Factors associated with polypharmacy and inappropriate medication use among older people: a longitudinal study of the Urban Health Centres Europe (UHCE)

doi:10.21203/rs.3.rs-1616586/v1

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Factors associated with polypharmacy and inappropriate medication use among older people: a longitudinal study of the Urban Health Centres Europe (UHCE)

https://doi.org/10.21203/rs.3.rs-1616586/v1

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Background: Polypharmacy and inappropriate medication use among older people is associated with adverse health consequences due to drug-drug interactions, drug-disease interactions, and adverse drug effects. The aim of this study is to assess the factors that are associated with polypharmacy and inappropriate medication use among community-dwelling older people in Europe.

Method: This longitudinal study used baseline and follow-up data from 1791 participants of the Urban Health Center European project in five European countries. We studied factors in the domains (a) demographic characteristics, (b) lifestyle and nutrition, and (c) health and health care use, Hierarchical logistic regression models were used to examine the factors associated with polypharmacy and inappropriate medication.

Results: Mean age was 79.6 years (SD±5.6 years); 60.8% were women; 45.2% had polypharmacy, and 41.8% had a high risk of inappropriate medication use. Participants who were women had higher odds of both having polypharmacy (OR=0.55;95%CI:0.42-0.72) and inappropriate medication use (OR=0.50;95%CI:0.39-0.65). Participants with a migration background (OR=1.67;95%CI:1.08-2.59), overweight (OR=1.37; 95%CI:1.04-1.79) and obesity (OR=1.78;95%CI:1.26-2.51) compared to ‘normal weight’, with lower physical HRQoL (OR=0.96, 95%CI:0.95-0.98), multi-morbidity (OR=3.73, 95%CI:2.18-6.37), frailty (OR=1.69, 95%CI:1.24-2.30), visited outpatient services (OR=1.77, 95%CI: 1.09-2.88) had higher odds of having polypharmacy. The associations with inappropriate medication use were similar.

Conclusions: Multiple factors in the domains of demography, lifestyle, nutrition, and health care use are associated with polypharmacy and inappropriate medication use. These provide starting points to improve interventions to reduce polypharmacy and inappropriate medication use. In the meantime, health professionals can apply these insights to identify subgroups of patients are at a high risk of polypharmacy and inappropriate medication use.

Trial registration: The intervention of the UHCE project was registered in the ISRCTN registry as ISRCTN52788952. The date of registration is 13/03/2017.

Polypharmacy

Inappropriate medication use

Older people

Sex

Migration background

BMI

frailty

outpatient services

Health related quality of life

People worldwide are living longer nowadays, but generally not in full health [1]. With increasing age, relatively more people experience multi-morbidity [2]. Patients with multi-morbidity are often prescribed more medications, leading to the possible increased risk of inappropriate prescribing [3]. The same type of medicine may be prescribed twice or a medication might not work very well due to the interference effects with other medication(s) (i.e. drug-drug interaction) [4, 5]; a medication used to treat or control a condition might have a negative effect on another condition (i.e. disease-drug interaction) [4]. Polypharmacy can be defined as using five or more medications simultaneously [6]. ‘Inappropriate medication use’ (IMU), as measured by the Medication Risk Questionnaire is an extension of the concept of polypharmacy; ‘inappropriate medication use’ is defined as a situation where the benefits offered by drugs do not outweigh the potential risks (for example by too large doses or by a too long duration) [7]. Polypharmacy and inappropriate medication use among older persons has been shown to be associated with adverse health consequences including risk of drug-drug interaction, adverse drug effects, falls, cognitive impairment and lower quality of life [4–6].

Studies have assessed which factors are associated with a higher risk of polypharmacy [8–13]. People with polypharmacy are generally older [8] and have a lower educational level than people without polypharmacy [9]. Former smokers were shown to have a higher risk of polypharmacy than persons who did not smoke [14]. Factors regarding health and health care use (e.g. falling, frailty, hospital admission, outpatient services use) have also been reported to be associated with a higher risk of polypharmacy [11, 12]. But relatively few studies evaluated how a comprehensive set of relevant factors (i.e. demographic, lifestyle and nutrition, as well as health and health care use) is associated with polypharmacy; also relatively few studies assess the association with inappropriate medication use [13, 15]. Hence, our study assesses which factors in the domains of (a) socio-demographic characteristics, (b) lifestyle and nutrition, and (c) health and health care are associated with polypharmacy and inappropriate medication use among community-dwelling older people in Europe.

Study Setting and Population

This study was performed within the framework of the Urban Health Centres Europe (UHCE) project. The project was conducted in five countries (the Netherlands, Greece, Croatia, Spain, United Kingdom) between May 2015 and June 2017 [16]. The project aims to prompt healthy ageing by approaches including a preventive multidimensional health assessment and integrated care pathways on appropriate medication prescription and adherence, prevention of fall risk, loneliness and frailty [17]. A total of 2325 participants who lived independently and were able to take part in the study for at least 6 months were recruited; 1215 were assigned to the integrated care pathway intervention; 1110 were assigned to the control group which applied the ‘care as usual’ [17]. Ethical committee procedures have been followed in all cities and approvals have been provided. Written informed consent was obtained from all participants. The study was registered as ISRCTN52788952. Further details on the interventions were described elsewhere [17, 18].

