Skip to main content

Uses and perceptions of medications among French older adults: results from the 2020 French Health Barometer survey

Abstract

Background

There are few studies reporting on self-medication, perceptions or difficulties older adults have with their medications. This study aimed to describe the uses and the perceptions of medications among older adults in France and to identify patient groups based on that information.

Methods

We used data from the 2020 ‘French Health Barometer’ – a nationally-representative cross-sectional survey. We assessed polypharmacy (five or more medications), self-medication, and patient perceptions of medications. Robust Poisson regression was used to investigate socio-demographic and health-related factors associated with the outcomes. Latent class analysis was used to identify patient groups classified by the use and the perceptions of medications. Factors associated with group assignment were investigated by multinomial logistic regression. All analyses were weighted.

Results

The study sample comprised 1,623 respondents aged 70–85 years. Polypharmacy and self-medication were reported in 23.5 and 48.7% of the older population, respectively. Polypharmacy was associated with increasing age, low education, and impaired health status. Self-medication was associated with female sex and high education. Among individuals taking at least 1 medication, 8.2% reported not to understand all their medications, and 9.7% having difficulty taking medications as prescribed. Among individuals taking at least 2 medications, 23.2% thought that they took too many medications. Three patient groups were identified: ‘Non-polypharmacy, positive perceptions’ (62.5%), ‘Polypharmacy, positive perceptions’ (28.0%), and ‘Negative perceptions’ (9.5%).

Conclusions

Polypharmacy and self-medication are common in French older adults. One segment of people reported negative perceptions of their medications regardless of their polypharmacy status. This underlines the difference between the objective and perceived measures of polypharmacy.

Peer Review reports

Introduction

The share of older adults continues to grow in France and globally [1]. The increase in the prevalence of multimorbidity in the ageing population is further accompanied by an increase in consumption of medications [2].

The concurrent use of five or more medications is commonly referred to as polypharmacy [3]. Polypharmacy is prevalent in older adults. A study using data from 17 European countries and Israel showed that the prevalence of polypharmacy among adults aged \(\ge\)65 years ranged between 26.3 and 39.9% [4]. Older people with polypharmacy are at a higher risk of adverse drug events and drug-drug interactions [3]. Polypharmacy is also associated with potentially inappropriate medication use, longer hospitalization, and mortality [3, 5].

A better understanding of patient perceptions of their medications may help reduce the burden of polypharmacy. The propensity to accept deprescribing notably depends on patient beliefs and attitudes towards medications, which can be complex. The study of Rozsnyai et al. found that although 97% of Swiss older patients with polypharmacy were satisfied with their medications, 16% felt that their medications were a burden to them. [6] A good knowledge of medications may influence patient perceptions, thus contributing to better health-related behaviors such as medication adherence [7]. However, when asking older adults about the purpose of their medications in another study, it appeared that about one third had difficulty indicating the purpose of one of their medications or more and that it was partly related to polypharmacy [8].

Besides prescription medications, older adults can use nonprescription medications for chronic or temporary ailments without consulting a doctor [9]. Despite its potential benefits, self-medication with nonprescription medications involves risks such as misdiagnosis and drug-drug interactions, thereby causing detrimental health effects to older population [10]. In France, a study found that over 50% of adults living in Paris metropolitan area had self-medicated in the past four weeks [11]. However, limited data are available on the older population.

In this study, we aimed to describe medication uses (polypharmacy, self-medication) and perceptions of medications in the older population in France and to investigate their associated factors. We also sought to use information about medication usage and patient perceptions to find distinct groups among older adults taking \(\ge\) 2 medications.

Methods

Study design and population

We used data from the ‘French Health Barometer’ survey conducted between January and March 2020 [12]. ‘French Health Barometer’ is a population-based telephone survey on health-related perceptions, attitudes, and behaviors. Random selection and weighting ensured the sample to be representative of the French-speaking population aged 18–85 years living in the community in France. Design weights, reflecting the individual selection probability (in the household and according to the individual phone equipment), were adjusted by calibration to match the target population structure in terms of age, gender, region of residency, town size, number of persons per household and education level, using data from the 2018 Labor Survey conducted by National Institute of Statistics and Economic Studies. Questions about uses and perceptions of medications were administered to respondents aged \(\ge\) 70 years.

