Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

The impact of the current economic crisis on mental health in Italy: evidence from two representative national surveys

The impact of the current economic crisis on mental health in Italy: evidence from two... Abstract Background Economic crises pose major threats to health. Research on the association between the current economic crisis and health is accumulating. Scant evidence is available on the impact of economic downturns on mental health in Italy, one of the European countries most affected by the economic crisis. Methods We used data from the 2005 and 2013 ‘Health Conditions and Use of Health Services’ surveys conducted by the Italian National Institute of Statistics to estimate Italian poor mental health prevalence in Italy and we applied Poisson regression analysis to explore how the risk (expressed as Prevalence Rate Ratios; PRR) of poor mental health has been impacted by the ongoing economic crisis, by gender and by different socio-economic strata. Results Poor mental health prevalence in Italy was 21.5% in 2005 and 25.1% in 2013. The risk of poor mental health increased between 2005 and 2013 by 17% in males (PRR: 1.17; 95%CI: 1.14–1.20) and by 4% in females (PRR: 1.04; 95%CI: 1.02–1.06), the increase being highest for young males (24%). Vulnerable subgroup is at higher risk of poor mental health but not differently affected by the impact of the economic crisis. Conclusion The economic crisis that hit Italy has posed threats to Italians’ mental health and wellbeing, with a higher impact on young male populations. As further evidence from prospective studies is accumulating, our findings suggest strengthened primary and secondary prevention interventions should be planned and implemented by the Italian National Health Service so as to counter economic downturns’ impact on population and individual-level health. Introduction Economic crises pose major threats to health.1 Although this might sound intuitive, the system, population and individual-level mechanisms mediating the health effects of recessions times and their quantification are yet to be fully understood. The ongoing economic crisis started in 2008 and is still affecting at different rates countries in Europe and at the global level.2 At the early stages of it, evidence on past economic crises was retrieved and pooled to guide experts’ expectations on its impact on health and public health.1,3–5 At that time, ambiguous evidence emerged reporting on both detrimental impact of increased unemployment rates and healthcare expenditure cuts on population and individual-level health, as well on the protective effect of reduced behaviour individual risk factors.1 Almost 10 years on, research on the association between the current economic crisis and health is accumulating;6–10 different types of data and study designs have been applied to assess and quantify how the ongoing recession is impacting on: health systems and service delivery, access to care, behavioural risk factors and—ultimately—on health outcomes in different settings.1 As ambiguity in the evidence persists, experts have stressed the importance of differentiating between physical and mental health.6 With regard to mental health, some data on the impact of the ongoing financial, economic and societal crisis on mental health is available from selected countries including Spain,8,9,11,12 Greece13,14 and England.15 Overall, there is evidence that the ongoing recession is negatively affecting mental health outcomes, namely depression and other mood disorders,9 anxiety and alcohol-related disorders,9 and suicides10,15,16—differently by gender, age, socio-demographic strata and, importantly, by setting.8 Italy is among the European countries that have been most affected by the crisis; national-level unemployment rates (intended as the number of unemployed people as a percentage of the labour force) rose sharply between 2008 and 2016 (+ 43%), reaching almost 12% in 2016, reported as the third highest rate in the European Union after Greece and Spain.17 Along the same lines, long-term unemployment (referring to the percentage of people who have been unemployed for 12 months or more among all unemployed) increased by 27.6% between 2008 and 2016 (58.3% in 2016).17 As for now, scant data is available on the impact of such crisis on health and mental health at the national and regional level.18 In February 2017, a European Journal of Public Health special supplement on the European Social Survey19 comprised an analysis on the effect of the economic crisis on mental health inequalities and depression in selected European countries,7 however it did not include data from Italy. The aim of the current paper is to explore—for the first time in Italy—the impact of the national current economic crisis on mental health and its social determinants, using data from a large national-representative survey carried out by the Italian Institute of Statistics (ISTAT). Methods We carried out a comprehensive analysis of two large national representative surveys conducted in 2005 and 2013 by ISTAT to assess how the risk of poor mental health varied before and after the origin (2008) of the ongoing economic crisis. ISTAT is the Italian national publicly funded body responsible for producing official statistics to be made available to the general population, research institutions and policy makers.20 Study setting and sources of data Data were derived from the Health Conditions and Use of Health Services (HCUHS) surveys conducted on a 5 year-basis by ISTAT to assess Italians’ health status, health-related behaviours and access to healthcare services. HCUHS surveys have been conducted in Italy since 1980 with increasing dimensions explored and content added, over subsequent editions, and are now a unique tool to explore, at the national level: health status and perceived health conditions of the Italian population, lifestyles, prevention and health promotion behaviours, as well as health systems features and healthcare services access.21,22 In the HCUHS survey sampling is carried out in a two-step process. The first sampling units are municipalities which are sampled with probabilities proportional to size (population). Within sampled municipalities a further cluster sampling is performed on households using population registers. The sampling technique adopted allows the survey sample to be representative of Italian macro regions (North-East, North-West, Centre, South and Islands), as well as of clusters of Local Health Units which delimits areas by healthcare system organization.22 The HCUHS survey consists of two parts: one part administered in person by trained personnel at the place of residence of respondents and a second self-administered part. Additional details on HCUHS survey design and methodology are described elsewhere and available from ISTAT.22 Mental health measurements The Mental Component Summary (MCS) score was considered as a proxy of mental health and wellbeing. MCS is derived from the 12-item Short-Form health survey (SF-12), an internationally validated survey that measures general population and patients’ perceived health status and quality of life23 and which is included in the HCUHS. SF-12 is derived from the longer 36-item SF-36 that was originally designed to survey health status in the Medical Outcomes Study (MOS).24 In the last two decades, SF-12 has been widely used in clinical practice and research, in health policy evaluations, as well as for surveys targeting the general population.24–31 SF-12 assess eight health dimensions and combine them in two summary indices: an overall measure of self-perceived physical health (Physical Component Summary, PCS) and an overall measure of self-perceived mental health (the Mental Component Summary, MCS). The MCS includes the following four dimensions: vitality, social functioning, role emotional and mental health. The SF-12 has been translated in different languages and validated in other countries in the context of the International Quality of Life Assessment (IQOLA) Project. Overall, translations and adaptations of the SF-12 have been carried out in almost 30 country-language combinations.23,32 The SF-12 was translated in Italian in 2001.33 The Italian SF-12 users’ manual provides the standardized algorithms to compile the PCS and MCS, guidelines on how to calculate the items’ score and practical recommendations on how to administer it. This allows the Italian version of the SF-12 to be used in a consistent way by researchers and their findings to be compared. SF-12 has been included in HCUHS since the 1999–2000 edition. Outcomes of interest and analysis The MCS score was used to derive a poor mental health binary