In the current study, we adopted a longitudinal design and used baseline data and data after 12-month follow-up of the UHCE project. Data was collected by self-reported questionnaires at both time points. In total of 2325 participants, participants who dropped out at follow-up (n = 482) were first excluded. Then, participants with missing data on polypharmacy (n = 24), on inappropriate medication use (n = 27), and on age and gender (n = 1) were further excluded. Thus, 1791 participants were included in this study. Due to the missing data on covariates, 340 participants were excluded in the main analysis (Fig. 1).

Measurements

Outcomes

Polypharmacy and inappropriate medication use were evaluated both at baseline and follow-up using Medication Risk Questionnaire (MRQ) [19]. MRQ is a 10-item validated self-administered tool that can identify participants at a higher risk of experiencing medication-related problems, notably for older population [19]. It covers polypharmacy and inappropriate prescribing, poor adherence, and multiple medical problems. Polypharmacy was measured by the question: Do you currently take five or more different medicines? [20]. Two items were dropped from the total score due to the poor performance [19, 21]. Supplementary Table S1 shows the items included. The sum of item scores in MRQ was used to indicate the risk level of inappropriate medication use. Risk score of 0 (lowest risk) to 8 (highest risk) indicate the level of risk [19]. Participants were classified as a low-risk group if the total score is lower than 4, and high-risk group if the score is equal or larger than 4 [21].

Associated factors

Based on literature [8, 9, 11, 12, 14], variables assessed at baseline from three domains were considered as associated factors: demographic factors, factors regarding lifestyle and nutrition, factors regarding health and health care use.

Socio-demographic factors

Socio-demographic factors included age (in years), sex (women/men), education level, country of residence (the Netherlands/ Greece/ Croatia/ Spain/ the UK), migration background (yes/no), and household composition (living with others/living alone). The level of education was reported as the highest level of education attained by a participant. It was classified into three categories according to the International Standard Classification of Education (ISCED): primary or less (ISCED 0–1), secondary or equivalent (ISCED 2–5), and tertiary or higher (ISCED 6–8) [22]. A participant was reported as having a migration background when the country of residence was not the country of birth.

Factors regarding lifestyle and nutrition

Factors regarding lifestyle and nutrition included smoking (yes/no), alcohol use (yes/no), physical activity, malnutrition (yes/no) and body mass index (BMI). One single question (i.e. Do you smoke at present?) was used to assess whether a person was a current smoker (yes/no) [16]. Alcohol use was assessed by three items of the Alcohol Use Disorders Identification Test (AUDIT-C), with a score from 0 (lowest risk) to 12 (highest risk) indicating the level of risk [23]. The variable was dichotomized (≥ 3 in women and ≥ 4 in men) to indicate whether a person was a hazardous drinker or have active alcohol use disorder (yes/no) [23]. The frequency of physical activity was assessed through answers to a question from the Frailty Instrument of the Survey of Health, Ageing and Retirement in Europe (SHARE-FI). The SHARE-FI was developed based on the existing questionnaire from SHARE [24]. Participants were asked to indicate the frequency of activities that require a low or medium energy levels, such as gardening, cleaning the car or going for a walk. Answer categories included ‘once a week or less’ and ‘more than once a week’ [25]. Malnutrition was measured with the Short Nutrition Assessment Questionnaire 65+ (SNAQ-65+) [26], which included the index of unintentional weight loss, mid-upper arm circumference (MUAC) and appetite and functional status. If a person lost 6 kg (13lbs) or more during the last 6 months, or 3 kg (6½ lbs) or more during the last month, or has a MUAC < 25 cm, he/she was classified as malnutrition. If a person had poor appetite last week and difficulties climbing a staircase, he/she was classified as at risk of malnutrition [26]. BMI was calculated using self-reported height and weight (kg/m2). Participants were classified as underweight /normal weight (< 24.9kg/m²), overweight (25–29.9kg/m²) and obese (≥ 30kg/m2), following WHO guidelines [25].

Factors regarding health and health care use

Health-related quality of life (HRQoL), multi-morbidity (yes/no), number of falls during the last year, frailty (yes/no), use of outpatient service (yes/no) and hospitalization (yes/no) during the last year were grouped in the factors regarding health and health care use. HRQoL was measured by the 12-Item Short-Form Health Survey (SF-12) [27]. The SF-12 includes 12 items encompassing eight health domains compiled in the Physical Component Summary (PCS) and Mental Component Summary (MCS), both ranging from 0 (lowest) to 100 (highest health status) [28]. Multi-morbidity was defined as having two or more common chronic conditions (i.e. heart attack, high blood pressure or hypertension, high blood cholesterol, stroke or cerebral vascular disease, diabetes or high blood sugar, chronic lung disease, asthma, arthritis, osteoporosis, cancer or malignant tumor, stomach or duodenal ulcer or peptic ulcer, Parkinson’s disease, cataract, and hip fracture or femoral fracture) [29]. Participants reported the number of falls during the past 12 months and the falling number was dichotomized into “none or once” and “twice or more”. Frailty was assessed with the Tilburg Frailty + Indicator (TFI), a validated questionnaire which contains 15 self-reported questions [30]. The overall frailty score is the sum of the 15 items (score range 0–15) in the questionnaire. Participants with a total score ≥ 5 were classified as frail [31]. Outpatient services use (yes/no) was assessed by whether a participant visited general practitioner or specialist during the last 12 months. Hospitalization (yes/no) was assessed by whether the participant had been admitted to hospital during last 12 months.