Measurement

Polypharmacy

Respondents reported the number of different medications currently taken in a day. A medication could contain one or multiple active components. Polypharmacy was defined as the use of five medications or more.

Self-medication

Respondents were asked whether they have ever self-medicated (Regularly/Occasionally/Never). Those who answered ‘Regularly’ or ‘Occasionally’ were both coded as ‘Yes’ while those who answered ‘Never’ were coded as ‘No’. Nonprescription drugs were grouped in six categories: drugs for fever, pain, and headache/drugs for cough and sore throat/drugs for stress and insomnia/drugs for heartburn and indigestion/vitamins and minerals/others drugs.

Patient perceptions of prescribed medications

Respondents taking \(\ge\) 1 medication were asked two questions:

  • ‘Do you understand the reasons why the medications were prescribed?’ (Yes, for all the medications/Yes, but not for all the medications/No). Respondents were coded as ‘Yes’ when they answered ‘Yes, for all the medications’, otherwise coded as ‘No’.

  • ‘Do you find it easy/difficult to take your medications as prescribed?’ (Easy/Sometimes difficult/Difficult). Those who answered ‘Sometimes difficult’ or ‘Difficult’ were both coded as ‘Difficult’ and asked to choose one or more of the following reasons for their difficulty: Have lots of medications to take/Run out of medications/Medications are hard to take/Changes in medication appearance/Have side effects/Other reasons.

Respondents taking \(\ge\) 2 medications were asked the third question:

  • ‘Do you think you take too many medications?’ (Yes/No)

Other variables

Socio-demographic variables included sex, age, living situation, socio-professional category, education level, and income. Socio-professional categories were defined using the French classification for occupations as follows: farmers/craftsmen and traders/intermediate professions/executive and intellectual professions/employees/manual workers [13]; retired persons were classified in their last job category. Income was regarded as the household income divided by the number of consumption units, using the Organization for Economic Co-operation and Development scale, to insure comparison of the standards of living of different size and compositions of households [14].

Health-related variables included frailty and three health variables of The Minimum European Health Module, namely self-perceived health, chronic conditions, and activity limitation [15]. Frailty was assessed according to Fried’s phenotype definition, adapted for declarative data with good performance [16, 17]. Nine questions were used to define the presence of five frailty components: involuntary weight loss, exhaustion, low physical activity, muscular weakness, and impaired mobility (Supplementary File–Table S1). Respondents having missing data on \(\le\) 2 frailty components were assigned a frailty status. Frailty was determined as follows: not frail (0 components), pre-frail (1–2 components) and frail (3–5 components).

Statistical analysis

Descriptive statistics were performed to summarize the characteristics of study population. We had five dichotomous outcomes (Yes/No): polypharmacy, self-medication, and three outcomes pertaining to patient perceptions (‘Think they take too many medications’, ‘Do not understand all medications’, ‘Have difficulty taking medications as prescribed’). We investigated their associated factors using robust Poisson regression. In cross-sectional studies, this method can be used to directly estimate prevalence ratios instead of odds ratios to avoid the overestimation of odds ratio when outcomes are not rare [18]. For each outcome, we built two multivariate models: Model 1 including only socio-demographic variables and Model 2 including socio-demographic and health-related variables. The number of medications taken was also added in Model 2 for the three outcomes pertaining to perceptions. Because health-related variables and the number of medications taken are potentially on the causal pathway between socio-demographic variables and our outcomes, adjustment for these variables could induce distorted associations of the outcomes with socio-demographic variables [19]. Model 1 was hence used to interpret associations of the outcomes with socio-demographic variables while Model 2 was used to interpret the associations with health-related variables and the number of medications taken. Model 1 and Model 2 of each outcome were conducted on the same sample in which there is no missing data on explanatory variables.

