Variation in the diagnosis and control of hypertension is not explained by conventional variables: Cross-sectional database study in English general practice

Abstract

BackgroundHypertension is a major cause of preventable disability and death globally and affects more than one in four adults in England. Unwarranted variation is variation in access, quality, outcome or value which is unexplained by differences in the condition or patient characteristics and which reduces quality and efficiency. Distinguishing unwarranted from variation due to clinical, organisational or patient factors can be challenging. We carried out this study to explore inter-practice variation in the diagnosis and management of hypertension in the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network database, a large, representative surveillance database.Methods and findingWe carried out a cross-sectional study using primary care data extracted from the electronic health records of 1,271,419 adults registered at RCGP RSC general practices on 31st December 2016. Logistic regression was used to indirectly standardise practice-level hypertension prevalence and control against the RCGP RSC population, adjusted for age, gender, ethnicity, deprivation, co-morbidity, NHS region and practice size. Inter-practice variation was demonstrated using funnel plots with 95% and 99.8% control limits. The prevalence of detected hypertension was 18.4% (95% CI 18.4–18.5), n = 234,165. Uncontrolled hypertension was present in 146,553 of 196,052 individuals, 25.2% (25.1–25.4), in whom blood pressure had been recorded in the previous year. Hypertension management varied markedly between practices with a three-fold difference in prevalence, 13.5–38.4%, and a four-fold difference in the proportion of uncontrolled hypertension, 11.8–47.9%. Despite adjustment for sociodemographic and practice characteristics funnel plots demonstrated marked over-dispersion.ConclusionsSubstantial variation in the prevalence of diagnosed hypertension and the management of hypertension was only partially explained by characteristics captured within a routine dataset. The over-dispersion suggests variation is not fully explained by these factors and that context, behaviour and processes of care delivery may contribute to variation. Routine data sources in isolation to not provide sufficient contextual data to diagnose the causes of variation.

Prescription Rates, Polypharmacy and Prescriber Variability in Swiss General Practice—A Cross-Sectional Database Study

“…Large primary care databases have been used before for measuring prescription rates and general practitioner (GP) variability in prescribing, but analyses have generally been limited to specific medication classes (e.g., antibiotics or opioids) ( Guthrie et al, 2015b ; Haastrup et al, 2016 ; Coyle et al, 2019 ) or populations (e.g., older patients) ( Aubert et al, 2016 ; Schnegg et al, 2020 ). Comprehensive assessments across all medication classes and patient demographics are needed to identify the specific medication classes contributing to polypharmacy and to develop targeted initiatives to improve prescribing practices.…”

Long-term Outcomes of a 1-year Hypertension Quality Improvement Initiative in a Large Health System

“…An analysis of 3 health systems participating in the San Francisco Bay Collaborative Research found the percentage of patients in control varied by as much as 32% across sites 30. A study in Great Britain identified that smaller practice size was associated with uncontrolled BP; however, much of the variation was uncharacterized 31. We found patient demographics and chronic conditions accounted for only a small amount of the variation in site-specific control.…”

“…30 A study in Great Britain identified that smaller practice size was associated with uncontrolled BP; however, much of the variation was uncharacterized. 31 We found patient demographics and chronic conditions accounted for only a small amount of the variation in site-specific control. Other potential explanations for the differences observed across locations that we could not explore in our analysis include variations in medication adherence and refractory hypertension.…”

Identification and classification of principal features for analyzing unwarranted clinical variation

“…The classification of features is shown in Figure 1 Race and ethnicity 18 (39) 18 [9][10][11][12][13][14][15]17,21,23,28,29,[35][36][37][38]45,49 Social Socioeconomic status 30 (65) 30 [9][10][11][12][13][14][15][16] Social risk factors 12 (26) 12 [10][11][12][13][14][15][16][18][19][20][21]45 Clinical characteristics Patient profile N/A Body mass index 7 (15) 7 20,23,34,[39][40][41]49 History Comorbidity 29 (63) 29 [9][10][11]13,[15]…”

“…The standard indices include Australia's Socioeconomic Indexes for Areas (SEIFA), 22 and Index of Relative Socioeconomic Disadvantage (IRSD), 16 the UK's Index of Multiple Deprivation (IMD). [23][24][25][26][27] The ad hoc measures include income levels, 11,12,15,19,[28][29][30][31][32][33] education levels, 11,15,28,34 neighbourhood income or rural setting, 15,19,20,35 non-English speakers, 18 and insurance type. [12][13][14][15][16]21,[35][36][37][38]…”