Statistical analyses

Descriptive statistics were used to describe the characteristics of the study population. Means and standard deviation (SD) were used to summarize the continuous variables, and frequencies and percentages for categorical variables. Characteristics of the participants were compared according to polypharmacy and inappropriate medication use by T-test for continuous variables and chi-square tests for categorical variables.

Hierarchical logistic regression model was used to estimate longitudinal associations between the factors and polypharmacy or inappropriate medication use. Four separate regression models were constructed to identify the factors related to polypharmacy and inappropriate medication use each. In model 1, all demographic factors at baseline were entered; in model 2, factors regarding lifestyle and nutrition at baseline were additionally added ; in model 3, factors regarding health and health care use at baseline were additionally added; In model 4, polypharmacy or inappropriate medication use at baseline was further added into the model to see the impact of the change in the outcomes. Whether the participants were divided into an intervention group or not (yes/no) was included as a covariate to all models. Odds ratios (OR) and 95% confidence intervals (95%CI) were calculated for each factor. P-values < 0.05 were considered statistically significant. The multi-collinearity test was performed to determine the correlation between the independent variables using a variance inflation factor (VIF). Collinearity exists when a VIF value greater than 10 [32]. All analyses were conducted in the IBM SPSS Statistics for Windows, version 25 Armonk, NY, USA: IBM Corp.

Non-response analysis

Compared to the population-for-analysis (n = 1451), participants excluded from the study due to the missing data (n = 340) were more likely to have a migration background (P < 0.05), and were less likely to have used outpatient services during the last year (P < 0.05).

Table 1 presents the characteristics of the study population at baseline. The mean age was 79.6 years (SD ± 5.6 years), and 60.8% were women. Of all participants, 45.2% had polypharmacy, and 41.8% was classified as having a high risk of inappropriate medication use.