We hypothesized that there were unobserved groups in the study population and that individuals in these groups differed in terms of the uses and the perceptions of medications. We then used latent class analysis (LCA) to group patients based on the five outcome variables. Respondents taking \(\ge\)2 medications were included in this analysis. The number of groups was determined by interpretability and the Akaike information criterion. The differences among patient groups were assessed through corrected design-based F-statistic [20]. Multivariate multinomial regression models were conducted to investigate factors associated with group assignment in the same strategy to that described in the preceding paragraph.

All analyses were weighted. Analyses were conducted using STATA 14. We used poLCA package in R to perform LCA.

Sensitivity analysis

As the missing data rate for income was around 10%, we performed sensitivity analyses using multiple imputation with chained equations to check the robustness of the results obtained from the complete case analyses. We imputed missing values for explanatory variables based on socio-demographic variables, health-related variables, polypharmacy, and self-medication. Multivariate regression models were then conducted with imputed dataset.

Results

Among 1,623 respondents included in this study, 9 respondents did not report the number of medications taken and 3 did not report the frequency of self-medication. Among 1,321 respondents taking \(\ge\) 1 medication, 4 respondents did not report their understanding of medications, and 3 did not answer the question about difficulty taking medications as prescribed. Among 1,080 respondents taking \(\ge\) 2 medications, 6 respondents did not answer whether they think they take too many medications.

Table 1 shows descriptive statistics of the study sample. Among the French population aged 70–85 years, 23.5% (95%CI: 20.9–26.3%) were on polypharmacy while 16.8% (95%CI: 14.7–19.1%) reported not to take any medication. In addition, 48.7% of respondents (95%CI: 45.7–51.7%) reported self-medication (2.8% regularly and 45.9% occasionally). Among nonprescription drug categories, drugs for fever, pain, and headache were the most reported (90.7% [95%CI: 88.3–92.7%]), followed by drugs for cough and sore throat (43.1% [95%CI: 39.2–47.2%]), drugs for heartburn and indigestion (30.9% [95%CI: 27.2–34.7%]), vitamins and minerals (30.4% [95%CI: 26.9–34.2%]), drugs for stress and insomnia (11.9% [95%CI: 9.6–14.6%]), and other drugs (7.7% [95%CI: 5.9–10.0%]) (Fig. 1).

Table 1 Descriptive statistics (unweighted sample size, weighted percentages)
Fig. 1
figure 1

Nonprescription medication use by age groups (N = 862)

Of those taking \(\ge\) 1 medication, 8.2% (95%CI: 6.3–10.4%) reported not to understand all their medications and 9.7% (95%CI: 7.7–12.1%) had difficulty taking medications as prescribed. ‘Have side effects’ (33.9% [95%CI: 23.5–46.1%]) was the most common barrier preventing patients from taking medications as prescribed, followed by ‘Changes in medication appearance’ (20.5% [95%CI: 12.0%–32.7%]), ‘Have lots of medications to take’ (16.1% [95%CI: 9.5–26.1%]), ‘Medications are hard to take’ (4.7% [95%CI: 1.7–12.6%]), and ‘Run out of medications’ (4.4% [95%CI: 1.8–10.2%]). Other reasons were mentioned by 24.7% (95%CI: 16.2–32.7%) of the respondents. Besides, 23.2% (95%CI: 20.2–26.5%) of the respondents taking \(\ge\) 2 medications thought that they took too many medications.

Table 2 displays the factors associated with polypharmacy and self-medication. Overall, polypharmacy was positively associated with increasing age, low education, very bad/bad/fair self-perceived health, presence of chronic conditions, activity limitation, and frailty. Self-medication was positively associated with female sex, high education, and negatively associated with frail status.

Table 2 Factors associated with polypharmacy and self-medication

Regarding the perceptions, the proportion of 70–85 years old who think that they take too many medications and the proportion of those who do not understand all their medications increased with the number of medications taken (Supplementary File–Tables S2 and S3). Females thought more often that they take too many medications than males. People aged 80–85 years and those with low income were less likely to understand their medications. Finally, having difficulty taking medications as prescribed was positively associated with increasing age (80–85 years), low income (< 1,500 euros), activity limitation, and frailty (Supplementary File–Table S4).

