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. 2025 Aug 15;2025:4661541. doi: 10.1155/da/4661541

Self-Medication in Individuals With Depression and Symptoms of Depression in the European Union: Prevalence and Associated Factors

Spencer Yeamans 1, Pilar Carrasco-Garrido 1,✉, Valentín Hernández-Barrera 1, Ángel Gil-De-Miguel 1
PMCID: PMC12373476  PMID: 40861244

Abstract

Depression is a growing public health problem in the European Union (EU), with many individuals turning to self-medication (SM) to manage their symptoms. This cross-sectional study uses data from the third wave of the European Health Interview Survey (EHIS; 2018–2020) to examine the prevalence and determinants of SM among people with recognized depression and depressive symptoms. A total of 25,701 respondents were analyzed. Prevalence of SM among individuals with recognized depression and symptoms of depression in the EU is 38.46% in men and 46.84% in women, varying considerably between countries. An important finding of this study is the impact of medication availability, with access to over-the-counter (OTC) medications outside of pharmacies nearly doubling SM likelihood (adjusted odds ratio [AOR] = 1.98). Additionally, the results reveal marked differences in how these men and women self-medicate. Specifically, women are more likely to self-medicate with depressive symptoms versus recognized depression (AOR = 1.28), whereas the opposite is observed in men (AOR = 0.69). Among women, younger age groups with depression symptoms are particularly likely to self-medicate (15–24 AOR = 1.60; 25–44 AOR = 1.93) and the results reinforce education as a strong predictor of SM (higher education vs. no education AOR = 5.63). Visits to medical/surgical specialists are also linked to SM in women (AOR = 1.32). This study also highlights potentially concerning relationships between SM and alcohol use in men with recognized depression (AOR = 1.42) and prescribed medicine (AOR = 1.68). Differences are also observable in the effect of employment on SM (AOR = 1.45) in men with depression symptoms and women with recognized depression. In contrast, physical activity (PA; high vs. low AOR = 1.32) and healthcare barriers (distance/transportation issues AOR = 1.89 in women; AOR = 1.55 in men, inability to afford care AOR = 1.38) display similar positive associations in men and women. Taken together, these findings underscore the complex and multifaceted nature of SM and point to potential gaps in depression care across the EU, emphasizing the need for gender-sensitive public health strategies and a closer look at OTC medication access.

Keywords: EHIS, European Health Interview Survey, gender, mental health, nonprescription, over-the-counter, sex

1. Introduction

Depression is a public health problem with the largest global burden of all mental disorders and is currently on the rise in the European Union (EU) [1, 2]. Varying across EU member states, the total prevalence of depression in the EU is under debate, ranging from 2.10% to 6.54% depending on the employed measurement tool [2, 3]. To deal with their symptomology, the self-medication (SM) hypothesis states that those suffering from psychological ailments such as depression may turn to the consumption of substances such as medication [4]. Undeniably, the link between depression and the consumption of medications, including psychopharmaceuticals but also opioids, with and without prescription, is well-established [5, 6]. A study in the United States working with university students found a connection between nonmedical use of prescription medicine (NMUP) and depression with respect to opioids, stimulants, sedatives, and antidepressants [7].

However, little research has investigated the link between depression and broader SM habits in general population. Though definitions vary, SM can be best defined as the use of over-the-counter (OTC) and prescription medications, herbal products, home-made remedies, nutritional supplements, and vitamins without strict adherence to authorized health care professionals´ instruction regarding indication, dose, and duration of treatment [8]. Like depression, SM is increasingly frequent [8], with reports emerging which cite eroding consumer trust in pharmacists and increasing information access as driving causes [9, 10]. Research from the United States found that the average consumer makes 26 trips annually to purchase OTC products, while only performing three doctors' visits during the same time frame [11].

Undoubtedly, responsible SM forms a key part of public health strategy and provides numerous benefits, including time and cost savings, quality of life improvements, and reduced strain on health care systems [12]. Concerningly, however, an Australian study found that less than 20% of patients reported often following dosage, frequency, and duration directions for nonprescription medicines [13]. Despite the fact that 71% of participants in a Scottish study perceived no risk from nonprescription medicines [14], irresponsible SM poses a multitude of risks. These include delayed disease diagnoses, antimicrobial resistance, comorbidities, drug–drug interactions, adverse drug reactions, misuse, and addiction [12]. As a result, understanding trends in SM is essential to broader public health. A previous study utilizing a 2-week sampling window established SM prevalence in the EU at 34.3% and discovered substantial SM prevalence variability between EU countries [15]. Additionally, this study established several socioeconomic, demographic, lifestyle, and health factors as determinants of SM in the EU.

Nonetheless, a number of questions remain surrounding the relationship between SM and vulnerable populations such as those with depression. Consequently, the goal of this study is to analyze SM prevalence and its associated factors in individuals ages 15 and older in the EU with recognized depression and symptoms of depression.

2. Materials and Methods

2.1. Data Source and Study Population

This observational study utilizes anonymized data from the third wave of the cross-sectional European Health Interview Surveys (EHISs) [16, 17]. Surveys were conducted between January 2018 and September 2020 on a broad representative sample of the population, consisting of the noninstitutionalized subjects aged 15 and over residing in EU countries. Twenty-six of the 27 EU member states are represented in this study, as France's data was unavailable via Eurostat. Results are comparable between countries due to the use of a common regulatory framework, including common variable definitions and conceptual guidelines, protocols for survey administration, and a standardized questionnaire, translated as needed to the local population. The total weighted sample size for this study is 25,701.

2.2. Study Measures

The dependent variable in this study is the dichotomous answer (yes/no) to the question “During the past 2 weeks, have you used any medicines or herbal medicines or vitamins not prescribed by a doctor?” excluding contraceptive pills or hormones used solely for contraception. In the construction of the dependent variable, we eliminated proxy answers to the dependent variable question for homogenization purposes.

To better analyze relationship between depression and SM, the study sample is partitioned into two mutually exclusive groups. The first group, labeled recognized depression, contains those who marked “yes” to the depression component of the question “During the past 12 months, have you had any of the following diseases or conditions?” The second group, labeled symptoms of depression, consists of those who do not enter into the previous grouping, but scored 10 points or more on the eight questions composing the Patient Health Questionaire-8 (PHQ-8). The PHQ-8 is Likert-type scaled tool validated for screening for depression in the EU [18, 19]. The symptoms of depression group does not contain participants from Spain, as the questions used to construct the PHQ-8 were excluded from Spain´s dataset. Results for both of these groups are also divided by sex.

This study also includes demographic, socioeconomic, lifestyle, and health independent variables. Demographic variables consist of age (separated into five categories: 15–24, 25–44, 45–64, 65–74, 75+), sex, marital status and living situation (single, married or living with partner, divorced, or widowed), degree of urbanization (cities, towns/suburbs, or rural), and nationality. Nationality has three potential responses: native-born, born in another EU member state, or born in a non-EU country. Due to anonymization practices, Malta´s nationality data was excluded. Socioeconomic variables are also utilized, including education, classified as no formal education (below ISCED 1), primary school (International Standard Classification of Education [ISCED] 1 or 2), secondary school (ISCED 3 or 4), or higher education (ISCED 5 or above). Employment status, categorized as employed, unemployed, or inactive (retirees, students, and those performing domestic tasks, carrying out compulsory service, or unable to work for health reasons) and income level, separated by quintiles within each respective country where the first quintile contains the lowest values and the fifth quintile contains the highest values, comprise the other socioeconomic variables. Also included in the study is a variable utilizing the Oslo Social Support Scale (OSSS-3), a standardized three-question tool for measuring social support as poor support, moderate support, or strong support [20]. Lifestyle variables include smoking (yes/no), vaping (yes/no), alcohol consumption (more than once a month and less than once a month), and physical activity (PA; low, moderate, and high) as defined by the methodology used in Jemna et al. 2022 [21].

This study also contains health and health system variables. To begin with, utilizing clusters created by Oleszkiewicz et al. [12], a variable was created to measure the impact of OTC medication availability on SM. Three clusters were formed using information from the legal acts on the websites of the countries' respective Ministries of Health, as well as from scientific literature. The first cluster, labeled pharmacy-only, contains countries where pharmacies have a monopoly on dispensing medicines: Austria, Belgium, Cyprus, Estonia, Finland, France, Greece, Lithuania, Luxembourg, Latvia, Malta, Slovakia, and Spain. The second cluster, labeled nonpharmacy, refers to countries where individuals may purchase medicines from trading points other than a pharmacy specified in general sales lists: Poland, Ireland, the Netherlands, Slovenia, Hungary, Italy, Czechia, Denmark, and Sweden. The third and final cluster, labeled limited nonpharmacy, encompasses countries with a limited range of medicines (i.e., medicines with a low therapeutic effect such as herbal medicines) sold in limited-service pharmacies (where it is not possible to consult with qualified staff when making a purchase): Bulgaria, Croatia, Germany, Portugal, Romania, and Switzerland. Additional health variables include the presence of a long-standing health problem, use of prescribed medicine in the past 2 weeks, visit to a general practitioner/family doctor, visit to medical or surgical specialist, and visit to a psychologist, psychotherapist, or psychiatrist in the past 12 months, all measured dichotomously (yes/no). A variable measuring self-perceived health, rated as very good/good, fair, and bad/very bad is also utilized. Finally, five variables evaluating unmet health care needs in the past 12 months with respect to waiting lists, distance or transportation problems, inability to afford medical examination or treatment, inability to afford prescribed medicines, and inability to afford mental health care are included, with possible answers including yes, no, and no need for health care.