Table 1
Baseline characteristics of the study sample (n = 1791) by polypharmacy (no /yes) and inappropriate medication use (low risk /high risk).
	Total ^b (n = 1791)	Polypharmacy			Inappropriate medication use
	Total ^b (n = 1791)	No n = 981 (54.8%)	Yes n = 810 (45.2%)	P-value^a	Low risk n = 1043 (58.2%)	High risk n = 748 (41.8%)	P-value^a
Demographic factors
Age (Year)	79.6 ± 5.6	79.0 ± 5.7	80.2 ± 5.3	< 0.001^d	79.7 ± 5.9	79.4 ± 5.0	0.20 ^d
Sex N(%)
Female	1089 (60.8)	616 (62.8)	473 (58.4)	0.06 ^e	376 (36.0)	326 (43.6)	0.001^e
Male	702 (39.2)	365 (37.2)	337 (41.6)	0.06 ^e	667 (64.0)	422 (56.4)	0.001^e
Educational level N(%)
Primary or less	446 (24.9)	261 (27.1)	185 (23.2)	0.06 ^e	244 (23.8)	202 (27.4)	0.23 ^e
Secondary	1142 (63.8)	601 (62.3)	541 (67.8)		677 (66.0)	465 (63.1)
Tertiary or higher	174 (9.7)	102 (10.6)	72 (9.0)		104 (10.1)	70 (9.5)
Country N(%)
The Netherlands	272 (15.2)	135 (13.8)	137 (16.9)	< 0.001^e	157 (15.1)	115 (15.4)	0.001^e
Greece	259 (14.5)	171 (17.4)	88 (10.9)		161 (15.4)	98 (13.1)
Croatia	427 (23.8)	256 (26.1)	171 (21.1)		278 (26.7)	149 (19.9)
Spain	395 (22.1)	252 (25.7)	143 (17.7)		224 (21.5)	171 (22.9)
The United Kingdom	438 (24.5)	167 (17.0)	271 (33.5)		223 (21.4)	215 (28.7)
Migration Background N(%)
No	1640 (91.6)	907 (92.8)	730 (90.1)	0.045^e	968 (92.8)	672 (89.8)	0.03^e
Yes	151 (8.4)	71 (7.2)	80 (9.9)	0.045^e	75 (7.2)	76 (10.2)	0.03^e
Household composition N(%)
Living with others	1106 (61.8)	635 (65.1)	471 (58.4)	0.004^e	654 (63.2)	452 (60.5)	0.25 ^e
Living alone	676 (37.7)	340 (34.9)	336 (41.6)	0.004^e	381 (36.8)	295 (39.5)	0.25 ^e
Factors regarding life style and nutrition
Smoking N(%)
No	1652 (92.2)	899 (92.0)	753 (93.2)	0.35 ^e	955 (91.7)	697 (93.7)	0.12 ^e
Yes	133 (7.4)	78 (8.0)	55 (6.8)	0.35 ^e	86 (8.3)	47 (6.3)	0.12 ^e
Alcohol use N(%)
No	1244 (69.5)	660 (71.7)	584 (75.5)	0.08 ^e	707 (71.8)	537 (75.8)	0.06 ^e
Yes	449 (25.1)	260 (28.3)	189 (24.5)	0.08 ^e	278 (28.2)	171 (24.2)	0.06 ^e
Exercise N(%)
More than once a week	1298 (72.5)	758 (77.8)	540 (67.2)	< 0.001^e	774 (74.8)	524 (70.5)	0.046^e
Once a week or less	480 (26.8)	216 (22.2)	264 (32.8)	< 0.001^e	261 (25.2)	219 (29.5)	0.046^e
Malnutrition N(%)
No	1516 (84.6)	833 (85.1)	683 (85.1)	0.99 ^e	888 (85.3)	628 (84.8)	0.75 ^e
Yes	266 (14.9)	146 (14.9)	120 (14.9)	0.99 ^e	153 (14.7)	113 (15.2)	0.75 ^e
BMI^c N(%)
Underweight and normal	607 (33.9)	360 (38.5)	247 (33.3)	0.001^e	386 (39.1)	221 (32.1)	< 0.001^e
Overweight	740 (41.3)	421 (45.1)	319 (43.0)		440(44.6)	300 (43.5)
Obese	328 (18.3)	153 (16.4)	175 (23.6)		160 (16.2)	168 (24.4)
Factors regarding health and health care use
Health-related quality of life (SF-12) (score)
Physical Component Summary	42.0 ± 11.9	44.9 ± 11.0	38.4 ± 12.0	< 0.001^d	43.6 ± 11.7	39.7 ± 11.9	< 0.001^d
Mental Component Summary	50.3 ± 10.7	50.8 ± 10.4	49.7 ± 11.0	0.03^d	50.4 ± 10.7	50.1 ± 10.7	0.52 ^d
