Predicting and preventing relapse of depression in primary care: a mixed methods study

BackgroundMost people with depression are managed in primary care. Relapse (reemergence of depression symptoms after improvement) is common and contributes to the burden and morbidity associated with depression. There is a lack of evidence-based approaches for risk-stratifying people according to risk of relapse and for preventing relapse in primary care.MethodsIn this mixed methods study, I initially reviewed studies looking to predict relapse of depression across all settings. I then attempted to derive and validate a prognostic model to predict relapse within 6-8 months in a primary care setting, using multilevel logistic regression analysis on individual participant data from seven studies (n=1244). Concurrently, a qualitative workstream, using thematic analysis, explored the perspectives of general practitioners (GPs) and people with lived experience of depression around relapse risk and prevention in practice.ResultsThe systematic review identified eleven models; none could currently be implemented in a primary care setting. The prognostic model developed in this study had inadequate predictive performance on internal validation (Cstatistic 0.60; calibration slope 0.81). I carried out twenty-two semi-structured interviews with GPs and twenty-three with people with lived experience of depression. People with lived experience of depression and GPs reflected that a discussion around relapse would be useful but was not routinely offered. Both participant groups felt there would be benefits to relapse prevention for depression being embedded within primary care.ConclusionsWe are currently unable to accurately predict an individual’s risk ofdepression relapse. The longer-term care of people with depression ingeneral practice could be improved by enabling continuity of care, increased consistency and clarity around follow-up arrangements, and focussed discussions around relapse risk and prevention. Scalable, brief relapse prevention interventions are needed, which would require policy change and additional resource. We need to better understand existing interventions and barriers to implementation in practice

Global Carbon Budget: Ocean carbon sink.

CO2 emissions from human activities, the main contributor to global climate change, are set to rise again in 2014 reaching 40 billion tonnes CO2 The natural carbon ‘sinks’ on land and in the ocean absorb on average 55% of the total CO2 emissions, thus slowing the rate of global climate change Increasing CO2 in the oceans is causing ocean acidificatio

Predicting and preventing relapse of depression in primary care: a mixed methods study

Background Most people with depression are managed in primary care. Relapse (re-emergence of depression symptoms after improvement) is common and contributes to the burden and morbidity associated with depression. There is a lack of evidence-based approaches for risk-stratifying people according to risk of relapse and for preventing relapse in primary care. Methods In this mixed methods study, I initially reviewed studies looking to predict relapse of depression across all settings. I then attempted to derive and validate a prognostic model to predict relapse within 6-8 months in a primary care setting, using multilevel logistic regression analysis on individual participant data from seven studies (n=1244). Concurrently, a qualitative workstream, using thematic analysis, explored the perspectives of general practitioners (GPs) and people with lived experience of depression around relapse risk and prevention in practice. Results The systematic review identified eleven models; none could currently be implemented in a primary care setting. The prognostic model developed in this study had inadequate predictive performance on internal validation (C-statistic 0.60; calibration slope 0.81). I carried out twenty-two semi-structured interviews with GPs and twenty-three with people with lived experience of depression. People with lived experience of depression and GPs reflected that a discussion around relapse would be useful but was not routinely offered. Both participant groups felt there would be benefits to relapse prevention for depression being embedded within primary care. Conclusions We are currently unable to accurately predict an individual’s risk of depression relapse. The longer-term care of people with depression in general practice could be improved by enabling continuity of care, increased consistency and clarity around follow-up arrangements, and focussed discussions around relapse risk and prevention. Scalable, brief relapse prevention interventions are needed, which would require policy change and additional resource. We need to better understand existing interventions and barriers to implementation in practice

Are there researcher allegiance effects in diagnostic validation studies of the PHQ-9? : A systematic review and meta-analysis

OBJECTIVES: To investigate whether an authorship effect is found that leads to better performance in studies conducted by the original developers of the Patient Health Questionnaire (PHQ-9) (allegiant studies). DESIGN: Systematic review with random effects bivariate diagnostic meta-analysis. Search strategies included electronic databases, examination of reference lists and forward citation searches. INCLUSION CRITERIA: Included studies provided sufficient data to calculate the diagnostic accuracy of the PHQ-9 against a gold standard diagnosis of major depression using the algorithm or the summed item scoring method at cut-off point 10. DATA EXTRACTION: Descriptive information, methodological quality criteria and 2×2 contingency tables. RESULTS: Seven allegiant and 20 independent studies reported the diagnostic performance of the PHQ-9 using the algorithm scoring method. Pooled diagnostic OR (DOR) for the allegiant group was 64.40, and 15.05 for non-allegiant studies group. The allegiance status was a significant predictor of DOR variation (p<0.0001).Five allegiant studies and 26 non-allegiant studies reported the performance of the PHQ-9 at recommended cut-off point of 10. Pooled DOR for the allegiant group was 49.31, and 24.96 for the non-allegiant studies. The allegiance status was a significant predictor of DOR variation (p=0.015).Some potential alternative explanations for the observed authorship effect including differences in study characteristics and quality were found, although it is not clear how some of them account for the observed differences. CONCLUSIONS: Allegiant studies reported better performance of the PHQ-9. Allegiance status was predictive of variation in the DOR. Based on the observed differences between independent and non-independent studies, we were unable to conclude or exclude that allegiance effects are present in studies examining the diagnostic performance of the PHQ-9. This study highlights the need for future meta-analyses of diagnostic validation studies of psychological measures to evaluate the impact of researcher allegiance in the primary studies

Closing the Coverage Gaps: Reducing Health Insurance Disparities in Massachusetts

Massachusetts has been exemplary in developing health insurance coverage policies to cover its residents. By 2019, the state's uninsurance rate was 3.0 percent, the lowest rate in the nation, representing about 204,000 uninsured residents. While the state's overall uninsured rate at a given point in time is low, more than twice as many people - 503,000, or 7.3 percent of the population - experienced a gap in coverage over the previous twelve months. And importantly, not all groups benefit equally. People who are Black or Hispanic, or who have lower incomes, experience significantly higher rates of uninsurance than the state population overall. As a result, these groups are more likely to face access barriers and financial insecurity associated with being uninsured.The purpose of this report is to begin charting a course toward closing the coverage gaps in Massachusetts, with a particular focus on creating a more racially and ethnically equitable system of coverage. The report and accompanying infographics describe the people in Massachusetts without health insurance and the barriers to coverage they face, including affordability, administrative complexity, and immigration, language, and cultural barriers. It then proposes a menu of policy options that address the specific circumstances in Massachusetts. The proposed options are meant to inform a statewide conversation about the best approaches to closing the remaining coverage gaps in Massachusetts and removing structural barriers that result in racial and ethnic disparities in health insurance coverage