2.3. Statistical Analysis

We calculated the prevalence of SM in individuals with recognized depression and symptoms of depression according to the PHQ-8 by country and for each independent variable, selected based on relevancy in the literature, via answers to the dependent variable. Furthermore, prevalences are stratified by sex and corresponding female-male odds ratios (ORs) with 95% confidence intervals (95% CIs) provided for all countries and independent variables. To estimate the independent effect of the study variables on SM, we calculated the adjusted ORs (AORs) and the 95%CIs via multivariable logistic regression analysis utilizing the methodology used by Hosmer Jr. et al. [22]. The independent variables for these models were selected using a stepwise variable selection procedure that began with univariate screening (p  < 0.25), followed by iterative refinement using Wald tests, partial likelihood ratio tests, and checks for confounding effects. Interaction was evaluated for both sex (male/female) and depression (recognized depression/depression symptoms) to measure the effect of SM. In total, nine multivariable logistic regression models were generated via combinations of the variables related to depression and sex. Estimates were made using the survey command (svy function) in Stata [23], enabling the incorporation of sample design and weights in all statistical calculations. Statistical significance is set as two-tailed α < 0.05. The figure was created in R using the ggplot2 package [24, 25].

3. Results

The characteristics of the study population are available in Table 1. 33.47% of the sample is male, while the other 66.53% is female. Additionally, 72.13% of the sample pertain to the recognized depression category with the remaining 27.87% belonging to the symptoms of depression group. In Supporting Information 1: Table A, the male and female prevalences for the recognized depression and symptoms of depression groups are stratified by country. These values are also displayed graphically via maps in Figure 1. It can be observed that the lowest and highest prevalence of SM, respectively, is amongst men with recognized depression in Spain at 13.66% (95% CI = 11.14–16.57) and females with recognized depression in Finland at 86.89% (95% CI = 81.64–91.03).

Table 1.

Descriptive table of depressed and noninstitutionalized residents aged 15 and over in the European Union.

Independent variables Male (N = 8602 [33.47%]) Female (N = 17,099 [66.53%]) Both sexes (N = 25,701 [100%])
N % N % N %
Age (years)
 15–24 618 9.20 1117 8.76 1735 8.93
 25–44 1903 29.41 2996 24.38 4899 26.31
 45–64 3066 38.15 5868 34.61 8934 35.97
 65–74 1417 10.99 2939 13.13 4356 12.31
 75+ 1598 12.24 4179 19.13 5777 16.49
Nationality⁣∗
 Native-born 7520 86.07 15,155 88.16 22,675 87.36
 Born in another EU state 323 3.23 718 4.16 1041 3.81
 Born in non-EU country 739 10.70 1198 7.68 1937 8.83
Degree of urbanization
 Cities 3227 44.00 6335 44.02 9562 44.01
 Towns and suburbs 2682 33.35 5431 33.08 8113 33.18
 Rural areas 2663 22.65 5271 22.90 7934 22.81
Marital status
 Single 2516 38.77 2986 22.38 5502 28.67
 Married or living with partner 4456 41.94 7578 44.42 12,034 43.47
 Divorced 699 7.21 4353 20.27 5052 15.26
 Widowed 913 12.07 2122 12.92 3035 12.60
Education level
 No formal education 262 1.88 929 3.63 1191 2.96
 Primary school 2863 30.03 6638 36.96 9501 34.30
 Secondary school 3400 50.42 5740 42.24 9140 45.37
 Higher education 1963 17.67 3597 17.18 5560 17.37
Employment status
 Employed 2720 39.10 4720 33.18 7440 35.44
 Unemployed 714 9.63 1007 7.36 1721 8.23
 Inactive 5071 51.28 11,227 59.46 16,298 56.33
Income level
 1st quintile 2067 29.57 4695 29.98 6762 29.82
 2nd quintile 1837 22.78 4121 24.42 5958 23.79
 3rd quintile 1703 19.62 3011 18.79 4714 19.11
 4th quintile 1372 15.79 2462 15.23 3834 15.45
 5th quintile 1138 12.24 1859 11.58 2997 11.83
Oslo Social Support Scale
 Poor support 5766 63.35 12,115 69.16 17,881 66.92
 Moderate support 2377 34.70 4192 29.00 6569 31.19
 Strong support 112 1.95 182 1.85 294 1.89
Smoking
 Yes 2660 40.26 3592 26.88 6252 32.01
 No 5844 59.74 13,278 73.12 19,122 67.99
Vaping
 Yes 364 6.52 469 3.83 833 4.86
 No 8078 93.48 16,306 96.17 24,384 95.14
Alcohol consumption
 More than once a month 2931 40.20 2949 20.62 5880 28.17
 Once a month or less 4636 59.80 12,083 79.38 16,719 71.83
Physical activity
 Low physical activity 5325 55.73 11,437 60.61 16,762 58.74
 Moderate physical activity 1343 15.09 2633 16.49 3976 15.95
 High physical activity 1875 29.19 2905 22.90 4780 25.31
Medication availability cluster
 Pharmacy-only 2285 14.32 5022 17.64 7307 16.36
 Nonpharmacy 3854 33.79 6956 34.94 10,810 34.50
 Limited pharmacy 2463 51.90 5121 47.42 7584 49.13
Long-standing health problem
 Yes 6982 78.29 14,191 82.13 21,173 80.66
 No 1564 21.71 2784 17.87 4348 19.34
Self-perceived health
 Very good/good 2237 30.88 4178 28.50 6415 29.41
 Fair (neither good nor bad) 3047 34.66 6872 40.72 9919 38.40
 Bad/very bad 3281 34.45 5966 30.78 9247 32.19
Use of prescribed medicine in the past 2 weeks
 Yes 7275 84.68 15,377 91.18 22,652 88.68
 No 1288 15.32 1661 8.82 2949 11.32
Visit to a general practitioner or family doctor⁣∗∗
 Yes 6508 71.72 14,042 80.19 20,550 76.94
 No 2053 28.28 2998 19.81 5051 23.06
Visit to a medical or surgical specialist⁣∗∗
 Yes 5553 64.02 11,924 72.80 17,477 69.44
 No 2964 35.98 5026 27.20 7990 30.56
Visit to a psychologist, psychotherapist, or psychiatrist⁣∗∗
 Yes 2469 33.92 5161 37.28 7630 36.00
 No 6012 66.08 11,750 62.72 17,762 64.00
Unmet need for health care due to waiting lists⁣∗∗
 Yes 2207 26.57 5080 30.88 7287 29.23
 No 5142 59.51 10,089 58.96 15,231 59.17
 No need for health care 1174 13.91 1799 10.15 2973 11.59
Unmet need for health care due to distance or transportation problems⁣∗∗
 Yes 736 9.99 1535 8.75 2271 9.23
 No 6618 77.49 13,510 80.62 20,128 79.42
 No need for health care 1165 12.52 1885 10.63 3050 11.35
Unmet need for health care due to inability to afford medical examination or treatment⁣∗∗
 Yes 1003 10.17 2333 13.10 3336 11.97
 No 5794 72.03 11,696 72.31 17,490 72.20
 No need for health care 1306 17.80 2171 14.59 3477 15.82
Unmet need for health care due to inability to afford prescribed medicines⁣∗∗
 Yes 849 9.61 1989 11.61 2838 10.84
 No 6201 76.42 12,673 78.22 18,874 77.53
 No need for health care 1047 13.97 1524 10.17 2571 11.63
Unmet need for health care due to inability to afford mental health care⁣∗∗
 Yes 699 9.53 1566 9.80 2265 9.70
 No 4548 61.54 8845 61.38 13,393 61.44
 No need for health care 2847 28.93 5773 28.81 8620 28.86
Self-medication in the past 2 weeks
 Yes 3282 38.46 8013 46.84 11,295 43.63
 No 5320 61.54 9086 53.16 14,406 56.37
Depression
 Recognized 5752 71.66 11,941 72.41 17,693 72.13
 Symptoms 2850 28.34 5158 27.59 8008 27.87
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Note: European Health Interview Survey Wave 3 (2018–2020). ⁣∗ = does not include data from Malta; ⁣∗∗ = in the past 12 months. Inactive = retirees, students, and those performing domestic tasks, carrying out compulsory service, or unable to work for health reasons. 1st quintile contains the lowest values and the 5th quintile the highest values.