Multi-morbidity N(%)
No	158 (8.8)	137 (14.0)	21 (2.6)	< 0.001^e	124 (11.9)	34 (4.5)	< 0.001^e
Yes	1629 (91.0)	840 (86.0)	789 (97.4)	< 0.001^e	915 (88.1)	714 (95.5)	< 0.001^e
Falling times (during the last year) N(%)
Zero or one time	1522 (85.0)	859 (88.6)	663 (82.4)	< 0.001^e	899 (87.3)	623 (83.6)	0.03^e
Two or more times	253 (14.1)	111 (11.4)	142 (17.6)	< 0.001^e	131 (12.7)	122 (16.4)	0.03^e
Frailty N(%)
No	804 (44.9)	516 (52.9)	288 (35.6)	< 0.001^e	525 (50.6)	279 (37.4)	< 0.001^e
Yes	979 (54.7)	459 (47.1)	520 (64.4)	< 0.001^e	513 (49.4)	466 (62.6)	< 0.001^e
Outpatient service use (during the last year) N(%)
No	153 (8.5)	113 (11.7)	40 (5.0)	< 0.001^e	120 (11.7)	33 (4.4)	< 0.001^e
Yes	1620 (90.5)	856 (88.3)	764 (95.0)	< 0.001^e	909 (88.3)	711 (95.6)	< 0.001^e
Hospitalization (during the last year) N(%)
No	1448 (80.8)	832 (85.6)	616 (76.9)	< 0.001^e	872 (84.6)	576 (77.6)	< 0.001^e
Yes	325 (18.1)	140 (14.4)	185 (23.1)	< 0.001^e	159 (15.4)	166 (22.4)	< 0.001^e
a. Presented as mean ± SD for continuous variables or N (%) for categorical variables; Significant P-values (< 0.05) are in bold.
b. Missing items: Educational level = 29(1.6%); Household composition = 9 (0.5%); Smoking = 6(0.3%); Alcohol use = 98 (5.5%); Exercise = 13 (0.7%); Malnutrition = 9(0.5%); BMI = 116(6.1%); Multi-morbidity = 4 (0.2%);Frailty = 8 (0.4%); Falling times during the last year = 16 (0.9%); Outpatient service = 18 (1.0%) ; Hospitalization = 18 (1.0%).
c. Abbreviations: BMI, body mass index.
d. P-values based on independent T test for continuous variables.
e. P-values based on chi-square test for categorical variables.

Polypharmacy was more frequently seen among older participants (P < 0.001) and among participants living alone (P = 0.004). Women had a higher probability of inappropriate medication use compared to men (P = 0.001). Participants with a migration background had a higher risk of polypharmacy (P = 0.045) and inappropriate medication use (P = 0.03) compared to participants without a migration background. In general, the UK had the lowest risks of polypharmacy and inappropriate medication use, while Greece had the highest risks (P < 0.001). Participants with polypharmacy (P < 0.001) or at high risk of inappropriate medication use (P = 0.046) were less likely to engage in physical activity more than once a week, and more subject to overweight (P < 0.01). The probabilities of polypharmacy or inappropriate medication use were higher in participants with lower physical HRQoL, experiencing multi-morbidity, and had fallen twice or more during the last year (all P < 0.05). The percentages of being frail, having visited outpatient services, and having been admitted to hospital during the past year were higher in participants with polypharmacy and at high risk of inappropriate medication use compared to the counterparts (all P < 0.001).

