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The 12-month prevalence of depression and health care utilization in the general population of Latvia
Journal of Affective Disorders 210 (2017) 204–210
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Research paper
The 12-month prevalence of depression and health care utilization in the general population of Latvia
MARK
⁎
Jelena Vrublevskaa, , Marcis Trapencierisb, Sigita Snikereb, Daiga Grinbergac, Biruta Velikac, Iveta Pudulec, Elmars Rancansa a b c
Department of Psychiatry and Narcology, Riga Stradins University, Tvaika street 2, LV-1005 Riga, Latvia Institute of Philosophy and Sociology, University of Latvia, Kalpaka bulv. 4, Riga, Latvia Centre for Disease Prevention and Control of Latvia, Department of Research, Statistics and Health Promotion, Latvia
A R T I C L E I N F O
A BS T RAC T
Keywords: Depression Epidemiology General population Prevalence
Background: This cross-sectional study aims to assess the 12-month prevalence of major and minor depression in the Latvian population, and to evaluate associated health care utilization. Methods: Trained interviewers conducted face-to-face interviews with a multistage stratified probability sample of the Latvian general population, ages 15–64 (n=3003). Participants were interviewed using the depression module of the Mini International Neuropsychiatric Interview. Self-reported health care utilization and somatic illness were also assessed. Multinomial logistic regressions were applied. Results: The 12-month prevalence of major depression was 7.9% (95%CI 7.0–8.9), while for minor depression it was 7.7% (95%CI 6.8–8.7). We did not find a substantial difference in the relative risk ratio (RRR 1.7 for female) for having major depression by gender. RRR of having major depression was higher for those who had used healthcare services six or more times (RRR 2.0), those who had three or more somatic disorders during the past 12 months (RRR 2.3), those who perceived their health status as being below average (RRR 8.3), and those who were occasional smokers (RRR 3.0). RRR of having minor depression was increased for those who had at least three somatic disorders (RRR 2.3), those who received disability pension (RRR 1.9), and those who perceived their health status to be below average (RRR 3.0). Limitations: The study was cross-sectional. Other psychiatric comorbidity was not assessed. Conclusions: This is the first population based study reporting the 12-month prevalence of depression in Latvia. Certain factors associated with depression have been found.
1. Introduction Depression is among the most commonly diagnosed mental disorders in adults. It is costly, and if left untreated, has considerable impact on individuals’ quality of life, on society and on the public health system (Mathers and Loncar, 2006). In addition to these socioeconomic impacts, depression is also associated with personal and interpersonal suffering, significant distress, impairment of functioning, disturbance to interpersonal relationships, and an increased risk of suicide (Rihmer, 2007; Zlotnick et al., 2000). Various studies have demonstrated a link between the prevalence of depression and socio-demographic factors, such as gender, marital status and economic hardship (Alonso et al., 2004; Ayuso-Mateos et al., 2001; Economou et al., 2013; Marcus et al., 2005). Depression is also a major cause of workplace absenteeism, diminished or lost productivity, and increased use of health care resources. It is associated with a level of disability ⁎
comparable to that of major medical illnesses (Moussavi et al., 2007), and has been associated with a variety of somatic diseases (von Korff, 2009), including cardiovascular disease (Vogelzangs et al., 2010), diabetes (Windle and Windle, 2013), and pulmonary disorders (Mikkelsen et al., 2004). In addition, there is evidence that a higher number of chronic health conditions is associated with a greater risk of depression (Gunn et al., 2012), and depressed individuals tend to use more primary care services than non-depressed individuals (Kessler et al., 1987; Shvartzman et al., 2005). Many studies have shown that multi-comorbidity is associated with higher health care utilization and costs (Lehnert et al., 2011). However, only a minority of patients with depression receive treatment for it from health care services (Bebbington et al., 2003; Unützer et al., 2000). As a consequence, both depression and multi-comorbidity pose a great burden on health care systems and societies. Medical help–seeking might represent an inefficient use of health care resources, if mental health issues are not
Corresponding author. E-mail addresses: [email protected] (J. Vrublevska), [email protected] (M. Trapencieris), [email protected] (S. Snikere), [email protected] (E. Rancans).
http://dx.doi.org/10.1016/j.jad.2016.12.031 Received 30 August 2016; Received in revised form 22 November 2016; Accepted 21 December 2016 Available online 28 December 2016 0165-0327/ © 2016 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 210 (2017) 204–210
J. Vrublevska et al.
Neuropsychiatric Interview (M.I.N.I), Version 6.0.0. The M.I.N.I is a structured screening interview that was validated by convergence with the SCID-P (Structured Clinical Interview for DSM-III-R Patient Version) and the CIDI (Composite International Diagnostic Interview), and by expert professional opinion (Sheehan et al., 1998). The good psychometric characteristics of the M.I.N.I, its ability to be administered rapidly, and its acceptability to patients made it a good choice for the research purposes (Pinninti et al., 2003). The M.I.N.I test results were interpreted into three categories: not depressed, major depression, or minor depression (according to DSM-IV-TR). The M.I.N.I used in this study has been previously translated for use in Latvian and Russian languages by authorship holders. To check for associations between depression prevalence and sociodemographic characteristics, the gender and age of participants were recorded. Age was recorded as a categorical variable (15–24, 25–34, 35–44, 45–64, and 55–64). The variable of health care utilization was calculated on the basis of the self-reported question: “How many times over the past 12 months have you … (…visited a family physician), (… visited a specialist (excluding the dentist)), (…been hospitalized (including day care)), (…called for an ambulance)”. For some analyses, the healthcare variables were dichotomized (did not use vs. used the service), for others categorized (“did not use”, “1–2 times”, “3–5 times”, “6–9 times”, “10 or more times”). Self-reported diagnosis of somatic illnesses was assessed by the question “Has a doctor over the past 12 months diagnosed you with any of the following illnesses (hypertension, diabetes, myocardial infarction, stenocardia, heart failure, rheumatoid arthritis, back diseases, pulmonary emphysema or chronic bronchitis, renal or urinary tract diseases, gastroenterological diseases, bronchial asthma, hypercholesterinaemia, cancer)". Other health status variables included: self-reported health status (assessed by the question “How would you rate your current health status?”, with the response options “good”, “fairly good”, “average”, “fairly poor”, “poor”. These were recoded into three categories “above average”, “average”, “below average”), smoking status (with responses categorized into “non-smoker”, “quitter/ex-smoker”, “occasional smoker”, or “regular smoker”), receipt of disability pension (assessed by the question “Do you currently receive disability pension?”), and absenteeism (assessed by the question “How many calendar days over the last 12 months did you skip work or did not perform everyday duties because of health issues?” The responses were categorized as “none”, “1–10 days”, or “11+ days”).
addressed during such clinical encounters (Kessler et al., 2002). Moreover, community-based cross-cultural studies show some indications that the prevalence of depression is increasing (Hidaka, 2012). By 2020, depression is prognosed to be the second leading cause of disability adjusted life years (DAYLY's), after cardio-vascular disease (Murray and Lopez, 1996). Therefore, detecting and treating depression will be especially important for decreasing disability, prolonging survival, and increasing the quality of life at a population level. Latvia is among the countries with the highest suicide rates in Europe (WHO Regional Office for Europe, 2016), and among those committing suicide each year, 80–85% suffer from at least one mood disorder (Mann et al., 2005). Latvia also has almost one of the lowest gross domestic product (GDP) per capita among European countries, and the lowest healthcare financing, making the study even more relevant (WHO Regional Office for Europe, 2016). The aimsof our study were to assess the 12-month prevalence of major and minor depression among the general population of Latvia, to assess health care utilization among people with depression, and to evaluate the association of depression with self-reported somatic illnesses. 2. Methods This study was a part of the Health Behaviour among the Latvian Adult Population survey, conducted in 2012. This survey was based on a nationally-representative, multistage stratified probability sample of 3004 persons between the ages of 15 and 64 years old (Pudule et al., 2013). The stratification variables included population density and nationality; a total of 32 strata were formed. Within those strata, a total of 390 starting addresses were randomly selected from the list of addresses, proportional to the population size in each stratum. Taking each of these addresses as a starting point, an additional seven to eight households in the vicinity were interviewed. The additional households were selected by a random route method (every third household in urban and semi-urban areas and every household in rural areas). In each sampled household, only one respondent was selected for interview, by using Kish tables and the closest birthday principle (Gaziano, 2005; Kish, 1949). Data was collected by face-to-face interviews in Latvian or Russian (language as preferred by respondent). Each sampled household was visited up to three times if no initial contact with the household or respondent was made. The fieldwork for the study was conducted from April to June 2012, and was carried out by 68 professional interviewers coordinated by five regional fieldwork coordinators and two fieldwork supervisors based in the city of Riga. Before conducting fieldwork, interviewers received training sessions covering survey methodology and the scales used. Up to eight interviewers participated in each session. The fieldwork agency followed the ESOMAR International Code on Market and Social Research (ESOMAR, 2015). The response rate, as calculated according to the American Association of Public Opinion Research, was 64.7% (Final Dispositions of Case Codes and Outcome Rates for Surveys, 2011). Data were weighted by gender, age groups, urbanization, region and nationality to account for different levels of non-response among various groups of the population (Pudule et al., 2013).
2.2. Statistical analysis Data analyses were carried out using the statistical package Stata (version 13). To account for complex survey sampling design, Taylor series linearization methods were used to calculate the standard errors. Statistical significance was evaluated at the 0.05 level of significance. The Rao-Scott Chi square test was used to test for different distributions of categorical variables between the “not depressed”, “minor depression”, and “major depression” respondent groups. Student's ttest was used for continuous variables. The Rao-Scott Chi square statistic was chosen over classical Pearson Chi square to account for the complex sampling design (Rao and Scott, 1984). Multinomial logistic regressions were used to calculate relative risk ratios (RRR) for both a crude (Model 1) and adjusted (Model 2) model. Multinomial logistic regression was used because the dependent variable (depression status) had more than two categories. It was also used to examine whether healthcare utilization was similar for individuals with major and minor depression and those not qualifying for a diagnosis of depression. Three categories of depression status (no depression, minor depression, major depression) were used for the dependent variable. Model 1 examined the association between depression status (as the dependent variable) and each of the factor variables, separately controlling for gender and age. The regression analyses performed for Model 2 examined the multivariate associations between depression
2.1. Measures Most of the measurements included in the survey questionnaire have been in use since 1998 as part of the Finbalt study, and have been pretested in previous surveys (Puska et al., 2003). The survey was conducted in the two most commonly spoken languages in Latvia – Latvian and Russian. In order to measure depression, the participants were interviewed using the depressive episode module of the Mini International 205
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relative risk ratio of having major depression was higher among females aged 45–54 years old (RRR 2.3), in people who had used healthcare services more than six times in the past 12 months (RRR 4.1), among those who had three or more somatic conditions (RRR 6.5), among those with more than 11 days absent from work during the past 12 months (RRR 2.6), among those who received disability pension (RRR 2.8), among those who perceived their health status to be average (RRR 3.1) or below average (RRR 13.1), and among those who were occasional (RRR 2.7) and regular smokers (RRR 2.1). The model for the relative risk of minor depression (as compared to no depression) reveals similar tendencies. Those who were absent from work for 1–10 days were more likely to have minor depression than they were to have major depression (RRR 1.8) or be non-depressed (RRR 1.5). In Model 2, there were higher odds of having major depression for those who had used healthcare services six or more times during the past 12 months, who had more than three somatic conditions, who perceived their health status as being average or below average, and who were occasional or regular smokers. More frequent use of healthcare services, and number of days absent from work in the last 12 months were not related with an increased likelihood of having minor depression. Receiving disability pension and reporting health status as average or below average was associated with a significantly higher likelihood of having minor depression. Use of any health care services during the last 12 months was higher among those with major depression, 87.7% (95% CI 82.8–91.3) and minor depression 81.2% (95% CI 75.6–85.7), as compared with use among the non-depressed population 73.1% (95% CI 71.4–74.8) (Fig. 1).