Abbreviations: EU, European Union; OR, odds ratio.

Figure 1.

Figure 1

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Self-medication prevalence (%) in noninstitutionalized residents aged 15 and over with recognized depression and symptoms of depression in the European Union. European Health Interview Survey Wave 3 (2018–2020). (a) Men with recognized depression. (b) Women with recognized depression. (c) Men with symptoms of depression. (d) Women with symptoms of depression.

SM prevalence with respect to demographic, socioeconomic, and lifestyle variables is displayed in Table 2. Total SM prevalence in individuals with recognized depression or symptoms of depression in the EU sits at 38.46% (95% CI = 37.54–39.39) in men and 46.84% (95% CI = 46.09–47.58) in women (OR: 1.41; 95% CI = 1.27–1.57). For women, SM prevalence is greater in those meeting PHQ-8 criteria for symptoms of depression than those who have recognized depression (49.87% vs. 45.68%; 95% CI = 48.45–51.30 and 44.80–46.56); in men, the opposite is true (33.25% vs. 40.52%; 95% CI = 31.59–34.95 and 39.42–41.62). In both men and women, SM is most prevalent for those aged 25–44 (44.16% and 56.00%, respectively; 95% CI = 42.42–45.90 and 54.49–57.50). It is also observable that SM prevalence increases with improved education level (no formal education: 15.57% vs. higher education: 59.06%; 95% CI = 12.88–18.61 and 57.29–60.86). Additionally, our findings show that in those with no formal education and recognized depression, men self-medicate more than their female counterparts (OR: 0.53; 95% CI = 0.31–0.86), and that this relationship inverts with greater educational level (higher education OR: 1.72; 95% CI = 1.46–2.01). Meanwhile, Table 3 contains SM prevalence by health and health system variables. The findings establish that SM prevalence in individuals with recognized depression or symptoms of depression is greater for those living in countries with non-pharmacy OTC medication availability versus those who live in countries with pharmacy-only medication sales (male: 42.63% vs. 35.29%, female: 51.91% vs. 40.85%; 95% CIs = male: 41.02–44.24 and 32.91–37.70, female: 50.64–53.17 and 39.12–42.62). Furthermore, the results show that SM prevalence for people with recognized depression or symptoms of depression is lower for those consuming prescription medicine (male: 38.06% vs., 41.32%, female: 46.60% vs. 50.18%; 95% CIs = male: 37.06–39.07 and 38.98–43.75, female: 45.82–47.38 and 47.64–52.69). Additionally, among population with recognized depression or symptoms of depression, we also find that SM prevalence is greater for those who report having unmet health needs in the past 12 months, such as physical difficulties in accessing health care (male: 55.50% vs. 37.88%, female: 52.71% vs. 53.31%; 95% CIs = male: 51.74–59.10 and 33.13–43.09, female: 49.59–55.75 and 48.85–57.70).

Table 2.

Self-medication prevalence by sex in depressed and noninstitutionalized residents aged 15 and over in the European Union according to demographic, socioeconomic, and lifestyle variables.