LCA yielded three groups among the respondents taking \(\ge\) 2 medications accounting for 69.2% of the French older population. Figure 2 demonstrates three group profiles. We characterized them as follows: Group 1 (‘Non-polypharmacy, positive perceptions’, n = 711) – people who were more likely to take 2–4 medications concurrently and to have positive perceptions of their medications, Group 2 (‘Polypharmacy, positive perceptions’, n = 279) – people who were more likely to take \(\ge\) 5 medications concurrently and to have positive perceptions of their medications, and Group 3 (‘Negative perceptions’, n = 90) – people who tended to think that they take too many medications (75.6%), that they do not understand all medications (54.3%), and that they have difficulty taking medications as prescribed (68.1%). There was no significant difference in the prevalence of self-medication among three groups, ranging from 38.2 to 54.7%. The three groups differed according to age, education, income, and all health-related factors (Supplementary File–Table S5). Table 3 showed that the assignment to Group 2 compared to Group 1 was associated with low education, presence of chronic conditions, activity limitation, and frailty. The assignment to Group 3 compared to Group 1 was associated with increasing age (80–85 years), low education, very bad/bad/fair self-perceived health, activity limitation, and frailty.

Fig. 2
figure 2

Profiles of the three patient groups derived from latent class analysis (model-based prevalence, %) (N = 1,080)

Table 3 Factors associated with group assignment (n = 954)

Our sensitivity analysis showed that the results obtained from the original and imputed datasets were similar with regards to the strength and the direction of associations (Supplementary File–Tables S6–S8).

Discussion

We observed that 23.5% of older adults aged 70–85 years in France were on polypharmacy, while nearly 50% practiced self-medication, although occasionally in the vast majority of cases. Most older adults believed that they understood their medications and had no difficulty taking medications as prescribed. However, about 23% of those taking \(\ge\) 2 medications thought they took too many medications. We also demonstrated a wide range of factors associated with polypharmacy, self-medication, and patient perceptions of medications. Furthermore, among older adults taking \(\ge\) 2 medications, we distinguished three patient groups that differed in terms of polypharmacy and perceptions of medications.

First, polypharmacy was common among older adults in our study. However, the observed prevalence of polypharmacy (23.5%) is lower than those reported from previous studies conducted in France and in other developed countries [21,22,23]. This can be explained by the heterogeneous measurements of the number of medications taken and also by different study populations. Regarding the associated factors, our findings reinforce the associations of polypharmacy with age, education, and health-related factors [24,25,26]. More attention is needed to prevent inappropriate polypharmacy among older people with low education and impaired health status. We did not observe any association between sex and polypharmacy. Previous findings on the association between sex and polypharmacy are inconsistent. No association was found in the study of Slater et al., [27, 28] while other studies concluded positive associations of polypharmacy with female sex [29], or male sex [30].

Second, our results showed that self-medication was common in older adults. The prevalence of self-medication in our study (48.7%) is higher than the average prevalence in people aged \(\ge\) 65 years from 14 European countries (26.3%) reported by Brandão et al. [31] The difference between the two results can be explained by varying data collection methods and patterns of self-medication across countries which depend on local healthcare policies and advertising regulations [32]. Consistent with prior studies, we found that women and adults with high education were more likely to self-medicate [11, 31]. Women have been described to be more interested in health-related information [33]; while people with high education tend to be well-informed by reliable sources, which probably make them feel more confident in purchasing nonprescription medications. The results suggest the necessity of conducting thorough interview and medication review, including nonprescription medications and dietary supplements used at home during medical consultations.

Third, regarding patient perceptions, women were seen to be less likely to think they take too many medications than men. One explanation could be that women tend to be more aware of their health conditions, visiting doctors more frequently than men to seek treatments [34, 35], thereby being more willing to take prescribed medications. The proportion of older adults reporting not understanding all medications increased with age and the number of medications taken. ‘Have difficulty taking medications as prescribed’ was associated with advancing age, low income, activity limitation, and frailty. This aligns with previous research showing that financial and physical constraints could prevent patients from taking medications as prescribed [36, 37]. However, since the French people have limited out-of-pocket expenses of medications [38], the underlying mechanism for the association with low income is uncertain. Although crude associations between three variables pertaining to socio-economic status (socio-professional category, education level, income) with the outcomes were found, only income remained significantly associated with ‘do not understand all medications’ and ‘have difficulty taking medications as prescribed’ in the multivariate analysis. Similarly, only education remained associated with polypharmacy and self-medication. This might reflect the complexity of evaluating social inequality in health risks at older ages [39]. Those associations thus need to be interpreted with caution.

