Dataset Information

Risk of depressive symptoms before and after the first hospitalisation for cancer: Evidence from a 16-year cohort study in the Czech Republic.

ABSTRACT:

Background

Whether depression risk starts increasing before cancer diagnosis, and whether cancer is an independent risk factor for depression, remain unclear. We aimed to quantify the risk of depressive symptoms before and after the first hospitalisation for cancer (as a proxy for cancer diagnosis) amongst patients with cancer.

Methods

We linked cohort data with national hospitalisation records in the Czech Republic. We followed 1056 incident cancer cases for up to 15 years before and 15 years after the first hospitalisation for cancer. Depressive symptoms were measured using the Centre for Epidemiological Studies-Depression (CES-D) scale. We used multilevel ordered logistic regression to assess the relationship between follow-up years (pre- and post-hospitalisation) and depressive symptoms amongst incident cancer cases. Propensity Score Matching was employed to match each case with a cancer-free control, to test the independent effect of cancer on depressive symptoms over time.

Results

Per one year of follow-up (whether pre- or post- hospitalisation) was associated with 1.07 (1.05-1.10) times more likely to have high severity of depressive symptoms amongst patients with cancer. The probability of having high severity of depressive symptoms increased from 25% at five years before hospitalisation to 33% at 7.5 years after hospitalisation. In parallel analyses amongst matched cancer-free controls, the risk of depressive symptoms had no significant changes during follow-up.

Limitations

Stratified analyses based on cancer types and stages of malignancy were infeasible.

Conclusions

The excess risk of depressive symptoms was apparent five years prior to the first hospitalisation for cancer. Using cancer-free matched controls, we confirmed that cancer was an independent predictor of depressive symptoms.

SUBMITTER: Lu W

PROVIDER: S-EPMC7456789 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk of depressive symptoms before and after the first hospitalisation for cancer: Evidence from a 16-year cohort study in the Czech Republic.

Lu Wentian W Pikhart Hynek H Peasey Anne A Kubinova Ruzena R Pitman Alexandra A Bobak Martin M

Journal of affective disorders 20200715

<h4>Background</h4>Whether depression risk starts increasing before cancer diagnosis, and whether cancer is an independent risk factor for depression, remain unclear. We aimed to quantify the risk of depressive symptoms before and after the first hospitalisation for cancer (as a proxy for cancer diagnosis) amongst patients with cancer.<h4>Methods</h4>We linked cohort data with national hospitalisation records in the Czech Republic. We followed 1056 incident cancer cases for up to 15 years before ...[more]

PMID: 32697719

Similar Datasets

Project description:ImportanceNeuropsychiatric symptoms, depressive symptoms in particular, are common in patients with dementia but whether depressive symptoms in adulthood increases the risk for dementia remains the subject of debate.ObjectiveTo characterize the trajectory of depressive symptoms over 28 years prior to dementia diagnosis to determine whether depressive symptoms carry risk for dementia.Design, setting, and participantsUp to 10 308 persons, aged 35 to 55 years, were recruited to the Whitehall II cohort study in 1985, with the end of follow-up in 2015. Data analysis for this study in a UK general community was conducted from October to December 2016.ExposuresDepressive symptoms assessed on 9 occasions between 1985 and 2012 using the General Health Questionnaire.Main outcomes and measuresIncidence of dementia (n = 322) between 1985 and 2015.ResultsOf the 10 189 persons included in the study, 6838 were men (67%) and 3351 were women (33%). Those reporting depressive symptoms in 1985 (mean follow-up, 27 years) did not have significantly increased risk for dementia (hazard ratio [HR], 1.21; 95% CI, 0.95-1.54) in Cox regression adjusted for sociodemographic covariates, health behaviors, and chronic conditions. However, those with depressive symptoms in 2003 (mean follow-up, 11 years) had an increased risk (HR, 1.72; 95% CI, 1.21-2.44). Those with chronic/recurring depressive symptoms (≥2 of 3 occasions) in the early study phase (mean follow-up, 22 years) did not have excess risk (HR, 1.02; 95% CI, 0.72-1.44) but those with chronic/recurring symptoms in the late phase (mean follow-up, 11 years) did have higher risk for dementia (HR, 1.67; 95% CI, 1.11-2.49). Analysis of retrospective depressive trajectories over 28 years, using mixed models and a backward time scale, shows that in those with dementia, differences in depressive symptoms compared with those without dementia became apparent 11 years (difference, 0.61; 95% CI, 0.09-1.13; P = .02) before dementia diagnosis and became more than 9 times larger at the year of diagnosis (difference, 5.81; 95% CI, 4.81-6.81; P < .001).Conclusions and relevanceDepressive symptoms in the early phase of the study corresponding to midlife, even when chronic/recurring, do not increase the risk for dementia. Along with our analysis of depressive trajectories over 28 years, these results suggest that depressive symptoms are a prodromal feature of dementia or that the 2 share common causes. The findings do not support the hypothesis that depressive symptoms increase the risk for dementia.

Project description:BackgroundThe Statutory Health Insurance scheme is one of two main schemes of health care system financing in Europe. This scheme mainly relies on wage-based contributions from employers and employees and is thus prone to business cycle fluctuations. This turned out to be a problem especially after the 2008 crisis. We estimate the magnitude of the effect of the business cycle on health insurance funds' revenues in the Czech Republic where the health care system financing is based on the Statutory Health Insurance scheme. The relationship between the business cycle and healthcare system's revenues has not been quantified to this date.MethodsWe use static and lagged regression models to estimate the impact of business cycle on health care system's revenues. The business cycle is proxied by eight different indicators (nominal GDP, unemployment, industrial production, recession index, business cycle index, GDP gap, consumer price index and consumer expenditure). Using quarterly data from 2000-2017, we examine the effect of business cycle on total revenues and its two main components: the employer-employee contributions and state contributions.ResultsHealth insurance funds' revenues display significant pro-cyclicality, which is mainly driven by employer-employee contributions. Out of all eight business cycle indicators, nominal GDP has the largest effect. In particular, the model estimates that if quarter-over-quarter GDP increases by 1%, then quarter-over-quarter healthcare system's revenues increase by 0.7% and quarter-over-quarter employer-employee contributions increase by 1.1%. The lagged effect of business cycle on healthcare system's revenues is smaller in magnitude. State contributions on behalf of economically inactive people do not display a significant relationship with business cycle in the static nor lagged model. The effect is consistent across different business cycle indicators, although the magnitudes of the effect vary.ConclusionThe results show large pro-cyclicality in healthcare system's revenues in Statutory Health Insurance schemes. Counter-cyclical mechanisms are needed to offset this loss of revenues during economic downturns to ensure sufficient resources in healthcare.

Dataset Information

Risk of depressive symptoms before and after the first hospitalisation for cancer: Evidence from a 16-year cohort study in the Czech Republic.

Background

Methods

Results

Limitations

Conclusions

Publications

Risk of depressive symptoms before and after the first hospitalisation for cancer: Evidence from a 16-year cohort study in the Czech Republic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets