A 40-Year Perspective on the Prevalence of Depression

Murphy, Jane M.; Laird, Nan M.; Monson, Richard R.; Sobol, Arthur M.; Leighton, Alexander H.

doi:10.1001/archpsyc.57.3.209

Cited by 279 publications

(158 citation statements)

References 51 publications

Supporting

Mentioning

138

Contrasting

Unclassified

Order By: Relevance

“…It has been demonstrated that inaccurate measurements decrease power so substantially that smaller samples with accurate assessments are preferable. 53 The incidence and aetiology of depression varies by age and gender, 54 hence it is germane to explore whether the G Â E interaction is specific to certain age or/and gender group. Across the studies exploring the 5-HTTLPR mediation of environmental adversity in the aetiology of depression, age appears to be a major factor, potentially accounting for substantial proportion of the heterogeneity in results.…”

Section: Methodsmentioning

confidence: 99%

The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: review and methodological analysis

2007

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: review and methodological analysis

2007

View full text Add to dashboard Cite

“…Depression is marked as a leading cause of disability and premature death in persons aged 18-44 years worldwide and it is expected to be the second leading cause of disability in persons of all age in 2020 [1,2].…”

Section: Introductionmentioning

confidence: 99%

Correlation between phq-9 score and physical activity level, risk factors and non-communicable diseases in patients in family medicine clinic

2011

View full text Add to dashboard Cite

AbstractThe work of physicians in primary health care is essential in prevention and early detection of health risk factors. To determine the incidence of depression among patients in family medicine clinics, as well as the correlation between depression, risk factors and NCDs, and to determine correlation between depression (PHQ-9 score) and the level of physical activity. The pilot study was conducted from January to March 2010 on 100 patients using the Patient Health Questionnaire (PHQ-9) and the International Physical Activity Questionnaire (IPAQ). The family doctor randomly interviewed and measured patients’ weight, height, blood pressure, fasting blood glucose and cholesterol and noted the presence of earlier diagnosed non-communicable diseases. The data for 92 patients were processed according to PHQ-9 and IPAQ guidelines. Out of 92 patients, 59 (64,1%) had PHQ-9 score ≤ 4 which suggests the absence of depression. The PHQ-9 score median was within normal limits, therefore, treatment was not required and there was no difference in PHQ-9 score median between patients with respect to age and sex. The level of physical activity was moderate in 39,1%, heavy in 35,9% and walking as physical activity in 25,0% of patients. Correlation coefficient between PHQ-9 score and MET score (r=−0,241) was statistically significant (p<0,05), as well as between PHQ -9 score and anxiety (r=0,27; p<0,01). Most studies show a correlation between depression and physical activity. It is essential to promote physical activity in order to prevent anxiety, depression and non-communicable diseases.

show abstract

“…Researchers have collected information using multiple informant reports even before appropriate techniques were developed for its analysis. For instance, in the Stirling County Study (Leighton, 1959;Murphy, 1980;Murphy et al, 2000), depression and/or anxiety were assessed using self reported information; in addition, physicians reported on the same subjects for a full range of psychiatric disorders. An early analysis investigated the relationship between self report of depression and/or anxiety and mortality where they found that the standardized mortality ratio (SMR) was 1.5 (Murphy et al, 1987); another study dealt with physician report of many psychiatric disorders and found that SMR was 1.6 (Murphy et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

“…To demonstrate the GEE approach, applies the technique to the Stirling County Study (Leighton, 1959;Murphy, 1980;Murphy et al, 2000), an investigation initiated in 1952 in a community of 20,000 residents in Atlantic Canada. use GEE models to predict mortality in a 16 year followup period of 1079 subjects from multiple informants (self report and physician report) about psychiatric disorders.…”

Section: Introductionmentioning

confidence: 99%

Marginal regression models with a time to event outcome and discrete multiple source predictors

et al. 2006

Self Cite

View full text Add to dashboard Cite

Information from multiple informants is frequently used to assess psychopathology. We consider marginal regression models with multiple informants as discrete predictors and a time to event outcome. We fit these models to data from the Stirling County Study; specifically, the models predict mortality from self report of psychiatric disorders and also predict mortality from physician report of psychiatric disorders. Previously, Horton et al. found little relationship between self and physician reports of psychopathology, but that the relationship of self report of psychopathology with mortality was similar to that of physician report of psychopathology with mortality. Generalized estimating equations (GEE) have been used to fit marginal models with multiple informant covariates; here we develop a maximum likelihood (ML) approach and show how it relates to the GEE approach. In a simple setting using a saturated model, the ML approach can be constructed to provide estimates that match those found using GEE. We extend the ML technique to consider multiple informant predictors with missingness and compare the method to using inverse probability weighted (IPW) GEE. Our simulation study illustrates that IPW GEE loses little efficiency compared with ML in the presence of monotone missingness. Our example data has non-monotone missingness; in this case, ML offers a modest decrease in variance compared with IPW GEE, particularly for estimating covariates in the marginal models. In more general settings, e.g. categorical predictors and piecewise exponential models, the likelihood parameters from the ML technique do not have the same interpretation as the GEE. Thus, the GEE is recommended to fit marginal models for its flexibility, ease of interpretation and comparable efficiency to ML in the presence of missing data.

show abstract

A 40-Year Perspective on the Prevalence of Depression

Cited by 279 publications

References 51 publications

The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: review and methodological analysis

The moderation by the serotonin transporter gene of environmental adversity in the aetiology of mental illness: review and methodological analysis

Correlation between phq-9 score and physical activity level, risk factors and non-communicable diseases in patients in family medicine clinic

Marginal regression models with a time to event outcome and discrete multiple source predictors

Contact Info

Product

Resources

About