Fourth, three patient groups were identified among older adults taking \(\ge\) 2 medications, including Group 1 (‘Non-polypharmacy, positive perceptions’), Group 2 (‘Polypharmacy, positive perceptions’), and Group 3 (‘Negative perceptions’). Based on the results, we were able to distinguish people with negative perceptions of their medications from those with positive perceptions. Older adults in Group 3 were less likely to report polypharmacy but more likely to think that they take too many medications than those in Group 2. This underlines the difference between the objective measure of polypharmacy (\(\ge\) 5 medications) and the perceived measure defined by the variable ‘think they take too many medications’. In health research, the subjective assessments have been shown to aid in capturing multiple-faceted concerns along with the objective assessments and predicting health outcomes [40]. It is thus essential to take the subjective measure of polypharmacy into account by questioning about patient perceptions in future research. Regarding the factors associated with the assignment to Group 2 and Group 3 compared to Group 1, some factors were found to be significantly associated but no major differences emerged. Similar to patient perceptions of health status [41], perceptions of medications might be influenced by a complex set of factors. These factors could include not only some patient-related factors which we observed but also treatment-related factors, pathologies, and physician-related factors which we did not assess in this study.

The strength of our study is that we used data from a nationally representative survey – the ‘French Health Barometer’. Although we did not use validated tools due to the limited duration of the survey interview, the survey questions used to explore patient perceptions of medications are similar to questions in validated questionnaires [42]. Our study has some limitations. First, we studied people in a limited age range (70–85 years) which prevents us from studying the phenomena at older age (> 85 years). Second, reported information on the number of medications taken and self-medication could be threatened by recall bias. Third, we did not have information on the assistance on preparing or taking medications, which can confound the associations of our outcomes with some factors. Finally, there was a lack of information on the therapeutic regimen complexity, the pathologies, and the physician–patient relationship that could have provided clues for a more accurate interpretation of the studied phenomena. These factors should be examined in future studies to develop a deeper understanding of patient perceptions.

Conclusion

Polypharmacy and self-medication are common in the French population aged 70–85 years. Most of older adults reported positive perceptions of their medications. However, there remained a group of older adults who tended to think that they take too many medications regardless of their polypharmacy status, having difficulty understanding and taking their medications as prescribed. The findings suggest the importance of the perceived measure of polypharmacy in research, which makes possible to identify difficulties and support needs in people who are not considered to be at risk according to objective polypharmacy indicators.

Availability of data and materials

The datasets generated during and analyzed during the current study are not publicly available but are available from the corresponding author (laure.carcaillon-bentata@santepubliquefrance.fr) on reasonable request.

References

  1. Tableaux de l’économie française. Institut national de la statistique et des études économiques. Accessed 21 May 2021.  https://www.insee.fr/fr/statistiques/fichier/4318291/TEF2020.pdf

  2. Halli-Tierney AD, Scarbrough C, Carroll D. Polypharmacy: evaluating risks and deprescribing. Am Fam Physician. 2019;100(1):32–8.

    PubMed  Google Scholar 

  3. Mehta RS, Kochar BD, Kennelty K, Ernst ME, Chan AT. Emerging approaches to polypharmacy among older adults. Nature Aging. 2021;1(4):347–56.

    Article  Google Scholar 

  4. Midão L, Giardini A, Menditto E, Kardas P, Costa E. Polypharmacy prevalence among older adults based on the survey of health, ageing and retirement in Europe. Arch Gerontol Geriatr. 2018;78:213–20. https://doi.org/10.1016/j.archger.2018.06.018.

    Article  PubMed  Google Scholar 

  5. Herr M, Grondin H, Sanchez S, et al. Polypharmacy and potentially inappropriate medications: a cross-sectional analysis among 451 nursing homes in France. Eur J Clin Pharmacol. 2017;73(5):601–8. https://doi.org/10.1007/s00228-016-2193-z.