Table 2 presents the results from the hierarchical logistic regression models with regard to polypharmacy. For each model, multi-collinearity between factors was within acceptable limits (all VIF < 2). After adjusting for all potential factors (model 3), female sex (OR = 0.55, 95%CI: 0.42–0.72) was associated with a lower risk of polypharmacy. Participants from Greece (OR = 0.43, 95%CI: 0.26–0.71), Croatia (OR = 0.42, 95%CI: 0.28–0.63) and Spain (OR = 0.56, 95%CI: 0.36–0.8) had a lower risk of polypharmacy compared to participants from the Netherlands. Having a migration background (OR = 1.67, 95%CI: 1.08–2.59), being overweight (OR = 1.37, 95%CI: 1.04–1.79) or being obese (OR = 1.78, 95%CI: 1.26–2.51) were associated with higher risk of polypharmacy. Higher level of physical HRQoL (OR = 0.96, 95%CI: 0.95–0.98) was associated with lower risk of polypharmacy. Participants with multi-morbidity (OR = 3.73, 95%CI: 2.18–6.37), being frail (OR = 1.69, 95%CI: 1.24–2.30), or having visited outpatient services during the last year (OR = 1.77, 95%CI: 1.09–2.88) had a higher risk of polypharmacy than counterparts.

Table 2

Hierarchical logistic regression model fitted on polypharmacy at follow-up (n = 1451^a).
	Polypharmacy (Model 1) ^d		Polypharmacy (Model 1) ^e		Polypharmacy (Model 3) ^f		Polypharmacy (Model 4) ^g
	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c
Demographic factors at baseline
Age (Year)	1.02 (1.00-1.04)	0.09	1.02 (0.99–1.04)	0.14	0.99 (0.97–1.02)	0.58	0.99 (0.97–1.02)	0.68
Sex (female)	0.78 (0.62–0.99)	0.045	0.73 (0.57–0.94)	0.01	0.55 (0.42–0.72)	< 0.001	0.75 (0.54–1.04)	0.09
Education level		0.04		0.12		0.41		0.54
Tertiary or higher (ref.)	1.00		1.00		1.00		1.00
Secondary	1.63 (1.05–2.51)	0.03	1.49 (0.96–2.33)	0.08	1.21 (0.80–1.85)	0.37	1.33 (0.80–2.21)	0.27
Primary	1.15 (0.78–1.7)	0.48	1.11 (0.75–1.66)	0.60	1.38 (0.86–2.21)	0.18	1.26 (0.71–2.22)	0.43
Country		< 0.001		< 0.001		< 0.001		< 0.001
The Netherlands (ref.)	1.00		1.00		1.00		1.00
Greece	0.59(0.38–0.92)	0.02	0.50 (0.32–0.78)	0.002	0.43 (0.26–0.71)	0.001	0.45 (0.24–0.82)	0.01
Croatia	0.72 (0.51–1.03)	0.07	0.58 (0.4–0.83)	0.003	0.42 (0.28–0.63)	< 0.001	0.47 (0.28–0.77)	0.003
Spain	0.58 (0.39–0.85)	0.006	0.56 (0.38–0.83)	0.004	0.55 (0.36–0.84)	0.005	0.68 (0.41–1.14)	0.15
The United Kingdom	1.62 (1.13–2.31)	0.008	1.7 (1.18–2.44)	0.004	1.52 (1.03–2.25)	0.03	1.27 (0.80–2.04)	0.31
Migration background (yes)	1.48 (0.98–2.23)	0.06	1.55 (1.02–2.35)	0.04	1.67 (1.08–2.59)	0.02	1.56 (0.91–2.65)	0.10
Living composition (living alone)	1.26 (0.99–1.60)	0.06	1.23 (0.96–1.58)	0.10	1.14 (0.87–1.48)	0.35	1.04 (0.76–1.43)	0.81
Factors regarding life style and nutrition at baseline
Smoke (yes)			1.09 (0.71–1.66)	0.70	1.01(0.65–1.57)	0.97	1.16 (0.68–1.97)	0.60
Alcohol risk (yes)			0.76 (0.59–0.98)	0.03	0.88 (0.67–1.16)	0.37	0.84 (0.60–1.17)	0.30
Exercise (once a week or less)			1.75 (1.35–2.27)	< 0.001	1.06 (0.79–1.43)	0.68	1.06 (0.74–1.51)	0.77
BMI ^b				< 0.001		0.003		0.03
Underweight and normal (ref.)			1.00		1.00		1.00
Overweight			1.47 (1.14–1.9)	0.003	1.37 (1.04–1.79)	0.02	1.40 (1.01–1.93)	0.045
Obese			1.99 (1.44–2.74)	< 0.001	1.78 (1.26–2.51)	0.001	1.70 (1.12–2.59)	0.01
Malnutrition (yes)			0.97 (0.7–1.34)	0.83	0.76 (0.53–1.08)	0.12	0.71 (0.46–1.08)	0.11
Factors regarding health and health care use at baseline
Health-related quality of life (SF-12) (score)
Physical Component Summary					0.96 (0.95–0.98)	< 0.001	0.98 (0.97–0.996)	0.01
Mental Component Summary					0.99 (0.98-1.00)	0.17	0.99 (0.98–1.01)	0.41
Multi-morbidity (yes)					3.73 (2.18–6.37)	< 0.001	2.31 (1.24–4.31)	0.01
Falling times during the last year (two or more times)					0.95 (0.68–1.32)	0.74	0.82 (0.55–1.24)	0.34
Frailty (yes)					1.69 (1.24–2.30)	0.001	1.44 (0.99–2.09)	0.05
Outpatient service use during the last year (yes)					1.77 (1.09–2.88)	0.02	1.24 (0.69–2.23)	0.47
Hospitalization (yes)					1.20 (0.88–1.63)	0.25	0.91 (0.63–1.33)	0.63
Polypharmacy at baseline
Polypharmacy (yes)							15.91 (11.94–21.21)	< 0.001
Block	75.47	< 0.001	45.44	< 0.001	147.40	< 0.001	435.16	< 0.001
Model summary	1918.15	0.95	1872.71	0.29	1725.31	0.69	1290.14	0.03
Nagelkerke R²	0.07		0.11		0.23		0.51
a. The number of missing cases is 340; the number of cases included in the analysis is 1451.
b. Abbreviations: BMI, body mass index; OR, odds ratio; CI, confidence interval.
c. Significant P-values (< 0.05) are in bold.
d. Model 1: Adjusted for intervention.
e. Model 2: Model 1 + Factors regarding life style and nutrition.
f. Model 3: Model 2 + Factors regarding health and health care use.
g. Model 4: Model 3 + Polypharmacy at baseline.