outcome, defined on the basis of the evidence available from the literature, as well as consultation with clinical psychiatrists and experts in the field. The MCS score cut off to derive the binary poor mental health variable was set using the average MCS score of individuals with a reported lifetime diagnosis of depression or anxiety disorders made by a physician (information included in the HCUHS), increased by 25%. The 25% increase was set with the rationale of including individuals with pre-morbid conditions. Other MCS score cut offs were derived from the literature to conduct sensitivity analysis.34 We report age-standardized prevalence of poor mental health (standard population: Italy 2005), descriptive analysis were conducted by gender strata and by survey year and percentage distributions of selected covariates were expressed accounting for sampling weights.35 Poisson regression analysis were conducted to estimate poor mental health Prevalence Rate Ratios and their 95% confidence intervals (PRR, CI 95%)36,37 in 2013 as compared to 2005 (respectively, after and before the origin of the ongoing economic crisis), accounting for age, physical comorbidity and social determinants in different gender strata. The following social determinants were accounted for in the models: education, occupation, marital status and nationality (foreign-born vs. Italy-born). In addition, two proxies of socioeconomic status (SES) were considered: housing conditions and perceived household economic resources. Interaction between survey year and all covariates of interest was tested. The modelling strategy was applied to estimate PRRs for the whole study sample by gender, as well as for selected strata: geographic area (northern, central and southern Italy), age (<60 years, ≥60 years) and their combinations. Analysis were conducted on the adult population (≥25 years) of HCUHS 2005 and 2013. HCUHS 2000 data were used to compile age-standardized prevalence of poor mental health by gender and poor mental health PRRs, as compared to other study years but were not merged into the study database as covariates of interest were not made available for year 2000. Results We analyzed a nationally representative sample of 187,423 individuals. The 2005 survey included 96,215 people and the 2013 survey included 91,208 people. Response rate (expressed as % responders on eligible individuals) was 86% in 2005 and 82.3% in 2013. The characteristics of the study population by gender and survey year are reported in table 1. 21.5% of the study population was reported to have poor mental health in 2005; the prevalence being higher in females (26.6%) than males (16.6%, table 2). In 2013, the age-standardized prevalence of poor mental health was 24.4%, 13.5% higher as compared to 2005 (24.4%). In lines with 2005, it was higher in females (28.6%), as compared to males (20.6%); however, the 2013–2005 increase was higher for males than females (+24.1% vs. 7.5%, overall 13.5 increase). Age-standardized prevalence of poor mental health in 2000 was 22.5%, higher for females; the 2000–2005% change was −4.8% for males and −4.9% for females (table 2). The prevalence of poor mental health in 2005 and in 2013 by physical comorbidity and by socio-demographic characteristics of the study population is reported in table 3. Table 1 Characteristics of the study population, by gender and study year Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Table 1 Characteristics of the study population, by gender and study year Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Table 2 Age-standardized prevalence of poor mental health and poor mental health Prevalence Rate Ratios (PRR; IC 95%), by year 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) Table 2 Age-standardized prevalence of poor mental health and poor mental health Prevalence Rate Ratios (PRR; IC 95%), by year 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) Table 3 Prevalence (%) of poor mental health by gender, survey year and co-variates of interest Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Table 3 Prevalence (%) of poor mental health by gender, survey year and co-variates of interest Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 As detailed in table 3, in 2005 the prevalence of poor mental health increased with increasing age both in males and in females; it was higher in individuals with physical comorbidities, in the South as compared to the North of Italy, in Italy-born as compared to foreign-born individuals and in singles, as compared to individuals with a spouse/partner. When considering social determinants, higher poor mental health prevalence was reported in disadvantaged categories: in less educated individuals as compared to individuals with university degree (23.2% vs. 11.6%), in unemployed individuals (24.2% vs. 12.1%), in individuals self-reporting inadequate economic resources (23.6% vs. 13.2%), and in individuals with poor housing conditions (19.2% vs. 15.8%). Similar patterns were reported in 2013 (table 3). The percentage increase of poor mental health between 2005 and 2013 is reported in tables 2 and 3, both for males and females. Overall, the higher 2013–2005 increase was reported for males; in particular younger individuals (between +53.3% in 25–29 years and +46.3% in 50–59 year), in males with no physical comorbidities (+38.5%), with higher education (33.6%), employed (+35.5%) and living with a spouse/partner (+28.7%). In the male population, the age-adjusted risk of poor mental health in males was 28% higher in 2013 as compared to 2005 (PRR: 1.28; 95%CI 1.24–1.33); in the female population it was 11% higher (PRR: 1.11; 95%CI 1.08–1.14). When adding survey year 2000 to the analysis (table 2) we report poor mental health risk in 2005 not to be higher as compared to 2000 for both males and female (males: PRR 0.96; 95%CI: 0.93–0.99, females: PRR 0.95; 95%CI: 0.93–0.97). In the final model, after adjusting for all co-variates of interest (table 4), the risk of poor mental health remained higher in 2013 as compared to 2005, both for males (PRR: 1.17; 95%CI: 1.14–1.20) and for females (PRR: 1.04; 95%CI: 1.02–1.06). Table 4 also reports poor mental health risks estimates in all considered categories of physical comorbidities, social determinants and socio-demographic characteristics. No interactions were reported between survey year and all covariates accounted for in the model, therefore no effect modifiers were included in the final regression analysis. When estimating the risk of poor mental health in 2013 as compared to 2005 for different strata, the highest risks of poor mental health was reported for males younger than 60 years, both in Northern (PRR: 1.23; 95%CI: 1.16–1.30) and Southern Italy (PRR: 1.25; 95%CI: 1.19–1.31). In the female population the risk was reported to increase in younger age categories (PRR: 1.11; 95%CI: 1.07–1.14) and fade away in the elderly (PRR: 0.97; 95%CI: 0.94–0.99). Table 4 Poor mental health Prevalence Rate Ratios (PRR; IC 95%) Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 a Adjusted for: age, year, geographic location, education, occupation, marital status, nationality, economic resources, housing conditions and chronic morbidity index (except when estimates refer to each item). Table 4 Poor mental health Prevalence Rate Ratios (PRR; IC 95%) Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 a Adjusted for: age, year, geographic location, education, occupation, marital status, nationality, economic resources, housing conditions and chronic morbidity index (except when estimates refer to each item). We conducted sensitivity analysis using diagnosis of depression or anxiety disorder (its corresponding average MCS score) as primary outcome, defined as risk of mental disorders. Age-adjusted risk of mental disorders was slightly higher in 2013 as compared to 2005 in males (PRR: 1.09; 95%CI 1.04–1.13) but did not increase in females (PRR: 0.95; 95%CI 0.92–0.98). In the final model when adjusting for all co-variates of interest the risk of mental disorders did not increase neither for males (PRR: 1.02; 95%CI 0.99–1.06), nor for females (PRR: 0.92; 95%CI 0.90–0.95). Discussion We analyze data from nearly 200,000 individuals from two large national representative surveys and report the risk of poor mental health to have increased in the Italian population between 2005 and 2013; this suggesting a negative impact of the ongoing economic crisis on mental health and wellbeing at the population level. In particular, the risk is higher in the male population (17% increase), and highest in young males (24% increase). The highest risk reported for young males is compatible with higher economic turmoil and job insecurity faced by those subgroups of the