particularly regarding the follow-up and dates that the morbidities and medications were identified on. For example, if the inclusion was met on February 1st 2016 for a patient, I assume the morbidities were assessed from January 1st 2004 through to January 31st 2016 (one day before the inclusion date), and medications were identified from January 1st 2015 through to January 31st 2016 (again one day before inclusion)? This is the level of detail in the figure that would be helpful to better understand the design.

2. On the point of morbidity and medication assessment prior to inclusion, this information (e.g., what were to morbidities and medications) is not summarized in the paper? I imagine this information could be helpful to understand differences in the patients with AB. Therefore, I wonder why this is excluded from the results since it appears to be assessed?

2. I am still unsure if this is truly a cross-sectional design as it does not appear to be a single snapshot of a patient at a given time-point. But instead there appears to be follow-up. For example, in the methods section and Figure 1, it states that patients were "followed until (i) death, (ii) start of hospitalisation in 2016 with a duration of >=90-days, (iii) end of continuous insurance period, or (iv) end of observation period, whichever occurred first". Based on this statement, the design still reads like a cohort study as you are following individual patients over time. With this in mind I wonder if it is a descriptive cohort study rather than a true cross-sectional design? Can the authors further clarify this. I think it would also help address the concerns from both reviewers regarding the potential for misclassification on the AB.

I understand the authors aim of the paper is only to assess the prevalence of AB in a younger population, which is of high interest. And while a cohort study that compares outcomes is not the aim, I do think further clarification on the follow-up and time of measurements is still warranted so that future work can replicate these findings and potentially conduct a cohort study to assess health outcomes.

Reviewer #2: Thank you for the revisions. I think the study design is much clearer now and the explanatory figures included helpful.

The work done to explain that its really a cross-sectional prevalence (assessed over a one year period) is clarifying; but at the same time, I feel the implications of this requires some discussion or at least some further expansion in the limitations.

To the best of my knowledge (but this would be worth the authors discussing in any case) the time period over which the cumulative effects of anticholinergic burden accrue are unclear. The important point about high anticholinergic burden -- as the authors are well aware -- is not that people are on a number of medications, but rather than these medications together create an exposure that results in negative health outcomes like falls and dementia. But over what time period does this exposure need to occur? Does it matter if someone has a high level of anticholinergic burden for a 30 day period while a set of medications interact? Or does that cumulative exposure need to be over a 30 day period or 60 day period or for >1 year? How does the study design chosen illuminate the relationship between exposure and outcomes; or in another way, do we know that the exposures measured and reported here are clinically relevant?

To be clear, I don't think the authors need to actually answer all the questions above (and indeed, I believe some are presently unanswerable); but rather this is the context in which to discuss the strengths and limitations of their study design. I am unclear why reporting the results of a cross sectional measure of 'cumulative' (as the authors define is) is an important finding -- but i would welcome the authors defense of why this is a valid and important measure, through expansion of the discussion section.

At the very least, I would think a clear limitation to the design should be added to state that longitudinal assessment of anticholinergic burden that accounts for dose is what would be needed to understand the link between anticholinergic burden exposure and negative health outcomes -- particularly in the younger population who may have higher than expected anticholinergic burden but to my knowledge have never been shown to develop the negative health outcomes observed in older adults.

I recognize that the authors have inserted this limitation related to the above: 'It should be kept in mind, however, that this measure might not be suitable for studies assessing the risk of outcomes associated with AB where a precise classification of exposed and unexposed time windows is important.' However in my mind, that is the important goal of this type of work. So discussing/addressing that limitation more fulsomely would be useful.

**********

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Reviewer #1: No

Reviewer #2: No

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26 May 2021

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

>> We thank the editor for this comment. We have checked and updated the reference list accordingly. We fixed a mistake where the reference

Campbell NL, Perkins AJ, Bradt P, Perk S, Wielage RC, Boustani MA, et al. Association of Anticholinergic Burden with Cognitive Impairment and Health Care Utilization Among a Diverse Ambulatory Older Adult Population. Pharmacotherapy. 2016;36(11):1123-31. Epub 2016/10/07. doi: 10.1002/phar.1843. PubMed PMID: 27711982; PubMed Central PMCID: PMCPMC5362375.

appeared in the reference list twice. This is also documented in the rebuttal letter. We did not cite any retracted manuscripts.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing the majority of the comments raised during the initial review. I believe the authors have substantially improved the manuscript. However, I do have a couple of additional questions that I would appreciate if the authors could address further regarding the study design.

1. I do appreciate that the authors have tried to generate a figure for the study design, however, it is not quite clarifying the cross sectional design to me. Particularly regarding the follow-up and dates that the morbidities and medications were identified on. For example, if the inclusion was met on February 1st 2016 for a patient, I assume the morbidities were assessed from January 1st 2004 through to January 31st 2016 (one day before the inclusion date), and medications were identified from January 1st 2015 through to January 31st 2016 (again one day before inclusion)? This is the level of detail in the figure that would be helpful to better understand the design.