Table 1 Description of health care utilization and health status of the sample, weighted data (n=3003). Total
Use of health care services Has not used services Primary care Specialized care Frequency of healthcare service use in the past 12 months Has not used 1–2 times 3–5 times 6–9 times 10 or more times Number of somatic diagnoses in the past 12 months None One Two Three or more Diagnosis of a chronic disease in the past 12 months Circulatory diseases Diabetes Arthritis Back pain Respiratory Gastroenterology Urinary Cancer Health status Good Average Poor
Male
Female
n
%
n
%
n
%
p
757 2101 1531
25.2 70.0 51.0
502 877 600
34.3 60.5 41.4
255 1224 931
16.7 78.9 60.0
< 0.0001 < 0.0001 < 0.0001 < 0.0001
757 795 699 384 368
25.2 26.3 23.4 12.8 12.4
502 393 280 152 129
34.3 27.1 19.3 10.6 8.7
255 402 419 232 239
16.7 25.5 27.2 14.9 15.8 < 0.0001
2020 616 232 135
67.3 20.5 7.7 4.6
1076 258 82 41
73.6 17.7 5.7 3.0
944 358 150 94
61.3 23.1 9.5 6.1
429 87 49 407 111 200 97 15
14.4 2.9 1.7 13.4 3.8 6.7 3.3 0.5
142 27 13 157 42 92 31 3
10.1 1.9 0.9 10.9 2.8 6.5 2.2 0.2
287 60 36 250 69 108 66 12
18.4 3.9 2.5 15.6 4.6 6.9 4.3 0.8
1604 1069 322
53.8 35.4 10.8
841 472 140
57.9 32.6 9.6
763 597 182
50.0 38.1 11.9
< 0.0001 0.0007 0.0009 0.0001 0.0088 0.6301 0.0014 0.0307 < 0.0001
4. Discussion To our best knowledge, this study is the first to determine the 12month prevalence of major depression and minor depression, as well as patterns of medical comorbidity and the association between depression and use of health care services in the Latvian setting. The first data on point prevalence of depression in Latvia was recently published (Rancans et al., 2014). However, in order to compare data among countries, it is crucial that identical methods are used throughout. Moreover, the most consistent rates in the literature are found for 12 months. The current study, conducted in a large, representative populationbased sample, has some important findings. First, the 12-month prevalence of major depression is in line with the estimates reported by other European countries (Wittchen et al., 2011). Second, the adjusted RRR for having major and minor depression is substantially higher in individuals with at least three somatic disorders. Third, a diagnosis of major depression is associated with significantly greater use of any health care services. The present study did not show any gender difference in the prevalence of major or minor depression. This finding is contrary with the evidence that depressive disorders are twice more common among women than among men. However, a study examining depression in 23 European countries among men and women aged 18–75 years old indicates that there is significant cross-national variation in this gender gap, with less pronounced differences in Ireland, Slovakia, and Nordic countries (Van de Velde et al., 2010). A similar lack of gender difference was also found in a study on the prevalence of major depression in the Danish general population (Ellervik et al., 2014). Additionally, a similar severity and symptomatology between genders was previously reported by Hamilton in 1967 (Hamilton, 1967). It is notable that in the current study, we didn’t distinguish between major depression and bipolar conditions. Bipolar depression is estimated to have no gender difference in prevalence (Fountoulakis, 2015). This should be considered as one of the limitations of our study. As this is the first study in the Latvian general population on the 12-month
and each factor variable, controlling for the presence of other variables in the model according to maximum likelihood ratios.
3. Results The final weighted sample included 3003 persons (1447 males and 1556 females); the questionnaire of one respondent had to be dropped due to insufficient data quality. A general description of the sample, healthcare utilization, frequency of use of health care services in the past 12 months, number of somatic diagnoses, and self-reported medical conditions are reported in Table 1. Table 2 presents the 12month prevalence of major and minor depression among both genders and across age groups. The 12-month prevalences of major depression and minor depression for the whole sample were 7.9% (95% CI 7.0 – 8.9) and 7.7% (95% CI 6.8–8.7), respectively. The proportion of the population without major or minor depression was higher among males (86.5% (95% CI 84.5–88.2) than females (82.6% (95% CI 80.7– 84.3)). Depression for both genders was most prevalent in the age group 45–54 years old (11.4%, 95% CI 9.1–14.1), while minor depression was most frequent in the age group 35–44 years old (8.8%, 95% CI 6.7–11.4) (F(7.96–23654.1)=2.53; p=0.0096). The prevalence of self-reported somatic illnesses among people with major, minor, and no depression is summarised in Table 3. Somatic conditions were more prevalent in the group with major depression. The most prevalent self-reported diseases were cardiovascular diseases (24.4%), back pain (21.5%), and gastroenterological diseases (14.1%). Table 4 displays the results from the multinomial logistic regressions. Model 1 was adjusted for gender and age, and reveals that the 206
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Table 2 Prevalence of major depression and minor depression, and associations with gender and age (n=3003). Variables
No depression %
95% CI
Minor depression %
95% CI
Major depression %
95% CI
Total Gender Male Female Age 15–24 25–34 35–44 45–54 55–64 Males 15–24 25–34 35–44 45–54 55–64 Females 15–24 25–34 35–44 45–54 55–64
84.4
83.1–85.7
7.7
6.8–8.7
7.9
7.0–8.9
86.5 82.6
84.5–88.2 80.7–84.3
6.9 8.4
5.7–8.4 7.1–9.8
6.6 9.1
5.4–8.1 7.8–10.5
0.0114
87.5 85.3 83.1 80.6 86.1
84.6–89.9 82.3–87.9 79.8–86.0 77.4–83.5 83.2–88.6
7.3 7.5 8.8 8.0 6.8
5.5–9.6 5.7–10.0 6.7–11.4 6.1–10.3 5.1–9.0
5.2 7.1 8.1 11.4 7.1
3.7–7.4 5.3–9.6 6.1–10.7 9.1–14.1 5.4–9.4
0.0096
90.0 86.2 83.0 84.9 88.7
86.1–92.9 81.8–90.0 77.5–87.4 80.2–88.7 84.2–92.0
5.2 7.2 10.0 5.3 7.0
3.3–8.2 4.8–10.8 6.8–14.6 3.2–8.6 4.5–10.8
4.7 6.6 7.0 9.8 4.4
2.8–8.0 4.3–10.1 4.2–11.3 6.8–13.9 2.5–7.7
0.0652
84.9 84.4 83.2 76.9 84.2
80.4–88.6 79.9–88.1 79.1–86.7 72.1–81.0 80.1–87.6
9.4 7.9 7.5 10.4 6.6
6.5–13.3 5.3–11.5 5.2–10.7 7.6–14.0 4.5–9.7
5.7 7.7 9.2 12.8 9.2
3.6–9.0 5.1–11.4 6.7–12.7 9.7–16.8 6.6–12.5
0.047
(Kessler et al., 2010; Strine et al., 2008). According to Model 1, having at least one somatic disease was associated with a 2.3-fold risk for major depression and 1.8-fold risk for minor depression. Presence of three or more somatic conditions was associated with a 6.5-fold risk for major depression and 3.5 fold risk for minor depression. Model 2 revealed that having three or more somatic disorders was associated with an increased risk of both major and minor depression (2.0-fold and 2.3-fold, respectively). The association of depression and somatic disease is reported by many authors (Evans et al., 2005; Patten et al., 2005; Verhaak et al., 2014). Studies have shown that depression is associated with various chronic medical conditions, such as cardiovascular disease (Abas et al., 2002), congestive heart failure (Turvey et al., 2002), diabetes (Fisher et al., 2012; Moussavi et al., 2007), renal disease (Lopes et al., 2002), COPD (Moussavi et al., 2007), and cerebrovascular accident (Hackett et al., 2005). It is reported that a comorbid state of depression worsens health as compared to depression alone (Moussavi et al., 2007). Our study shows, that somatic conditions are more prevalent in people with major depression compared to those who have minor depression and who are non-depressed. Although there are numerous studies that have examined depression in people with somatic disorders, many of them suffer from important limitations in terms of generalizability. Our study was conducted in a randomly sampled general population. However, we have to take into account that the presence of medical conditions was self-reported. Patients suffering with mental disorders are usually characterized as high utilizers of health services. These patients tend to have twice as many general medical visits not related with mental care, and have higher rates of specialist visits (Demers, 1995). A study conducted by Hämäläinen et al., (2004) found that health services are used more frequently by people with more severe, prolonged and subjectively more disabling depressive episodes. Moreover, seeking medical care might represent an inefficient use of health care services (Kessler et al., 2002). In our study, reporting six or more visits to any healthcare service in the past 12 months was associated with a 2.0-fold increased risk of having major depression. However, having minor depression was not associated with higher healthcare utilization. This finding is in contrary with studies that report that depressive symptoms are associated with the same or a greater level of service utilization and impairment as clinical depression is (Johnson et al., 1992). It is noteworthy that in the current study, we did not assess the reason for visits to healthcare specialists.