Table 3 presents the results from the hierarchical logistic regression models with regard to inappropriate medication use. For each model, multi-collinearity factors was within acceptable limits (all VIF < 2). After adjusting for all potential factors (model 3), older age (OR = 0.96, 95%CI: 0.94–0.98) or female sex (OR = 0.50, 95%CI: 0.39–0.65) were associated with lower risk of inappropriate medication use. Participants from Croatia (OR = 0.63, 95%CI: 0.41–0.98) were less likely to be at high risk of inappropriate medication use than those from the Netherlands. Having a migration background (OR = 1.81, 95%CI: 1.19–2.77), being obesity (OR = 1.57, 95%CI: 1.13–2.19), having multi-morbidity (OR = 1.87, 95%CI: 1.19–2.93), being frail (OR = 1.75, 95%CI: 1.30–2.36), or having visited outpatient services during the last year (OR = 2.57, 95%CI: 1.54–4.29) were associated with high risk of inappropriate medication use. Higher level of physical HRQoL (OR = 0.98, 95%CI: 0.96–0.99) was associated with lower risk of inappropriate medication use.

Table 3

Hierarchical logistic regression model fitted on inappropriate medication use at follow-up (n = 1451^a).
	Inappropriate medication use (Model 1) ^d		Inappropriate medication use (Model 2) ^e		Inappropriate medication use (Model 3) ^f		Inappropriate medication use (Model 4) ^g
	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c	OR (95%CI) ^b	P-value^c
Demographic factors at baseline
Age (Year)	0.98 (0.96-1.00)	0.08	0.98 (0.96-1.00)	0.08	0.96 (0.94–0.98)	0.001	0.96 (0.94–0.99)	0.004
Sex (female)	0.65 (0.52–0.83)	< 0.001	0.61 (0.48–0.78)	< 0.001	0.50 (0.39–0.65)	< 0.001	0.65 (0.49–0.87)	0.004
Education level		0.02		0.05		0.12		0.18
Tertiary or higher (ref.)	1.00		1.00		1.00		1.00
Secondary	1.67 (1.09–2.56)	0.02	1.57 (1.02–2.41)	0.04	1.18 (0.79–1.76)	0.43	1.24 (0.80–1.93)	0.34
Primary	1.12 (0.76–1.64)	0.57	1.10 (0.75–1.63)	0.63	1.54 (0.98–2.41)	0.06	1.57 (0.96–2.56)	0.07
Country		0.01		< 0.001		< 0.001		0.003
The Netherlands (ref.)	1.00		1.00		1.00		1.00
Greece	0.73 (0.47–1.14)	0.16	0.66 (0.42–1.03)	0.07	0.68 (0.42–1.11)	0.12	0.72 (0.42–1.23)	0.23
Croatia	0.82 (0.57–1.17)	0.27	0.68 (0.47–0.99)	0.04	0.59 (0.39–0.87)	0.008	0.63 (0.41–0.98)	0.04
Spain	0.90 (0.62–1.33)	0.61	0.87 (0.59–1.28)	0.48	0.86 (0.57–1.29)	0.46	1.01 (0.64–1.58)	0.98
The United Kingdom	1.36 (0.95–1.94)	0.09	1.38 (0.96–1.98)	0.08	1.31 (0.90–1.90)	0.17	1.36 (0.90–2.06)	0.14
Migration background (yes)	1.70 (1.13–2.55)	0.01	1.74 (1.15–2.61)	0.008	1.81 (1.19–2.77)	0.006	1.79 (1.12–2.84)	0.02
Living composition (living alone)	1.27 (1.00-1.62)	0.06	1.24 (0.97–1.59)	0.08	1.14 (0.88–1.48)	0.31	1.12 (0.84–1.48)	0.45
Factors regarding life style and nutrition at baseline
Smoke (yes)			0.91 (0.60–1.40)	0.68	0.84 (0.54–1.3)	0.43	1.04 (0.65–1.67)	0.87
Alcohol risk (yes)			0.70 (0.54–0.90)	0.006	0.77 (0.59–1.01)	0.06	0.80 (0.60–1.07)	0.14
Exercise (once a week or less)			1.27 (0.98–1.64)	0.07	0.89 (0.66–1.18)	0.41	0.93 (0.67–1.27)	0.63
BMI ^b				0.002		0.03		0.08
Underweight and normal (ref.)			1.00		1.00		1.00
Overweight			1.34 (1.04–1.72)	0.02	1.23 (0.95–1.60)	0.12	1.17 (0.88–1.56)	0.29
Obese			1.77 (1.29–2.44)	< 0.001	1.57 (1.13–2.19)	0.007	1.53 (1.06–2.20)	0.02
Malnutrition (yes)			1.12 (0.81–1.54)	0.50	0.92 (0.66–1.30)	0.64	0.86 (0.59–1.50)	0.43
Factors regarding health and health care use at baseline
Health-related quality of life (SF-12) (score)
Physical Component Summary					0.98 (0.96–0.99)	< 0.001	0.99 (0.98-1.00)	0.14
Mental Component Summary					1.00 (0.99–1.02)	0.74	1.00 (0.99–1.02)	0.68
Multi-morbidity (yes)					1.87 (1.19–2.93)	0.006	1.50 (0.92–2.43)	0.10
Falling times during the last year (two or more times)					0.92 (0.67–1.28)	0.64	0.90 (0.62–1.29)	0.55
Frailty (yes)					1.75 (1.30–2.36)	< 0.001	1.61 (1.16–2.23)	0.005
Outpatient service use during the last year (yes)					2.57 (1.54–4.29)	< 0.001	1.98 (1.13–3.48)	0.02
Hospitalization (yes)					1.14 (0.84–1.54)	0.40	1.03 (0.74–1.43)	0.86
Inappropriate medication use at baseline
Inappropriate medication use (high)							6.83 (5.29–8.80)	< 0.001
Block	40.19	< 0.001	26.74	< 0.001	94.37	< 0.001	241.77	< 0.001
Model summary	1926.28	0.55	1899.54	0.99	1805.17	0.24	1563.40	0.85
Nagelkerke R²	0.037		0.061		0.14		0.33
a. The number of missing cases is 340; the number of cases included in the analysis is 1451.
b. Abbreviations: BMI, body mass index; OR, odds ratio; CI, confidence interval.
c. Significant P-values (< 0.05) are in bold.
d. Model 1: Adjusted for intervention.
e. Model 2: Model 1 + Factors regarding life style and nutrition.
f. Model 3: Model 2 + Factors regarding health and health care use.
g. Model 4: Model 3 + Inappropriate medication use at baseline.

Supplementary Table S2 presents the association between polypharmacy and inappropriate medication use in the study population. Compared with participants without polypharmacy at follow-up, participants with polypharmacy were more likely to be at a high risk of inappropriate medication use (P < 0.001).

The present study assessed the factors, which are longitudinally associated with polypharmacy and inappropriate medication use in a large sample of older community-dwelling people in Europe. Multiple factors (i.e. sex, migration background, HRQoL, multi-morbidity, BMI, frailty, and outpatients service use during the last year) were found to be significantly associated with polypharmacy and inappropriate medication use.

In the present study, no association was found between age and polypharmacy. This is not in line with the findings from previous studies, which reported that older people are more likely to have polypharmacy [8, 10]. A possible explanations could be the relatively high age of the participants in our study, in comparison with previous studies. A nationwide report from Italy found that medication use increases steeply until the age of 85–90 years and then declines substantially [33]. The reason might be that medication prescribing and utilization is generally treated with caution among very old people [34]. This may explain the absence of the association between age and polypharmacy in our study population with a mean age of 80. It may also explain that in our study there was a negative association between age and inappropriate medication use. Further studies are needed to explore association between age and polypharmacy and inappropriate medication use among older people.

We found that women were less likely to have polypharmacy and were at a lower risk of inappropriate medication use than men, which is consistent with previous study [35]. The possible explanation could be that women are more likely to care about their health status; they consult doctors more regularly and earlier than men [35]. Therefore, women might accumulate more experience and knowledge on medication use than men of the same age. We recommend that health practitioners pay more attention on sex differences regarding medication use.

