Psychosocial and sociodemographic predictors of attrition in a longitudinal study of major depression in primary care: the predictD-Spain study

Bellón, Juan Ángel; Luna, Juan de Dios; Moreno, B.; Montón-Franco, Carmen; GildeGómez-Barragán, María Josefa; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Motrico, Emma; Martinez-Cañavate, María Teresa; Oliván-Blázquez, Bárbara; Vázquez-Medrano, Ana; Sánchez-Artiaga, María Soledad; March, Sebastià; Muñoz-García, María del Mar; Moreno-Peral, Patrícia; Nazareth, Irwin; King, Michael; Torres-González, Francisco

doi:10.1136/jech.2008.085290

Cited by 27 publications

(42 citation statements)

References 35 publications

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“…It represents the heterogeneous and variable nature of depression seen in primary care (Rubenstein et al, 2007). Data are of good quality with recruitment rates in the range of those reported in longitudinal studies of depression in primary care (Bellon et al, 2010). Fitting a PPOM allowed us to examine the associations between participants charateristics and categories of length of antidepressant use taking into account the ordinal nature of the outcome.…”

Section: Strengthsmentioning

confidence: 99%

What factors influence long-term antidepressant use in primary care? Findings from the Australian diamond cohort study

Ambresin

Palmer

Densley

et al. 2015

Journal of Affective Disorders

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Section: Strengthsmentioning

confidence: 99%

What factors influence long-term antidepressant use in primary care? Findings from the Australian diamond cohort study

Ambresin

Palmer

Densley

et al. 2015

Journal of Affective Disorders

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“…50,51 Missing data have been dealt with rigorously using multiple imputation methods to overcome possible biases related to complete case analysis. Depression outcome was assessed with the PHQ-9 diagnostic criteria, which have good levels of agreement with diagnoses of independent mental health professionals.…”

Section: Strengthsmentioning

confidence: 99%

“…11,14 Data are of good quality, with recruitment and attrition rates in the range of those reported in longitudinal studies of depression in primary care. 50,51 Missing data have been dealt with rigorously using multiple imputation methods to overcome possible biases related to complete case analysis. Depression outcome was assessed with the PHQ-9 diagnostic criteria, which have good levels of agreement with diagnoses of independent mental health professionals.…”

Section: Strengthsmentioning

confidence: 99%

Self-Rated Health and Long-Term Prognosis of Depression

Ambresin

Chondros

Dowrick

et al. 2014

The Annals of Family Medicine

125

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PURPOSE Indicators of prognosis should be considered to fully inform clinical decision making in the treatment of depression. This study examines whether self-rated health predicts long-term depression outcomes in primary care. METHODSOur analysis was based on the first 5 years of a prospective 10-year cohort study underway since January 2005 conducted in 30 randomly selected Australian primary care practices. Participants were 789 adult patients with a history of depressive symptoms. Main outcome measures include risks, risk differences, and risk ratios of major depressive syndrome (MDS) on the Patient Health Questionnaire.RESULTS Retention rates during the 5 years were 660 (84%), 586 (74%), 560 (71%), 533 (68%), and 517 (66%). At baseline, MDS was present in 27% (95% CI, 23%-30%). Cross-sectional analysis of baseline data showed participants reporting poor or fair self-rated health had greater odds of chronic illness, MDS, and lower socioeconomic status than those reporting good to excellent self-rated health. For participants rating their health as poor to fair compared with those rating it good to excellent, risk ratios of MDS were 2.10 (95% CI, 1.60-2.76), 2.38 (95% CI, 1.77-3.20), 2.22 (95% CI, 1.70-2.89), 1.73 (95% CI, 1.30-2.28), and 2.15 (95% CI, 1.59-2.90) at 1, 2, 3, 4, and 5 years, after accounting for missing data using multiple imputation. After adjusting for age, sex, multimorbidity, and depression status and severity, self-rated health remained a predictor of MDS up to 5 years.CONCLUSIONS Self-rated health offers family physicians an efficient and simple way to identify patients at risk of poor long-term depression outcomes and to inform therapeutic decision making. INTRODUCTIOND epression is the largest contributor to the burden of disease in high-income countries, and further increases are expected.1,2 Of those who seek professional help for depression, 80% turn to a primary care physician. 3 The high prevalence of depressive symptoms in primary care patients highlights the crucial role of primary care in the detection and treatment of depression. 4,5 In clinical practice understanding the prognosis for an individual is central to fully inform treatment decisions, 6 yet prognosis has received limited attention compared with therapeutic and etiological research. 6,7 Validated prognostic indices provide estimates of the probability of an outcome for those with a particular condition 7 and offer improved accuracy in prognostic assumptions that influence clinical decisions. The accuracy of family physicians in predicting the course of depression is described as fair, 9 although as in other conditions it has been studied relatively little. Self-rated health may be a candidate for a simple and transportable prognostic index. It provides a singlequestion assessment of one's subjective health, encompassing physical functioning, as well as psychosocial aspects of health.25,26 Self-rated health has been used as a reliable, quick assessment for population health monitoring. [27][28][29] Two lar...

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“…We decided to include an interaction in the model when the likelihood ratio test was significant at p<0.05. We used inverse probability weighting [41], [42] to adjust for a possible attrition bias due to participants lost to follow-up. All reported P values were two-sided.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

et al. 2014

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BackgroundThere are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months.MethodsA prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces.ResultsThe cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit.ConclusionsThe predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care.

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Psychosocial and sociodemographic predictors of attrition in a longitudinal study of major depression in primary care: the predictD-Spain study

Cited by 27 publications

References 35 publications

What factors influence long-term antidepressant use in primary care? Findings from the Australian diamond cohort study

What factors influence long-term antidepressant use in primary care? Findings from the Australian diamond cohort study

Self-Rated Health and Long-Term Prognosis of Depression

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

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