    Article  PubMed  Google Scholar 

  6. Rozsnyai Z, Jungo KT, Reeve E, et al. What do older adults with multimorbidity and polypharmacy think about deprescribing? The LESS study - a primary care-based survey. BMC Geriatr. 2020;20(1):435. https://doi.org/10.1186/s12877-020-01843-x.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sjölander M, Eriksson M, Glader EL. The association between patients’ beliefs about medicines and adherence to drug treatment after stroke: a cross-sectional questionnaire survey. BMJ Open. 2013;3(9): e003551. https://doi.org/10.1136/bmjopen-2013-003551.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hoisnard L, Santos-Eggimann B, Chauvin P, Hiance-Delahaye A, Herr M. Do older adults know the purpose of their medications? a survey among community-dwelling people. Eur J Clin Pharmacol. 2019;75(2):255–63. https://doi.org/10.1007/s00228-018-2575-5.

    CAS  Article  PubMed  Google Scholar 

  9. Hanlon JT, Fillenbaum GG, Ruby CM, Gray S, Bohannon A. Epidemiology of over-the-counter drug use in community dwelling elderly: United States perspective. Drugs Aging. 2001;18(2):123–31. https://doi.org/10.2165/00002512-200118020-00005.

    CAS  Article  PubMed  Google Scholar 

  10. Hughes CM, McElnay JC, Fleming GF. Benefits and risks of self medication. Drug Saf. 2001;24(14):1027–37. https://doi.org/10.2165/00002018-200124140-00002.

    CAS  Article  PubMed  Google Scholar 

  11. Vanhaesebrouck A, Vuillermoz C, Robert S, Parizot I, Chauvin P. Who self-medicates? results from structural equation modeling in the greater Paris area, France. PLoS ONE. 2018;13(12): e0208632. https://doi.org/10.1371/journal.pone.0208632.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Baromètre de Santé Publique France 2020. Santé Publique France. Accessed 28 May 2021.  https://www.santepubliquefrance.fr/docs/barometre-de-sante-publique-france-2020.-questionnaire

  13. Nomenclature des professions et catégories socioprofessionnelles 2017. Institut national de la statistique et des études économiques. Accessed 21 Nov 2021.  https://www.insee.fr/fr/information/2497952

  14. Consumption unit. Institut national de la statistique et des études économiques. Accessed 25 Nov 2021.  https://www.insee.fr/en/metadonnees/definition/c1802

  15. Cox B, van Oyen H, Cambois E, et al. The reliability of the minimum European health module. Int J Public Health. 2009;54(2):55–60. https://doi.org/10.1007/s00038-009-7104-y.

    Article  PubMed  Google Scholar 

  16. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56. https://doi.org/10.1093/gerona/56.3.m146.

    CAS  Article  PubMed  Google Scholar 

  17. Le Cossec C, Perrine AL, Beltzer N, Fuhrman C, Carcaillon-Bentata L. Pre-frailty, frailty, and multimorbidity: prevalences and associated characteristics from two French national surveys. J Nutr Health Aging. 2016;20(8):860–9. https://doi.org/10.1007/s12603-016-0802-2.

    Article  PubMed  Google Scholar 

  18. Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3:21. https://doi.org/10.1186/1471-2288-3-21.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488–95. https://doi.org/10.1097/EDE.0b013e3181a819a1.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee ES, Forthofer RN. Analyzing Complex Survey Data. Quantitative Applications in the Social Sciences. Thousand Oaks: SAGE Publications, Inc; 2nd edition; 2005.

  21. Haider SI, Johnell K, Thorslund M, Fastbom J. Analysis of the association between polypharmacy and socioeconomic position among elderly aged > or =77 years in Sweden. Clin Ther. 2008;30(2):419–27. https://doi.org/10.1016/j.clinthera.2008.02.010.

    Article  PubMed  Google Scholar 

  22. Ellenbogen MI, Wang P, Overton HN, et al. Frequency and predictors of polypharmacy in US Medicare patients: a cross-sectional analysis at the patient and physician levels. Drugs Aging. 2020;37(1):57–65. https://doi.org/10.1007/s40266-019-00726-0.

