scholarly journals Identifying determinants of diabetes risk and outcomes for people with severe mental illness: a mixed-methods study

2021 ◽  
Vol 9 (10) ◽  
pp. 1-194
Author(s):  
Jennie Lister ◽  
Lu Han ◽  
Sue Bellass ◽  
Jo Taylor ◽  
Sarah L Alderson ◽  
...  
Keyword(s):  
Health Care ◽  
Mental Illness ◽  
Clinical Practice ◽  
Qualitative Study ◽  
Confidence Interval ◽  
Health Outcomes ◽  
Severe Mental Illness ◽  
Quantitative Study ◽  
Practice Research ◽  
Clinical Practice Research Datalink

Background People with severe mental illness experience poorer health outcomes than the general population. Diabetes contributes significantly to this health gap. Objectives The objectives were to identify the determinants of diabetes and to explore variation in diabetes outcomes for people with severe mental illness. Design Under a social inequalities framework, a concurrent mixed-methods design combined analysis of linked primary care records with qualitative interviews. Setting The quantitative study was carried out in general practices in England (2000–16). The qualitative study was a community study (undertaken in the North West and in Yorkshire and the Humber). Participants The quantitative study used the longitudinal health records of 32,781 people with severe mental illness (a subset of 3448 people had diabetes) and 9551 ‘controls’ (with diabetes but no severe mental illness), matched on age, sex and practice, from the Clinical Practice Research Datalink (GOLD version). The qualitative study participants comprised 39 adults with diabetes and severe mental illness, nine family members and 30 health-care staff. Data sources The Clinical Practice Research Datalink (GOLD) individual patient data were linked to Hospital Episode Statistics, Office for National Statistics mortality data and the Index of Multiple Deprivation. Results People with severe mental illness were more likely to have diabetes if they were taking atypical antipsychotics, were living in areas of social deprivation, or were of Asian or black ethnicity. A substantial minority developed diabetes prior to severe mental illness. Compared with people with diabetes alone, people with both severe mental illness and diabetes received more frequent physical checks, maintained tighter glycaemic and blood pressure control, and had fewer recorded physical comorbidities and elective admissions, on average. However, they had more emergency admissions (incidence rate ratio 1.14, 95% confidence interval 0.96 to 1.36) and a significantly higher risk of all-cause mortality than people with diabetes but no severe mental illness (hazard ratio 1.89, 95% confidence interval 1.59 to 2.26). These paradoxical results may be explained by other findings. For example, people with severe mental illness and diabetes were more likely to live in socially deprived areas, which is associated with reduced frequency of health checks, poorer health outcomes and higher mortality risk. In interviews, participants frequently described prioritising their mental illness over their diabetes (e.g. tolerating antipsychotic side effects, despite awareness of harmful impacts on diabetes control) and feeling overwhelmed by competing treatment demands from multiple morbidities. Both service users and practitioners acknowledged misattributing physical symptoms to poor mental health (‘diagnostic overshadowing’). Limitations Data may not be nationally representative for all relevant covariates, and the completeness of recording varied across practices. Conclusions People with severe mental illness and diabetes experience poorer health outcomes than, and deficiencies in some aspects of health care compared with, people with diabetes alone. Future work These findings can inform the development of targeted interventions aimed at addressing inequalities in this population. Study registration National Institute for Health Research (NIHR) Central Portfolio Management System (37024); and ClinicalTrials.gov NCT03534921. Funding This project was funded by the NIHR Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 9, No. 10. See the NIHR Journals Library website for further project information.

scholarly journals rdrugtrajectory: An R Package for the Analysis of Drug Prescriptions in Electronic Health Care Records

2021 ◽  
Author(s):  
Anthony Nash ◽  
Tingyee E Chang ◽  
Benjamin Wan ◽  
M. Zameel Cader
Keyword(s):  
Health Care ◽  
Clinical Practice ◽  
Drug Prescription ◽  
Clinical Information ◽  
R Package ◽  
Drug Prescriptions ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Electronic Health ◽  
Health Care Records

Primary care electronic health care records are rich with patient and clinical information. Studying electronic health care records has resulted in marked improvements to national health care processes and patient-care decision making, and is a powerful supplementary source of data for drug discovery effort. We present the R package rdrugtrajectory, designed to yield demographic and patient-level characteristics of drug prescriptions in the UK Clinical Practice Research Datalink dataset. The package operates over Clinical Practice Research Datalink Gold clinical, referral and therapy datasets and includes features such as first drug prescriptions analysis, cohort-wide prescription information, cumulative drug prescription events, the longitudinal trajectory of drug prescriptions, and a survival analysis timeline builder to identify risks related to drug prescription switching. The rdrugtrajectory package has been made freely available via the GitHub repository.

