Evaluating the safety of mental health-related prescribing in UK primary care: a cross-sectional study using the Clinical Practice Research Datalink (CPRD)

Abstract

BackgroundMost patients with mental illness are managed in primary care, yet there is a lack of data exploring potential prescribing safety issues in this setting for this population.ObjectivesExamine the prevalence of, between-practice variation in, and patient and practice-level risk factors for, 18 mental health-related potentially hazardous prescribing indicators and four inadequate medication monitoring indicators in UK primary care.MethodCross-sectional analyses of routinely collected electronic health records from 361 practices contributing to Clinical Practice Research Datalink GOLD database. The proportion of patients ‘at risk’ (based on an existing diagnosis, medication, age and/or sex) triggering each indicator and composite indicator was calculated. To examine between-practice variation, intraclass correlation coefficient (ICC) and median OR (MOR) were estimated using two-level logistic regression models. The relationship between patient and practice characteristics and risk of triggering composites including 16 of the 18 prescribing indicators and four monitoring indicators were assessed using multilevel logistic regression.Results9.4% of patients ‘at risk’ (151 469 of 1 611 129) triggered at least one potentially hazardous prescribing indicator; between practices this ranged from 3.2% to 24.1% (ICC 0.03, MOR 1.22). For inadequate monitoring, 90.2% of patients ‘at risk’ (38 671 of 42 879) triggered at least one indicator; between practices this ranged from 33.3% to 100% (ICC 0.26, MOR 2.86). Patients aged 35–44, females and those receiving more than 10 repeat prescriptions were at greatest risk of triggering a prescribing indicator. Patients aged less than 25, females and those with one or no repeat prescription were at greatest risk of triggering a monitoring indicator.ConclusionPotentially hazardous prescribing and inadequate medication monitoring commonly affect patients with mental illness in primary care, with marked between-practice variation for some indicators. These findings support health providers to identify improvement targets and inform development of improvement efforts to reduce medication-related harm.

Understanding the medication safety challenges for patients with mental illness in primary care: a scoping review

“…Primary care encompasses settings which “practice in the context of family and community” [ 23 ] and include services such as general practice, community pharmacy, community mental health teams (CMHT), and elderly care/nursing homes. Whilst several review papers have summarised the evidence concerning medication safety in mental health inpatient care [ 17 , 24 , 25 ], the available literature for primary care is fragmented despite the emergence of recent studies [ 26 , 27 ]. This is important as 90% of patients with mental illness are treated solely in primary care [ 28 ], and this setting accounts for a greater estimated proportion of errors with medication in England per year than secondary care for the general population (38.4% compared to 19.9%) [ 29 ].…”

Evaluating patient-reported outcome measures in Peru: a cross-sectional study of satisfaction and net promoter score using the 2016 EnSuSalud survey

“…We calculated ICC from multilevel ordinal logistic regression models for each categorical outcome and multilevel linear regression for NPS 10 , first as null models and then controlling for individual-level factors (demographics, self-rated health and visit type and purpose) to address case-mix differences between facilities. We used the Spearman-Brown formula, |n*ICC|1+|n-1*ICC, where n is sample size per facility, to calculate reliability and estimate the minimum number of respondents required per facility to obtain adequate (>0.70) or strong (>0.90) reliability for each outcome 30…”

“…We used the Spearman-Brown formula, , where n is sample size per facility, to calculate reliability and estimate the minimum number of respondents required per facility to obtain adequate (>0.70) or strong (>0.90) reliability for each outcome. 30 …”

“…) , where n is sample size per facility, to calculate reliability and estimate the minimum number of respondents required per facility to obtain adequate (>0.70) or strong (>0.90) reliability for each outcome. 30 Second, to show discrimination between facilities, we generated facility-specific means (empirical Bayes estimates) and SEs from the multilevel ordinal logistic regression models of each outcome adjusted for case mix. We calculated the Spearman rank correlation for facility scores on satisfaction and NPS.…”

Challenge of optimising medication in people with severe mental illness

“…In this issue of BMJ Quality and Safety, Dr Khawagi and colleagues evaluate the safety of mental health related prescribing in general practice in the UK. 10 They report on prevalence and between-practice variation in 22 indicators of mental healthrelated prescribing that suggest potentially hazardous prescribing (n=18) or inadequate monitoring of medication (n=4). The indicators had been developed in a previous Delphi study 11 to target safety and monitoring of medication used to treat SMI, anxiety, insomnia, depression and dementia.…”