Does shortage of GPs matter? A cross-sectional study of practice population life expectancy.

BACKGROUND: There are not enough GPs in England. Access to general practice and continuity of care are declining. AIM: To investigate whether practice characteristics are associated with life expectancy of practice populations. DESIGN AND SETTING: A cross-sectional ecological study of patient life expectancy from 2015-2019. METHOD: Selection of independent variables was based on conceptual frameworks describing general practice's influence on outcomes. Sixteen non-correlated variables were entered into multivariable weighted regression models: population characteristics (Index of Multiple Deprivation, region, % White ethnicity, and % on diabetes register); practice organisation (total NHS payments to practices expressed as payment per registered patient, full-time equivalent fully qualified GPs, GP registrars, advanced nurse practitioners, other nurses, and receptionists per 1000 patients); access (% seen on the same day); clinical performance (% aged ≥45 years with blood pressure checked, % with chronic obstructive pulmonary disease vaccinated against flu, % with diabetes in glycaemic control, and % with coronary heart disease on antiplatelet therapy); and the therapeutic relationship (% continuity). RESULTS: Deprivation was strongly negatively associated with life expectancy. Regions outside London and White ethnicity were associated with lower life expectancy. Higher payment per patient, full-time equivalent fully qualified GPs per 1000 patients, continuity, % with chronic obstructive pulmonary disease having the flu vaccination, and % with diabetes with glycaemic control were associated with higher life expectancy; the % being seen on the same day was associated with higher life expectancy in males only. The variable aged ≥45 years with blood pressure checked was a negative predictor in females. CONCLUSION: The number of GPs, continuity of care, and access in England are declining, and it is worrying that these features of general practice were positively associated with life expectancy.

Variations in documentation of atrial fibrillation predicted by population and service level characteristics in primary health care in England.

BACKGROUND: Identifying features associated with atrial fibrillation (AF) documentation could inform screening. This study used published data to describe differences in documented and estimated AF prevalence in general practices, and explored predictors of variations in AF prevalence. METHODS: Cross-sectional study of 7318 general practices in England. Descriptive and inferential statistics were undertaken. Multiple linear regression was used to model the difference between estimated AF and documented AF, adjusted for population, practice and practice performance variables. RESULTS: Documented AF prevalence was lower than estimated (- 0.55% 95% confidence intervals, -1.89, 2.99). The proportion of variability accounted for in the final regression model was 0.25. Factors positively associated with AF documentation (increase in difference between estimated and documented), were patients 65-74 years, 75 years +, Black or South Asian ethnicity, diabetes mellitus and practices in East and Midlands of England. Eight variables (female patients, deprivation score, heart failure and peripheral artery disease, total patients per practice, full-time GPs and nurses; and location in South of England) were negatively associated with AF documentation (reduction in difference). CONCLUSION: Variations in AF documentation were predicted by several practice and population characteristics. Screening could target these sources of variation to decrease variation and improve AF documentation.

Predictors of hypertension detection in English general practices: a cross sectional study.

BACKGROUND: Worldwide, high systolic blood pressure is the leading risk factor for deaths and disability-adjusted life-years but has been historically under-detected. This study aimed to quantify differences between estimated and practice-detected prevalences of hypertension across English general practices, and to determine how variations in detected prevalence could be explained by population-level and service-level factors. METHODS: Descriptive statistics, pair wise correlations between the independent variables and a multivariable regression analysis were undertaken. In the regression model, the outcome was detected hypertension prevalence, adjusted for estimated prevalence, person-related and disease-related determinants of illness and characteristics of general practices. RESULTS: Detected prevalence was substantially lower than estimated prevalence (mean difference 16.23%; standard deviation 2.88%). Higher detected prevalence was associated with increased deprivation, increased non-white ethnicity and urban location. Lower detected prevalence was associated with larger list sizes, more general practitioners and being located in the South outside London. The final multivariable model's adjusted R2 value was 0.75. CONCLUSIONS: Substantial under-detection of hypertension is widespread across England. Independent of estimated prevalence, factors associated with greater morbidity and population density predicted higher detected prevalence. Identifying patients with undetected hypertension and coordinating care for these patients will require further resources and logistical support in community settings.

Socioeconomic deprivation scores as predictors of variations in NHS practice payments: a longitudinal study of English general practices 2013-2017.