Table 3 Prevalence of somatic illnesses among people with no depression, with minor depression, and with major depression (n=3003). Variables
Cardiovascular disease Diabetes Arthritis Back pain Respiratory Gastroenterology Urinary Cancer
No depression
Major depression %
p
%
Minor depression %
13.1
18.1
24.4
< 0.0001
2.2 1.3 12.3 3.4 5.7 2.9 0.3
6.8 4.6 16.3 4.3 10.3 5.5 0.8
5.7 3.0 21.5 7.5 14.1 5.9 2.7
< 0.0001 0.0003 < 0.0001 0.0054 < 0.0001 0.0063 < 0.0001
p
prevalence of depression, no exact comparison with existing national studies can be made. The total prevalence of minor depression was 7.7%. The literature suggests that the prevalence of minor depression, as defined by DSMIV criteria, is between 2.6% and 4.5% (Hermens et al., 2004). For those with any chronic somatic conditions, these rates are even higher. Evidence suggests that individuals with depressive symptoms have an increased risk for major depression (Fogel et al., 2006). Depressive symptoms are also associated with functional disability, medical comorbidity (Pickett et al., 2014) and poor subjective health status (Lyness et al., 2006). In our study minor depression was almost twice as prevalent as in other countries. One possible explanation could be that the health system in Latvia is severely underfunded. Total health expenditure in 2012 was only US$1188 purchasing power parity (PPP) per capita (corresponding to 6% of GDP), which was the third lowest amount spent on health in the EU (WHO Regional Office for Europe, 2016). Furthermore, only about 57% (2012) of total spending came from public sources. This share is lower only in Bulgaria and Cyprus. The proportion of the population reporting an unmet medical need because of costs doubled during the financial crisis, reaching more than 14% in 2011 before falling to just above 10% in 2012 (Eurostat, 2016). The prevalence of major and minor depression varied with age in both men and women, but no consistent age-related pattern was found. The observation of decreased prevalence in the oldest age group for both disorders is consistent with findings from developed countries 207
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Table 4 Relative risk ratios of major depression and minor depression, according to gender, age and health related variables. Model 1 Factor
Gender Male Female Age 15–24 25–34 35–44 45–54 55–64 Frequency of healthcare service use in the past 12 months Has not used 1–2 times 3–5 times 6 or more times Number of somatic diagnoses in the past 12 months None One Two Three more more Number of days absent in the past 12 months None 1–10 days 11+ days Receive disability pension No Yes Perceived health Above average Average Below average Smoking status Non-smoker Quitter/ex-smoker Occasional smoker Regular smoker
Model 2
RRR major depression vs nondepressed (95% CIe)
RRR minor depression vs nondepressed (95% CIe)
RRR minor depression vs major depression (95% CIe)
RRR major depression vs nondepressed (95% CIe)
RRR minor depression vs nondepressed (95% CIe)
RRR minor depression vs major depression (95% CIe)
1.0 1.4(a) (1.1–1.9)
1.0 1.3 (1.0–1.7)
1.0 0.9 (0.6–1.3)
1.0 1.7(b) (1.2–2.4)
1.0 1.4(d) (1.0–1.9)
1.0 0.8 (0.5–1.3)
1.0 1.4 (0.9–2.3) 1.6(a) (1.0–2.6) 2.3(c) (1.5–3.6) 1.3 (0.8–2.2)
1.0 1.1 1.3 1.2 0.9
1.0 0.8 (0.4–1.4) 0.8 (0.4–1.4) 0.5(a) (0.3–0.9) 0.7 (0.4–1.3)
1.0 1.1 (0.7–1.9) 1.0 (0.6–1.8) 1.0 (0.6–1.7) 0.4(b) (0.2–0.7)
1.0 0.9 (0.6–1.4) 0.9 (0.6–1.4) 0.7(d) (0.4–1.1) 0.4(b) (0.3–0.7)
1.0 0.8 0.9 0.7 1.1
1.0 1.7(a) (1.0–2.7) 2.3(b) (1.4–3.6) 4.1(c) (2.6–6.4)
1.0 1.7(b) (1.1–2.5) 1.4 (0.9–2.2) 1.6(b) (1.1–2.4)
1.0 1.0 (0.5–1.8) 0.6 (0.3–1.2) 0.4(b) (0.2–0.7)
1.0 1.6(0) (1.0–2.5) 1.7(a) (1.1–2.9) 2.0(b) (1.2–3.4)
1.0 1.3 (0.9–2.0) 1.0 (0.6–1.6) 0.8 (0.5–1.4)
1.0 0.9 (0.5–1.6) 0.6(d) (0.3–1.1) 0.4(a) (0.2–0.8)
1.0 2.3(c) (1.6–3.2) 2.5(c) (1.5–4.0) 6.5(c) (3.9–10.7)
1.0 1.8(c) (1.3–2.5) 1.5 (0.9–2.2) 3.5(c) (2.0–6.2)
1.0 0.8 (0.5–1.3) 0.6 (0.3–1.2) 0.6 (0.3–1.1)
1.0 1.2 (0.8–1.8) 1.0 (0.6–1.7) 2.1(b) (1.2–3.8)
1.0 1.4(d) (1.0–2.1) 1.1 (0.6–2.0) 2.3(b) (1.3–4.3)
1.0 1.2 (0.7–1.9) 1.1 (0.5–2.4) 1.1 (0.5–2.4)
1.0 0.9 (0.6–1.3) 2.6(c) (1.9–3.6)
1.0 1.5(a) (1.1–2.1) 1.4 (0.9–2.1)
1.0 1.8(a) (1.0–3.0) 0.5(b) (0.3–0.9)
1.0 0.7 (0.4–1.1) 1.3 (0.9–1.8)
1.0 1.4(d) (1.0–2.1) 1.1 (0.7–1.7)
1.0 2.0(b) (1.2–3.5) 0.9 (0.5–1.5)
1.0 2.8(c) (1.8–4.3)
1.0 2.5(c) (1.6–4)
1.0 0.9 (0.5–1.6)
1.0 1.0 (0.6–1.7)
1.0 1.9(a) (1.1–3.2)
1.0 1.8(d) (0.9–3.5)
1.0 3.1(c) (2.2–4.5) 13.1(c) (8.6–20.0)
1.0 2.2(c) (1.6–3.1) 4.0(c) (2.5–6.4)
1.0 0.7 (0.5–1.1) 0.3(c) (0.2–0.5)
1.0 2.5(c) (1.7–3.7) 8.3(c) (5.1–13.7)
1.0 2.0(c) (1.4–2.9) 3.0(c) (1.7–5.3)
1.0 0.8 (0.5–1.3) 0.4(b) (0.2–0.7)
1.0 1.1 (1.6–3.2) 2.7(c) (1.5–4.8) 2.1(c) (1.5–2.9)
1.0 1.3 (0.8–1.9) 1.5 (0.8–2.9) 1.3(d) (1.0–1.9)
1.0 1.1 (0.6–2.0) 0.6 (0.3–1.3) 0.7(d) (0.4–1.0)
1.0 1.3 (0.8–2.0) 3.0(c) (1.6–5.5) 2.2(c) (1.5–3.1)
1.0 1.3 (0.9–2.0) 1.6 (0.8–3.1) 1.3 (0.9–1.9)
1.0 1.0 (0.6–1.9) 0.5 (0.2–1.3) 0.6(b) (0.4–1.0)
(0.7–1.6) (0.8–1.9) (0.8–1.8) (0.6–1.4)
(0.4–1.5) (0.5–1.7) (0.3–1.3) (0.5–2.2)
Model 1 results from multinomial logistic regressions expressed as relative risk ratio (RRR) with respective 95% confidence intervals controlled for gender and age. Model 2 results from multinomial logistic regressions expressed as relative risk ratio (RRR) with respective 95% confidence intervals controlled for all factor variables (gender, age, frequency of use of healthcare services, number of self-reported somatic diagnosis in the past 12 months, number of absent days in the past 12 months, receiving disability pension, perceived health, and smoking status). Bolded text indicates a significant difference from the reference group. a p < 0.05 b p < =0.01 c p < =0.001 d p < 0.1 e CI: Confidence interval.
Previous research suggests that clinically significant depression is highly prevalent in primary care settings (Rucci et al., 2003). Despite the fact that depression is often comorbid with medical conditions, the Latvian National Health Service data show that in 2013 general practitioners saw only 4423 unique patients with a mood disorder diagnosis, while more than 50 000 patients were diagnosed as having a neurotic disorder, dominantly somatoform autonomic dysfunction (Pulmanis et al., 2014). Therefore, Latvian data confirms that depression is highly under-diagnosed by health care professionals (Burns et al., 2000). This may contribute to complications, mortality, and increased health care expenditures. It is to be noted that depressive patients seen in healthcare settings have a greater level of comorbidity of chronic medical diseases than patients seen in the mental health setting (Lawrence and Kisely, 2010). In Model 2 in our study, both major and minor depression was
Fig. 1. Use of health care services during last 12 months.
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trainings on depression diagnostic practices in medical settings, using screening questionnaires, and providing guidance on diagnosing depression could increase timely recognition and improve treatments.
associated with a higher risk of having three or more somatic disorders in last 12 months. However, minor depression was associated as well with having a disability pension, but was not associated with a higher risk of higher utilization of medical care services. Previous studies suggest that depression is a considerable cause not only of increased use of health care resources, but also of work place absenteeism and diminished productivity (Hendriks et al., 2015; Suzuki et al., 2015). In our study, receiving disability pension was associated with an almost 2.0-fold risk of having minor depression. Model 1 revealed that being absent from work for 11 or more days during the past 12 months was associated with a 2.6-fold risk of having major depression. Shorter periods of absence from work were associated with a 1.5-fold risk of having minor depression. In population based samples, the prevalence of tobacco dependence is 40–60% higher among people with major depression than it is among the general population (Anda et al., 1990). Tobacco smoking appears to increase the risk for depression (John et al., 2004), and depression appears to sustain tobacco smoking, as demonstrated by the finding that individuals with depression have a much harder time quitting smoking, and require more attempts before they quit (Hughes, 2007). In 2012, 34% of the Latvian population were regular smokers, including 52% of males and 18% of females. 4.5% were occasional smokers (Martinsone and Pelne, 2013). According to Model 2, the RRR for having major depression was two times higher among regular smokers and three times higher among occasional smokers (as compared to non-smokers). We did not find an increased risk of having minor depression among those who were current or occasional smokers. The strengths of this study include the large, population based sample, the use of a diagnostic interview for the diagnosis of major and minor depression, and the inclusion of a wide spectrum of age groups. Another important strength is that only specially trained interviewers were involved in data collection. On the other hand, several methodological limitations should be considered. Due to the cross-sectional nature of this study, we are not able to draw conclusions about causality. We did not have information on course or previous episodes of depression. Moreover, the diagnostic interview covered only depression, and excluded investigation of other psychiatric disorders. The fact that non responders might have different characteristics from responders is also one considerable limitation (Korkeila et al., 2001). Finally, recall bias may have influenced some measures, such as professionally-diagnosed somatic conditions in the past 12 months and health service utilization.
