2 resultados para Primary-care Patients
em Glasgow Theses Service
Resumo:
In Scotland, life expectancy and health outcomes are strongly tied to socioeconomic status. Specifically, socioeconomically deprived areas suffer disproportionately from high levels of premature multimorbidity and mortality. To tackle these inequalities in health, challenges in the most deprived areas must be addressed. One avenue that merits attention is the potential role of general medical practitioners (GPs) in helping to address health inequalities, particularly due to their long-term presence in deprived communities, their role in improving patient and population health, and their potential advocacy role on behalf of their patients. GPs can be seen as what Lipsky calls ‘street-level bureaucrats’ due to their considerable autonomy in the decisions they make surrounding individual patient needs, yet practising under the bureaucratic structure of the NHS. While previous research has examined the applicability of Lipsky’s framework to the role of GPs, there has been very little research exploring how GPs negotiate between the multiple identities in their work, how GPs ‘socially construct’ their patients, how GPs view their potential role as ‘advocate’, and what this means in terms of the contribution of GPs to addressing existing inequalities in health. Using semi-structured interviews, this study explored the experience and views of 24 GPs working in some of Scotland’s most deprived practices to understand how they might combat this growing health divide via the mitigation (and potential prevention) of existing health inequalities. Participants were selected based on several criteria including practice deprivation level and their individual involvement in the Deep End project, which is an informal network comprising the 100 most deprived general practices in Scotland. The research focused on understanding GPs’ perceptions of their work including its broader implications, within their practice, the communities within which they practise, and the health system as a whole. The concept of street-level bureaucracy proved to be useful in understanding GPs’ frontline work and how they negotiate dilemmas. However, this research demonstrated the need to look beyond Lipsky’s framework in order to understand how GPs reconcile their multiple identities, including advocate and manager. As a result, the term ‘street-level professional’ is offered to capture more fully the multiple identities which GPs inhabit and to explain how GPs’ elite status positions them to engage in political and policy advocacy. This study also provides evidence that GPs’ social constructions of patients are linked not only to how GPs conceptualise the causes of health inequalities, but also to how they view their role in tackling them. In line with this, the interviews established that many GPs felt they could make a difference through advocacy efforts at individual, community and policy/political levels. Furthermore, the study draws attention to the importance of practitioner-led groups—such as the Deep End project—in supporting GPs’ efforts and providing a platform for their advocacy. Within this study, a range of GPs’ views have been explored based on the sample. While it is unclear how common these views are amongst GPs in general, the study revealed that there is considerable scope for ‘political GPs’ who choose to exercise discretion in their communities and beyond. Consequently, GPs working in deprived areas should be encouraged to use their professional status and political clout not only to strengthen local communities, but also to advocate for policy change that might potentially affect the degree of disadvantage of their patients, and levels of social and health inequalities more generally.
Resumo:
Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.