Our results also showed that participants with a migration background were more likely to be at risk of polypharmacy and high risk of inappropriate medication use. The explanation could be that participants with a migration background have personal beliefs about medication that rely on their background (e.g. cultural, core values, religious and health-related experience), which may differ from the healthcare providers of the country they reside in [36]. Additionally, the communication between participants with a migration background and healthcare providers could be challenging due to the possible language differences [37]. Furthermore, participants with migration background might know less well about the local health system (e.g. health insurance coverage, treatment process) and might experience barriers when receiving the appropriate health service including medication prescribing [38]. Thus, services adapted to specific needs of population with migration background, for example, translated and simplified information on prescription use, may reduce polypharmacy and inappropriate medication use [39].

We found that a higher physical HRQoL was associated with a lower risk of polypharmacy, as well as a lower risk of inappropriate medication use. Meanwhile, mental HRQoL was not significantly associated with polypharmacy or inappropriate medication use. These findings are consistent with previous studies in the USA [40–42].

Our results showed that older people with multi-morbidity were more likely to have polypharmacy and a high risk of inappropriate medication use. People with multiple chronic conditions may be treated by multiple medical specialists, and may consequently use multiple medications [15, 43]. Inappropriate medication use and poor adherence by the patient may be due to lack of adequate communication between prescribers and pharmacies [3]. Practitioners need to pay extra attention on medication use among older people with multi-morbidity.

Our study showed that frail older people are more likely to be at high risk of both polypharmacy and inappropriate medication use. The explanation could be that frail people are more likely to have multi-morbidity [2, 25, 43].

We found that participants who used outpatient services during the last 12 months were more likely to have polypharmacy and at high risk of inappropriate medication use. Hu et al. showed that more frequent access of outpatients services, is associated with more prescriptions and a higher risk of inappropriate medication use [43].

Methodological Considerations

Our results need to be interpreted with caution. The population in this study is aged 75 years and older; so, we cannot generalize our findings to the entire community-dwelling older population. Medication risk was assessed by a self-reported questionnaire (the MRQ), which could introduce recall bias. However, the MRQ has been validated by various studies [44, 45]. Future studies may use digital medical records in order to obtain detailed medication information.

Multiple factors in demography, lifestyle, nutrition, and health care use are associated with polypharmacy and inappropriate medication use. These provide starting points to improve interventions to reduce polypharmacy and inappropriate medication use. In the meantime, health professionals can apply these insights to identify subgroups of patients at a high risk of polypharmacy and inappropriate medication use.

Abbreviation	Full name
UHCE	Urban Health Centres Europe
MRQ	Medication Risk Questionnaire
ISCED	the International Standard Classification of Education
BMI	Body mass index
AUDIT-C	the Alcohol Use Disorders Identification Test
SNAQ-65+	the Short Nutrition Assessment Questionnaire 65+
MUAC	Mid-upper arm circumference
WHO	World Health Organization
HRQoL	Health-related quality of life
SF-12	the 12-Item Short-Form Health Survey
PCS	Physical Component Summary
MCS	Mental Component Summary
TFI	the Tilburg Frailty Indicator
SD	Standard deviation
OR	Odds ratios
95%CI	95% confidence intervals
VIF	Variance Inflation Factor

Ethics approval and consent to participate:

The study was approved by the Medical Ethics Committees in all participating cities and funded by the European Commission Executive Agency for Health and Consumers. The names of the review board and the approval references are: Rotterdam, The Netherlands: Medische Ethische Toetsings Commissie (METC) - Erasmus MC Rotterdam; 08/01/2015; MEC-2014-661; Pallini, Greece: The Ethics and Scientific board - Latriko Palaiou Falirou Hospital; 04/03/2015; 20150304-01; Rijeka, Croatia: The Ethical Committee - Faculty of Medicine University of Rijeka; 07-04-2014; 2170-24-01-14-02; Valencia, Spain: Comisión de Investigación - Consorcio Hospital General Universitario de Valencia. 29/01/2015; CICHGUV-2015-01-29; Manchester, United Kingdom: NRES Committee West Midlands - Coventry & Warwickshire; 06-03-2015; 15/WM/0080; NRES Committee South Central Berkshire B; 29-20-2014; 14/SC/1349. Written informed consent was obtained from all participants [16]. The study was registered in the ISRCTN registry as ISRCTN52788952. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication: Not applicable.

Availability of data and materials: The datasets analyzed during the current study are not publicly available due to privacy/ethical restrictions but are available from the corresponding author on reasonable request.

Conflict of Interest: The authors have no conflicts.

Funding: Urban Health Centres Europe is funded by the European Union, Consumers, Health, Agriculture and Food Executive Agency (CHAFEA), third health program, number 20131201.

Authors’ Contributions: L.Z.Y. and H.R.: study concept and design. C.F., T.R., V.V., F.M.R., A.V., T.A.B., A.R., and H.R.: acquisition of subjects and data. L.Z.Y., J.Y.H., and H.R.: analysis and interpretation of data. L.Z.Y. and J.Y.H.: preparation of manuscript. All authors: critical revision and final approval of manuscript.

Acknowledgements: We thank all participating older persons and all organizations and professionals involved in the Urban Health Centres Europe project.

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No competing interests reported.

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Factors associated with polypharmacy and inappropriate medication use among older people: a longitudinal study of the Urban Health Centres Europe (UHCE)

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