population.4,38 In fact, in Italy, in line with global trends, the unemployment raise associated with the economic crisis hit most male populations as compared to females (+109% in 2008–2013 in males vs. +54% in females).39 The negative impact of unemployment, low pay and job insecurity on mental health and wellbeing is echoed in evidence from individual-level research.38,40 We also report that social determinants play a key role on mental health with vulnerable subgroup of the populations, including less educated individuals and those reporting job insecurity and lower socioeconomic status, being at higher risk of poor mental health. However, our data show different socio-demographic strata not to have been differently affected by the impact of the economic crisis in Italy. We interpret such finding as the economic crisis not worsening vulnerable populations’ conditions but, instead, increasing the share of vulnerable (i.e. unemployed) people. Such interpretation is supported by unemployment rate trends. From a methodological point of view it is not straightforward to quantitatively assess how and by how much macroeconomic features (i.e. the current economic crisis) impact on population health and health outcomes. Different conceptual frameworks have been proposed in the literature to inform analysis’ models and different study designs have been applied to assess the impact of recession times on health.1,6 In the field of mental health, the majority of the studies that aimed at assessing the impact of the current economic crisis selected suicide rates as their primary outcome.10,14–16 In fact, solid evidence is available in the literature on the positive association between times of recession and increased suicide rates. On the contrary, scanter and more heterogeneous evidence is available on other mental health outcomes, including mood and anxiety disorders, global functioning and wellbeing measures. We believe that although suicide rates is a suitable outcome for several reasons (available from administrative data and routinely collected, difficult to misclassify, comparable between different settings), other mental health and wellbeing outcomes—including the one we selected—might be more useful to explore the effects and mediators of the crisis and to inform prevention strategies. Among other strengths of our study the fact that we could count on a large and representative sample size; the most recent evidence published on the impact of the economic crisis on mental health in Europe7 considered data from 21 European countries summing a total sample size almost half of ours. Nonetheless, taking into account that with large sample sizes statistically significant findings are more easily derived, when interpreting data, we put more emphasis on effect sizes rather than significance. Despite being Italy among the European countries most severely affected by the global economic and financial crisis, there is paucity of Italian research on its impact on health, as compared to other European countries, ours being the first Italian national-level data to explore general population’s mental health and wellbeing effects. Our study has also imitations that need to be acknowledged. First, we did not use a direct measurement of the crisis but applied a ‘before and after’ approach instead; this has however already been done in previous research on the topic in other settings and provided valuable insight.9,41 Second, we analyzed only two points in time (2005 and 2013) which does not allow to derive trends, nor it allows to establish that the reported differences between 2005 and 2013 are influenced by the crisis and do not merely reflect previous trends. However, we analyzed data from 2000 HCUHS and report poor mental health risk in 2005 not to be higher as compared to 2000 which suggests the increase reported between 2005 and 2013 might be associated with incident economic downturns. Second, although MCS score is a very sensitive parameter its interpretation is not straightforward. The poor mental health binary outcome we derived from individual-level MCS score not only allows to better interpret data but also was intended to define subgroup of the population with poor mental conditions, including pre-morbid status that can be targeted by prevention. In conclusion, the economic crisis that hit Italy 9 years ago and whose recovery still appear to be slow has posed threats to Italians’ mental health and wellbeing with a higher impact on young males populations. As additional sources of data need to be further analyzed with prospective approaches to accumulate additional evidence on causal relations, our findings suggest strengthened primary and secondary prevention interventions should be planned and implemented by the National Health Service so as to counter economic downturns’ impact on population and individual-level health. Funding No dedicated funding was received to support this manuscript. Conflicts of interest: None declared. Key points Data from two large national representative surveys (over 200,000 individuals) suggest poor mental health increased in the Italian population during the economic crisis. The increased risk of poor mental health is highest in male and young populations (17% and 24% increase, respectively). Social determinants play a key role on mental health with vulnerable subgroup being at higher risk of poor mental health, but not differently affected by the impact of the economic crisis. Our data suggest pre-morbid mental health conditions might be targeted by prevention programmes so as to counter economic downturns’ impact on population health. References 1 Karanikolos M , Mladovsky P , Cylus J , et al. Financial crisis, austerity, and health in Europe . Lancet 2013 ; 381 : 1323 – 31 . Google Scholar CrossRef Search ADS PubMed 2 The Economist . The origins of the financial crisis. Available at: https://www.economist.com/news/schoolsbrief/21584534-effects-financial-crisis-are-still-being-felt-five-years-article ( 2 August 2017 , date last accessed). 3 Glonti K , Gordeev VS , Goryakin Y , et al. A systematic review on health resilience to economic crises . PLoS One 2015 ; 10 : e0123117 . Google Scholar CrossRef Search ADS PubMed 4 Stuckler D , Basu S , Suhrcke M , et al. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis . Lancet 2009 ; 374 : 315 – 23 . Google Scholar CrossRef Search ADS PubMed 5 Suhrcke M , Stuckler D , Suk JE , et al. The impact of economic crises on communicable disease transmission and control: a systematic review of the evidence . PLoS One 2011 ; 6 : e20724 . Google Scholar CrossRef Search ADS PubMed 6 Karanikolos M , Heino P , McKee M , et al. Effects of the global financial crisis on health in high-income oecd countries: a narrative review . Int J Health Serv 2016 ; 46 : 208 – 40 . Google Scholar CrossRef Search ADS PubMed 7 Reibling N , Beckfield J , Huijts T , Schmidt-Catran A , Thomson KH , Wendt C . Depressed during the depression: has the economic crisis affected mental health inequalities in Europe? Findings from the European Social Survey (2014) special module on the determinants of health . Eur J Public Health 2017 ; 27 : 47 – 54 . Google Scholar CrossRef Search ADS PubMed 8 Bartoll X , Palencia L , Malmusi D , et al. The evolution of mental health in Spain during the economic crisis . Eur J Public Health 2014 ; 24 : 415 – 8 . Google Scholar CrossRef Search ADS PubMed 9 Gili M , Roca M , Basu S , et al. The mental health risks of economic crisis in Spain: evidence from primary care centres, 2006 and 2010 . Eur J Public Health 2013 ; 23 : 103 – 8 . Google Scholar CrossRef Search ADS PubMed 10 Lopez Bernal JA , Gasparrini A , Artundo CM , McKee M . The effect of the late 2000s financial crisis on suicides in Spain: an interrupted time-series analysis . Eur J Public Health 2013 ; 23 : 732 – 6 . Google Scholar CrossRef Search ADS PubMed 11 Iglesias GC , Saiz MP , G-P Gonzalez MP , Bousono GM , et al. Effects of the economic crisis on demand due to mental disorders in Asturias: data from the Asturias Cumulative Psychiatric Case Register (2000–2010) . Actas espanolas de psiquiatria 2014 ; 42 : 108 – 15 . Google Scholar PubMed 12 Roca M , Gili M , Garcia-Campayo J , Garcia-Toro M . Economic crisis and mental health in Spain . Lancet 2013 ; 382 : 1977 – 8 . Google Scholar CrossRef Search ADS PubMed 13 Mylona K , Tsiantou V , Zavras D , et al. Determinants of self-reported frequency of depressive symptoms in Greece during economic crisis . Public health 2014 ; 128 : 752 – 4 . Google Scholar CrossRef Search ADS PubMed 14 Simou E , Koutsogeorgou E . Effects of the economic crisis on health and healthcare in Greece in the literature from 2009 to 2013: a systematic review . Health Policy 2014 ; 115 : 111 – 9 . Google Scholar CrossRef Search ADS PubMed 15 Barr B , Taylor-Robinson D , Scott-Samuel A , et al. Suicides associated with the 2008-10 economic recession in England: time trend analysis . BMJ 2012 ; 345 : e5142 . Google Scholar CrossRef Search ADS PubMed 16 Fountoulakis KN , Kawohl W , Theodorakis PN , et al. Relationship of suicide rates to economic variables in Europe: 2000–2011 . Br J Psychiatry 2014 ; 205 : 486 – 96 . Google Scholar CrossRef Search ADS PubMed 17 Organisation for Economic Co-operation and Development (OECD) Data . Unemployment. Available at: https://data.oecd.org/unemp/unemployment-rate.htm-indicator-chart ( 23 August 2017 , date last accessed). 