References Abas, M., et al., 2002. Depression and mortality in a high-risk population. 11-year followup of the Medical Research Council elderly hypertension trial. Br. J. Psychiatry: J. Ment. Sci. 181, 123–128. Alonso, J., et al., 2004. Use of mental health services in Europe: results from the European study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr. Scand. Suppl. 109, 47–54. Anda, R.F., et al., 1990. Depression and the dynamics of smoking. A national perspective. J. Am. Med. Assoc. 264, 1541–1545. Ayuso-Mateos, J.L., et al., 2001. Depressive disorders in Europe: prevalence figures from the ODIN study. Br. J. Psychiatry.: J. Ment. Sci. 179, 308–316. Bebbington, P., et al., 2003. Unequal access and unmet need: neurotic disorders and the use of primary care services. Int. Rev. Psychiatry 15, 115–122. Burns, B.J., et al., 2000. General medical and specialty mental health service use for major depression. Int. J. Psychiatry Med. 30, 127–143. Demers, M., 1995. Frequent users of ambulatory health care in Quebec: the case of doctor-shoppers. CMAJ: Can. Med. Assoc. J.=J. De. L′Assoc. Med. Can. 153, 37–42. Economou, M., et al., 2013. Major depression in the Era of economic crisis: a replication of a cross-sectional study across Greece. J. Affect. Disord. 145, 308–314. Ellervik, C., et al., 2014. Prevalence of depression, quality of life and antidepressant treatment in the danish general suburban population study. Nord. J. Psychiatry 68, 507–512. ESOMAR, 2015. International Code on Market and Social Research. 〈https://www. esomar.org/〉. Eurostat statistical database, 2016. 〈http://ec.europa.eu/eurostat/portal/page/portal/ eurostat/home〉 (accessed October 2016). Evans, D.L., et al., 2005. Mood disorders in the medically ill: scientific review and recommendations. Biol. Psychiatry 58, 175–189. Fisher, E.B., et al., 2012. Co-occurrence of diabetes and depression: conceptual considerations for an emerging global health challenge. J. Affect. Disord. 142, S56–S66. Fogel, J., et al., 2006. Minor depression as a predictor of the first onset of major depressive disorder over a 15-year follow-up. Acta Psychiatr. Scand. 113, 36–43. Fountoulakis, K., N., 2015. Bipolar Disorder: an Evidence-Based Guide to Manic Depression. Springer-Verlag, Berlin Heidelberg. Gaziano, C., 2005. Comparative analysis of within-householdwithin-household respondent selection techniques. Public Opin. Q. 69, 124–157. Gunn, J., et al., 2012. The association between chronic illness, multimorbidity and depressive symptoms in an Australian primary care cohort. Soc. Psychiatry Psychiatr. Epidemiol. 47, 175–184. Hackett, M.L., et al., 2005. Frequency of depression after stroke: a systematic review of observational studies. Stroke; J. Cereb. Circ. 36, 1330–1340. Hämäläinen, J., et al., 2004. Use of health services for major depressive episode in Finland. J. Affect. Disord. 79, 105–112. Hamilton, M., 1967. Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6, 278–296. Hendriks, S.M., et al., 2015. Long-term work disability and absenteeism in anxiety and depressive disorders. J. Affect. Disord. 178, 121–130. Hermens, M.L.M., et al., 2004. The prognosis of minor depression in the general population: a systematic review. Gen. Hosp. Psychiatry 26, 453–462. Hidaka, B.H., 2012. Depression as a disease of modernity: explanations for increasing prevalence. J. Affect. Disord. 140, 205–214. Hughes, J.R., 2007. Depression during tobacco abstinence. Nicotine Tob. Res.: Off. J. Soc. Res. Nicotine Tob. 9, 443–446. John, U., et al., 2004. Depressive disorders are related to nicotine dependence in the population but do not necessarily hamper smoking cessation. J. Clin. Psychiatry 65, 169–176. Johnson, J., et al., 1992. Service utilization and social morbidity associated with depressive symptoms in the community. J. Am. Med. Assoc. 267, 1478–1483. Kessler, D., et al., 2002. Detection of depression and anxiety in primary care: follow up study. BMJ (Clin. Res. Ed.) 325, 1016–1017. Kessler, L.G., et al., 1987. Psychiatric diagnoses of medical service users: evidence from the Epidemiologic Catchment Area Program. Am. J. Public Health 77, 18–24. Kessler, R.C., et al., 2010. Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depress. Anxiety 27, 351–364. Kish, L., 1949. A procedure for objective respondent selection within the household. J. Am. Stat. Assoc. 44, 380–387. Korkeila, K., et al., 2001. Non-response and related factors in a nation-wide health survey. Eur. J. Epidemiol. 17, 991–999. Lawrence, D., Kisely, S., 2010. Inequalities in healthcare provision for people with severe mental illness. J. Psychopharmacol. 24, 61–68. Lehnert, T., et al., 2011. Review: health care utilization and costs of elderly persons with multiple chronic conditions. Med. Care Res. Rev.: MCRR 68, 387–420. Lopes, A.A., et al., 2002. Depression as a predictor of mortality and hospitalization among hemodialysis patients in the United States and Europe. Kidney Int. 62, 199–207. Lyness, J.M., et al., 2006. Outcomes of minor and subsyndromal depression among elderly patients in primary care settings. Ann. Intern. Med. 144, (496-W484).
5. Conclusion In a conclusion, this is the first study reporting the 12-month prevalence of depression in our country. The 12-month prevalence of depression in Latvia is concordant with that in other European countries. Frequent healthcare utilization in the past 12 months, the presence of at least three somatic disorders, the perception of health as being poor, and participating in occasional smoking were the factors with the strongest association with major depression. The presence of three medical conditions, receiving a disability pension, and poor subjective health status were the most correlated with minor depression. The high prevalence of both major and minor depression suggests diagnostic and treatment issues must be resolved at national level. These findings call for effectively targeted interventions to reduce the burden associated with major and minor depression diagnoses. Our findings outline certain individuals who might be at a higher risk of having depression. The factors associated with major or minor depression could be used as indicators for health providers to initiate screening for depression. It is also important to monitor individuals with minor depression, taking into account their increased risk of developing major depression. We suggest improvement of diagnosis and treatment of depression in the primary care setting. Introducing 209
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associated characteristics. J. Affect. Disord. 76, 171. Sheehan, D.V., et al., 1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59 (Suppl 20), 22–33. Shvartzman, P., et al., 2005. Health services utilization by depressive patients identified by the MINI questionnaire in a primary care setting. Scand. J. Prim. Health Care 23, 18–25. Strine, T.W., et al., 2008. Depression and anxiety in the United States: findings from the 2006 Behavioral Risk Factor Surveillance System. Psychiatr. Serv. 59, 1383–1390. Suzuki, T., et al., 2015. Relationship between sickness presenteeism (WHO-HPQ) with depression and sickness absence due to mental disease in a cohort of Japanese workers. J. Affect. Disord. 180, 14–20. Turvey, C.L., et al., 2002. Prevalence and correlates of depressive symptoms in a community sample of people suffering from heart failure. J. Am. Geriatr. Soc. 50, 2003–2008. Unützer, J., et al., 2000. Care for depression in HMO patients aged 65 and older. J. Am. Geriatr. Soc. 48, 871–878. Van de Velde, S., et al., 2010. Gender differences in depression in 23 European countries. cross-national variation in the gender gap in depression. Soc. Sci. Med. 71, 305–313. Verhaak, P.F.M., et al., 2014. Depression, disability and somatic diseases among elderly. J. Affect. Disord. 167, 187–191. Vogelzangs, N., et al., 2010. Cardiovascular disease in persons with depressive and anxiety disorders. J. Affect. Disord. 125, 241–248. von Korff, M., 2009. Global perspectives on mental-physical comorbidity. In: von Korff, M.R., Scott, K.M., Gureje, O. (Eds.), Global Perspectives on Mental-Physical Comorbidity in the World Health Organization World Merntal Health Surveys. Cambridge University Press Cambridge, 1–14. WHO, 2016. Regional office for Europe. European Health for all Database (HFA-DB). 〈http://data.euro.who.int/hfadb/〉. (accesed October 2016). Windle, M., Windle, R.C., 2013. Recurrent depression, cardiovascular disease, and diabetes among middle-aged and older adult women. J. Affect. Disord. 150, 895–902. Wittchen, H.U., et al., 2011. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol.: J. Eur. Coll. Neuropsychopharmacol. 21, 655–679. Zlotnick, C., et al., 2000. The relationship between quality of interpersonal relationships and major depressive disorder: findings from the National Comorbidity survey. J. Affect. Disord. 59, 205–215.
Mann, J.J., et al., 2005. Suicide prevention strategies: a systematic review. J. Am. Med. Assoc. 294, 2064–2074. Marcus, S.M., et al., 2005. Gender differences in depression: findings from the STAR*D study. J. Affect. Disord. 87, 141–150. Martinsone, U., Pelne, A., 2013. SMĒĶĒŠANAS IZPLATĪBA UN SEKAS LATVIJĀ 2012. GADĀ. Tematiskais ziņojums. (Prevalence of smoking and related consequences in Latvia in 2012. Thematic report). Slimību profilakses un kontroles centrs, Rīga. Mathers, C.D., Loncar, D., 2006. Projections of global mortality and burden of disease from 2002 to 2030. Plos Med. 3, (e442-e442). Mikkelsen, R.L., et al., 2004. Anxiety and depression in patients with chronic obstructive pulmonary disease (COPD). A review. Nord. J. Psychiatry 58, 65–70. Moussavi, S., et al., 2007. Depression, chronic diseases, and decrements in health: results from the World health surveys. Lancet 370, 851–858. Murray, C.J.L., Lopez, A.D., 1996. The Global Burden of Disease. Harvard University Press, Boston. Patten, S.B., et al., 2005. Long-term medical conditions and major depression: strength of association for specific conditions in the general population. Can. J. Psychiatry 50, 195–202. Pickett, Y.R., et al., 2014. Healthcare use among older primary care patients with minor depression. Am. J. Geriatr. Psychiatry.: Off. J. Am. Assoc. Geriatr. Psychiatry. 22, 207–210. Pinninti, N.R., et al., 2003. MINI international neuropsychiatric schedule: clinical utility and patient acceptance. Eur. Psychiatry.: J. Assoc. Eur. Psychiatr. 18, 361–364. Pudule, I., Grīnberga, D., Velika, B., Gavare, I., Villeruša, A., 2013. Latvijas iedzīvotāju veselību ietekmējošo paradumu pētījums, 2012. (Health Behaviour among Latvian Adult Population, 2012). Pulmanis, T., Pelne, A., Taube, M., 2014. Psihiskā veselība Latvijā 2013. gadā. Tematiskais ziņojums. (Mental Health in Latvia. A Thematic Report). Slimību profilakses un kontroles centrs, Rīga. Puska, P., et al., 2003. Health behaviour in Estonia, Finland and Lithuania 1994–1998. Standardized comparison. Eur. J. Public Health 13, 11–17. Rancans, E., et al., 2014. The point prevalence of depression and associated sociodemographic correlates in the general population of Latvia. J. Affect. Disord. 156, 104–110. Rao, J.N.K., Scott, A.J., 1984. On chi-squared tests for multi-way tables with cell proportions estimated from survey data. Ann. Stat.. Rihmer, Z., 2007. Suicide risk in mood disorders. Curr. Opin. Psychiatry 20, 17–22. Rucci, P., et al., 2003. Subthreshold psychiatric disorders in primary care: prevalence and
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
The 12-month prevalence of depression and health care utilization in the general population of Latvia
MARK
⁎
Jelena Vrublevskaa, , Marcis Trapencierisb, Sigita Snikereb, Daiga Grinbergac, Biruta Velikac, Iveta Pudulec, Elmars Rancansa a b c
Department of Psychiatry and Narcology, Riga Stradins University, Tvaika street 2, LV-1005 Riga, Latvia Institute of Philosophy and Sociology, University of Latvia, Kalpaka bulv. 4, Riga, Latvia Centre for Disease Prevention and Control of Latvia, Department of Research, Statistics and Health Promotion, Latvia
A R T I C L E I N F O
A BS T RAC T
Keywords: Depression Epidemiology General population Prevalence
Background: This cross-sectional study aims to assess the 12-month prevalence of major and minor depression in the Latvian population, and to evaluate associated health care utilization. Methods: Trained interviewers conducted face-to-face interviews with a multistage stratified probability sample of the Latvian general population, ages 15–64 (n=3003). Participants were interviewed using the depression module of the Mini International Neuropsychiatric Interview. Self-reported health care utilization and somatic illness were also assessed. Multinomial logistic regressions were applied. Results: The 12-month prevalence of major depression was 7.9% (95%CI 7.0–8.9), while for minor depression it was 7.7% (95%CI 6.8–8.7). We did not find a substantial difference in the relative risk ratio (RRR 1.7 for female) for having major depression by gender. RRR of having major depression was higher for those who had used healthcare services six or more times (RRR 2.0), those who had three or more somatic disorders during the past 12 months (RRR 2.3), those who perceived their health status as being below average (RRR 8.3), and those who were occasional smokers (RRR 3.0). RRR of having minor depression was increased for those who had at least three somatic disorders (RRR 2.3), those who received disability pension (RRR 1.9), and those who perceived their health status to be below average (RRR 3.0). Limitations: The study was cross-sectional. Other psychiatric comorbidity was not assessed. Conclusions: This is the first population based study reporting the 12-month prevalence of depression in Latvia. Certain factors associated with depression have been found.