>> The reviewer’s understanding of the time frames is correct. We have heeded the reviewer’s suggestion and created a new version of figure 1 where we clarified the timeframes in which morbidities and medications were assessed. In particular, we have now defined the day of inclusion in a footnote so that it is clear to which date the periods refer.

2. On the point of morbidity and medication assessment prior to inclusion, this information (e.g., what were to morbidities and medications) is not summarized in the paper? I imagine this information could be helpful to understand differences in the patients with AB. Therefore, I wonder why this is excluded from the results since it appears to be assessed?

>> We thank the reviewer for this comment; In addition to the detailed information provided in Table S1, we have now added the following paragraph to summarize information on morbidity, medication as well as healthcare utilization in the study population, stratified by ACB status (ACB=1, ACB=2, ACB≥3 and persons without ACB) on page 7 lines 141-146

>> Page 7 lines 141-146: For example, compared to persons with lower or no ACB, persons with ACB≥3, had higher prevalences of psychiatric and behavioral, musculoskeletal as well as endocrine and metabolic diseases. They were prescribed medications from a higher number of different prescribers and had higher prevalences of cardiovascular therapy, analgesics and psychiatric medication. Moreover, persons with ACB≥3 were, on average, more frequently hospitalized, remained hospitalized for longer periods and had a higher prevalence of nursing home residency and obesity.

2. I am still unsure if this is truly a cross-sectional design as it does not appear to be a single snapshot of a patient at a given time-point. But instead there appears to be follow-up. For example, in the methods section and Figure 1, it states that patients were "followed until (i) death, (ii) start of hospitalisation in 2016 with a duration of >=90-days, (iii) end of continuous insurance period, or (iv) end of observation period, whichever occurred first". Based on this statement, the design still reads like a cohort study as you are following individual patients over time. With this in mind I wonder if it is a descriptive cohort study rather than a true cross-sectional design? Can the authors further clarify this. I think it would also help address the concerns from both reviewers regarding the potential for misclassification on the AB.

I understand the authors aim of the paper is only to assess the prevalence of AB in a younger population, which is of high interest. And while a cohort study that compares outcomes is not the aim, I do think further clarification on the follow-up and time of measurements is still warranted so that future work can replicate these findings and potentially conduct a cohort study to assess health outcomes.

>> We apologize that some parts of the description of the study design still created confusion. We fully understand that the expression “followed until…” was misleading here. We have now re-written this part of the methods section and hope it is clear now that this sentence should simply describe the observation period in 2016 that was used to assess the use of MAC. In a cross-sectional study based on primary data, one would ask patients, for example, about their MAC use in the past year. An assessment period of one year for MAC use seems reasonable to ensure that the assessment of MAC is robust regarding potential seasonal variations. In our study which was based on secondary data, this corresponds to defining an assessment period for MAC use of one year. Maybe this comparison is also helpful to explain the other measurement periods. In a primary data study, one would ask patients, for example, if they ever had a certain comorbidity (e.g. cancer). In our study based on secondary data, it is not possible to ask patients directly and pose the “ever” question. To compensate for that as much as possible, we used all the information available on this person in the database before 2016. This may also explain the measurement period we used for co-medication. In a primary data study, one would ask the patient about recent medication use (e.g. past 12 months). Analogously, we used an assessment period of one year to assess co-medication. We now expand on this also in the methods section and hope that this clarifies the study design.

>> Page 2 lines 60-62: For all included persons, the available (continuous) observation period in 2016 was used to assess the use of MAC. For persons with a hospitalization starting in 2016 and with a duration of ≥90 days, MAC use was only assessed until the start of this hospitalization (Figure 1).

>> Page 2 lines 65-71: The coding of morbidities was assessed any time prior to observation period (starting from 2004) through records of ≥1 ICD-10-GM inpatient or outpatient diagnoses or records of ≥1 codes of relevant operations, procedures or outpatient services as well as participation in disease management plans. This approach, i.e. taking into account all information on morbidity available for a person before 2016, aims to compensate for the fact that with secondary data, a person cannot be asked if he or she ever had a certain disease, as it would be done in a study based on primary data. Treatment with medication excluding MAC was assessed within 365 days before start of observation period (excluding start of observation period) based on records of ≥1 outpatient dispensations.

>> We also would like to point out that other cross-sectional studies based on claims data have used a similar approach in regards to the definition of observation periods, so for claims data analysis this is not an unusual approach (e.g. Simon et al, Pediatr Rheumatol Online J. 2020; 18: 43. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275412/) or Mapel et al BMC Health Serv Res. 2011; 11: 43. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3050697/)

Reviewer #2: Thank you for the revisions. I think the study design is much clearer now and the explanatory figures included helpful.