18 Mattei G , Ferrari S , Pingani L , Rigatelli M . Short-term effects of the 2008 Great Recession on the health of the Italian population: an ecological study . Soc Psychiatry Psychiatr Epidemiol 2014 ; 49 : 851 – 8 . Google Scholar CrossRef Search ADS PubMed 19 Beckfield J , Balaj M , McNamara CL , et al. The health of European populations: introduction to the special supplement on the 2014 European Social Survey (ESS) rotating module on the social determinants of health. Eur. J Public Health 2017 ; 27 : 3 – 7 . 20 Istituto Italiano di Statistica (ISTAT) . About Italian National Institute of Statistics. Available at: http://www.istat.it/en/about-istat ( 2 September 2017 date last accessed). 21 Istituto Nazionale di Statistica (ISTAT) - Il sistema di Indagini Sociali Multiscopo. Contenuti e metodologia delle indagini. Metodi e Norme, n. 31 - Roma, 2006 . 22 ISTAT . Indagine Multiscopo sulle Famiglie “Condizioni di salute e ricorso ai servizi sanitari 2013” Documentazione tecnica e descrizione del file. [Italian Institute of Statistics. Health Conditions and Use of Health Services (HCUHS) surveys. Supporting methodological notes], Rome 2016 Avaialbe at http://www.istat.it/it/files/2011/01/metadati_condizioni_salute_2013_new.zip? title=Condizioni+di+salute+-+09%2Ffeb%2F2016+-+Metadati+-+Anni+2012-2013.zip (28 August 2017, date last accessed). 23 Ware J Jr , Kosinski M , Keller SD . A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity . Med Care 1996 ; 34 : 220 – 33 . Google Scholar CrossRef Search ADS PubMed 24 Ware JE Jr , Sherbourne CD . The MOS 36-item short-form health survey (SF-36). I . Conceptual framework and item selection. Med Care 1992 ; 30 : 473 – 83 . 25 Ellis C , Grubaugh AL , Egede LE . Factors associated with SF-12 physical and mental health quality of life scores in adults with stroke . J Stroke Cerebrovasc Dis 2013 ; 22 : 309 – 17 . Google Scholar CrossRef Search ADS PubMed 26 Forero CG , Vilagut G , Adroher ND , Alonso J . Multidimensional item response theory models yielded good fit and reliable scores for the Short Form-12 questionnaire . J Clin Epidemiol 2013 ; 66 : 790 – 801 . Google Scholar CrossRef Search ADS PubMed 27 Konig HH , Heider D , Lehnert T , Riedel-Heller SG , Angermeyer MC , Matschinger H , et al. Health status of the advanced elderly in six European countries: results from a representative survey using EQ-5D and SF-12 . Health Qual Life Outcomes 2010 ; 8 : 143 . Google Scholar CrossRef Search ADS PubMed 28 McBurney CR , Eagle KA , Kline-Rogers EM , et al. Health-related quality of life in patients 7 months after a myocardial infarction: factors affecting the Short Form-12 . Pharmacotherapy 2002 ; 22 : 1616 – 22 . Google Scholar CrossRef Search ADS PubMed 29 Mols F , Pelle AJ , Kupper N . Normative data of the SF-12 health survey with validation using postmyocardial infarction patients in the Dutch population . Qual Life Res 2009 ; 18 : 403 – 14 . Google Scholar CrossRef Search ADS PubMed 30 Vilagut G , Forero CG , Pinto-Meza A , et al. The mental component of the short-form 12 health survey (SF-12) as a measure of depressive disorders in the general population: results with three alternative scoring methods . Value Health 2013 ; 16 : 564 – 73 . Google Scholar CrossRef Search ADS PubMed 31 Webster KE , Feller JA . Comparison of the short form-12 (SF-12) health status questionnaire with the SF-36 in patients with knee osteoarthritis who have replacement surgery . Knee Surg Sports Traumatol Arthrosc 2016 ; 24 : 2620 – 6 . Google Scholar CrossRef Search ADS PubMed 32 Ware JE , Keller SD , Gandek B , et al. Evaluating translations of health status surveys: lessons from the IQOLA project . Int J Technol Assess Health Care 1995 ; 11 : 525 . Google Scholar CrossRef Search ADS PubMed 33 Goad JA , Taitel MS , Fensterheim LE , Cannon AE . Vaccinations administered during off-clinic hours at a national community pharmacy: implications for increasing patient access and convenience . Ann Fam Med 2013 ; 11 : 429 – 36 . Google Scholar CrossRef Search ADS PubMed 34 Kiely KM , Butterworth P . Validation of four measures of mental health against depression and generalized anxiety in a community based sample . Psychiatry Res 2015 ; 225 : 291 – 8 . Google Scholar CrossRef Search ADS PubMed 35 Caranci N , Di Martino M , Gargiulo L , et al. Metodi per l’analisi dei dati, Indicazioni pratiche e note d’uso, Approfondimenti sull’indagine multiscopo Istat Salute 2005, Monitor, 3° supplemento, n. 22, 2008 . 36 Zocchetti C , Consonni D , Bertazzi PA . Relationship between prevalence rate ratios and odds ratios in cross-sectional studies . Int J Epidemiol 1997 ; 26 : 220 . Google Scholar CrossRef Search ADS PubMed 37 Barros AJ , Hirakata VN . Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio . BMC Med Res Methodol 2003 ; 3 : 21 . Google Scholar CrossRef Search ADS PubMed 38 Fiori F , Rinesi F , Spizzichino D , Di Giorgio G . Employment insecurity and mental health during the economic recession: an analysis of the young adult labour force in Italy . Soc Sci Med 2016 ; 153 : 90 – 8 . Google Scholar CrossRef Search ADS PubMed 39 Eurostat . Unemployment statistics. Available at: http://ec.europa.eu/eurostat/statistics-explained/index.php/Unemployment_statistics (23 December 2016, date last accessed). 40 Theodossiou I . The effects of low-pay and unemployment on psychological well-being: a logistic regression approach . J Health Econ 1998 ; 17 : 85 – 104 . Google Scholar CrossRef Search ADS PubMed 41 Odone A, Signorelli C, Rodrigues LC. Tuberculosis and the economic crisis: an old threat for the new European agenda . Scand J Public Health 2014 ; 42 : 834 – 5 . CrossRef Search ADS PubMed © The Author(s) 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

The impact of the current economic crisis on mental health in Italy: evidence from two representative national surveys

Download PDF
6 pages

Loading next page...
 
/lp/ou_press/the-impact-of-the-current-economic-crisis-on-mental-health-in-italy-Z0lNbRIDq1

References (34)

Publisher
Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
ISSN
1101-1262
eISSN
1464-360X
DOI
10.1093/eurpub/ckx220
Publisher site
See Article on Publisher Site

Abstract

Abstract Background Economic crises pose major threats to health. Research on the association between the current economic crisis and health is accumulating. Scant evidence is available on the impact of economic downturns on mental health in Italy, one of the European countries most affected by the economic crisis. Methods We used data from the 2005 and 2013 ‘Health Conditions and Use of Health Services’ surveys conducted by the Italian National Institute of Statistics to estimate Italian poor mental health prevalence in Italy and we applied Poisson regression analysis to explore how the risk (expressed as Prevalence Rate Ratios; PRR) of poor mental health has been impacted by the ongoing economic crisis, by gender and by different socio-economic strata. Results Poor mental health prevalence in Italy was 21.5% in 2005 and 25.1% in 2013. The risk of poor mental health increased between 2005 and 2013 by 17% in males (PRR: 1.17; 95%CI: 1.14–1.20) and by 4% in females (PRR: 1.04; 95%CI: 1.02–1.06), the increase being highest for young males (24%). Vulnerable subgroup is at higher risk of poor mental health but not differently affected by the impact of the economic crisis. Conclusion The economic crisis that hit Italy has posed threats to Italians’ mental health and wellbeing, with a higher impact on young male populations. As further evidence from prospective studies is accumulating, our findings suggest strengthened primary and secondary prevention interventions should be planned and implemented by the Italian National Health Service so as to counter economic downturns’ impact on population and individual-level health. Introduction Economic crises pose major threats to health.1 Although this might sound intuitive, the system, population and individual-level mechanisms mediating the health effects of recessions times and their quantification are