1. Introduction Depression is among the most commonly diagnosed mental disorders in adults. It is costly, and if left untreated, has considerable impact on individuals’ quality of life, on society and on the public health system (Mathers and Loncar, 2006). In addition to these socioeconomic impacts, depression is also associated with personal and interpersonal suffering, significant distress, impairment of functioning, disturbance to interpersonal relationships, and an increased risk of suicide (Rihmer, 2007; Zlotnick et al., 2000). Various studies have demonstrated a link between the prevalence of depression and socio-demographic factors, such as gender, marital status and economic hardship (Alonso et al., 2004; Ayuso-Mateos et al., 2001; Economou et al., 2013; Marcus et al., 2005). Depression is also a major cause of workplace absenteeism, diminished or lost productivity, and increased use of health care resources. It is associated with a level of disability ⁎
comparable to that of major medical illnesses (Moussavi et al., 2007), and has been associated with a variety of somatic diseases (von Korff, 2009), including cardiovascular disease (Vogelzangs et al., 2010), diabetes (Windle and Windle, 2013), and pulmonary disorders (Mikkelsen et al., 2004). In addition, there is evidence that a higher number of chronic health conditions is associated with a greater risk of depression (Gunn et al., 2012), and depressed individuals tend to use more primary care services than non-depressed individuals (Kessler et al., 1987; Shvartzman et al., 2005). Many studies have shown that multi-comorbidity is associated with higher health care utilization and costs (Lehnert et al., 2011). However, only a minority of patients with depression receive treatment for it from health care services (Bebbington et al., 2003; Unützer et al., 2000). As a consequence, both depression and multi-comorbidity pose a great burden on health care systems and societies. Medical help–seeking might represent an inefficient use of health care resources, if mental health issues are not
Corresponding author. E-mail addresses: [email protected] (J. Vrublevska), [email protected] (M. Trapencieris), [email protected] (S. Snikere), [email protected] (E. Rancans).
http://dx.doi.org/10.1016/j.jad.2016.12.031 Received 30 August 2016; Received in revised form 22 November 2016; Accepted 21 December 2016 Available online 28 December 2016 0165-0327/ © 2016 Elsevier B.V. All rights reserved.
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Neuropsychiatric Interview (M.I.N.I), Version 6.0.0. The M.I.N.I is a structured screening interview that was validated by convergence with the SCID-P (Structured Clinical Interview for DSM-III-R Patient Version) and the CIDI (Composite International Diagnostic Interview), and by expert professional opinion (Sheehan et al., 1998). The good psychometric characteristics of the M.I.N.I, its ability to be administered rapidly, and its acceptability to patients made it a good choice for the research purposes (Pinninti et al., 2003). The M.I.N.I test results were interpreted into three categories: not depressed, major depression, or minor depression (according to DSM-IV-TR). The M.I.N.I used in this study has been previously translated for use in Latvian and Russian languages by authorship holders. To check for associations between depression prevalence and sociodemographic characteristics, the gender and age of participants were recorded. Age was recorded as a categorical variable (15–24, 25–34, 35–44, 45–64, and 55–64). The variable of health care utilization was calculated on the basis of the self-reported question: “How many times over the past 12 months have you … (…visited a family physician), (… visited a specialist (excluding the dentist)), (…been hospitalized (including day care)), (…called for an ambulance)”. For some analyses, the healthcare variables were dichotomized (did not use vs. used the service), for others categorized (“did not use”, “1–2 times”, “3–5 times”, “6–9 times”, “10 or more times”). Self-reported diagnosis of somatic illnesses was assessed by the question “Has a doctor over the past 12 months diagnosed you with any of the following illnesses (hypertension, diabetes, myocardial infarction, stenocardia, heart failure, rheumatoid arthritis, back diseases, pulmonary emphysema or chronic bronchitis, renal or urinary tract diseases, gastroenterological diseases, bronchial asthma, hypercholesterinaemia, cancer)". Other health status variables included: self-reported health status (assessed by the question “How would you rate your current health status?”, with the response options “good”, “fairly good”, “average”, “fairly poor”, “poor”. These were recoded into three categories “above average”, “average”, “below average”), smoking status (with responses categorized into “non-smoker”, “quitter/ex-smoker”, “occasional smoker”, or “regular smoker”), receipt of disability pension (assessed by the question “Do you currently receive disability pension?”), and absenteeism (assessed by the question “How many calendar days over the last 12 months did you skip work or did not perform everyday duties because of health issues?” The responses were categorized as “none”, “1–10 days”, or “11+ days”).
addressed during such clinical encounters (Kessler et al., 2002). Moreover, community-based cross-cultural studies show some indications that the prevalence of depression is increasing (Hidaka, 2012). By 2020, depression is prognosed to be the second leading cause of disability adjusted life years (DAYLY's), after cardio-vascular disease (Murray and Lopez, 1996). Therefore, detecting and treating depression will be especially important for decreasing disability, prolonging survival, and increasing the quality of life at a population level. Latvia is among the countries with the highest suicide rates in Europe (WHO Regional Office for Europe, 2016), and among those committing suicide each year, 80–85% suffer from at least one mood disorder (Mann et al., 2005). Latvia also has almost one of the lowest gross domestic product (GDP) per capita among European countries, and the lowest healthcare financing, making the study even more relevant (WHO Regional Office for Europe, 2016). The aimsof our study were to assess the 12-month prevalence of major and minor depression among the general population of Latvia, to assess health care utilization among people with depression, and to evaluate the association of depression with self-reported somatic illnesses. 2. Methods This study was a part of the Health Behaviour among the Latvian Adult Population survey, conducted in 2012. This survey was based on a nationally-representative, multistage stratified probability sample of 3004 persons between the ages of 15 and 64 years old (Pudule et al., 2013). The stratification variables included population density and nationality; a total of 32 strata were formed. Within those strata, a total of 390 starting addresses were randomly selected from the list of addresses, proportional to the population size in each stratum. Taking each of these addresses as a starting point, an additional seven to eight households in the vicinity were interviewed. The additional households were selected by a random route method (every third household in urban and semi-urban areas and every household in rural areas). In each sampled household, only one respondent was selected for interview, by using Kish tables and the closest birthday principle (Gaziano, 2005; Kish, 1949). Data was collected by face-to-face interviews in Latvian or Russian (language as preferred by respondent). Each sampled household was visited up to three times if no initial contact with the household or respondent was made. The fieldwork for the study was conducted from April to June 2012, and was carried out by 68 professional interviewers coordinated by five regional fieldwork coordinators and two fieldwork supervisors based in the city of Riga. Before conducting fieldwork, interviewers received training sessions covering survey methodology and the scales used. Up to eight interviewers participated in each session. The fieldwork agency followed the ESOMAR International Code on Market and Social Research (ESOMAR, 2015). The response rate, as calculated according to the American Association of Public Opinion Research, was 64.7% (Final Dispositions of Case Codes and Outcome Rates for Surveys, 2011). Data were weighted by gender, age groups, urbanization, region and nationality to account for different levels of non-response among various groups of the population (Pudule et al., 2013).
2.2. Statistical analysis Data analyses were carried out using the statistical package Stata (version 13). To account for complex survey sampling design, Taylor series linearization methods were used to calculate the standard errors. Statistical significance was evaluated at the 0.05 level of significance. The Rao-Scott Chi square test was used to test for different distributions of categorical variables between the “not depressed”, “minor depression”, and “major depression” respondent groups. Student's ttest was used for continuous variables. The Rao-Scott Chi square statistic was chosen over classical Pearson Chi square to account for the complex sampling design (Rao and Scott, 1984). Multinomial logistic regressions were used to calculate relative risk ratios (RRR) for both a crude (Model 1) and adjusted (Model 2) model. Multinomial logistic regression was used because the dependent variable (depression status) had more than two categories. It was also used to examine whether healthcare utilization was similar for individuals with major and minor depression and those not qualifying for a diagnosis of depression. Three categories of depression status (no depression, minor depression, major depression) were used for the dependent variable. Model 1 examined the association between depression status (as the dependent variable) and each of the factor variables, separately controlling for gender and age. The regression analyses performed for Model 2 examined the multivariate associations between depression
2.1. Measures Most of the measurements included in the survey questionnaire have been in use since 1998 as part of the Finbalt study, and have been pretested in previous surveys (Puska et al., 2003). The survey was conducted in the two most commonly spoken languages in Latvia – Latvian and Russian. In order to measure depression, the participants were interviewed using the depressive episode module of the Mini International 205
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relative risk ratio of having major depression was higher among females aged 45–54 years old (RRR 2.3), in people who had used healthcare services more than six times in the past 12 months (RRR 4.1), among those who had three or more somatic conditions (RRR 6.5), among those with more than 11 days absent from work during the past 12 months (RRR 2.6), among those who received disability pension (RRR 2.8), among those who perceived their health status to be average (RRR 3.1) or below average (RRR 13.1), and among those who were occasional (RRR 2.7) and regular smokers (RRR 2.1). The model for the relative risk of minor depression (as compared to no depression) reveals similar tendencies. Those who were absent from work for 1–10 days were more likely to have minor depression than they were to have major depression (RRR 1.8) or be non-depressed (RRR 1.5). In Model 2, there were higher odds of having major depression for those who had used healthcare services six or more times during the past 12 months, who had more than three somatic conditions, who perceived their health status as being average or below average, and who were occasional or regular smokers. More frequent use of healthcare services, and number of days absent from work in the last 12 months were not related with an increased likelihood of having minor depression. Receiving disability pension and reporting health status as average or below average was associated with a significantly higher likelihood of having minor depression. Use of any health care services during the last 12 months was higher among those with major depression, 87.7% (95% CI 82.8–91.3) and minor depression 81.2% (95% CI 75.6–85.7), as compared with use among the non-depressed population 73.1% (95% CI 71.4–74.8) (Fig. 1).