scholarly journals Exploring Severe Mental Illness and Diabetes: Protocol for a Longitudinal, Observational, and Qualitative Mixed Methods Study

10.2196/13407 ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. e13407 ◽  
Author(s):  
Sue Bellass ◽  
Johanna Taylor ◽  
Lu Han ◽  
Stephanie L Prady ◽  
David Shiers ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Care ◽  
Mental Illness ◽  
Mixed Methods ◽  
Severe Mental Illness ◽  
Qualitative Interviews ◽  
Care Staff ◽  
Clinical Practice Research Datalink ◽  
Diabetes Outcomes ◽  
Diabetic Outcomes

Background The average life expectancy for people with a severe mental illness (SMI) such as schizophrenia or bipolar disorder is 15 to 20 years less than that for the population as a whole. Diabetes contributes significantly to this inequality, being 2 to 3 times more prevalent in people with SMI. Various risk factors have been implicated, including side effects of antipsychotic medication and unhealthy lifestyles, which often occur in the context of socioeconomic disadvantage and health care inequality. However, little is known about how these factors may interact to influence the risk of developing diabetes and poor diabetic outcomes, or how the organization and provision of health care may contribute. Objective This study aims to identify the determinants of diabetes and to explore variation in diabetes outcomes for people with SMI. Methods This study will employ a concurrent mixed methods design combining the interrogation of electronic primary care health records from the Clinical Practice Research Datalink (CPRD GOLD) with qualitative interviews with adults with SMI and diabetes, their relatives and friends, and health care staff. The study has been funded for 2 years, from September 2017 to September 2019, and data collection has recently ended. Results CPRD and linked health data will be used to explore the association of sociodemographics, illness, and health care–related factors with both the development and outcomes of type 2 diabetes in people with SMI. Experiences of managing the comorbidity and accessing health care will be explored through qualitative interviews using topic guides informed by evidence synthesis and expert consultation. Findings from both datasets will be merged to develop a more comprehensive understanding of diabetes risks, interventions, and outcomes for people with SMI. Findings will be translated into recommendations for interventions and services using co-design workshops. Conclusions Improving diabetes outcomes for people with SMI is a high-priority area nationally and globally. Understanding how risk factors combine to generate high prevalence of diabetes and poor diabetic outcomes for this population is a necessary first step in developing health care interventions to improve outcomes for people with diabetes and SMI. Trial Registration ClinicalTrials.gov NCT03534921; https://clinicaltrials.gov/ct2/show/NCT03534921

scholarly journals Assessment of Effectiveness of Seasonal Influenza Vaccination During Pregnancy in Preventing Influenza Infection in Infants in England, 2013–2014 and 2014–2015

2019 ◽  
Author(s):  
Jemma L Walker ◽  
Hongxin Zhao ◽  
Gavin Dabrera ◽  
Nick Andrews ◽  
Sarah L Thomas ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Confidence Interval ◽  
Influenza Vaccination ◽  
Seasonal Influenza ◽  
Influenza Infection ◽  
Screening Method ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Uptake Data ◽  
Maternal Influenza

AbstractMaternal influenza vaccination is increasingly recognized to protect infants from influenza infection in their first 6 months. We used the screening method to estimate vaccine effectiveness (VE) against laboratory-confirmed influenza in infants in England, using newly available uptake data from the Clinical Practice Research Datalink pregnancy register, matched on week of birth and region and adjusted for ethnicity. We found VE of 66% (95% confidence interval [CI], 18%–84%) in the 2013–2014 season and 50% (95% CI, 11%–72%) in 2014–2015, with similar VE against influenza-related hospitalization. VE against the dominant circulating influenza strain was higher, at 78% (95% CI, 16%–94%) against H1N1 in 2013–2014, and 60% (95% CI, 16%–81%) against H3N2 in 2014–2015.

scholarly journals Varenicline versus nicotine replacement therapy for long-term smoking cessation: an observational study using the Clinical Practice Research Datalink