yet to be fully understood. The ongoing economic crisis started in 2008 and is still affecting at different rates countries in Europe and at the global level.2 At the early stages of it, evidence on past economic crises was retrieved and pooled to guide experts’ expectations on its impact on health and public health.1,3–5 At that time, ambiguous evidence emerged reporting on both detrimental impact of increased unemployment rates and healthcare expenditure cuts on population and individual-level health, as well on the protective effect of reduced behaviour individual risk factors.1 Almost 10 years on, research on the association between the current economic crisis and health is accumulating;6–10 different types of data and study designs have been applied to assess and quantify how the ongoing recession is impacting on: health systems and service delivery, access to care, behavioural risk factors and—ultimately—on health outcomes in different settings.1 As ambiguity in the evidence persists, experts have stressed the importance of differentiating between physical and mental health.6 With regard to mental health, some data on the impact of the ongoing financial, economic and societal crisis on mental health is available from selected countries including Spain,8,9,11,12 Greece13,14 and England.15 Overall, there is evidence that the ongoing recession is negatively affecting mental health outcomes, namely depression and other mood disorders,9 anxiety and alcohol-related disorders,9 and suicides10,15,16—differently by gender, age, socio-demographic strata and, importantly, by setting.8 Italy is among the European countries that have been most affected by the crisis; national-level unemployment rates (intended as the number of unemployed people as a percentage of the labour force) rose sharply between 2008 and 2016 (+ 43%), reaching almost 12% in 2016, reported as the third highest rate in the European Union after Greece and Spain.17 Along the same lines, long-term unemployment (referring to the percentage of people who have been unemployed for 12 months or more among all unemployed) increased by 27.6% between 2008 and 2016 (58.3% in 2016).17 As for now, scant data is available on the impact of such crisis on health and mental health at the national and regional level.18 In February 2017, a European Journal of Public Health special supplement on the European Social Survey19 comprised an analysis on the effect of the economic crisis on mental health inequalities and depression in selected European countries,7 however it did not include data from Italy. The aim of the current paper is to explore—for the first time in Italy—the impact of the national current economic crisis on mental health and its social determinants, using data from a large national-representative survey carried out by the Italian Institute of Statistics (ISTAT). Methods We carried out a comprehensive analysis of two large national representative surveys conducted in 2005 and 2013 by ISTAT to assess how the risk of poor mental health varied before and after the origin (2008) of the ongoing economic crisis. ISTAT is the Italian national publicly funded body responsible for producing official statistics to be made available to the general population, research institutions and policy makers.20 Study setting and sources of data Data were derived from the Health Conditions and Use of Health Services (HCUHS) surveys conducted on a 5 year-basis by ISTAT to assess Italians’ health status, health-related behaviours and access to healthcare services. HCUHS surveys have been conducted in Italy since 1980 with increasing dimensions explored and content added, over subsequent editions, and are now a unique tool to explore, at the national level: health status and perceived health conditions of the Italian population, lifestyles, prevention and health promotion behaviours, as well as health systems features and healthcare services access.21,22 In the HCUHS survey sampling is carried out in a two-step process. The first sampling units are municipalities which are sampled with probabilities proportional to size (population). Within sampled municipalities a further cluster sampling is performed on households using population registers. The sampling technique adopted allows the survey sample to be representative of Italian macro regions (North-East, North-West, Centre, South and Islands), as well as of clusters of Local Health Units which delimits areas by healthcare system organization.22 The HCUHS survey consists of two parts: one part administered in person by trained personnel at the place of residence of respondents and a second self-administered part. Additional details on HCUHS survey design and methodology are described elsewhere and available from ISTAT.22 Mental health measurements The Mental Component Summary (MCS) score was considered as a proxy of mental health and wellbeing. MCS is derived from the 12-item Short-Form health survey (SF-12), an internationally validated survey that measures general population and patients’ perceived health status and quality of life23 and which is included in the HCUHS. SF-12 is derived from the longer 36-item SF-36 that was originally designed to survey health status in the Medical Outcomes Study (MOS).24 In the last two decades, SF-12 has been widely used in clinical practice and research, in health policy evaluations, as well as for surveys targeting the general population.24–31 SF-12 assess eight health dimensions and combine them in two summary indices: an overall measure of self-perceived physical health (Physical Component Summary, PCS) and an overall measure of self-perceived mental health (the Mental Component Summary, MCS). The MCS includes the following four dimensions: vitality, social functioning, role emotional and mental health. The SF-12 has been translated in different languages and validated in other countries in the context of the International Quality of Life Assessment (IQOLA) Project. Overall, translations and adaptations of the SF-12 have been carried out in almost 30 country-language combinations.23,32 The SF-12 was translated in Italian in 2001.33 The Italian SF-12 users’ manual provides the standardized algorithms to compile the PCS and MCS, guidelines on how to calculate the items’ score and practical recommendations on how to administer it. This allows the Italian version of the SF-12 to be used in a consistent way by researchers and their findings to be compared. SF-12 has been included in HCUHS since the 1999–2000 edition. Outcomes of interest and analysis The MCS score was used to derive a poor mental health binary outcome, defined on the basis of the evidence available from the literature, as well as consultation with clinical psychiatrists and experts in the field. The MCS score cut off to derive the binary poor mental health variable was set using the average MCS score of individuals with a reported lifetime diagnosis of depression or anxiety disorders made by a physician (information included in the HCUHS), increased by 25%. The 25% increase was set with the rationale of including individuals with pre-morbid conditions. Other MCS score cut offs were derived from the literature to conduct sensitivity analysis.34 We report age-standardized prevalence of poor mental health (standard population: Italy 2005), descriptive analysis were conducted by gender strata and by survey year and percentage distributions of selected covariates were expressed accounting for sampling weights.35 Poisson regression analysis were conducted to estimate poor mental health Prevalence Rate Ratios and their 95% confidence intervals (PRR, CI 95%)36,37 in 2013 as compared to 2005 (respectively, after and before the origin of the ongoing economic crisis), accounting for age, physical comorbidity and social determinants in different gender strata. The following social determinants were accounted for in the models: education, occupation, marital status and nationality (foreign-born vs. Italy-born). In addition, two proxies of socioeconomic status (SES) were considered: housing conditions and perceived household economic resources. Interaction between survey year and all covariates of interest was tested. The modelling strategy was applied to estimate PRRs for the whole study sample by gender, as well as for selected strata: geographic area (northern, central and southern Italy), age (<60 years, ≥60 years) and their combinations. Analysis were conducted on the adult population (≥25 years) of HCUHS 2005 and 2013. HCUHS 2000 data were used to compile age-standardized prevalence of poor mental health by gender and poor mental health PRRs, as compared to other study years but were not merged into the study database as covariates of interest were not made available for year 2000. Results We analyzed a nationally representative sample of 187,423 individuals. The 2005 survey included 96,215 people and the 2013 survey included 91,208 people. Response rate (expressed as % responders on eligible individuals) was 86% in 2005 and 82.3% in 2013. The characteristics of the study population by gender and survey year are reported in table 1. 