Table 1 Description of health care utilization and health status of the sample, weighted data (n=3003). Total
Use of health care services Has not used services Primary care Specialized care Frequency of healthcare service use in the past 12 months Has not used 1–2 times 3–5 times 6–9 times 10 or more times Number of somatic diagnoses in the past 12 months None One Two Three or more Diagnosis of a chronic disease in the past 12 months Circulatory diseases Diabetes Arthritis Back pain Respiratory Gastroenterology Urinary Cancer Health status Good Average Poor
Male
Female
n
%
n
%
n
%
p
757 2101 1531
25.2 70.0 51.0
502 877 600
34.3 60.5 41.4
255 1224 931
16.7 78.9 60.0
< 0.0001 < 0.0001 < 0.0001 < 0.0001
757 795 699 384 368
25.2 26.3 23.4 12.8 12.4
502 393 280 152 129
34.3 27.1 19.3 10.6 8.7
255 402 419 232 239
16.7 25.5 27.2 14.9 15.8 < 0.0001
2020 616 232 135
67.3 20.5 7.7 4.6
1076 258 82 41
73.6 17.7 5.7 3.0
944 358 150 94
61.3 23.1 9.5 6.1
429 87 49 407 111 200 97 15
14.4 2.9 1.7 13.4 3.8 6.7 3.3 0.5
142 27 13 157 42 92 31 3
10.1 1.9 0.9 10.9 2.8 6.5 2.2 0.2
287 60 36 250 69 108 66 12
18.4 3.9 2.5 15.6 4.6 6.9 4.3 0.8
1604 1069 322
53.8 35.4 10.8
841 472 140
57.9 32.6 9.6
763 597 182
50.0 38.1 11.9
< 0.0001 0.0007 0.0009 0.0001 0.0088 0.6301 0.0014 0.0307 < 0.0001
4. Discussion To our best knowledge, this study is the first to determine the 12month prevalence of major depression and minor depression, as well as patterns of medical comorbidity and the association between depression and use of health care services in the Latvian setting. The first data on point prevalence of depression in Latvia was recently published (Rancans et al., 2014). However, in order to compare data among countries, it is crucial that identical methods are used throughout. Moreover, the most consistent rates in the literature are found for 12 months. The current study, conducted in a large, representative populationbased sample, has some important findings. First, the 12-month prevalence of major depression is in line with the estimates reported by other European countries (Wittchen et al., 2011). Second, the adjusted RRR for having major and minor depression is substantially higher in individuals with at least three somatic disorders. Third, a diagnosis of major depression is associated with significantly greater use of any health care services. The present study did not show any gender difference in the prevalence of major or minor depression. This finding is contrary with the evidence that depressive disorders are twice more common among women than among men. However, a study examining depression in 23 European countries among men and women aged 18–75 years old indicates that there is significant cross-national variation in this gender gap, with less pronounced differences in Ireland, Slovakia, and Nordic countries (Van de Velde et al., 2010). A similar lack of gender difference was also found in a study on the prevalence of major depression in the Danish general population (Ellervik et al., 2014). Additionally, a similar severity and symptomatology between genders was previously reported by Hamilton in 1967 (Hamilton, 1967). It is notable that in the current study, we didn’t distinguish between major depression and bipolar conditions. Bipolar depression is estimated to have no gender difference in prevalence (Fountoulakis, 2015). This should be considered as one of the limitations of our study. As this is the first study in the Latvian general population on the 12-month
and each factor variable, controlling for the presence of other variables in the model according to maximum likelihood ratios.
3. Results The final weighted sample included 3003 persons (1447 males and 1556 females); the questionnaire of one respondent had to be dropped due to insufficient data quality. A general description of the sample, healthcare utilization, frequency of use of health care services in the past 12 months, number of somatic diagnoses, and self-reported medical conditions are reported in Table 1. Table 2 presents the 12month prevalence of major and minor depression among both genders and across age groups. The 12-month prevalences of major depression and minor depression for the whole sample were 7.9% (95% CI 7.0 – 8.9) and 7.7% (95% CI 6.8–8.7), respectively. The proportion of the population without major or minor depression was higher among males (86.5% (95% CI 84.5–88.2) than females (82.6% (95% CI 80.7– 84.3)). Depression for both genders was most prevalent in the age group 45–54 years old (11.4%, 95% CI 9.1–14.1), while minor depression was most frequent in the age group 35–44 years old (8.8%, 95% CI 6.7–11.4) (F(7.96–23654.1)=2.53; p=0.0096). The prevalence of self-reported somatic illnesses among people with major, minor, and no depression is summarised in Table 3. Somatic conditions were more prevalent in the group with major depression. The most prevalent self-reported diseases were cardiovascular diseases (24.4%), back pain (21.5%), and gastroenterological diseases (14.1%). Table 4 displays the results from the multinomial logistic regressions. Model 1 was adjusted for gender and age, and reveals that the 206
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Table 2 Prevalence of major depression and minor depression, and associations with gender and age (n=3003). Variables
No depression %
95% CI
Minor depression %
95% CI
Major depression %
95% CI
Total Gender Male Female Age 15–24 25–34 35–44 45–54 55–64 Males 15–24 25–34 35–44 45–54 55–64 Females 15–24 25–34 35–44 45–54 55–64
84.4
83.1–85.7
7.7
6.8–8.7
7.9
7.0–8.9
86.5 82.6
84.5–88.2 80.7–84.3
6.9 8.4
5.7–8.4 7.1–9.8
6.6 9.1
5.4–8.1 7.8–10.5
0.0114
87.5 85.3 83.1 80.6 86.1
84.6–89.9 82.3–87.9 79.8–86.0 77.4–83.5 83.2–88.6
7.3 7.5 8.8 8.0 6.8
5.5–9.6 5.7–10.0 6.7–11.4 6.1–10.3 5.1–9.0
5.2 7.1 8.1 11.4 7.1
3.7–7.4 5.3–9.6 6.1–10.7 9.1–14.1 5.4–9.4
0.0096
90.0 86.2 83.0 84.9 88.7
86.1–92.9 81.8–90.0 77.5–87.4 80.2–88.7 84.2–92.0
5.2 7.2 10.0 5.3 7.0
3.3–8.2 4.8–10.8 6.8–14.6 3.2–8.6 4.5–10.8
4.7 6.6 7.0 9.8 4.4
2.8–8.0 4.3–10.1 4.2–11.3 6.8–13.9 2.5–7.7
0.0652
84.9 84.4 83.2 76.9 84.2
80.4–88.6 79.9–88.1 79.1–86.7 72.1–81.0 80.1–87.6
9.4 7.9 7.5 10.4 6.6
6.5–13.3 5.3–11.5 5.2–10.7 7.6–14.0 4.5–9.7
5.7 7.7 9.2 12.8 9.2
3.6–9.0 5.1–11.4 6.7–12.7 9.7–16.8 6.6–12.5
0.047
(Kessler et al., 2010; Strine et al., 2008). According to Model 1, having at least one somatic disease was associated with a 2.3-fold risk for major depression and 1.8-fold risk for minor depression. Presence of three or more somatic conditions was associated with a 6.5-fold risk for major depression and 3.5 fold risk for minor depression. Model 2 revealed that having three or more somatic disorders was associated with an increased risk of both major and minor depression (2.0-fold and 2.3-fold, respectively). The association of depression and somatic disease is reported by many authors (Evans et al., 2005; Patten et al., 2005; Verhaak et al., 2014). Studies have shown that depression is associated with various chronic medical conditions, such as cardiovascular disease (Abas et al., 2002), congestive heart failure (Turvey et al., 2002), diabetes (Fisher et al., 2012; Moussavi et al., 2007), renal disease (Lopes et al., 2002), COPD (Moussavi et al., 2007), and cerebrovascular accident (Hackett et al., 2005). It is reported that a comorbid state of depression worsens health as compared to depression alone (Moussavi et al., 2007). Our study shows, that somatic conditions are more prevalent in people with major depression compared to those who have minor depression and who are non-depressed. Although there are numerous studies that have examined depression in people with somatic disorders, many of them suffer from important limitations in terms of generalizability. Our study was conducted in a randomly sampled general population. However, we have to take into account that the presence of medical conditions was self-reported. Patients suffering with mental disorders are usually characterized as high utilizers of health services. These patients tend to have twice as many general medical visits not related with mental care, and have higher rates of specialist visits (Demers, 1995). A study conducted by Hämäläinen et al., (2004) found that health services are used more frequently by people with more severe, prolonged and subjectively more disabling depressive episodes. Moreover, seeking medical care might represent an inefficient use of health care services (Kessler et al., 2002). In our study, reporting six or more visits to any healthcare service in the past 12 months was associated with a 2.0-fold increased risk of having major depression. However, having minor depression was not associated with higher healthcare utilization. This finding is in contrary with studies that report that depressive symptoms are associated with the same or a greater level of service utilization and impairment as clinical depression is (Johnson et al., 1992). It is noteworthy that in the current study, we did not assess the reason for visits to healthcare specialists.