The work done to explain that its really a cross-sectional prevalence (assessed over a one year period) is clarifying; but at the same time, I feel the implications of this requires some discussion or at least some further expansion in the limitations.

To the best of my knowledge (but this would be worth the authors discussing in any case) the time period over which the cumulative effects of anticholinergic burden accrue are unclear. The important point about high anticholinergic burden -- as the authors are well aware -- is not that people are on a number of medications, but rather than these medications together create an exposure that results in negative health outcomes like falls and dementia. But over what time period does this exposure need to occur? Does it matter if someone has a high level of anticholinergic burden for a 30 day period while a set of medications interact? Or does that cumulative exposure need to be over a 30 day period or 60 day period or for >1 year? How does the study design chosen illuminate the relationship between exposure and outcomes; or in another way, do we know that the exposures measured and reported here are clinically relevant?

To be clear, I don't think the authors need to actually answer all the questions above (and indeed, I believe some are presently unanswerable); but rather this is the context in which to discuss the strengths and limitations of their study design. I am unclear why reporting the results of a cross sectional measure of 'cumulative' (as the authors define is) is an important finding -- but i would welcome the authors defense of why this is a valid and important measure, through expansion of the discussion section.

>> We fully agree that the points the reviewer raised are very important and - even though partly unanswerable - should be mentioned in the discussion to sensitize the reader to uncertainties in the field. Therefore, we now expand on this in the discussion in a separate paragraph (page 15, lines 244-253). With respect to the ‘cumulative” AB: We hope our answer regarding the study design we provided in response to reviewer 1 explains to a certain extent why we think this measure is not contradictory to a cross-sectional study. Also in a cross-sectional study based on primary data, one would ask a patients about MAC use e.g. in the past year, and then there are different options how to categorize / classify this information to describe the prevalence of AB. Apart from describing the persons regarding the highest category of AB reached during the study period, we calculated this additional measure because – unlike the aforementioned measure - it allowed us to assess the proportion of AB attributable to a certain class of MAC or to a certain physician specialty. We think this additional measure is helpful as it provides insights into the medication and sectors of care that contribute to the AB in the population.

>> Page 15 lines 244-253: Our study showed that there are persons with an AB considered to be clinically relevant in all age groups. This demonstrates the need to conduct studies on potentially harmful effects not only in older adults but also in children, adolescents, and the entire adult population. However, it has to be kept in mind that there are a lot of unanswered questions in regards to how AB can cause or contribute to clinically relevant adverse effects. For example, the time period over which the cumulative effects of anticholinergic burden may accrue and possibly produce harms are unclear. Also the role of type and dosage of single MACs and their overlap are not well understood. When planning a study on the risk of AB, this means that classifying persons as exposed or unexposed bears a high level of uncertainty, so robustness of findings would need to be assessed by comprehensive sensitivity analyses. Also in many other regards, studies on the risk of outcomes associated with AB are challenging, e.g. regarding issues such as confounding by indication, unmeasured confounding and time-varying exposure.

At the very least, I would think a clear limitation to the design should be added to state that longitudinal assessment of anticholinergic burden that accounts for dose is what would be needed to understand the link between anticholinergic burden exposure and negative health outcomes -- particularly in the younger population who may have higher than expected anticholinergic burden but to my knowledge have never been shown to develop the negative health outcomes observed in older adults.

I recognize that the authors have inserted this limitation related to the above: 'It should be kept in mind, however, that this measure might not be suitable for studies assessing the risk of outcomes associated with AB where a precise classification of exposed and unexposed time windows is important.' However in my mind, that is the important goal of this type of work. So discussing/addressing that limitation more fulsomely would be useful.

>> We apologize that we had not sufficiently addressed this limitation yet. We now expand on this in the limitations section and – as also mentioned above –have added a paragraph addressing the open questions in this regard.

>> Page 16 lines 274-279: Nonetheless, this approach is not equivalent to other studies that had more information on dosage and used more sophisticated methods to take it into account. Finally, in our study we have not assessed AB in a longitudinal manner, which – in view of the aforementioned unanswered questions about clinically relevant AB levels – would be essential in a subsequent risk study to understand the potential link between AB exposure and negative health outcomes. Such risk studies are particularly needed in the younger population where it is even less clear if such a link exists at all.

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Reviewer #1: No

Reviewer #2: No

Submitted filename: Reinold_et_al_2021_Answers_to_the_Reviewers.docx

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3 Jun 2021

Anticholinergic burden: first comprehensive analysis using claims data shows large variation by age and sex

PONE-D-20-37319R2

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7 Jun 2021

PONE-D-20-37319R2

Anticholinergic burden: first comprehensive analysis using claims data shows large variation by age and sex

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