21.5% of the study population was reported to have poor mental health in 2005; the prevalence being higher in females (26.6%) than males (16.6%, table 2). In 2013, the age-standardized prevalence of poor mental health was 24.4%, 13.5% higher as compared to 2005 (24.4%). In lines with 2005, it was higher in females (28.6%), as compared to males (20.6%); however, the 2013–2005 increase was higher for males than females (+24.1% vs. 7.5%, overall 13.5 increase). Age-standardized prevalence of poor mental health in 2000 was 22.5%, higher for females; the 2000–2005% change was −4.8% for males and −4.9% for females (table 2). The prevalence of poor mental health in 2005 and in 2013 by physical comorbidity and by socio-demographic characteristics of the study population is reported in table 3. Table 1 Characteristics of the study population, by gender and study year Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Table 1 Characteristics of the study population, by gender and study year Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Males Females 2005(N = 45539) % 2013(N = 43041) % 2005(N = 50676) % 2013(N = 48167) % Age group 25–9 9.2 7.6 8.3 6.6 30–39 22.9 18.5 20.4 17.2 40–49 20.6 21.9 18.8 20.3 50–59 17.5 18.6 17.2 17.4 60–69 14.6 15.7 14.5 15.6 70–79 10.8 11.7 13.0 13.0 80–89 3.9 5.4 6.6 8.4 ≥90 0.5 0.7 1.3 1.6 Physical comorbidity None 54.5 49.3 44.1 39.7 Low 17.1 20.8 17.4 19.3 Median 15.7 16.0 19.3 18.3 High 12.7 13.9 19.1 22.7 Geographic location NorthernItaly 47.0 46.5 46.5 46.2 Central Italy 19.5 19.9 19.9 20.2 Southern Italy and Islands 33.5 33.6 33.6 33.5 Education University degree 10.8 12.9 10.2 13.8 High school degree 25.6 30.6 23.0 27.4 Technical school degree 38.4 38.2 32.0 31.7 Primary school degree or lower 25.2 18.3 34.8 27.2 Occupation Employed 63.4 57.1 37.1 36.5 Unemployed 4.7 9.4 4.2 8.8 Retired/not employed 32.0 33.5 58.7 54.7 Marital status Married/with partner 78.6 75.0 69.1 65.4 Single 11.3 14.2 16.1 18.9 Other 10.1 10.9 14.8 15.7 Nationality Italy-born 96.0 93.5 96.4 92.9 Foreign-born 4.0 6.5 3.6 7.2 Economic resources Adequate 70.2 62.6 67.5 60.4 Inadequate 29.8 37.4 32.5 39.6 Housing conditions Good 85.9 90.8 86.6 91.4 Bad 14.1 9.2 13.4 8.6 Table 2 Age-standardized prevalence of poor mental health and poor mental health Prevalence Rate Ratios (PRR; IC 95%), by year 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) Table 2 Age-standardized prevalence of poor mental health and poor mental health Prevalence Rate Ratios (PRR; IC 95%), by year 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) 2000 2005 2013 Percentage difference (2000–2005) Percentage difference (2005–2013) Males Age-standardized prevalence (%) 17.4% 16.6% 20.6% −4.60% 24.10% PRR (95%CI) 1 0.96 (0.93–0.99) 1.20 (1.16–1.24) Females Age-standardized prevalence (%) 27.9% 26.6% 28.6% −4.66% 7.52% PRR (95%CI) 1 0.95 (0.93–0.97) 1.02 (1.00–1.05) All Age-standardized prevalence (%) 22.5% 21.5% 24.4% −4.44% 13.49% PRR (95%CI) 1 0.95 (0.93–0.98) 1.10 (1.07–1.12) Table 3 Prevalence (%) of poor mental health by gender, survey year and co-variates of interest Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Table 3 Prevalence (%) of poor mental health by gender, survey year and co-variates of interest Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 Males Females 2005 (%) (n = 45.539) 2013 (%) (n = 43.041) Percentage change (%) (2005–2013) 2005 (%) (n = 50.676) 2013 (%) (n = 48.167) Percentage change (%) (2005–2013) Age group 25–29 10.5 16.1 53.3 16.9 21.8 29.0 30–39 12.1 16.1 33.1 18.8 21.9 16.5 40–49 14.0 18.8 34.3 20.6 24.5 18.9 50–59 16.0 23.4 46.3 25.3 29.3 15.8 60–69 18.5 20.9 13.0 30.1 27.8 −7.6 70–79 25.3 25.0 −1.2 36.8 36.4 −1.1 80–89 31.3 32.1 2.6 44.5 44.4 −0.2 ≥90 44.4 40.1 −9.7 49.9 52.3 4.8 Physical comorbidity None 9.6 13.3 38.5 14.6 16.1 10.3 Low 14.2 17.6 23.9 22.6 22.2 −1.8 Median 20.9 24.2 15.8 30.3 29.6 −2.3 High 42.3 48.8 15.4 51.8 56.4 8.9 Geographic location NorthernItaly 15.6 19.7 26.3 25.2 27.3 8.3 Central Italy 16.5 20.4 23.6 27.7 28.9 4.3 Southern Italy and Islands 17.2 22.8 32.6 26.5 31.1 17.4 Education University degree 11.6 15.5 33.6 17.6 21.6 22.7 High school degree 13.0 18.3 40.8 20.3 24.4 20.2 Technical school degree 15.3 20.6 34.6 23.1 27.7 19.9 Primary school degree or lower 23.2 29.5 27.2 35.3 38.6 9.3 Occupation Employed 12.1 16.4 35.5 19.7 22.7 15.2 Unemployed 24.2 31.7 31.0 24.3 30.8 26.7 Retired/not employed 23.4 25.5 9.0 30.3 32.7 7.9 Marital status Married/with partner 15.7 20.2 28.7 23.2 26.2 12.9 Single 18.5 21.7 17.3 34.8 34.6 -0.6 Other 18.9 24.4 29.1 30.5 33.4 9.5 Nationality Italy-born 16.5 21.2 28.5 26.6 29.6 11.3 Foreign-born 12.2 16.2 32.8 15.0 19.7 31.3 Economic resources Adequate 13.2 16.0 21.2 21.8 23.5 7.8 Inadequate 23.6 29.0 22.9 35.1 37.1 5.7 Housing conditions Good 15.8 20.2 27.8 25.5 28.5 11.8 Bad 19.2 27.2 41.7 30.4 33.0 8.6 As detailed in table 3, in 2005 the prevalence of poor mental health increased with increasing age both in males and in females; it was higher in individuals with physical comorbidities, in the South as compared to the North of Italy, in Italy-born as compared to foreign-born individuals and in singles, as compared to individuals with a spouse/partner. When considering social determinants, higher poor mental health prevalence was reported in disadvantaged categories: in less educated individuals as compared to individuals with university degree (23.2% vs. 11.6%), in unemployed individuals (24.2% vs. 12.1%), in individuals self-reporting inadequate economic resources (23.6% vs. 13.2%), and in individuals with poor housing conditions (19.2% vs. 15.8%). Similar patterns were reported in 2013 (table 3). The percentage increase of poor mental health between 2005 and 2013 is reported in tables 2 and 3, both for males and females. Overall, the higher 2013–2005 increase was reported for males; in particular younger individuals (between +53.3% in 25–29 years and +46.3% in 50–59 year), in males with no physical comorbidities (+38.5%), with higher education (33.6%), employed (+35.5%) and living with a spouse/partner (+28.7%). In the male population, the age-adjusted risk of poor mental health in males was 28% higher in 2013 as compared to 2005 (PRR: 1.28; 95%CI 1.24–1.33); in the female population it was 11% higher (PRR: 1.11; 95%CI 1.08–1.14). When adding survey year 2000 to the analysis (table 2) we report poor mental health risk in 2005 not to be higher as compared to 2000 for both males and female (males: PRR 0.96; 95%CI: 0.93–0.99, females: PRR 0.95; 95%CI: 0.93–0.97). In the final model, after adjusting for all co-variates of interest (table 4), the risk of poor mental health remained higher in 2013 as compared to 2005, both for males (PRR: 1.17; 95%CI: 1.14–1.20) and for females (PRR: 1.04; 95%CI: 1.02–1.06). Table 4 also reports poor mental health risks estimates in all considered categories of physical comorbidities, social determinants and socio-demographic characteristics. No interactions were reported between survey year and all covariates accounted for in the model, therefore no effect modifiers were included in the final regression analysis. When estimating the risk of poor mental health in 2013 as compared to 2005 for different strata, the highest risks of poor mental health was reported for males younger than 60 years, both in Northern (PRR: 1.23; 95%CI: 1.16–1.30) and Southern Italy (PRR: 1.25; 95%CI: 1.19–1.31). In the female population the risk was reported to increase in younger age categories (PRR: 1.11; 95%CI: 1.07–1.14) and fade away in the elderly (PRR: 0.97; 95%CI: 0.94–0.99). Table 4 Poor mental health Prevalence Rate Ratios (PRR; IC 95%) Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 a Adjusted for: age, year, geographic location, education, occupation, marital status, nationality, economic resources, housing conditions and chronic morbidity index (except when estimates refer to each item). Table 4 Poor mental health Prevalence Rate Ratios (PRR; IC 95%) Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 Males (n = 88.580) Females (n = 98.843) PRRa (IC 95%) P value PRRa (IC 95%) P value Year 2005 1.0 1.0 2013 1.17 (1.14–1.20) <.0001 1.04 (1.02–1.06) 0.0002 Geographic location Norther Italy 1.0 1.0 Central and Southern Italy 1.02 (0.99–1.05) 0.1964 1.05 (1.03–1.07) <0.0001 Education University degree 1.0 1.0 High school degree 1.05 (0.99–1.11) 