2020 ◽  
Vol 24 (9) ◽  
pp. 1-46
Author(s):  
Neil M Davies ◽  
Amy E Taylor ◽  
Gemma MJ Taylor ◽  
Taha Itani ◽  
Tim Jones ◽  
...  
Keyword(s):  
Primary Care ◽  
Smoking Cessation ◽  
Clinical Practice ◽  
Confidence Interval ◽  
Replacement Therapy ◽  
Nicotine Replacement Therapy ◽  
Instrumental Variable ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Nicotine Replacement

Background Smoking is the leading avoidable cause of illness and premature mortality. The first-line treatments for smoking cessation are nicotine replacement therapy and varenicline. Meta-analyses of experimental studies have shown that participants allocated to the varenicline group were 1.57 times (95% confidence interval 1.29 to 1.91 times) as likely to be abstinent 6 months after treatment as those allocated to the nicotine replacement therapy group. However, there is limited evidence about the effectiveness of varenicline when prescribed in primary care. We investigated the effectiveness and rate of adverse events of these medicines in the general population. Objective To estimate the effect of prescribing varenicline on smoking cessation rates and health outcomes. Data sources Clinical Practice Research Datalink. Methods We conducted an observational cohort study using electronic medical records from the Clinical Practice Research Datalink. We extracted data on all patients who were prescribed varenicline or nicotine replacement therapy after 1 September 2006 who were aged ≥ 18 years. We investigated the effects of varenicline on smoking cessation, all-cause mortality and cause-specific mortality and hospitalisation for: (1) chronic lung disease, (2) lung cancer, (3) coronary heart disease, (4) pneumonia, (5) cerebrovascular disease, (6) diabetes, and (7) external causes; primary care diagnosis of myocardial infarction, chronic obstructive pulmonary disease, depression, or prescription for anxiety; weight in kg; general practitioner and hospital attendance. Our primary outcome was smoking cessation 2 years after the first prescription. We investigated the baseline differences between patients prescribed varenicline and patients prescribed nicotine replacement therapy. We report results using multivariable-adjusted, propensity score and instrumental variable regression. Finally, we developed methods to assess the relative bias of the different statistical methods we used. Results People prescribed varenicline were healthier at baseline than those prescribed nicotine replacement therapy in almost all characteristics, which highlighted the potential for residual confounding. Our instrumental variable analysis results found little evidence that patients prescribed varenicline had lower mortality 2 years after their first prescription (risk difference 0.67, 95% confidence interval –0.11 to 1.46) than those prescribed nicotine replacement therapy. They had similar rates of all-cause hospitalisation, incident primary care diagnoses of myocardial infarction and chronic obstructive pulmonary disease. People prescribed varenicline subsequently attended primary care less frequently. Patients prescribed varenicline were more likely (odds ratio 1.46, 95% confidence interval 1.42 to 1.50) to be abstinent 6 months after treatment than those prescribed nicotine replacement therapy when estimated using multivariable-adjusted for baseline covariates. Patients from more deprived areas were less likely to be prescribed varenicline. However, varenicline had similar effectiveness for these groups. Conclusion Patients prescribed varenicline in primary care were more likely to quit smoking than those prescribed nicotine replacement therapy, but there was little evidence that they had lower rates of mortality or morbidity in the 4 years following the first prescription. There was little evidence of heterogeneity in effectiveness across the population. Future work Future research should investigate the decline in prescribing of smoking cessation products; develop an optimal treatment algorithm for smoking cessation; use methods for using instruments with survival outcomes; and develop methods for comparing multivariable-adjusted and instrumental variable estimates. Limitations Not all of our code lists were validated, body mass index and Index of Multiple Deprivation had missing values, our results may suffer from residual confounding, and we had no information on treatment adherence. Trial registration This trial is registered as NCT02681848. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 9. See the NIHR Journals Library website for further project information.

scholarly journals Clustering of physical health multimorbidity in 68,392 people with severe mental illness and matched comparators: a lifetime prevalence analysis of United Kingdom primary care data

2021 ◽  
Author(s):  
Naomi J Launders ◽  
Joseph F Hayes ◽  
Gabriele Price ◽  
David PJ Osborn
Keyword(s):  
Mental Illness ◽  
Physical Health ◽  
Severe Mental Illness ◽  
Neurological Disease ◽  
Health Conditions ◽  
Drug Misuse ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Disease Cluster ◽  
Younger Age