Table 3 Prevalence of somatic illnesses among people with no depression, with minor depression, and with major depression (n=3003). Variables
Cardiovascular disease Diabetes Arthritis Back pain Respiratory Gastroenterology Urinary Cancer
No depression
Major depression %
p
%
Minor depression %
13.1
18.1
24.4
< 0.0001
2.2 1.3 12.3 3.4 5.7 2.9 0.3
6.8 4.6 16.3 4.3 10.3 5.5 0.8
5.7 3.0 21.5 7.5 14.1 5.9 2.7
< 0.0001 0.0003 < 0.0001 0.0054 < 0.0001 0.0063 < 0.0001
p
prevalence of depression, no exact comparison with existing national studies can be made. The total prevalence of minor depression was 7.7%. The literature suggests that the prevalence of minor depression, as defined by DSMIV criteria, is between 2.6% and 4.5% (Hermens et al., 2004). For those with any chronic somatic conditions, these rates are even higher. Evidence suggests that individuals with depressive symptoms have an increased risk for major depression (Fogel et al., 2006). Depressive symptoms are also associated with functional disability, medical comorbidity (Pickett et al., 2014) and poor subjective health status (Lyness et al., 2006). In our study minor depression was almost twice as prevalent as in other countries. One possible explanation could be that the health system in Latvia is severely underfunded. Total health expenditure in 2012 was only US$1188 purchasing power parity (PPP) per capita (corresponding to 6% of GDP), which was the third lowest amount spent on health in the EU (WHO Regional Office for Europe, 2016). Furthermore, only about 57% (2012) of total spending came from public sources. This share is lower only in Bulgaria and Cyprus. The proportion of the population reporting an unmet medical need because of costs doubled during the financial crisis, reaching more than 14% in 2011 before falling to just above 10% in 2012 (Eurostat, 2016). The prevalence of major and minor depression varied with age in both men and women, but no consistent age-related pattern was found. The observation of decreased prevalence in the oldest age group for both disorders is consistent with findings from developed countries 207
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Table 4 Relative risk ratios of major depression and minor depression, according to gender, age and health related variables. Model 1 Factor
Gender Male Female Age 15–24 25–34 35–44 45–54 55–64 Frequency of healthcare service use in the past 12 months Has not used 1–2 times 3–5 times 6 or more times Number of somatic diagnoses in the past 12 months None One Two Three more more Number of days absent in the past 12 months None 1–10 days 11+ days Receive disability pension No Yes Perceived health Above average Average Below average Smoking status Non-smoker Quitter/ex-smoker Occasional smoker Regular smoker
Model 2
RRR major depression vs nondepressed (95% CIe)
RRR minor depression vs nondepressed (95% CIe)
RRR minor depression vs major depression (95% CIe)
RRR major depression vs nondepressed (95% CIe)
RRR minor depression vs nondepressed (95% CIe)
RRR minor depression vs major depression (95% CIe)
1.0 1.4(a) (1.1–1.9)
1.0 1.3 (1.0–1.7)
1.0 0.9 (0.6–1.3)
1.0 1.7(b) (1.2–2.4)
1.0 1.4(d) (1.0–1.9)
1.0 0.8 (0.5–1.3)
1.0 1.4 (0.9–2.3) 1.6(a) (1.0–2.6) 2.3(c) (1.5–3.6) 1.3 (0.8–2.2)
1.0 1.1 1.3 1.2 0.9
1.0 0.8 (0.4–1.4) 0.8 (0.4–1.4) 0.5(a) (0.3–0.9) 0.7 (0.4–1.3)
1.0 1.1 (0.7–1.9) 1.0 (0.6–1.8) 1.0 (0.6–1.7) 0.4(b) (0.2–0.7)
1.0 0.9 (0.6–1.4) 0.9 (0.6–1.4) 0.7(d) (0.4–1.1) 0.4(b) (0.3–0.7)
1.0 0.8 0.9 0.7 1.1
1.0 1.7(a) (1.0–2.7) 2.3(b) (1.4–3.6) 4.1(c) (2.6–6.4)
1.0 1.7(b) (1.1–2.5) 1.4 (0.9–2.2) 1.6(b) (1.1–2.4)
1.0 1.0 (0.5–1.8) 0.6 (0.3–1.2) 0.4(b) (0.2–0.7)
1.0 1.6(0) (1.0–2.5) 1.7(a) (1.1–2.9) 2.0(b) (1.2–3.4)
1.0 1.3 (0.9–2.0) 1.0 (0.6–1.6) 0.8 (0.5–1.4)
1.0 0.9 (0.5–1.6) 0.6(d) (0.3–1.1) 0.4(a) (0.2–0.8)
1.0 2.3(c) (1.6–3.2) 2.5(c) (1.5–4.0) 6.5(c) (3.9–10.7)
1.0 1.8(c) (1.3–2.5) 1.5 (0.9–2.2) 3.5(c) (2.0–6.2)
1.0 0.8 (0.5–1.3) 0.6 (0.3–1.2) 0.6 (0.3–1.1)
1.0 1.2 (0.8–1.8) 1.0 (0.6–1.7) 2.1(b) (1.2–3.8)
1.0 1.4(d) (1.0–2.1) 1.1 (0.6–2.0) 2.3(b) (1.3–4.3)
1.0 1.2 (0.7–1.9) 1.1 (0.5–2.4) 1.1 (0.5–2.4)
1.0 0.9 (0.6–1.3) 2.6(c) (1.9–3.6)
1.0 1.5(a) (1.1–2.1) 1.4 (0.9–2.1)
1.0 1.8(a) (1.0–3.0) 0.5(b) (0.3–0.9)
1.0 0.7 (0.4–1.1) 1.3 (0.9–1.8)
1.0 1.4(d) (1.0–2.1) 1.1 (0.7–1.7)
1.0 2.0(b) (1.2–3.5) 0.9 (0.5–1.5)
1.0 2.8(c) (1.8–4.3)
1.0 2.5(c) (1.6–4)
1.0 0.9 (0.5–1.6)
1.0 1.0 (0.6–1.7)
1.0 1.9(a) (1.1–3.2)
1.0 1.8(d) (0.9–3.5)
1.0 3.1(c) (2.2–4.5) 13.1(c) (8.6–20.0)
1.0 2.2(c) (1.6–3.1) 4.0(c) (2.5–6.4)
1.0 0.7 (0.5–1.1) 0.3(c) (0.2–0.5)
1.0 2.5(c) (1.7–3.7) 8.3(c) (5.1–13.7)
1.0 2.0(c) (1.4–2.9) 3.0(c) (1.7–5.3)
1.0 0.8 (0.5–1.3) 0.4(b) (0.2–0.7)
1.0 1.1 (1.6–3.2) 2.7(c) (1.5–4.8) 2.1(c) (1.5–2.9)
1.0 1.3 (0.8–1.9) 1.5 (0.8–2.9) 1.3(d) (1.0–1.9)
1.0 1.1 (0.6–2.0) 0.6 (0.3–1.3) 0.7(d) (0.4–1.0)
1.0 1.3 (0.8–2.0) 3.0(c) (1.6–5.5) 2.2(c) (1.5–3.1)
1.0 1.3 (0.9–2.0) 1.6 (0.8–3.1) 1.3 (0.9–1.9)
1.0 1.0 (0.6–1.9) 0.5 (0.2–1.3) 0.6(b) (0.4–1.0)
(0.7–1.6) (0.8–1.9) (0.8–1.8) (0.6–1.4)
(0.4–1.5) (0.5–1.7) (0.3–1.3) (0.5–2.2)
Model 1 results from multinomial logistic regressions expressed as relative risk ratio (RRR) with respective 95% confidence intervals controlled for gender and age. Model 2 results from multinomial logistic regressions expressed as relative risk ratio (RRR) with respective 95% confidence intervals controlled for all factor variables (gender, age, frequency of use of healthcare services, number of self-reported somatic diagnosis in the past 12 months, number of absent days in the past 12 months, receiving disability pension, perceived health, and smoking status). Bolded text indicates a significant difference from the reference group. a p < 0.05 b p < =0.01 c p < =0.001 d p < 0.1 e CI: Confidence interval.
Previous research suggests that clinically significant depression is highly prevalent in primary care settings (Rucci et al., 2003). Despite the fact that depression is often comorbid with medical conditions, the Latvian National Health Service data show that in 2013 general practitioners saw only 4423 unique patients with a mood disorder diagnosis, while more than 50 000 patients were diagnosed as having a neurotic disorder, dominantly somatoform autonomic dysfunction (Pulmanis et al., 2014). Therefore, Latvian data confirms that depression is highly under-diagnosed by health care professionals (Burns et al., 2000). This may contribute to complications, mortality, and increased health care expenditures. It is to be noted that depressive patients seen in healthcare settings have a greater level of comorbidity of chronic medical diseases than patients seen in the mental health setting (Lawrence and Kisely, 2010). In Model 2 in our study, both major and minor depression was
Fig. 1. Use of health care services during last 12 months.
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trainings on depression diagnostic practices in medical settings, using screening questionnaires, and providing guidance on diagnosing depression could increase timely recognition and improve treatments.
associated with a higher risk of having three or more somatic disorders in last 12 months. However, minor depression was associated as well with having a disability pension, but was not associated with a higher risk of higher utilization of medical care services. Previous studies suggest that depression is a considerable cause not only of increased use of health care resources, but also of work place absenteeism and diminished productivity (Hendriks et al., 2015; Suzuki et al., 2015). In our study, receiving disability pension was associated with an almost 2.0-fold risk of having minor depression. Model 1 revealed that being absent from work for 11 or more days during the past 12 months was associated with a 2.6-fold risk of having major depression. Shorter periods of absence from work were associated with a 1.5-fold risk of having minor depression. In population based samples, the prevalence of tobacco dependence is 40–60% higher among people with major depression than it is among the general population (Anda et al., 1990). Tobacco smoking appears to increase the risk for depression (John et al., 2004), and depression appears to sustain tobacco smoking, as demonstrated by the finding that individuals with depression have a much harder time quitting smoking, and require more attempts before they quit (Hughes, 2007). In 2012, 34% of the Latvian population were regular smokers, including 52% of males and 18% of females. 4.5% were occasional smokers (Martinsone and Pelne, 2013). According to Model 2, the RRR for having major depression was two times higher among regular smokers and three times higher among occasional smokers (as compared to non-smokers). We did not find an increased risk of having minor depression among those who were current or occasional smokers. The strengths of this study include the large, population based sample, the use of a diagnostic interview for the diagnosis of major and minor depression, and the inclusion of a wide spectrum of age groups. Another important strength is that only specially trained interviewers were involved in data collection. On the other hand, several methodological limitations should be considered. Due to the cross-sectional nature of this study, we are not able to draw conclusions about causality. We did not have information on course or previous episodes of depression. Moreover, the diagnostic interview covered only depression, and excluded investigation of other psychiatric disorders. The fact that non responders might have different characteristics from responders is also one considerable limitation (Korkeila et al., 2001). Finally, recall bias may have influenced some measures, such as professionally-diagnosed somatic conditions in the past 12 months and health service utilization.