Independent variables Recognized depression Depression symptoms⁣∗ Both groups
Male Female OR female Male Female OR female Male Female OR female
Total 40.52 (39.42–41.62) 45.68 (44.80–46.56) 1.23 (1.08–1.41) 33.25 (31.59–34.95) 49.87 (48.45–51.30) 1.99 (1.66–2.39) 38.46 (37.54–39.39) 46.84 (46.09–47.58) 1.41 (1.27–1.57)
Agea,b,c,d,e,f
 15–24 years 47.73 (43.92–51.41) 49.12 (46.11–52.23) 1.06 (0.64–1.73) 25.19 (20.66–30.48) 51.67 (47.20–56.12) 3.17 (2.53–4.08) 40.84 (37.79–43.93) 49.93 (47.37–52.43) 1.44 (0.97–2.14)
 25–44 years 44.35 (42.38–46.35) 54.48 (52.74–56.20) 1.50 (1.14–1.97) 43.53 (39.95–47.09) 60.76 (57.74–63.74) 2.00 (1.28–3.15) 44.16 (42.42–45.90) 56.00 (54.49–57.50) 1.60 (1.27–2.03)
 45–64 years 38.12 (36.43–39.86) 44.67 (43.24–46.11) 1.31 (1.07–1.60) 28.80 (26.05–31.68) 53.57 (50.90–56.27) 2.85 (2.07–3.92) 35.84 (34.39–37.34) 46.65 (45.39–47.93) 1.56 (1.31–1.86)
 65–74 years 35.81 (32.48–39.26) 41.38 (39.06–43.72) 1.26 (0.92–1.74) 37.99 (33.29–42.67) 45.42 (41.18–49.58) 1.36 (0.91–2.02) 36.57 (33.85–39.36) 42.34 (40.30–44.37) 1.27 (0.99–1.63)
 75+ years 36.20 (32.79–39.79) 35.50 (33.38–37.68) 0.97 (0.69–1.35) 28.67 (25.21–32.54) 39.46 (36.88–42.03) 1.62 (1.22–2.15) 32.84 (30.32–35.40) 37.17 (35.53–38.84) 1.21 (0.96–1.52)
Nationality⁣∗a,b,c,f
 Native-born 39.40 (38.21–40.58) 45.20 (44.26–46.14) 1.27 (1.10–1.45) 34.16 (32.34–36.00) 49.83 (48.32–51.33) 1.91 (1.58–2.32) 37.92 (36.93–38.92) 46.50 (45.70–47.29) 1.42 (1.27–1.59)
 Born in another EU state 41.32 (34.88–47.65) 55.62 (51.32–59.77) 1.78 (0.85–3.71) 30.14 (22.18–38.63) 49.53 (42.29–56.64) 2.27 (1.68–3.22) 37.51 (32.60–42.82) 54.05 (50.33–57.63) 1.96 (1.05–3.64)
 Born in non-EU country 48.17 (44.75–51.59) 44.71 (41.65–47.78) 0.87 (0.54–1.39) 24.28 (19.92–29.30) 50.44 (44.90–56.08) 3.17 (2.54–4.07) 41.44 (38.62–44.34) 46.04 (43.33–48.71) 1.20 (0.82–1.77)
Degree of urbanizationa,c,d,e,f
 Cities 41.98 (40.32–43.65) 46.19 (44.87–47.52) 1.19 (0.97–1.44) 36.68 (34.01–39.36) 52.29 (50.09–54.47) 1.89 (1.40–2.54) 40.55 (39.13–41.97) 47.83 (46.69–48.96) 1.34 (1.14–1.58)
 Towns and suburbs 39.40 (37.54–41.34) 44.09 (42.57–45.61) 1.21 (0.96–1.53) 30.84 (28.00–33.87) 48.76 (46.28–51.33) 2.13 (1.49–3.05) 37.08 (35.47–38.68) 45.35 (44.05–46.66) 1.40 (1.15–1.71)
 Rural areas 38.46 (36.09–40.89) 46.53 (44.65–48.40) 1.39 (1.24–1.57) 30.82 (27.68–34.09) 47.33 (44.46–50.20) 2.01 (1.55–2.62) 35.90 (33.99–37.85) 46.77 (45.21–48.35) 1.57 (1.29–1.91)
Marital statusa,b,c,d,e,f
 Single 43.23 (41.53–44.95) 49.98 (48.13–51.80) 1.31 (1.00–1.71) 35.06 (32.04–38.23) 57.07 (53.91–60.13) 2.46 (1.58–3.83) 41.43 (39.95–42.95) 51.78 (50.20–53.37) 1.52 (1.21–1.90)
 Married or living with partner 38.70 (37.00–40.44) 46.04 (44.76–47.34) 1.35 (1.15–1.59) 35.76 (33.31–38.32) 50.34 (48.06–52.58) 1.82 (1.46–2.27) 37.78 (36.36–39.20) 47.10 (45.98–48.22) 1.47 (1.28–1.67)
 Divorced 34.68 (30.19–39.18) 38.30 (36.24–40.37) 1.17 (0.60–2.04) 27.24 (22.75–32.18) 43.47 (40.86–46.16) 2.05 (1.30–3.25) 31.38 (28.10–34.64) 40.30 (38.68–41.94) 1.48 (1.01–2.16)
 Widowed 39.85 (36.66–42.97) 46.56 (44.19–48.93) 1.31 (0.90–1.91) 24.56 (20.28–29.04) 53.09 (48.79–57.40) 3.47 (1.95–6.18) 35.48 (32.90–38.12) 48.08 (46.02–50.18) 1.68 (1.22–2.32)
Education levela,b,c,d,e,f
 No formal education 25.44 (18.44–32.98) 13.10 (10.33–16.36) 0.44 (0.18–0.82) 26.87 (16.95–38.25) 24.29 (17.65–31.96) 0.87 (0.51–1.49) 25.90 (20.05–32.17) 15.57 (12.88–18.61) 0.53 (0.31–0.86)
 Primary school 32.50 (30.54–34.48) 36.10 (34.69–37.53) 1.17 (0.88–1.56) 24.13 (21.54–26.85) 41.33 (39.14–43.57) 2.21 (1.57–3.12) 29.87 (28.30–31.48) 37.68 (36.49–38.89) 1.42 (1.13–1.78)
 Secondary school 43.61 (42.04–45.16) 51.65 (50.31–52.99) 1.38 (1.13–1.68) 35.20 (32.75–37.68) 55.86 (53.59–58.11) 2.32 (1.73–3.13) 41.34 (40.03–42.68) 52.74 (51.58–53.89) 1.58 (1.34–1.87)
 Higher education 45.37 (42.75–48.03) 58.00 (55.92–60.10) 1.66 (1.37–2.01) 46.48 (42.06–50.78) 61.96 (58.55–65.36) 1.87 (1.40–2.51) 45.67 (43.40–47.92) 59.06 (57.29–60.86) 1.72 (1.46–2.01)
Employment statusa,b,c,d,e,f
 Employed 38.96 (36.91–41.02) 44.27 (42.67–45.91) 1.48 (1.19–1.82) 24.47 (21.70–27.43) 48.76 (46.04–51.49) 1.83 (1.29–2.59) 34.82 (33.14–36.54) 45.45 (44.07–46.86) 1.56 (1.30–1.87)
 Unemployed 43.58 (41.19–45.96) 41.88 (40.11–43.70) 1.09 (0.67–1.78) 29.95 (26.56–33.49) 45.14 (42.25–48.13) 4.80 (2.23–10.37) 39.66 (37.67–41.65) 42.78 (41.25–44.31) 1.42 (0.92–2.20)
 Inactive 40.13 (37.62–42.70) 44.84 (42.78–46.90) 1.13 (095–1.35) 36.49 (32.60–40.45) 51.84 (48.47–55.23) 2.11 (1.72–2.60) 39.09 (36.96–41.23) 46.74 (45.00–48.53) 1.37 (1.19–1.57)
Income levela,b,c,d,e,f
 1st quintile 38.17 (35.44–41.01) 49.04 (46.76–51.33) 1.24 (0.94–1.65) 41.87 (37.50–46.65) 52.61 (48.76–56.35) 2.94 (1.95–4.42) 39.19 (36.81–41.57) 49.99 (48.04–51.96) 1.56 (1.23–1.97)
 2nd quintile 43.04 (39.93–46.32) 49.63 (46.98–52.35) 0.93 (0.69–1.26) 42.52 (37.24–47.91) 56.41 (52.28–60.46) 1.92 (1.37–2.71) 42.90 (40.22–45.71) 51.65 (49.39–53.88) 1.14 (0.89–1.45)
 3rd quintile 43.23 (41.48–44.98) 53.03 (51.51–54.51) 1.21 (0.91–1.61) 44.43 (41.43–47.42) 59.37 (56.77–61.91) 1.87 (1.51–3.04) 43.54 (42.05–45.07) 54.60 (53.30–55.89) 1.37 (1.07–1.75)
 4th quintile 35.81 (32.58–39.26) 37.94 (35.06–40.88) 1.56 (1.18–2.05) 20.16 (15.43–25.85) 54.83 (47.65–61.78) 1.54 (1.06–2.24) 32.32 (29.46–35.21) 40.48 (37.77–43.20) 1.55 (1.24–1.94)
 5th quintile 39.23 (37.67–40.82) 42.24 (41.08–43.39) 1.30 (0.96–1.77) 28.10 (26.03–30.28) 45.29 (43.55–47.06) 1.75 (1.12–2.72) 35.70 (34.43–36.98) 43.17 (42.21–44.14) 1.42 (1.10–1.83)
Oslo Social Support Scalea,b,c,e
 Poor support 38.44 (37.03–39.86) 44.13 (43.06–45.20) 1.26 (1.08–1.47) 33.05 (31.00–35.20) 49.46 (47.68–51.26) 1.98 (1.58–2.49) 36.83 (35.67–38.01) 45.54 (44.63–46.46) 1.43 (1.26–1.63)
 Moderate support 44.12 (42.22–46.02) 49.78 (48.08–51.47) 1.25 (0.97–1.62) 32.33 (29.36–35.39) 50.17 (47.59–52.84) 2.10 (1.52–2.92) 41.05 (39.44–42.67) 49.89 (48.46–51.31) 1.43 (1.16–1.76)
 Strong support 24.74 (18.47–32.52) 46.93 (39.96–53.67) 2.69 (0.73–9.90) 48.58 (36.21–62.06) 42.54 (33.25–52.31) 0.78 (0.23–1.91) 31.38 (25.50–38.35) 45.46 (39.82–51.00) 1.82 (0.68–4.87)
Smokinga,e
 Yes 43.96 (42.24–45.69) 46.07 (44.44–47.70) 1.09 (0.84–1.40) 33.36 (30.59–36.16) 52.57 (49.46–55.66) 2.21 (1.43–3.41) 41.24 (39.76–42.71) 47.48 (46.03–48.92) 1.29 (1.04–1.60)
 No 38.18 (36.76–39.62) 45.73 (44.69–46.78) 1.36 (1.18–1.57) 33.24 (31.16–35.39) 49.44 (47.82–51.04) 1.96 (1.63–2.35) 36.69 (35.52–37.89) 46.83 (45.95–47.71) 1.52 (1.35–1.70)
Vapingb,c,d,e,f
 Yes 44.15 (39.72–48.67) 58.96 (54.66–63.14) 1.82 (0.96–3.44) 41.46 (35.07–48.11) 59.54 (50.85–67.28) 2.07 (1.36–3.43) 43.30 (39.65–47.04) 59.08 (55.20–62.75) 1.89 (1.08–3.31)
 No 40.41 (39.28–41.56) 45.28 (44.38–46.18) 1.22 (1.07–1.39) 32.77 (31.05–34.55) 50.01 (48.54–51.46) 2.05 (1.71–2.45) 38.27 (37.31–39.23) 46.60 (45.83–47.36) 1.41 (1.26–1.57)
Alcohol consumptiona,b,c,e,f
 More than once a month 46.88 (45.07–48.69) 49.43 (47.46–51.40) 1.11 (0.87–1.41) 36.77 (33.95–39.70) 53.74 (50.24–57.16) 1.99 (1.39–2.87) 44.15 (42.62–45.70) 50.48 (48.78–52.20) 1.29 (1.05–1.57)
 Once a month or less 36.75 (35.31–38.21) 45.08 (44.05–46.10) 1.41 (1.19–1.67) 31.28 (29.12–33.53) 50.91 (49.24–52.55) 2.28 (1.80–2.88) 35.19 (33.98–36.41) 46.71 (45.84–47.58) 1.61 (1.40–1.86)
Physical activitya,b,c,d,e,f
 Low physical activity 36.51 (35.04–37.99) 41.20 (40.08–42.33) 1.22 (1.02–1.45) 29.42 (27.38–31.56) 46.64 (44.87–48.42) 2.09 (1.72–2.55) 34.32 (33.12–35.54) 42.79 (41.84–43.75) 1.43 (1.25–1.64)
 Moderate physical activity 43.09 (40.24–45.96) 48.81 (46.62–50.97) 1.26 (0.94–1.68) 39.16 (34.64–43.57) 49.79 (46.4–53.35) 1.54 (0.91–2.62) 41.98 (39.56–44.39) 49.08 (47.25–50.94) 1.33 (1.03–1.72)
 High physical activity 46.44 (44.43–48.43) 54.87 (53.10–56.64) 1.40 (1.06–1.84) 38.83 (35.35–42.43) 61.73 (58.5–64.88) 2.54 (1.59–4.05) 44.66 (42.91–46.41) 56.43 (54.88–57.98) 1.60 (1.27–2.03)
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Note: 1st quintile contains the lowest values and the 5th quintile the highest values. European Health Interview Survey Wave 3 (2018–2020). Inactive, retirees, students, and those performing domestic tasks, carrying out compulsory service, or unable to work for health reasons. ⁣∗ = according to the PHQ-8; ⁣∗∗ = does not include data from Malta.