Objective: To investigate the clustering of physical health multimorbidity in people with severe mental illness (SMI) compared to matched comparators. Design: A cohort-nested analysis of lifetime diagnoses of physical health conditions. Setting: Over 1,800 UK general practices (GP) contributing to Clinical Practice Research DataLink (CPRD) Gold or Aurum databases. Participants: 68,392 adult patients with a diagnosis of SMI between 2000 and 2018, with at least one year of follow up data, matched 1:4 to patients without an SMI diagnosis, on age, sex, GP, and year of GP registration. Main outcome measures: Odds ratios for 24 physical health conditions derived using Elixhauser and Charlson comorbidity indices. We controlled for age, sex, region, and ethnicity; and then additionally for smoking status, alcohol and drug misuse and body mass index. We defined multimorbidity clusters using Multiple Correspondence Analysis and K-Means cluster analysis and described them based on the observed/expected ratio. Results: Patients with a diagnosis of SMI had an increased odds of 19 of 24 physical health conditions and had a higher prevalence of multimorbidity at a younger age compared to comparators (aOR: 2.47; 95%CI: 2.25 to 2.72 in patients aged 20-29). Smoking, obesity, alcohol, and drug misuse were more prevalent in the SMI group and adjusting for these reduced the odds ratio of all comorbid conditions. In patients with multimorbidity (SMI cohort: n=22,843, comparators: n=68,856), we identified six multimorbidity clusters in the SMI cohort, and five in the comparator cohort. Five profiles were common to both. The "hypertension and varied multimorbidity" cluster was most common: 49.8% in the SMI cohort, and 56.7% in comparators. 41.5% of the SMI cohort were in a "respiratory and neurological disease" cluster, compared to 28.7% of comparators. Conclusions: Physical health multimorbidity clusters similarly in people with and without SMI, though patients with SMI develop multimorbidity earlier and a greater proportion fall into a "respiratory and neurological disease" cluster. There is a need for interventions aimed at younger-age multimorbidity in those with SMI.

scholarly journals Exploring Severe Mental Illness and Diabetes: Protocol for a Longitudinal, Observational, and Qualitative Mixed Methods Study (Preprint)

2019 ◽  
Author(s):  
Sue Bellass ◽  
Johanna Taylor ◽  
Lu Han ◽  
Stephanie L Prady ◽  
David Shiers ◽  
...  
Keyword(s):  
Risk Factors ◽  
Health Care ◽  
Mental Illness ◽  
Mixed Methods ◽  
Severe Mental Illness ◽  
Qualitative Interviews ◽  
Care Staff ◽  
Clinical Practice Research Datalink ◽  
Diabetes Outcomes ◽  
Diabetic Outcomes

BACKGROUND The average life expectancy for people with a severe mental illness (SMI) such as schizophrenia or bipolar disorder is 15 to 20 years less than that for the population as a whole. Diabetes contributes significantly to this inequality, being 2 to 3 times more prevalent in people with SMI. Various risk factors have been implicated, including side effects of antipsychotic medication and unhealthy lifestyles, which often occur in the context of socioeconomic disadvantage and health care inequality. However, little is known about how these factors may interact to influence the risk of developing diabetes and poor diabetic outcomes, or how the organization and provision of health care may contribute. OBJECTIVE This study aims to identify the determinants of diabetes and to explore variation in diabetes outcomes for people with SMI. METHODS This study will employ a concurrent mixed methods design combining the interrogation of electronic primary care health records from the Clinical Practice Research Datalink (CPRD GOLD) with qualitative interviews with adults with SMI and diabetes, their relatives and friends, and health care staff. The study has been funded for 2 years, from September 2017 to September 2019, and data collection has recently ended. RESULTS CPRD and linked health data will be used to explore the association of sociodemographics, illness, and health care–related factors with both the development and outcomes of type 2 diabetes in people with SMI. Experiences of managing the comorbidity and accessing health care will be explored through qualitative interviews using topic guides informed by evidence synthesis and expert consultation. Findings from both datasets will be merged to develop a more comprehensive understanding of diabetes risks, interventions, and outcomes for people with SMI. Findings will be translated into recommendations for interventions and services using co-design workshops. CONCLUSIONS Improving diabetes outcomes for people with SMI is a high-priority area nationally and globally. Understanding how risk factors combine to generate high prevalence of diabetes and poor diabetic outcomes for this population is a necessary first step in developing health care interventions to improve outcomes for people with diabetes and SMI. CLINICALTRIAL ClinicalTrials.gov NCT03534921; https://clinicaltrials.gov/ct2/show/NCT03534921

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