Depressive symptoms among adults 18–69 years in Italy: results from the Italian behavioural risk factor surveillance system, 2007

Binkin, Nancy; Gigantesco, Antonella; Ferrante, Gianluigi; Baldissera, Sandro

doi:10.1007/s00038-009-0106-y

Cited by 13 publications

(13 citation statements)

References 27 publications

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“…In addition, our data were in line with those describing higher depression rates in women than men (e.g. Binkin et al, 2010;De Girolamo et al, 2006;Linden, Vodermaier, Mackenzie, & Greig, 2012), even though other studies (Annunziata, Muzzatti, Bidoli, & Veronesi, 2012;Ciaramella & Poli, 2001;Mitchell et al, 2011) did not report association between depression and gender in oncological samples.…”

Section: Discussionsupporting

confidence: 93%

“…Being woman, low education, economic problems, being divorced/ separated or widowed as well as suffering from a chronic disease are the main risk factors for depression (Binkin, Gigantesco, Ferrante, & Baldissera, 2010;De Girolamo et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The risk of suffering from depression is greatest at the moment of receiving a diagnosis and at the beginning of therapies, and decreases 6 months after the diagnosis (Irwin, 2013). Similarly to the general population (Binkin et al, 2010;ESEMeD/MHEDEA 2000Investigators, 2004, depression in oncological patients is associated with a worse quality of life (QoL) and more functional limitations. Furthermore, in oncology, there is a lower adherence to treatments, an increase in pain perception, a reduction in the tolerance of side effects of oncological therapies, longer absence from work and more recourse to the health system services (including longer hospitalisation) (Ballenger et al, 2001;Irwin, 2013;Smith, 2015;Torta, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Intensity and prevalence of depressive states in cancer inpatients: a large sample descriptive study

et al. 2016

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In cancer patients, depression causes suffering during the whole disease trajectory and it also influences the personal perception of well-being as well as treatment adherence. Consequently, its better definition is needed for planning more tailored supportive programmes. This study was aimed to provide information on depressive state intensity and prevalence in an heterogeneous sample of cancer inpatients. In addition, associations were studied between depressive state and different socio-demographic and clinical factors. A total of 1,147 consecutive adult cancer inpatients completed the Center for Epidemiologic Studies Scale on Depression together with a form for collecting socio-demographic and clinical data. The mean score of depression was 16.9 (SD = 9.3). There were differences in depression intensity associated with gender (p < .001), age (p = .001) and cancer type (p < .001), but not with education level (p = .282) or marital status (p = .436). Of the entire sample 13.9% had depressive states; this percentage raised to 26.2% if a less stringent criterion was used. These data reinforce the importance of a clinical and research focus on depression in oncology. As differences according to gender, age and diagnosis exist in depression prevalence and intensity, tailored supportive intervention should be planned and verified for effectiveness and efficacy.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intensity and prevalence of depressive states in cancer inpatients: a large sample descriptive study

et al. 2016

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“…Older age, female gender, lower education attainment, financial difficulties, living alone, and not being employed showed a strong association with depression (Binkin et al 2010). In the multivariate analysis, the odds of depression decreased for all risk factor observed ( Table 2).…”

Section: Depressionmentioning

confidence: 99%

Social determinants effects from the Italian risk factor surveillance system PASSI

Minardi

Campostrini

Carrozzi³

et al. 2011

Int J Public Health

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“…The data used for the analysis is from the PASSI (Progressi delle Aziende Sanitarie per la Salute or Progress in the Italian Local Health Units) surveillance system in Italy [ 13 ], the details of which are described by Baldissera et al [ 14 ], as well as Minardi et al [ 15 ] and Binkin et al [ 16 ], who have used the PASSI data for analysis of health outcomes and risk factors. PASSI raw data are not openly available outside the PASSI network; however, their use for research purposes is encouraged once permission is obtained from the National Coordinating Group at the Italian National Institute of Health (Istituto Superiore di Sanità).…”

Section: Methodsmentioning

confidence: 99%

Application of the varying coefficient model to the behaviour risk factor surveillance data in Italy: a study of changing smoking prevalence among sub-populations

Assaf

Campostrini

2015

BMC Public Health

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BackgroundBehaviour risk factor surveillance (BRFS) data can be an important source of information for studying changes in various health outcomes and risk factors. Results obtained from surveillance data analysis are vital for informing health policy interventions, particularly with regards to evolutionary aspects. The objective of this analysis was to recommend a method that can be used for analysing trends in the association among variables from large public health data sets. This was demonstrated by examining the changing effects of various covariates, representing different sub-populations, on smoking status over time.MethodsIn our work, we propose the use of varying coefficient models (VCM) with non-parametric techniques to catch the dynamics of the evolutionary processes under study. This is a useful method, which allows coefficients to vary with time using smooth functions. Italian BRFS data from 2008-2012 was used with a sample size of 185,619 observations. In the application, a time VCM is fit for a smoking status binary outcome variable using the P-spline estimation method. The model includes ten independent variables comprising socio-demographic, health risk and behaviour variables.ResultsThe VCM fit for the data indicates that the coefficients for some of the categories for the age and the alcohol consumption variables varied with time. The main results show that Italians aged 18-29 and 40-49 had higher odds of being smokers compared to those aged 60-69; however, these odds significantly decreased in the period 2008-2012. In addition, those who do not drink had lower odds for being a smoker compared to high risk drinkers and these odds decreased further during the observation period.ConclusionThe application of the VCM to the BRFS data in Italy has shown that this method can be useful in detecting which sub-populations require interventions. Although the results have shown a decrease in the odds of being a smoker for certain age groups and non-drinkers, other sub-populations have not decreased their odds and health inequalities remain. This observation indicates that efforts and interventions are still required to target these non-changing sub-populations in order to modify their smoking behaviour.

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Depressive symptoms among adults 18–69 years in Italy: results from the Italian behavioural risk factor surveillance system, 2007

Cited by 13 publications

References 27 publications

Intensity and prevalence of depressive states in cancer inpatients: a large sample descriptive study

Intensity and prevalence of depressive states in cancer inpatients: a large sample descriptive study

Social determinants effects from the Italian risk factor surveillance system PASSI

Application of the varying coefficient model to the behaviour risk factor surveillance data in Italy: a study of changing smoking prevalence among sub-populations

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