Abbreviations: EU, European Union; OR, odds ratio.

aStatistically significant self-medication differences in men with recognized depression.

bStatistically significant self-medication differences in women with recognized depressed.

cStatistically significant self-medication differences in men with depression symptoms.

dStatistically significant self-medication differences in women with depression symptoms.

eStatistically significant self-medication differences in men for both groups.

fStatistically significant self-medication differences in women for both groups.

Table 3.

Self-medication prevalence by sex in depressed and noninstitutionalized residents aged 15 and over in the European Union according to health variables.

Independent variables Recognized depression Depression symptoms⁣∗ Both groups
Male Female OR female Male Female OR female Male Female OR female
Medication availability clustera,b,c,d,e,f
 Pharmacy-only 32.66 (30.09–35.27) 37.45 (35.58–39.30) 1.23 (1.04–1.46) 47.41 (41.39–53.18) 61.40 (56.73–65.91) 1.76 (1.34–2.30) 35.29 (32.91–37.70) 40.85 (39.12–42.62) 1.26 (1.09–1.47)
 Nonpharmacy 45.42 (43.42–47.43) 52.28 (50.68–53.85) 1.32 (1.16–1.49) 37.30 (34.63–40.01) 51.28 (49.19–53.38) 1.77 (1.49–2.09) 42.63 (41.02–44.24) 51.91 (50.64–53.17) 1.45 (1.31–1.61)
 Limited nonpharmacy 40.09 (38.58–41.61) 45.06 (43.81–46.32) 1.22 (0.96–1.55) 27.39 (25.16–29.65) 46.09 (43.98–48.22) 2.26 (1.57–3.25) 36.62 (35.37–37.91) 45.33 (44.25–46.41) 1.43 (1.18–1.74)
Long-standing health problema,b,e
 Yes 38.78 (37.55–40.01) 46.21 (45.26–47.16) 1.36 (1.17–1.56) 34.01 (32.06–35.98) 49.51 (47.85–51.16) 1.90 (1.53–2.35) 37.49 (36.45–38.53) 47.04 (46.21–47.86) 1.48 (1.31–1.67)
 No 47.27 (44.81–49.78) 43.23 (40.97–45.47) 0.85 (0.61–1.18) 30.99 (27.82–34.41) 50.77 (47.96–53.63) 2.29 (1.60–3.29) 41.93 (39.91–43.94) 46.18 (44.43–47.97) 1.19 (0.92–1.53)
Self-perceived healtha,b,d,e,f
 Very good/good 43.66 (41.70–45.63) 48.25 (46.63–49.89) 1.20 (0.94–1.53) 34.18 (30.98–37.37) 50.57 (47.79–53.32) 1.97 (1.35–2.86) 41.23 (39.54–42.9) 48.85 (47.45–50.25) 1.36 (1.11–1.66)
 Fair 37.64 (35.87–39.49) 46.85 (45.51–48.20) 1.46 (1.19–1.79) 35.32 (32.35–38.44) 51.63 (49.25–54.02) 1.95 (1.38–2.76) 37.05 (35.50–38.61) 48.01 (46.84–49.18) 1.57 (1.32–1.87)
 Bad/very bad 40.70 (38.78–42.67) 41.45 (39.80–43.08) 1.03 (0.81–1.31) 30.84 (28.30–33.46) 47.51 (45.23–49.86) 2.03 (1.59–2.59) 37.40 (35.85–38.98) 43.51 (42.17–44.85) 1.29 (1.07–1.55)
Use of prescribed medicinea,b,e,f
 Yes 39.80 (38.61–41.00) 45.46 (44.55–46.38) 1.26 (1.10–1.45) 33.64 (31.82–35.49) 49.65 (48.15–51.16) 1.94 (1.59–2.38) 38.06 (37.06–39.07) 46.60 (45.82–47.38) 1.42 (1.27–1.59)
 No 45.41 (42.52–48.25) 49.08 (46.07–52.18) 1.16 (0.79–1.70) 31.33 (27.23–35.57) 52.53 (47.92–56.83) 2.42 (1.61–3.65) 41.32 (38.98–43.75) 50.18 (47.64–52.69) 1.40 (1.06–1.93)
Visit to a general practitioner or family doctor⁣∗∗a,c,e
 Yes 37.85 (36.58–39.12) 45.58 (44.62–46.56) 1.37 (1.19–1.59) 34.86 (32.82–36.96) 49.20 (47.55–50.83) 1.81 (1.46–2.24) 37.06 (35.97–38.14) 46.52 (45.69–47.36) 1.47 (1.31–1.66)
 No 48.23 (46.08–50.43) 46.33 (44.27–48.39) 0.93 (0.68–1.25) 30.09 (27.29–33.03) 52.16 (49.28–55.05) 2.53 (1.80–3.57) 42.32 (40.58–44.11) 48.31 (46.63–49.99) 1.27 (1.00–1.61)
Visit to a medical or surgical specialist⁣∗∗b,d,f
 Yes 40.73 (39.37–42.12) 46.99 (45.97–48.01) 1.29 (1.10–1.51) 34.23 (32.14–36.42) 54.79 (53.03–56.50) 2.32 (1.85–2.92) 38.92 (37.77–40.09) 48.98 (48.09–49.85) 1.51 (1.32–1.71)
 No 40.51 (38.65–42.36) 41.98 (40.27–43.73) 1.06 (0.84–1.35) 31.73 (29.08–34.53) 39.89 (37.43–42.35) 1.43 (1.05–1.93) 37.93 (36.39–39.47) 41.30 (39.89–42.72) 1.15 (0.95–1.39)
Visit to a psychologist, psychotherapist, or psychiatrist⁣∗∗b,d,e,f
 Yes 40.37 (38.68–42.06) 50.20 (48.89–51.50) 1.49 (1.22–1.82) 35.86 (31.09–40.82) 57.64 (54.11–61.13) 2.43 (2.01–2.99) 39.90 (38.32–41.52) 51.08 (49.86–52.31) 1.57 (1.30–1.89)
 No 40.65 (39.20–42.11) 42.00 (40.82–43.17) 1.06 (0.88–1.26) 32.95 (31.16–34.75) 48.37 (46.82–49.94) 1.91 (1.57–2.32) 37.76 (36.62–38.89) 44.34 (43.40–45.29) 1.31 (1.15–1.50)
Unmet need for health care due to waiting lists⁣∗∗a,b,c,d,e,f
 Yes 44.75 (42.64–46.86) 51.60 (50.01–53.16) 1.31 (1.02−1.69) 39.64 (36.07–43.37) 56.13 (53.49–58.68) 1.95 (1.31–2.88) 43.51 (41.68–45.34) 52.80 (51.47–54.16) 1.45 (1.17–1.79)
 No 37.33 (35.93–38.77) 42.52 (41.40–43.66) 1.24 (1.05–1.47) 31.63 (29.53–33.75) 47.85 (46.00–49.74) 1.98 (1.60–2.46) 35.66 (34.48–36.85) 43.97 (43.01–44.95) 1.41 (1.23 (1.62)
 No need for health care 46.23 (43.16–49.29) 47.14 (44.33–50.04) 1.03 (0.82–1.25) 30.72 (26.63–35.05) 44.99 (40.96–49.16) 1.84 (1.06–3.21) 41.41 (38.93–43.95) 46.44 (44.14–48.83) 1.23 (0.90–1.67)
Unmet need for health care due to distance or transportation problems⁣∗∗a,b,e,f
 Yes 55.50 (51.74–59.10) 52.71 (49.59–55.75) 0.89 (0.53–1.50) 37.88 (33.13–43.09) 53.31 (48.85–57.70) 1.87 (1.23–2.21) 49.47 (46.48–52.49) 52.90 (50.34–55.40) 1.15 (0.75–1.74)
 No 38.44 (37.21–39.68) 44.70 (43.74–45.67) 1.29 (1.12–1.49) 33.32 (31.39–35.29) 49.73 (48.11–51.37) 1.98 (1.62–2.41) 37.05 (36.01–38.09) 46.02 (45.19–46.86) 1.45 (1.28–1.63)
 No need for health care 43.22 (40.08–46.45) 48.93 (46.11–51.74) 1.25 (1.02–1.59) 29.99 (25.64–34.55) 48.28 (44.37–52.33) 2.18 (1.36–3.48) 39.20 (36.60–41.85) 48.71 (46.44–51.03) 1.47 (1.11–1.96)
Unmet need for health care due to inability to afford medical examination or treatment⁣∗∗a,b,c,d,e,f
 Yes 50.41 (46.69–54.01) 52.14 (49.57–54.64) 1.07 (0.71–1.61) 37.38 (32.40–42.89) 58.05 (54.26–61.78) 2.32 (1.50–3.58) 46.32 (43.32–49.38) 53.95 (51.82–56.03) 1.36 (0.98–1.87)
 No 37.99 (36.70–39.29) 44.68 (43.65–45.72) 1.32 (1.13–1.54) 33.95 (31.87–36.08) 51.04 (49.26–52.84) 2.02 (1.61–2.55) 36.92 (35.82–38.03) 46.29 (45.40–47.19) 1.47 (1.29–1.67)
 No need for health care 47.46 (44.66–50.23) 47.02 (44.59–49.48) 0.98 (0.69–1.40) 29.25 (25.73–33.08) 42.69 (39.29–46.16) 1.80 (1.10–2.95) 41.58 (39.33–43.86) 45.58 (43.60–47.59) 1.18 (0.88–1.58)
Unmet need for health care due to inability to afford prescribed medicines⁣∗∗a,c,d,e
 Yes 49.05 (45.23–52.82) 48.66 (46.01–51.33) 0.98 (0.62–1.55) 47.16 (41.73–52.66) 50.60 (46.38–54.69) 1.15 (0.69–1.90) 48.44 (45.36–51.60) 49.22 (46.97–51.46) 1.03 (0.73–1.46)
 No 38.74 (37.48–39.99) 45.53 (44.53–46.53) 1.32 (1.14–1.53) 31.44 (29.45–33.47) 51.93 (50.24–53.64) 2.35 (1.89–2.93) 36.82 (35.76–37.90) 47.18 (46.32–48.04) 1.53 (1.35–1.73)
 No need for health care 47.92 (44.71–51.07) 47.64 (44.74–50.67) 0.99 (0.65–1.52) 32.70 (28.7–37.06) 43.58 (39.58–47.62) 1.59 (0.93–2.71) 42.78 (40.24–45.37) 46.22 (43.84–48.62) 1.15 (0.82–1.61)
Unmet need for health care due to inability to afford mental health care⁣∗∗a,b,d,e,f
 Yes 45.46 (42.05–48.85) 54.55 (51.83–57.18) 1.44 (0.95–2.18) 41.24 (33.68–49.32) 60.49 (54.81–66.27) 2.18 (1.09–4.34) 44.81 (41.69–47.94) 55.58 (53.13–57.99) 1.54 (1.06–2.22)
 No 39.90 (38.52–41.27) 45.14 (44.05–46.24) 1.24 (1.05–1.46) 32.92 (30.48–35.36) 51.92 (49.82–54.05) 2.20 (1.65–2.94) 38.32 (37.13–39.53) 46.58 (45.61–47.56) 1.40 (1.21–1.62)
 No need for health care 39.98 (37.65–42.34) 44.88 (43.00–46.75) 1.22 (0.94–1.59) 32.94 (30.35–35.50) 48.31 (46.16–50.52) 1.90 (1.45–2.49) 36.93 (35.19–38.67) 46.34 (44.92–47.76) 1.47 (1.22–1.78)
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Note: European Health Interview Survey Wave 3 (2018–2020). ⁣∗ = according to the PHQ-8; ⁣∗∗ = in the past 12 months.