0.0910 1.03 (0.99–1.07) 0.1830 Technical school degree 1.01 (0.95–1.06) 0.8119 1.01 (0.97–1.06) 0.5027 Primary school degree or lower 1.07 (1.01–1.13) 0.0200 1.09 (1.04–1.14) 0.0001 Occupation Employed 1.0 1.0 Unemployed 1.54 (1.47–1.61) <0.0001 1.15 (1.10–1.20) <0.0001 Retired/not employed 1.08 (1.03–1.13) 0.0008 0.98 (0.95–1.01) 0.1596 Marital status Married/with partner 1.0 1.0 Single 1.06 (1.02–1.10) 0.0047 1.01 (0.99–1.04) 0.2652 Other 1.15 (1.10–1.20) <0.0001 1.11 (1.08–1.14) <0.0001 Nationality Italy-born 1.0 1.0 Foreign-born 0.89 (0.82–0.96) 0.0045 0.83 (0.78–0.88) <0.0001 Economic resources adequate 1.00 1.0 inadequate 1.54 (1.49–1.58) <0.0001 1.37 (1.34–1.40) <0.0001 Housing conditions Good 1.00 1.0 Bad 1.12 (1.08–1.16) <0.0001 1.13 (1.10–1.16) <0.0001 a Adjusted for: age, year, geographic location, education, occupation, marital status, nationality, economic resources, housing conditions and chronic morbidity index (except when estimates refer to each item). We conducted sensitivity analysis using diagnosis of depression or anxiety disorder (its corresponding average MCS score) as primary outcome, defined as risk of mental disorders. Age-adjusted risk of mental disorders was slightly higher in 2013 as compared to 2005 in males (PRR: 1.09; 95%CI 1.04–1.13) but did not increase in females (PRR: 0.95; 95%CI 0.92–0.98). In the final model when adjusting for all co-variates of interest the risk of mental disorders did not increase neither for males (PRR: 1.02; 95%CI 0.99–1.06), nor for females (PRR: 0.92; 95%CI 0.90–0.95). Discussion We analyze data from nearly 200,000 individuals from two large national representative surveys and report the risk of poor mental health to have increased in the Italian population between 2005 and 2013; this suggesting a negative impact of the ongoing economic crisis on mental health and wellbeing at the population level. In particular, the risk is higher in the male population (17% increase), and highest in young males (24% increase). The highest risk reported for young males is compatible with higher economic turmoil and job insecurity faced by those subgroups of the population.4,38 In fact, in Italy, in line with global trends, the unemployment raise associated with the economic crisis hit most male populations as compared to females (+109% in 2008–2013 in males vs. +54% in females).39 The negative impact of unemployment, low pay and job insecurity on mental health and wellbeing is echoed in evidence from individual-level research.38,40 We also report that social determinants play a key role on mental health with vulnerable subgroup of the populations, including less educated individuals and those reporting job insecurity and lower socioeconomic status, being at higher risk of poor mental health. However, our data show different socio-demographic strata not to have been differently affected by the impact of the economic crisis in Italy. We interpret such finding as the economic crisis not worsening vulnerable populations’ conditions but, instead, increasing the share of vulnerable (i.e. unemployed) people. Such interpretation is supported by unemployment rate trends. From a methodological point of view it is not straightforward to quantitatively assess how and by how much macroeconomic features (i.e. the current economic crisis) impact on population health and health outcomes. Different conceptual frameworks have been proposed in the literature to inform analysis’ models and different study designs have been applied to assess the impact of recession times on health.1,6 In the field of mental health, the majority of the studies that aimed at assessing the impact of the current economic crisis selected suicide rates as their primary outcome.10,14–16 In fact, solid evidence is available in the literature on the positive association between times of recession and increased suicide rates. On the contrary, scanter and more heterogeneous evidence is available on other mental health outcomes, including mood and anxiety disorders, global functioning and wellbeing measures. We believe that although suicide rates is a suitable outcome for several reasons (available from administrative data and routinely collected, difficult to misclassify, comparable between different settings), other mental health and wellbeing outcomes—including the one we selected—might be more useful to explore the effects and mediators of the crisis and to inform prevention strategies. Among other strengths of our study the fact that we could count on a large and representative sample size; the most recent evidence published on the impact of the economic crisis on mental health in Europe7 considered data from 21 European countries summing a total sample size almost half of ours. Nonetheless, taking into account that with large sample sizes statistically significant findings are more easily derived, when interpreting data, we put more emphasis on effect sizes rather than significance. Despite being Italy among the European countries most severely affected by the global economic and financial crisis, there is paucity of Italian research on its impact on health, as compared to other European countries, ours being the first Italian national-level data to explore general population’s mental health and wellbeing effects. Our study has also imitations that need to be acknowledged. First, we did not use a direct measurement of the crisis but applied a ‘before and after’ approach instead; this has however already been done in previous research on the topic in other settings and provided valuable insight.9,41 Second, we analyzed only two points in time (2005 and 2013) which does not allow to derive trends, nor it allows to establish that the reported differences between 2005 and 2013 are influenced by the crisis and do not merely reflect previous trends. However, we analyzed data from 2000 HCUHS and report poor mental health risk in 2005 not to be higher as compared to 2000 which suggests the increase reported between 2005 and 2013 might be associated with incident economic downturns. Second, although MCS score is a very sensitive parameter its interpretation is not straightforward. The poor mental health binary outcome we derived from individual-level MCS score not only allows to better interpret data but also was intended to define subgroup of the population with poor mental conditions, including pre-morbid status that can be targeted by prevention. In conclusion, the economic crisis that hit Italy 9 years ago and whose recovery still appear to be slow has posed threats to Italians’ mental health and wellbeing with a higher impact on young males populations. As additional sources of data need to be further analyzed with prospective approaches to accumulate additional evidence on causal relations, our findings suggest strengthened primary and secondary prevention interventions should be planned and implemented by the National Health Service so as to counter economic downturns’ impact on population and individual-level health. Funding No dedicated funding was received to support this manuscript. Conflicts of interest: None declared. Key points Data from two large national representative surveys (over 200,000 individuals) suggest poor mental health increased in the Italian population during the economic crisis. The increased risk of poor mental health is highest in male and young populations (17% and 24% increase, respectively). Social determinants play a key role on mental health with vulnerable subgroup being at higher risk of poor mental health, but not differently affected by the impact of the economic crisis. Our data suggest pre-morbid mental health conditions might be targeted by prevention programmes so as to counter economic downturns’ impact on population health. References 1 Karanikolos M , Mladovsky P , Cylus J , et al. Financial crisis, austerity, and health in Europe . Lancet 2013 ; 381 : 1323 – 31 . Google Scholar CrossRef Search ADS PubMed 2 The Economist . The origins of the financial crisis. Available at: https://www.economist.com/news/schoolsbrief/21584534-effects-financial-crisis-are-still-being-felt-five-years-article ( 2 August 2017 , date last accessed). 