    Article  PubMed  Google Scholar 

  23. Drusch S, Le Tri T, Ankri J, Zureik M, Herr M. Decreasing trends in potentially inappropriate medications in older people: a nationwide repeated cross-sectional study. BMC Geriatr. 2021;21(1):621. https://doi.org/10.1186/s12877-021-02568-1.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Herr M, Robine JM, Pinot J, Arvieu JJ, Ankri J. Polypharmacy and frailty: prevalence, relationship, and impact on mortality in a French sample of 2350 old people. Pharmacoepidemiol Drug Saf. 2015;24(6):637–46. https://doi.org/10.1002/pds.3772.

    Article  PubMed  Google Scholar 

  25. Husson N, Watfa G, Laurain MC, et al. Characteristics of polymedicated (≥ 4) elderly: a survey in a community-dwelling population aged 60 years and over. J Nutr Health Aging. 2014;18(1):87–91. https://doi.org/10.1007/s12603-013-0337-8.

    CAS  Article  PubMed  Google Scholar 

  26. Katsimpris A, Linseisen J, Meisinger C, Volaklis K. The association between polypharmacy and physical function in older adults: a systematic review. J Gen Intern Med. 2019;34(9):1865–73. https://doi.org/10.1007/s11606-019-05106-3.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Slater N, White S, Venables R, Frisher M. Factors associated with polypharmacy in primary care: a cross-sectional analysis of data from The English Longitudinal Study of Ageing (ELSA). BMJ Open. 2018;8(3):e020270. https://doi.org/10.1136/bmjopen-2017-020270.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Bjerrum L L, Søgaard J J, Hallas J J, Kragstrup J. Polypharmacy: correlations with sex, age and drug regimen. a prescription database study. Eur J Clin Pharmacol. 1998;54(3):197–202. https://doi.org/10.1007/s002280050445.

    Article  PubMed  Google Scholar 

  29. Linjakumpu T, Hartikainen S, Klaukka T, Veijola J, Kivelä SL, Isoaho R. Use of medications and polypharmacy are increasing among the elderly. J Clin Epidemiol. 2002;55(8):809–17. https://doi.org/10.1016/s0895-4356(02)00411-0.

    Article  PubMed  Google Scholar 

  30. Slabaugh SL, Maio V, Templin M, Abouzaid S. Prevalence and risk of polypharmacy among the elderly in an outpatient setting: a retrospective cohort study in the Emilia-Romagna region. Italy Drugs Aging. 2010;27(12):1019–28. https://doi.org/10.2165/11584990-000000000-00000.

    Article  PubMed  Google Scholar 

  31. Brandão GR, Teixeira L, Araújo L, Paúl C, Ribeiro O. Self-medication in older European adults: prevalence and predictive factors. Arch Gerontol Geriatr. 2020;91: 104189. https://doi.org/10.1016/j.archger.2020.104189.

    Article  PubMed  Google Scholar 

  32. Bergmann JF. Self-medication: from European regulatory directives to therapeutic strategy. Fundam Clin Pharmacol. 2003;17(3):275–80. https://doi.org/10.1046/j.1472-8206.2003.00141.x.

    CAS  Article  PubMed  Google Scholar 

  33. Ek S. Gender differences in health information behaviour: a finnish population-based survey. Health Promot Int. 2015;30(3):736–45. https://doi.org/10.1093/heapro/dat063.

    Article  PubMed  Google Scholar 

  34. Bertakis KD, Azari R, Helms LJ, Callahan EJ, Robbins JA. Gender differences in the utilization of health care services. J Fam Pract. 2000;49(2):147–52.