BACKGROUND: A previous study found that variables related to population health needs were poor predictors of cross-sectional variations in practice payments. AIM: To investigate whether deprivation scores predicted variations in the increase over time of total payments to general practices per patient, after adjustment for potential confounders. DESIGN AND SETTING: Longitudinal multilevel model for 2013-2017; 6900 practices (84.4% of English practices). METHOD: Practices were excluded if total adjusted payments per patient were <£10 or >£500 per patient or if deprivation scores were missing. Main outcome measures were adjusted total NHS payments; calculated by dividing total NHS payments, after deductions and premises payments, by the number of registered patients in each practice. A total of 17 independent variables relating to practice population and organisational factors were included in the model after checking for collinearity. RESULTS: After adjustment for confounders and the logarithmic transformation of the dependent and main independent variables (due to extremely skewed [positive] distribution of payments), practice deprivation scores predicted very weakly longitudinal variations in total payments' slopes. For each 10% increase in the Index of Multiple Deprivation score, practice payments increased by only 0.06%. The large sample size probably explains why eight of the 17 confounders were significant predictors, but with very small coefficients. Most of the variability was at practice level (intraclass correlation = 0.81). CONCLUSION: The existing NHS practice payment formula has demonstrated very little redistributive potential and is unlikely to substantially narrow funding gaps between practices with differing workloads caused by the impact of deprivation.

Predicting declines in perceived relationship continuity using practice deprivation scores: a longitudinal study in primary care.

BACKGROUND: Increased relationship continuity in primary care is associated with better health outcomes, greater patient satisfaction, and fewer hospital admissions. Greater socioeconomic deprivation is associated with lower levels of continuity, as well as poorer health outcomes. AIM: To investigate whether deprivation scores predicted variations in the decline over time of patient-perceived relationship continuity of care, after adjustment for practice organisational and population factors. DESIGN AND SETTING: An observational study in 6243 primary care practices with more than one GP, in England, using a longitudinal multilevel linear model, 2012-2017 inclusive. METHOD: Patient-perceived relationship continuity was calculated using two questions from the GP Patient Survey. The effect of deprivation on the linear slope of continuity over time was modelled, adjusting for nine confounding variables (practice population and organisational factors). Clustering of measurements within general practices was adjusted for by using a random intercepts and random slopes model. Descriptive statistics and univariable analyses were also undertaken. RESULTS: Relationship continuity declined by 27.5% between 2012 and 2017, and at all deprivation levels. Deprivation scores from 2012 did not predict variations in the decline of relationship continuity at practice level, after accounting for the effects of organisational and population confounding variables, which themselves did not predict, or weakly predicted with very small effect sizes, the decline of continuity. Cross-sectionally, continuity and deprivation were negatively correlated within each year. CONCLUSION: The decline in relationship continuity of care has been marked and widespread. Measures to maximise continuity will need to be feasible for individual practices with diverse population and organisational characteristics.

Population health needs as predictors of variations in NHS practice payments: a cross-sectional study of English general practices in 2013-2014 and 2014-2015.

BACKGROUND: NHS general practice payments in England include pay for performance elements and a weighted component designed to compensate for workload, but without measures of specific deprivation or ethnic groups. AIM: To determine whether population factors related to health needs predicted variations in NHS payments to individual general practices in England. DESIGN AND SETTING: Cross-sectional study of all practices in England, in financial years 2013-2014 and 2014-2015. METHOD: Descriptive statistics, univariable analyses (examining correlations between payment and predictors), and multivariable analyses (undertaking multivariable linear regressions for each year, with logarithms of payments as the dependent variables, and with population, practice, and performance factors as independent variables) were undertaken. RESULTS: Several population variables predicted variations in adjusted total payments, but inconsistently. Higher payments were associated with increases in deprivation, patients of older age, African Caribbean ethnic group, and asthma prevalence. Lower payments were associated with an increase in smoking prevalence. Long-term health conditions, South Asian ethnic group, and diabetes prevalence were not predictive. The adjusted R2 values were 0.359 (2013-2014) and 0.374 (2014-2015). A slightly different set of variables predicted variations in the payment component designed to compensate for workload. Lower payments were associated with increases in deprivation, patients of older age, and diabetes prevalence. Smoking prevalence was not predictive. There was a geographical differential. CONCLUSION: Population factors related to health needs were, overall, poor predictors of variations in adjusted total practice payments and in the payment component designed to compensate for workload. Revising the weighting formula and extending weighting to other payment components might better support practices to address these needs.

Population characteristics, mechanisms of primary care and premature mortality in England: a cross-sectional study.