3 Glonti K , Gordeev VS , Goryakin Y , et al. A systematic review on health resilience to economic crises . PLoS One 2015 ; 10 : e0123117 . Google Scholar CrossRef Search ADS PubMed 4 Stuckler D , Basu S , Suhrcke M , et al. The public health effect of economic crises and alternative policy responses in Europe: an empirical analysis . Lancet 2009 ; 374 : 315 – 23 . Google Scholar CrossRef Search ADS PubMed 5 Suhrcke M , Stuckler D , Suk JE , et al. The impact of economic crises on communicable disease transmission and control: a systematic review of the evidence . PLoS One 2011 ; 6 : e20724 . Google Scholar CrossRef Search ADS PubMed 6 Karanikolos M , Heino P , McKee M , et al. Effects of the global financial crisis on health in high-income oecd countries: a narrative review . Int J Health Serv 2016 ; 46 : 208 – 40 . Google Scholar CrossRef Search ADS PubMed 7 Reibling N , Beckfield J , Huijts T , Schmidt-Catran A , Thomson KH , Wendt C . Depressed during the depression: has the economic crisis affected mental health inequalities in Europe? Findings from the European Social Survey (2014) special module on the determinants of health . Eur J Public Health 2017 ; 27 : 47 – 54 . Google Scholar CrossRef Search ADS PubMed 8 Bartoll X , Palencia L , Malmusi D , et al. The evolution of mental health in Spain during the economic crisis . Eur J Public Health 2014 ; 24 : 415 – 8 . Google Scholar CrossRef Search ADS PubMed 9 Gili M , Roca M , Basu S , et al. The mental health risks of economic crisis in Spain: evidence from primary care centres, 2006 and 2010 . Eur J Public Health 2013 ; 23 : 103 – 8 . Google Scholar CrossRef Search ADS PubMed 10 Lopez Bernal JA , Gasparrini A , Artundo CM , McKee M . The effect of the late 2000s financial crisis on suicides in Spain: an interrupted time-series analysis . Eur J Public Health 2013 ; 23 : 732 – 6 . Google Scholar CrossRef Search ADS PubMed 11 Iglesias GC , Saiz MP , G-P Gonzalez MP , Bousono GM , et al. Effects of the economic crisis on demand due to mental disorders in Asturias: data from the Asturias Cumulative Psychiatric Case Register (2000–2010) . Actas espanolas de psiquiatria 2014 ; 42 : 108 – 15 . Google Scholar PubMed 12 Roca M , Gili M , Garcia-Campayo J , Garcia-Toro M . Economic crisis and mental health in Spain . Lancet 2013 ; 382 : 1977 – 8 . Google Scholar CrossRef Search ADS PubMed 13 Mylona K , Tsiantou V , Zavras D , et al. Determinants of self-reported frequency of depressive symptoms in Greece during economic crisis . Public health 2014 ; 128 : 752 – 4 . Google Scholar CrossRef Search ADS PubMed 14 Simou E , Koutsogeorgou E . Effects of the economic crisis on health and healthcare in Greece in the literature from 2009 to 2013: a systematic review . Health Policy 2014 ; 115 : 111 – 9 . Google Scholar CrossRef Search ADS PubMed 15 Barr B , Taylor-Robinson D , Scott-Samuel A , et al. Suicides associated with the 2008-10 economic recession in England: time trend analysis . BMJ 2012 ; 345 : e5142 . Google Scholar CrossRef Search ADS PubMed 16 Fountoulakis KN , Kawohl W , Theodorakis PN , et al. Relationship of suicide rates to economic variables in Europe: 2000–2011 . Br J Psychiatry 2014 ; 205 : 486 – 96 . Google Scholar CrossRef Search ADS PubMed 17 Organisation for Economic Co-operation and Development (OECD) Data . Unemployment. Available at: https://data.oecd.org/unemp/unemployment-rate.htm-indicator-chart ( 23 August 2017 , date last accessed). 18 Mattei G , Ferrari S , Pingani L , Rigatelli M . Short-term effects of the 2008 Great Recession on the health of the Italian population: an ecological study . Soc Psychiatry Psychiatr Epidemiol 2014 ; 49 : 851 – 8 . Google Scholar CrossRef Search ADS PubMed 19 Beckfield J , Balaj M , McNamara CL , et al. The health of European populations: introduction to the special supplement on the 2014 European Social Survey (ESS) rotating module on the social determinants of health. Eur. J Public Health 2017 ; 27 : 3 – 7 . 20 Istituto Italiano di Statistica (ISTAT) . About Italian National Institute of Statistics. Available at: http://www.istat.it/en/about-istat ( 2 September 2017 date last accessed). 21 Istituto Nazionale di Statistica (ISTAT) - Il sistema di Indagini Sociali Multiscopo. Contenuti e metodologia delle indagini. Metodi e Norme, n. 31 - Roma, 2006 . 22 ISTAT . Indagine Multiscopo sulle Famiglie “Condizioni di salute e ricorso ai servizi sanitari 2013” Documentazione tecnica e descrizione del file. [Italian Institute of Statistics. Health Conditions and Use of Health Services (HCUHS) surveys. Supporting methodological notes], Rome 2016 Avaialbe at http://www.istat.it/it/files/2011/01/metadati_condizioni_salute_2013_new.zip? title=Condizioni+di+salute+-+09%2Ffeb%2F2016+-+Metadati+-+Anni+2012-2013.zip (28 August 2017, date last accessed). 23 Ware J Jr , Kosinski M , Keller SD . A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity . Med Care 1996 ; 34 : 220 – 33 . Google Scholar CrossRef Search ADS PubMed 24 Ware JE Jr , Sherbourne CD . The MOS 36-item short-form health survey (SF-36). I . Conceptual framework and item selection. Med Care 1992 ; 30 : 473 – 83 . 25 Ellis C , Grubaugh AL , Egede LE . Factors associated with SF-12 physical and mental health quality of life scores in adults with stroke . J Stroke Cerebrovasc Dis 2013 ; 22 : 309 – 17 . Google Scholar CrossRef Search ADS PubMed 26 Forero CG , Vilagut G , Adroher ND , Alonso J . Multidimensional item response theory models yielded good fit and reliable scores for the Short Form-12 questionnaire . J Clin Epidemiol 2013 ; 66 : 790 – 801 . Google Scholar CrossRef Search ADS PubMed 27 Konig HH , Heider D , Lehnert T , Riedel-Heller SG , Angermeyer MC , Matschinger H , et al. Health status of the advanced elderly in six European countries: results from a representative survey using EQ-5D and SF-12 . Health Qual Life Outcomes 2010 ; 8 : 143 . Google Scholar CrossRef Search ADS PubMed 28 McBurney CR , Eagle KA , Kline-Rogers EM , et al. Health-related quality of life in patients 7 months after a myocardial infarction: factors affecting the Short Form-12 . Pharmacotherapy 2002 ; 22 : 1616 – 22 . Google Scholar CrossRef Search ADS PubMed 29 Mols F , Pelle AJ , Kupper N . Normative data of the SF-12 health survey with validation using postmyocardial infarction patients in the Dutch population . Qual Life Res 2009 ; 18 : 403 – 14 . Google Scholar CrossRef Search ADS PubMed 30 Vilagut G , Forero CG , Pinto-Meza A , et al. The mental component of the short-form 12 health survey (SF-12) as a measure of depressive disorders in the general population: results with three alternative scoring methods . Value Health 2013 ; 16 : 564 – 73 . Google Scholar CrossRef Search ADS PubMed 31 Webster KE , Feller JA . Comparison of the short form-12 (SF-12) health status questionnaire with the SF-36 in patients with knee osteoarthritis who have replacement surgery . Knee Surg Sports Traumatol Arthrosc 2016 ; 24 : 2620 – 6 . Google Scholar CrossRef Search ADS PubMed 32 Ware JE , Keller SD , Gandek B , et al. Evaluating translations of health status surveys: lessons from the IQOLA project . Int J Technol Assess Health Care 1995 ; 11 : 525 . Google Scholar CrossRef Search ADS PubMed 33 Goad JA , Taitel MS , Fensterheim LE , Cannon AE . Vaccinations administered during off-clinic hours at a national community pharmacy: implications for increasing patient access and convenience . Ann Fam Med 2013 ; 11 : 429 – 36 . Google Scholar CrossRef Search ADS PubMed 34 Kiely KM , Butterworth P . Validation of four measures of mental health against depression and generalized anxiety in a community based sample . Psychiatry Res 2015 ; 225 : 291 – 8 . Google Scholar CrossRef Search ADS PubMed 35 Caranci N , Di Martino M , Gargiulo L , et al. Metodi per l’analisi dei dati, Indicazioni pratiche e note d’uso, Approfondimenti sull’indagine multiscopo Istat Salute 2005, Monitor, 3° supplemento, n. 22, 2008 . 36 Zocchetti C , Consonni D , Bertazzi PA . Relationship between prevalence rate ratios and odds ratios in cross-sectional studies . Int J Epidemiol 1997 ; 26 : 220 . Google Scholar CrossRef Search ADS PubMed 37 Barros AJ , Hirakata VN . Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio . BMC Med Res Methodol 2003 ; 3 : 21 . Google Scholar CrossRef Search ADS PubMed 38 Fiori F , Rinesi F , Spizzichino D , Di Giorgio G . Employment insecurity and mental health during the economic recession: an analysis of the young adult labour force in Italy . Soc Sci Med 2016 ; 153 : 90 – 8 . Google Scholar CrossRef Search ADS PubMed 39 Eurostat . Unemployment statistics. Available at: http://ec.europa.eu/eurostat/statistics-explained/index.php/Unemployment_statistics (23 December 2016, date last accessed). 40 Theodossiou I . The effects of low-pay and unemployment on psychological well-being: a logistic regression approach . J Health Econ 1998 ; 17 : 85 – 104 . Google Scholar CrossRef Search ADS PubMed 41 Odone A, Signorelli C, Rodrigues LC. Tuberculosis and the economic crisis: an old threat for the new European agenda . Scand J Public Health 2014 ; 42 : 834 – 5 . CrossRef Search ADS PubMed © The Author(s) 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

The European Journal of Public Health – Oxford University Press

Published: Dec 27, 2017

There are no references for this article.