Abbreviation: OR, odds ratio.

aStatistically significant self-medication differences in men with recognized depression.

bStatistically significant self-medication differences in women with recognized depressed.

cStatistically significant self-medication differences in men with depression symptoms.

dStatistically significant self-medication differences in women with depression symptoms.

eStatistically significant self-medication differences in men for both groups.

fStatistically significant self-medication differences in women for both groups.

The findings of the multivariable analysis of the independent variables are presented in Table 4. In our results, we find that women are more likely to SM (both groups AOR = 1.37; 95% CI = 1.18–1.60). Simultaneously, the findings indicate that men self-medicate less with depression symptoms (AOR = 0.69; 95% CI = 0.56–0.84), while the opposite is true in women (AOR = 1.28; 95% CI = 1.11–1.48). These results also show that in women, age is a significant determinant for SM, with those ages 15–24 (both groups AOR = 1.50; 95% CI = 1.09–2.07) and 25–44 (both groups AOR = 1.47; 95% CI = 1.15–1.89) being most susceptible to self-medicate (AORs calculated vs. ages 75+). Additionally, the findings demonstrate that for the study population, higher education is associated with greater SM when compared with those lacking formal education (AOR = 3.74; 95% CI = 2.83–4.93), especially among women (both groups AOR = 5.63; 95% CI = 4.20–7.54). Furthermore, we find that employment increases in SM in individuals with recognized depression and symptoms of depression (vs. unemployment AOR = 1.45; 95% CI = 1.12–1.88). Moreover, in men with recognized depression, consuming alcohol more than once a month is also associated with greater odds of self-medicating (AOR = 1.36; 95% CI = 1.11–1.66). Our analysis also demonstrates that high levels of PA while depressed is associated with greater SM in contrast with low levels of PA (both groups AOR = 1.38; 95% CI = 1.18–1.60). Finally, we show that individuals with recognized depression self-medicate more in countries with wider accessibility to medication (AOR = 2.10; 95% CI = 1.86–2.36), while the opposite is true for individuals with symptoms of depression (AOR = 0.69; 95% CI = 0.59–0.82).

Table 4.

Multivariable analysis of self-medication in depressed and noninstitutionalized residents aged 15 and over in the European Union according to demographic, socioeconomic, lifestyle, and health variables.

Independent variables Recognized depression Depression symptoms⁣∗ Both groups
Male Female Both sexes Male Female Both sexes Male Female Both sexes
Sex (ref. male)
 Female — — 1.41 (1.22–1.64) — — 2.25 (1.86–2.71) — — 1.37 (1.18–1.60)
Depression diagnosis tool (ref. recognized depression)
 Depression symptoms — — — — — — 0.69 (0.56–0.84) 1.28 (1.11–1.48) 0.70 (0.57–0.86)
Women with depression symptoms (ref. men with recognized depression) — — — — — — — — 1.75 (1.36–2.26)
Age (ref. 75+ years)
 15–24 years 1.16 (0.67–2.02) 1.47 (0.98–2.18) 1.15 (0.82–1.60) 0.77 (0.43–1.37) 1.60 (1.02–2.51) 1.15 (0.80–1.64) 1.01 (0.67–1.53) 1.50 (1.09–2.07) 1.06 (0.81–1.39)
 25–44 years 1.00 (0.65–1.54) 1.35 (1.01–1.81) 1.12 (0.86–1.46) 1.17 (0.72–1.92) 1.93 (1.33–2.78) 1.38 (0.98–1.95) 1.07 (0.78–1.46) 1.47 (1.15–1.89) 1.15 (0.93–1.43)
 45–64 years 0.89 (0.59–1.35) 1.02 (0.81–1.27) 0.95 (0.76–1.19) 0.72 (0.48–1.10) 1.45 (1.08–1.95) 0.99 (0.77–1.29) 0.86 (0.64–1.16) 1.07 (0.89–1.29) 0.94 (0.79–1.12)
 65–74 years 0.80 (0.50–1.28) 1.04 (0.83–1.31) 0.95 (0.76–1.20) 1.34 (0.89–2.03) 1.08 (0.79–1.47) 1.20 (0.93–1.54) 0.98 (0.70–1.36) 1.06 (0.88–1.28) 1.00 (0.84–1.20)
Degree of urbanization (ref. rural areas)
 Cities — — — 1.37 (1.00–1.88) — — — — —
 Towns and suburbs — — — 1.00 (0.70–1.45) — — — — —
Education level (ref. no formal education)
 Primary school — — — — 1.92 (1.21–3.05) 1.51 (1.00–2.29) 0.93 (0.56–1.53) 2.75 (2.10–3.61) 2.06 (1.57–2.70)
 Secondary school — — — — 2.84 (1.78–4.55) 2.32 (1.52–3.54) 1.50 (0.93–2.42) 4.66 (3.53–6.14) 3.25 (2.48–4.25)
 Higher education — — — — 3.37 (2.04–5.57) 2.79 (1.78–4.37) 1.67 (1.02–2.71) 5.63 (4.20–7.54) 3.74 (2.83–4.93)
Employment status (ref. unemployed)
 Employed — 1.52 (1.06–2.18) 1.33 (1.00–1.77) 2.86 (1.57–5.22) — 1.65 (0.98–2.76) — 1.52 (1.09–2.11) 1.45 (1.12–1.88)
 Inactive — 1.28 (0.90–1.83) 1.20 (0.89–1.61) 1.26 (0.69–2.29) — 1.05 (0.62–1.76) — 1.23 (0.89–1.72) 1.18 (0.90–1.54)
Income level (ref. 1st quintile)
 2nd quintile — — — 1.09 (0.71–1.65) — — — — —
 3rd quintile — — — 1.30 (0.79–2.14) — — — — —
 4th quintile — — — 1.70 (1.10–2.62) — — — — —
 5th quintile — — — 1.59 (0.99–2.57) — — — — —
Alcohol consumption (ref. once a month or less)
 More than once a month 1.42 (1.12–1.81) — 1.20 (1.03–1.40) — — — 1.36 (1.11–1.66) — 1.16 (1.02–1.33)
Physical activity (ref. low physical activity)
 Moderate physical activity 1.27 (0.94–1.72) 1.22 (1.00–1.48) 1.22 (1.02–1.46) 1.59 (1.05–2.41) 1.01 (0.72–1.4) 1.09 (0.83–1.44) 1.26 (0.98–1.63) 1.15 (0.97–1.37) 1.14 (0.98–1.34)
 High physical activity 1.30 (0.99–1.71) 1.50 (1.21–1.87) 1.39 (1.17–1.66) 1.53 (1.05–2.22) 1.43 (1.02–2.00) 1.37 (1.05–1.78) 1.32 (1.05–1.66) 1.50 (1.25–1.81) 1.38 (1.18–1.60)
Medication availability cluster (ref. pharmacy-only)
 Nonpharmacy 2.23 (1.82–2.72) 1.55 (1.37–1.76) 2.10 (1.86–2.36) 0.64 (0.48–0.86) 0.71 (0.59–0.87) 0.69 (0.59–0.82) 1.89 (1.59–2.25) 1.42 (1.27–1.59) 1.98 (1.78–2.21)
 Limited pharmacy 1.43 (1.12–1.84) 1.09 (0.92–1.28) 1.32 (1.15–1.53) 0.38 (0.27–0.54) 0.52 (0.40–0.66) 0.46 (0.38–0.57) 1.17 (0.95–1.44) 0.95 (0.82–1.10) 1.15 (1.02–1.31)
Long-standing health problem (ref. no)
 Yes — 1.35 (1.07–1.70) — — — — — — —
Use of prescribed medicine in the past 2 weeks (ref. no)
 Yes — — — 1.68 (1.12–2.52) — — — 1.27 (1.04–1.55) 1.02 (0.85–1.21)
Visit to a medical or surgical specialist⁣∗∗ (ref. no)
 Yes — 1.21 (1.01–1.45) 1.24 (1.05–1.46) — 1.65 (1.30–2.08) 1.49 (1.23–1.81) — 1.32 (1.13–1.53) 1.34 (1.17–1.54)
Unmet need for health care due to distance or transportation problems⁣∗∗ (ref. no need for health care)
 Yes 1.91 (1.06–3.46) — 1.50 (1.05–2.15) — — — 1.89 (1.18–3.04) 1.55 (1.20–1.99) 1.35 (0.99–1.85)
 No 0.88 (0.63–1.25) — 0.84 (0.67–1.06) — — — 0.97 (0.73–1.28) 1.06 (0.87–1.29) 0.88 (0.72–1.09)
Unmet need for health due to inability to afford medical examination or treatment⁣∗∗ (ref. no need for health care)
 Yes — — — — 2.02 (1.32–3.08) 1.97 (1.40–2.77) — — 1.38 (1.07–1.79)
 No — — — — 1.47 (1.06–2.03) 1.42 (1.09–1.86) — — 1.00 (0.82–1.22)
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Note: Inactive, retirees, students, and those performing domestic tasks, carrying out compulsory service, or unable to work for health reasons. 1st quintile contains the lowest values and the 5th quintile the highest values. European Health Interview Survey Wave 3 (2018–2020). ⁣∗ = according to the PHQ-8; ⁣∗∗ = in the past 12 months;