References Abas, M., et al., 2002. Depression and mortality in a high-risk population. 11-year followup of the Medical Research Council elderly hypertension trial. Br. J. Psychiatry: J. Ment. Sci. 181, 123–128. Alonso, J., et al., 2004. Use of mental health services in Europe: results from the European study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr. Scand. Suppl. 109, 47–54. Anda, R.F., et al., 1990. Depression and the dynamics of smoking. A national perspective. J. Am. Med. Assoc. 264, 1541–1545. Ayuso-Mateos, J.L., et al., 2001. Depressive disorders in Europe: prevalence figures from the ODIN study. Br. J. Psychiatry.: J. Ment. Sci. 179, 308–316. Bebbington, P., et al., 2003. Unequal access and unmet need: neurotic disorders and the use of primary care services. Int. Rev. Psychiatry 15, 115–122. Burns, B.J., et al., 2000. General medical and specialty mental health service use for major depression. Int. J. Psychiatry Med. 30, 127–143. Demers, M., 1995. Frequent users of ambulatory health care in Quebec: the case of doctor-shoppers. CMAJ: Can. Med. Assoc. J.=J. De. L′Assoc. Med. Can. 153, 37–42. Economou, M., et al., 2013. Major depression in the Era of economic crisis: a replication of a cross-sectional study across Greece. J. Affect. Disord. 145, 308–314. Ellervik, C., et al., 2014. Prevalence of depression, quality of life and antidepressant treatment in the danish general suburban population study. Nord. J. Psychiatry 68, 507–512. ESOMAR, 2015. International Code on Market and Social Research. 〈https://www. esomar.org/〉. Eurostat statistical database, 2016. 〈http://ec.europa.eu/eurostat/portal/page/portal/ eurostat/home〉 (accessed October 2016). Evans, D.L., et al., 2005. Mood disorders in the medically ill: scientific review and recommendations. Biol. Psychiatry 58, 175–189. Fisher, E.B., et al., 2012. Co-occurrence of diabetes and depression: conceptual considerations for an emerging global health challenge. J. Affect. Disord. 142, S56–S66. Fogel, J., et al., 2006. Minor depression as a predictor of the first onset of major depressive disorder over a 15-year follow-up. Acta Psychiatr. Scand. 113, 36–43. Fountoulakis, K., N., 2015. Bipolar Disorder: an Evidence-Based Guide to Manic Depression. Springer-Verlag, Berlin Heidelberg. Gaziano, C., 2005. Comparative analysis of within-householdwithin-household respondent selection techniques. Public Opin. Q. 69, 124–157. Gunn, J., et al., 2012. The association between chronic illness, multimorbidity and depressive symptoms in an Australian primary care cohort. Soc. Psychiatry Psychiatr. Epidemiol. 47, 175–184. Hackett, M.L., et al., 2005. Frequency of depression after stroke: a systematic review of observational studies. Stroke; J. Cereb. Circ. 36, 1330–1340. Hämäläinen, J., et al., 2004. Use of health services for major depressive episode in Finland. J. Affect. Disord. 79, 105–112. Hamilton, M., 1967. Development of a rating scale for primary depressive illness. Br. J. Soc. Clin. Psychol. 6, 278–296. Hendriks, S.M., et al., 2015. Long-term work disability and absenteeism in anxiety and depressive disorders. J. Affect. Disord. 178, 121–130. Hermens, M.L.M., et al., 2004. The prognosis of minor depression in the general population: a systematic review. Gen. Hosp. Psychiatry 26, 453–462. Hidaka, B.H., 2012. Depression as a disease of modernity: explanations for increasing prevalence. J. Affect. Disord. 140, 205–214. Hughes, J.R., 2007. Depression during tobacco abstinence. Nicotine Tob. Res.: Off. J. Soc. Res. Nicotine Tob. 9, 443–446. John, U., et al., 2004. Depressive disorders are related to nicotine dependence in the population but do not necessarily hamper smoking cessation. J. Clin. Psychiatry 65, 169–176. Johnson, J., et al., 1992. Service utilization and social morbidity associated with depressive symptoms in the community. J. Am. Med. Assoc. 267, 1478–1483. Kessler, D., et al., 2002. Detection of depression and anxiety in primary care: follow up study. BMJ (Clin. Res. Ed.) 325, 1016–1017. Kessler, L.G., et al., 1987. Psychiatric diagnoses of medical service users: evidence from the Epidemiologic Catchment Area Program. Am. J. Public Health 77, 18–24. Kessler, R.C., et al., 2010. Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depress. Anxiety 27, 351–364. Kish, L., 1949. A procedure for objective respondent selection within the household. J. Am. Stat. Assoc. 44, 380–387. Korkeila, K., et al., 2001. Non-response and related factors in a nation-wide health survey. Eur. J. Epidemiol. 17, 991–999. Lawrence, D., Kisely, S., 2010. Inequalities in healthcare provision for people with severe mental illness. J. Psychopharmacol. 24, 61–68. Lehnert, T., et al., 2011. Review: health care utilization and costs of elderly persons with multiple chronic conditions. Med. Care Res. Rev.: MCRR 68, 387–420. Lopes, A.A., et al., 2002. Depression as a predictor of mortality and hospitalization among hemodialysis patients in the United States and Europe. Kidney Int. 62, 199–207. Lyness, J.M., et al., 2006. Outcomes of minor and subsyndromal depression among elderly patients in primary care settings. Ann. Intern. Med. 144, (496-W484).
5. Conclusion In a conclusion, this is the first study reporting the 12-month prevalence of depression in our country. The 12-month prevalence of depression in Latvia is concordant with that in other European countries. Frequent healthcare utilization in the past 12 months, the presence of at least three somatic disorders, the perception of health as being poor, and participating in occasional smoking were the factors with the strongest association with major depression. The presence of three medical conditions, receiving a disability pension, and poor subjective health status were the most correlated with minor depression. The high prevalence of both major and minor depression suggests diagnostic and treatment issues must be resolved at national level. These findings call for effectively targeted interventions to reduce the burden associated with major and minor depression diagnoses. Our findings outline certain individuals who might be at a higher risk of having depression. The factors associated with major or minor depression could be used as indicators for health providers to initiate screening for depression. It is also important to monitor individuals with minor depression, taking into account their increased risk of developing major depression. We suggest improvement of diagnosis and treatment of depression in the primary care setting. Introducing 209
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associated characteristics. J. Affect. Disord. 76, 171. Sheehan, D.V., et al., 1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59 (Suppl 20), 22–33. Shvartzman, P., et al., 2005. Health services utilization by depressive patients identified by the MINI questionnaire in a primary care setting. Scand. J. Prim. Health Care 23, 18–25. Strine, T.W., et al., 2008. Depression and anxiety in the United States: findings from the 2006 Behavioral Risk Factor Surveillance System. Psychiatr. Serv. 59, 1383–1390. Suzuki, T., et al., 2015. Relationship between sickness presenteeism (WHO-HPQ) with depression and sickness absence due to mental disease in a cohort of Japanese workers. J. Affect. Disord. 180, 14–20. Turvey, C.L., et al., 2002. Prevalence and correlates of depressive symptoms in a community sample of people suffering from heart failure. J. Am. Geriatr. Soc. 50, 2003–2008. Unützer, J., et al., 2000. Care for depression in HMO patients aged 65 and older. J. Am. Geriatr. Soc. 48, 871–878. Van de Velde, S., et al., 2010. Gender differences in depression in 23 European countries. cross-national variation in the gender gap in depression. Soc. Sci. Med. 71, 305–313. Verhaak, P.F.M., et al., 2014. Depression, disability and somatic diseases among elderly. J. Affect. Disord. 167, 187–191. Vogelzangs, N., et al., 2010. Cardiovascular disease in persons with depressive and anxiety disorders. J. Affect. Disord. 125, 241–248. von Korff, M., 2009. Global perspectives on mental-physical comorbidity. In: von Korff, M.R., Scott, K.M., Gureje, O. (Eds.), Global Perspectives on Mental-Physical Comorbidity in the World Health Organization World Merntal Health Surveys. Cambridge University Press Cambridge, 1–14. WHO, 2016. Regional office for Europe. European Health for all Database (HFA-DB). 〈http://data.euro.who.int/hfadb/〉. (accesed October 2016). Windle, M., Windle, R.C., 2013. Recurrent depression, cardiovascular disease, and diabetes among middle-aged and older adult women. J. Affect. Disord. 150, 895–902. Wittchen, H.U., et al., 2011. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol.: J. Eur. Coll. Neuropsychopharmacol. 21, 655–679. Zlotnick, C., et al., 2000. The relationship between quality of interpersonal relationships and major depressive disorder: findings from the National Comorbidity survey. J. Affect. Disord. 59, 205–215.
Mann, J.J., et al., 2005. Suicide prevention strategies: a systematic review. J. Am. Med. Assoc. 294, 2064–2074. Marcus, S.M., et al., 2005. Gender differences in depression: findings from the STAR*D study. J. Affect. Disord. 87, 141–150. Martinsone, U., Pelne, A., 2013. SMĒĶĒŠANAS IZPLATĪBA UN SEKAS LATVIJĀ 2012. GADĀ. Tematiskais ziņojums. (Prevalence of smoking and related consequences in Latvia in 2012. Thematic report). Slimību profilakses un kontroles centrs, Rīga. Mathers, C.D., Loncar, D., 2006. Projections of global mortality and burden of disease from 2002 to 2030. Plos Med. 3, (e442-e442). Mikkelsen, R.L., et al., 2004. Anxiety and depression in patients with chronic obstructive pulmonary disease (COPD). A review. Nord. J. Psychiatry 58, 65–70. Moussavi, S., et al., 2007. Depression, chronic diseases, and decrements in health: results from the World health surveys. Lancet 370, 851–858. Murray, C.J.L., Lopez, A.D., 1996. The Global Burden of Disease. Harvard University Press, Boston. Patten, S.B., et al., 2005. Long-term medical conditions and major depression: strength of association for specific conditions in the general population. Can. J. Psychiatry 50, 195–202. Pickett, Y.R., et al., 2014. Healthcare use among older primary care patients with minor depression. Am. J. Geriatr. Psychiatry.: Off. J. Am. Assoc. Geriatr. Psychiatry. 22, 207–210. Pinninti, N.R., et al., 2003. MINI international neuropsychiatric schedule: clinical utility and patient acceptance. Eur. Psychiatry.: J. Assoc. Eur. Psychiatr. 18, 361–364. Pudule, I., Grīnberga, D., Velika, B., Gavare, I., Villeruša, A., 2013. Latvijas iedzīvotāju veselību ietekmējošo paradumu pētījums, 2012. (Health Behaviour among Latvian Adult Population, 2012). Pulmanis, T., Pelne, A., Taube, M., 2014. Psihiskā veselība Latvijā 2013. gadā. Tematiskais ziņojums. (Mental Health in Latvia. A Thematic Report). Slimību profilakses un kontroles centrs, Rīga. Puska, P., et al., 2003. Health behaviour in Estonia, Finland and Lithuania 1994–1998. Standardized comparison. Eur. J. Public Health 13, 11–17. Rancans, E., et al., 2014. The point prevalence of depression and associated sociodemographic correlates in the general population of Latvia. J. Affect. Disord. 156, 104–110. Rao, J.N.K., Scott, A.J., 1984. On chi-squared tests for multi-way tables with cell proportions estimated from survey data. Ann. Stat.. Rihmer, Z., 2007. Suicide risk in mood disorders. Curr. Opin. Psychiatry 20, 17–22. Rucci, P., et al., 2003. Subthreshold psychiatric disorders in primary care: prevalence and
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