    CAS  PubMed  Google Scholar 

  35. Thompson AE, Anisimowicz Y, Miedema B, Hogg W, Wodchis WP, Aubrey-Bassler K. The influence of gender and other patient characteristics on health care-seeking behaviour: a QUALICOPC study. BMC Fam Pract. 2016;17:38. https://doi.org/10.1186/s12875-016-0440-0.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Piette JD, Heisler M, Wagner TH. Cost-related medication underuse among chronically ill adults: the treatments people forgo, how often, and who is at risk. Am J Public Health. 2004;94(10):1782–7. https://doi.org/10.2105/ajph.94.10.1782.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Figueiredo D, Teixeira L, Poveda V, Paúl C, Santos-Silva A, Costa E. Predictors of difficulty in medication intake in Europe: a cross-country analysis based on SHARE. Aging Dis. 2016;7(3):246–53. https://doi.org/10.14336/AD.2015.0925.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Marc C, Héam J-C, Mikou M, Portela M. Les dépenses de santé en. Paris: DREES; 2019.

    Google Scholar 

  39. Grundy E, Holt G. The socioeconomic status of older adults: how should we measure it in studies of health inequalities? J Epidemiol Community Health. 2001;55(12):895–904. https://doi.org/10.1136/jech.55.12.895.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Cleary PD. Subjective and objective measures of health: which is better when? J Health Serv Res Policy. 1997;2(1):3–4. https://doi.org/10.1177/135581969700200102.

    CAS  Article  PubMed  Google Scholar 

  41. Machón M, Vergara I, Dorronsoro M, Vrotsou K, Larrañaga I. Self-perceived health in functionally independent older people: associated factors. BMC Geriatr. 2016;16:66. https://doi.org/10.1186/s12877-016-0239-9.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Reeve E, Low LF, Shakib S, Hilmer SN. Development and validation of the revised patients’ attitudes towards deprescribing (rPATD) questionnaire: versions for older adults and caregivers. Drugs Aging. 2016;33(12):913–28. https://doi.org/10.1007/s40266-016-0410-1.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the French Health Barometer team of the French National Public Health Agency for their work and support. They also thank Solène Drusch, PhD Candidate at Université Paris-Saclay, UVSQ, INSERM, CESP, Anti-infective evasion and pharmacoepidemiology, for reviewing preliminary version of this paper.

Funding

This study received no external funding. The French Health Barometer survey was funded by the French National Public Health Agency.

Author information

Authors and Affiliations

Authors

Contributions

M.H, L.CB: conceptualized the study. M.H, L.CB, ATQ.T: designed the methodology. ATQ.T: performed the analyses under the supervision of M.H and L.CB. M.H, L.CB, ATQ.T: interpreted the results. ATQ.T: drafted the manuscript. M.H, L.CB: reviewed and edited the manuscript. N.S: reviewed the manuscript and contributed to the data analysis. J.A: reviewed the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Laure Carcaillon-Bentata.

Ethics declarations

Ethics approval and consent to participate

The French Health Barometer surveys were approved by the French Data Protection Authority (Commission Nationale de l’Informatique et des Libertés, CNIL). No personal identifiable information was collected. The anonymity of participants and the confidentiality of their data were guaranteed. All participants gave their informed consent before the telephone interview. As the main investigator of the surveys, the French National Public Health Agency has full permission to access and use these data.

All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

List of the variables composing the frailty phenotype. Table S2. Factors associated with ‘Think that they take too many medications’. Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables. Table S3. Factors associated with ‘Think they do not understand all their medications’. Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables. Table S4. Factors associated with ‘Have difficulty taking their medications as prescribed’. Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables. Table S5. Statistical description of three patient groups, n (weighted percentages) (n=1,080). Table S6. Factors associated with polypharmacy and self–medication (results from imputed dataset). Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables. Table S7. Factors associated with three outcomes pertaining to patient perceptions of medications (results from imputed dataset). Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables. Table S8. Factors associated with group assignment (n=1,080) (results from imputed dataset). Model 1: adjusted for socio-demographic variables. Model 2: adjusted for socio-demagraphic and health-related variables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tran, A.TQ., Soullier, N., Ankri, J. et al. Uses and perceptions of medications among French older adults: results from the 2020 French Health Barometer survey. BMC Geriatr 22, 602 (2022). https://doi.org/10.1186/s12877-022-03289-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-022-03289-9

Keywords

  • Polypharmacy
  • Self-medication
  • Perceptions
  • Older adults
  • Health survey