4. Discussion

To the authors' knowledge, this article is the first to detail SM prevalence and associated factors in individuals with recognized depression and symptoms of depression in the EU. Compared to the 34.3% SM prevalence found in the general EU population (28.5% in men and 39.7% in women) [15], SM prevalence among individuals with recognized depression and symptoms of depression is higher at 38.46% (95% CI = 37.54–39.39) and 46.84% (95% CI = 46.09–47.58) for men and women, respectively. Previous studies have linked depression to the NMUP of prescription medicine such as opioids, stimulants, sedatives, and antidepressants in American university students [7], with similar results found in the misuse and NMUP of opioids in a systematic review and a study of Australian patients [5, 6]. OTC analgesic-use for pains directly stemming from depression has also been documented [26]. Furthermore, a Polish survey of doctors found that mental health issues increased the likelihood of a patient self-medicating before visiting a doctor [10]. Our study adds to the observed linkage between medication consumption without prescription and depression on a broader scale than previous studies, including not only medications but also vitamins and herbal medicines. These findings lend support to the SM hypothesis [4], which posits that individuals with mental illness often turn to SM to manage their symptoms. This hypothesis traditionally focuses on the link between depression and the consumption of psychoactive substances [27, 28], but it is noteworthy that SM also includes many self-care practices, such as consuming vitamins or medicinal herbs, that individuals looking to alleviate their depression or its symptoms may adopt. The results also show that SM prevalence in this population varies substantially between EU countries, with the highest cumulative SM prevalence in found in Finland (73.04% in men and 84.80% in women) and the lowest in Italy (19.68% in men and 24.74% in women). This same phenomenon—Northern and Eastern Europe self-medicating more frequently than Western and Southern Europe—is visible in general EU population [15], highlighting the importance of socio-cultural, economic, and governmental factors in shaping our health habits.

One such influence is the availability of OTC medication. It has been logically suggested that increased OTC medication distribution points (such as supermarkets) proliferate SM [12]. The results of this study support this idea, finding that nonpharmacy medication purchasing significantly increases SM (vs. pharmacy-only purchasing AOR = 1.98; 95% CI = 1.78–2.21), though a 2018 Italian study found no statistically significant relationship between alternative distribution points and nonprescription medicine purchasing [29]. In our study, a key difference exists between those with recognized depression and symptoms of depression. Puzzlingly, while those with recognized depression self-medicate more frequently in countries with nonpharmacy sale of medications (vs. pharmacy-only sale: AOR = 2.10; 95% CI = 1.86–2.36), those with symptoms of depression in these countries self-medicate 31% less (vs. pharmacy-only sale: AOR = 0.69; 95% CI = 0.59–0.82). Perhaps participants with symptoms of depression living in countries with more open access to OTC medications see these medications as more commonplace and less useful for dealing with symptoms of depression. On the other hand, those with recognized depression may have identified the value of SM in dealing with their depression and increased access facilitates this usage. Nonetheless, a similarly puzzling result to our study was found in research working with cannabis, where participants living in U.S. states where cannabis is legal were less likely to use it to self-medicate for depression [30], though contradictory results were found in another study [31]. Regardless of the cause, what these results make clear is that further research is required to understand the effect of OTC medication availability on SM, especially given the economic and social benefits of responsible SM in Europe [32].

Additionally, our results reinforce previous research identifying greater SM among women [15]. Specifically, this study supports these findings amongst those with recognized depression (AOR = 1.41; 95% CI = 1.22–1.64) and symptoms of depression (AOR = 2.25; 95% CI = 1.86–2.71). In line with this study, a systematic review by Votaw et al. [33] demonstrated that gender serves as a moderator for the association between symptoms of depression and NMUP of benzodiazepine, with a stronger association found in women compared to men. Interestingly, we also find that men and women exhibit different behaviors with respect to recognized depression and symptoms of depression. While women are more likely to self-medicate with only symptoms of depression rather than recognized depression (AOR = 1.28; 95% CI = 1.11–1.48), men self-medicate less with only symptoms of depression than with recognized depression (AOR = 0.69; 95% CI = 0.56–0.84). This suggests that women may self-medicate proactively in response to depression symptomology, while men may wait until greater recognition of their depression, such as a diagnosis by a medical professional. This is encapsulated by the finding that women with depression symptoms are 75% more likely than men with recognized depression to self-medicate (AOR = 1.75; 95% CI = 1.36–2.26). This result could be explained by previous research showing that women are more proactive health decision makers, including seeking for external health information and displaying concern about health consequences [34]. Simultaneously, women are often conditioned to turn to medication [35], while men are accustomed to “toughing it out” and avoiding medication [36].

Along the same lines, multiple other variables display differing relationships for men and women, highlighting differences in how they self-medicate and deal with depression. First, we find that age is an influential variable specifically for women, with those aged 15–24 (symptoms of depression AOR = 1.60; 95% CI = 1.02–2.51) and 25–44 (symptoms of depression AOR = 1.93; 95% CI = 1.33–2.78) most likely to self-medicate. Previous research has found an age discrepancy between the peaks in prevalence of depression (ages 14–25) and antidepressant use (ages 45+) [37], which, coupled with our findings, suggests that young women may SM their depression until later receiving antidepressant treatment, especially for those with only symptoms of depression. Providing further backing for this idea, a study from the United States found that onset of depressive disorder at earlier ages was associated with higher likelihood of NMUP [38]. Second, this study finds that while education is influential for men at the highest level of education (both groups vs. no formal education AOR = 1.67; 95% CI = 1.02–2.71), each level of improved education increases the probability of SM in women, particularly for women symptoms of depression (higher education vs. no formal education AOR = 3.37; 95% CI = 2.04–5.57). Education has been strongly correlated with SM across a multitude of demographics, including general population in the EU [15]. Improved health literacy, greater confidence to make self-care decisions, increased questioning of doctor's orders, and improved social networking (which leads to medication sharing) are just a few of the reasons why education is positively associated with SM [15]. The results of this study suggest these motivations are amplified for women with symptoms of depression and less present for other study groups.

