Background The Patient Health Questionnaire-9 (PHQ-9) is a brief tool to assess the presence and severity of depressive symptoms. This study aimed to validate and calibrate the PHQ-9 to determine appropriate cut-off points for different degrees of severity of depression in Argentina. Methods We conducted a cross-sectional study on an intentional sample of adult ambulatory care patients with different degrees of severity of depression. All patients who completed the PHQ-9 were further interviewed by a trained clinician with the Mini International Neuropsychiatric Interview (MINI) and the Beck Depression Inventory-II (BDI-II). Reliability and validity tests, including receiver operating curve analysis, were performed. Results One hundred sixty-nine patients were recruited with a mean age of 47.4 years (SD = 14.8), of whom 102 were females (60.4%). The local PHQ-9 had high internal consistency (Cronbach’s alpha = 0.87) and satisfactory convergent validity with the BDI-II scale [Pearson’s correlation = 0.88 (p < 0.01)]. For the diagnosis of Major Depressive Episode (MDE) according to the MINI, a PHQ-9 ≥ 8 was the optimal cut-off point found (sensitivity 88.2%, specificity 86.6%, PPV 90.91%). The local version of PHQ-9 showed good ability to discriminate among depression severity categories according to the BDI-II scale. The best cut off points were 6–8 for mild cases, 9–14 for moderate and 15 or more for severe depressive symptoms respectively. Conclusions The Argentine version of the PHQ-9 questionnaire has shown acceptable validity and reliability for both screening and severity assessment of depressive symptoms.
There is an increasing research interest in targeting interventions at the neighborhood level to prevent obesity. Healthy urban environments require including residents’ perspectives to help understanding how urban environments relate to residents’ food choices and physical activity levels. We describe an innovative community-driven process aimed to develop environmental recommendations for obesity prevention. We conducted this study in a low-income area in Madrid (Spain), using a collaborative citizen science approach. First, 36 participants of two previous Photovoice projects translated their findings into policy recommendations, using an adapted logical framework approach. Second, the research team grouped these recommendations into strategies for obesity prevention, using the deductive analytical strategy of successive approximation. Third, through a nominal group session including participants, researchers, public health practitioners and local policy-makers, we discussed and prioritized the obesity prevention recommendations. Participants identified 12 policy recommendations related to their food choices and 18 related to their physical activity. The research team grouped these into 11 concrete recommendations for obesity prevention. The ‘top-three’ ranked recommendations were: (1) to adequate and increase the number of public open spaces; (2) to improve the access and cost of existing sports facilities and (3) to reduce the cost of gluten-free and diabetic products.
Introduction To review the geographical exposure measures used to characterize the tobacco environment in terms of density of and proximity to tobacco outlets, and its association with smoking-related outcomes. Methods We used PubMed and Google Scholar to find articles published until December 2019. The search was restricted to studies which 1) measured the density of and/or proximity to tobacco outlets and 2) included associations with smoking outcomes. The extraction was coordinated by several observers. We gathered data on the place of exposure, methodological approaches, and smoking outcomes. Results Forty articles were eligible out of 3,002 screened papers. Different density and proximity measures were described. 47.4% density calculations were based on simple counts (number of outlets within an area). Kernel Density Estimations and other measures weighted by the size of the area (outlets/sq km), population, and road length were identified. 81.3% of the articles which assessed proximity to tobacco outlets used length distances estimated through the street network. Higher density values were mostly associated with higher smoking prevalence (76.2%), greater tobacco use and smoking initiation (64.3%); and lower cessation outcomes (84.6%). Proximity measures were not associated with any smoking outcome except with cessation (62.5%). Conclusion Associations between the density of tobacco outlets and smoking outcomes were found regardless of the exposure measure applied. Further research is warranted to better understand how proximity to tobacco outlets may influence on smoking outcomes. This systematic review discusses methodological gaps in the literature and provides insights for future studies exploring the tobacco environment. Implications Our findings pose some methodological lessons to improve the exposure measures on the tobacco outlet environment. To solve these methodological gaps is crucial to understanding the influence of the tobacco environment on the smoking outcomes. Activity spaces should be considered in further analyses since individuals are exposed to tobacco beyond their residence or school neighbourhood. Further studies in this research area demand density estimations weighted by the size of the area, population, or road length; or measured using Kernel Density Estimations. Proximity calculations should be measured through the street network and should consider travel times apart from the length-distance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.