OBJECTIVES: Health systems with strong primary care tend to have better population outcomes, but in many countries demand for care is growing. We sought to identify mechanisms of primary care that influence premature mortality. DESIGN: We developed a conceptual model of the mechanisms by which primary care influences premature mortality, and undertook a cross-sectional study in which population and primary care variables reflecting the model were used to explain variations in mortality of those aged under 75 years. The premature standardised mortality ratios (SMRs) for each practice, available from the Department of Health, had been calculated from numbers of deaths in the 5 years from 2006 to 2010. A regression model was undertaken with explanatory variables for the year 2009/2010, and repeated to check stability using data for 2008/2009 and 2010/2011. SETTING: All general practices in England were eligible for inclusion and, of the total of 8290, complete data were available for 7858. RESULTS: Population variables, particularly deprivation, were the most powerful predictors of premature mortality, but the mechanisms of primary care depicted in our model also affected mortality. The number of GPs/1000 population and detection of hypertension were negatively associated with mortality. In less deprived practices, continuity of care was also negatively associated with mortality. CONCLUSIONS: Greater supply of primary care is associated with lower premature mortality even in a health system that has strong primary care (England). Health systems need to sustain the capacity of primary care to deliver effective care, and should assist primary care providers in identifying and meeting the needs of socioeconomically deprived groups.

Association of primary care characteristics with variations in mortality rates in England: an observational study.

BACKGROUND: Wide variations in mortality rates persist between different areas in England, despite an overall steady decline. To evaluate a conceptual model that might explain how population and service characteristics influence population mortality variations, an overall null hypothesis was tested: variations in primary healthcare service do not predict variations in mortality at population level, after adjusting for population characteristics. METHODOLOGY/PRINCIPAL FINDINGS: In an observational study of all 152 English primary care trusts (geographical groupings of population and primary care services, total population 52 million), routinely available published data from 2008 and 2009 were modelled using negative binomial regression. Counts for all-cause, coronary heart disease, all cancers, stroke, and chronic obstructive pulmonary disease mortality were analyzed using explanatory variables of relevant population and service-related characteristics, including an age-correction factor. The main predictors of mortality variations were population characteristics, especially age and socio-economic deprivation. For the service characteristics, a 1% increase in the percentage of patients on a primary care hypertension register was associated with decreases in coronary heart disease mortality of 3% (95% CI 1-4%, p = 0.006) and in stroke mortality of 6% (CI 3-9%, p<0.0001); a 1% increase in the percentage of patients recalling being better able to see their preferred doctor was associated with decreases in chronic obstructive pulmonary disease mortality of 0.7% (CI 0.2-2.0%, p = 0.02) and in all cancer mortality of 0.3% (CI 0.1-0.5%, p = 0.009) (continuity of care). The study found no evidence of an association at primary care trust population level between variations in achievement of pay for performance and mortality. CONCLUSIONS/SIGNIFICANCE: Some primary healthcare service characteristics were also associated with variations in mortality at population level, supporting the conceptual model. Health care system reforms should strengthen these characteristics by delivering cost-effective evidence-based interventions to whole populations, and fostering sustained patient-provider partnerships.

Association of features of primary health care with coronary heart disease mortality.

CONTEXT: The goal of US health care reform is to extend access. In England, with a universal access health system, coronary heart disease (CHD) mortality rates have decreased by more than two-fifths in the last decade, but variations in rates between local populations persist. OBJECTIVE: To identify which features of populations and primary health care explain variations in CHD mortality rates between the 152 primary care trust populations in England. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional study in England of all 152 primary care trusts (total registered population, 54.3 million in 2008) using a hierarchical regression model with age-standardized CHD mortality rate as the dependent variable, and population characteristics (index of multiple deprivation, smoking, ethnicity, and registers of individuals with diabetes) and service characteristics (level of provision of primary care services, levels of detected hypertension, pay for performance data) as candidate explanatory variables. MAIN OUTCOME MEASURES: Age-standardized CHD mortality rates in 2006, 2007, and 2008. RESULTS: The mean age-standardized CHD mortality rates per 100,000 European Standard Population were 97.9 (95% confidence interval [CI], 94.9-100.9) in 2006, 93.5 (95% CI, 90.4-96.5) in 2007, and 88.4 (95% CI, 85.7-91.1) in 2008. In all 3 years, 4 population characteristics were significantly positively associated with CHD mortality (index of multiple deprivation, smoking, white ethnicity, and registers of individuals with diabetes), and 1 service characteristic (levels of detected hypertension) was significantly negatively associated with CHD mortality (adjusted r(2) = 0.66 in 2006, adjusted r(2) = 0.68 in 2007, and adjusted r(2) = 0.67 in 2008). Other service characteristics did not contribute significantly to the model. CONCLUSION: In England, variations in CHD mortality are predominantly explained by population characteristics; however, greater detection of hypertension is associated with lower CHD mortality.