Third, we find that being employed (vs. unemployed) is significantly associated with SM (both groups and sexes AOR = 1.45; 95% CI = 1.12–1.88), perhaps due to time limitations for doctor's visits for the employed. Interestingly, this relationship for men is found in those with symptoms of depression, while in women, it is for those with recognized depression, which may reflect distinct motivations for why men and women self-medicate. For example, employed men with symptoms of depression may self-medicate in response to stresses from work, while employed men with recognized depression may already have other methods for dealing with job stress. Fourth, we find a noteworthy relationship between SM and alcohol in men. Specifically, SM is significantly associated with alcohol consumption in men with recognized depression (AOR = 1.42; 95% CI = 1.12–1.81), corroborating previous studies linking alcohol consumption to SM [15, 39]. Given the vulnerability of this population and incompatibility between antidepressants and alcohol, this potentially dangerous phenomenon should be further investigated.

Fifth, our findings unveil that the use of prescribed medicine for men with depressive symptoms is significantly associated with SM (AOR = 1.68; 95% CI = 1.12–2.52). The same was not found in general EU population as there is typically a trade-off between prescription and non-prescription medicines [15], suggesting that this may be a trait unique to men with depressive symptoms. Contrarily, in all female study groups but no male study groups, SM is associated with visits to medical or surgical specialists (both groups AOR = 1.32; 95% CI = 1.13–1.53), whereas in EU general population, this relationship was found in both men and women. This may be suggestive of reduced self-care by men with recognized depression or symptoms of depression compared to general male population in the EU. Surprisingly, visits to mental health specialists nor general practitioners (who often serve as an entry point for treatment of patients with depression) [40] did not factor into our multivariable models.

Nonetheless, men and women with recognized depression and symptoms of depression are similar in terms of SM with respect to several other variables. Our study finds that engaging in high levels of PA is associated with SM (vs. low PA, total both sexes AOR = 1.38; 95% CI = 1.18–1.60). This is likely explained by overlapping self-care behaviors between PA and concern about one's health, which may include self-medicating with vitamins and other nutritional supplements. Our results also suggest that SM is motivated by difficulties in accessing health care systems due to distance or transportation problems for both men (both groups AOR = 1.89; 95% CI = 1.18–3.04) and women (both groups AOR = 1.55; 95% CI = 1.20–1.99). This highlights the importance of increasing healthcare accessibility, especially in rural or other poorly communicated areas. Interestingly, inability to afford mental health care did not contribute to our multivariable models, though lack of ability to afford general treatment/examination did in the model for both sexes/groups (AOR = 1.38; 95% CI = 1.07–1.79). Previous research found similar relationships between SM and healthcare access, as well as SM and exercise, in general EU population [15], suggesting these variables behave similarly in this demographic and in the general population.

Finally, this study is subject to a series of limitations. First, due to the cross-sectional nature of the data, causality cannot be determined. Second, the European Health Survey does not identify specific active pharmaceutical ingredients nor does it identify groups of medicines for specific diseases, conditions, or disorders. It is also worth noting that the EHIS references sex, which may inadequately capture the range of diversity and social, political, and economic forces expressed in gender [41]. Additionally, it is possible that social desirability bias could have led to underreporting on SM, depression, and symptoms of depression, affecting both prevalences and associated factors. This is furthered by the fact that differences existed between countries in data collection methods and sampling design, potentially altering results and reducing comparability between countries [42]. For this study, individuals interviewed face-to-face or over the phone may have felt additional social pressure to underreport SM, depression, and symptoms of depression vs. those who filled out self-administered written or online surveys.

Moreover, EHIS data collection occurred nonsimultaneously across the EU over multiple years and seasons, and consequently, seasonal and annual variance of conditions that provoke SM and depression could have been impactful. Specifically, lower temperatures are associated with greater OTC respiratory medication sales [43], and as a result, SM prevalence could be greater in colder months. Simultaneously, depression is also more frequent in colder months, which could alter consumption habits and the strength of the relationship [44]. The vast majority of EHIS responses were collected in autumn. Along the same lines, three countries (Germany, Spain, and Malta) collected data after the beginning of the COVID-19 pandemic, which could alter recognized depression, symptoms of depression, and SM prevalences [45]. An additional limitation is that the data is self-reported and thus susceptible to recall bias, though the narrow 2-week window of the dependent variable question and PHQ-8 questions should have reduced some of this effect. Additionally, the nonresponse rate, ranging from 12% to 78% across countries [45], may be influential as those who did not participate could have shared insights into SM, even if the direction of this effect is indeterminable. Similarly, Spain's nonparticipation in the PHQ questions used to form the symptoms group could have also impacted the findings. Finally, criticism has been levied against the utility of the PHQ-8 [46]; however, these criticisms are oriented principally toward its use as a measure of depression prevalence, not the presence of symptoms of depression as used in this study. Studies have shown that compared to the PHQ-9 (which includes an additional question about suicide), the PHQ-8 undergoes minimal sensitivity loss and no specificity loss [47].

Nonetheless, the quality report of the third wave of the EHIS details that the data underwent validation, calibration, and nonresponse adjustments procedures to minimize the effect of all potential sources of sampling and nonsampling errors, resulting in dataset that is highly harmonized and allows for a high degree of comparability across EU member states [45]. Coupled with the robust weighted sample size of 25,701, the authors feel that none of the aforementioned limitations should dampen the strength or relevancy of the findings.

5. Conclusion

In conclusion, this study finds that individuals with recognized depression or depressive symptoms engage in SM more frequently than the general population, and there are differences in the factors affecting their consumption. A key finding of this study is that the availability of OTC medications outside of pharmacies increases their usage among individuals with recognized depression or depressive symptoms. Furthermore, this study reveals differences in SM between men and women with recognized depression and symptoms of depression. Specifically, women were more likely to self-medicate in response to depressive symptoms, while men did so more often when depression was formally recognized. Other influencing variables such as age, education, employment, alcohol consumption, prescription drug use, and healthcare utilization also showed distinct associations in men and women. In contrast, factors like physical activity and difficulty accessing healthcare behave similarly in men and women. Going forward, research should take a closer look into SM by vulnerable populations, such as individuals suffering from depression, to ensure their consumption is informed, safe, and beneficial. Additionally, given the variability in medication access across member states and its significant impact on consumption, there is a need for greater examination of regulations surrounding OTC medication access.

Acknowledgments

Thanks are extended to Eurostat for providing the microdata used in this study.

Data Availability Statement

Data requests must be made to Eurostat. Instructions for requesting data access can be found at https://ec.europa.eu/eurostat/web/microdata/european-health-interview-survey. The code for data processing is available upon request to the authors.

Ethics Statement

The microdata was anonymized by the Eurostat office in accordance with the EU regulation 2018/255 on the European Health Interview Survey. The Universidad Rey Juan Carlos Ethics Committee confirmed the lack of need for ethics committee approval for this project.

Disclosure

All authors have read and approved the final manuscript. The responsibility for all conclusions drawn from the data lies entirely with the authors, not Eurostat. The funding source had no involvement in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

All authors contributed to the study conception and design. Spencer Yeamans, Valentin Hernández-Barrera, and Pilar Carrasco-Garrido performed material preparation and data analysis. Spencer Yeamans wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript.

Funding

Spencer Yeamans is the recipient of funding from a Universidad Rey Juan Carlos predoctoral program reference code (PREDOC20-107).

Supporting Information

Additional supporting information can be found online in the Supporting Information section.

Supporting Information 1

Table A. Self-medication prevalence by country and sex.

4661541.f1.pdf (273.5KB, pdf)
Supporting Information 2

STROBE Checklist.

4661541.f2.pdf (156.2KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information 1

Table A. Self-medication prevalence by country and sex.

4661541.f1.pdf (273.5KB, pdf)
Supporting Information 2

STROBE Checklist.

4661541.f2.pdf (156.2KB, pdf)

Data Availability Statement

Data requests must be made to Eurostat. Instructions for requesting data access can be found at https://ec.europa.eu/eurostat/web/microdata/european-health-interview-survey. The code for data processing is available upon request to the authors.

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