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Article

Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes

School of Geography and Life Sciences Institute, Queen Mary University of London, London E1 4NS, UK
Int. J. Environ. Res. Public Health 2016, 13(9), 870; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13090870
Submission received: 29 April 2016 / Revised: 9 August 2016 / Accepted: 23 August 2016 / Published: 1 September 2016
(This article belongs to the Collection Health Care and Diabetes)

Abstract

:
Background: Enhanced quality of care and improved access are central to effective primary care management of long term conditions. However, research evidence is inconclusive in establishing a link between quality of primary care, or access, and adverse outcomes, such as unplanned hospitalisation. Methods: This paper proposes a structural equation model for quality and access as latent variables affecting adverse outcomes, such as unplanned hospitalisations. In a case study application, quality of care (QOC) is defined in relation to diabetes, and the aim is to assess impacts of care quality and access on unplanned hospital admissions for diabetes, while allowing also for socio-economic deprivation, diabetes morbidity, and supply effects. The study involves 90 general practitioner (GP) practices in two London Clinical Commissioning Groups, using clinical quality of care indicators, and patient survey data on perceived access. Results: As a single predictor, quality of care has a significant negative impact on emergency admissions, and this significant effect remains when socio-economic deprivation and morbidity are allowed. In a full structural equation model including access, the probability that QOC negatively impacts on unplanned admissions exceeds 0.9. Furthermore, poor access is linked to deprivation, diminished QOC, and larger list sizes. Conclusions: Using a Bayesian inference methodology, the evidence from the analysis is weighted towards negative impacts of higher primary care quality and improved access on unplanned admissions. The methodology of the paper is potentially applicable to other long term conditions, and relevant when care quality and access cannot be measured directly and are better regarded as latent variables.

1. Introduction

Improving quality of, and access to, primary care are major strategic foci in transforming primary care in the face of growing demand and the increasing burden of long term conditions [1]. For example, in the English National Health Service (NHS), a primary care incentive scheme known as the Quality Outcomes Framework (or QOF) was introduced in 2004/2005 to promote improved chronic disease management [2]. The impact of this scheme on performance and care quality is measured by a set of indicators within various clinical domains (disease types or groupings).
Although findings are not consistent, quality of primary care may reduce unplanned emergency hospital admissions, which have major cost implications [3,4,5]. There is debate about impacts on adverse outcomes (e.g., premature mortality, avoidable hospitalisations) of quality as measured by the QOF indicators [6,7].
Unplanned admissions may also be reduced by improved access [8]. Access to care can be defined in various ways [9,10]. Utilisation-based measures of access provide a partial picture [11], and many UK studies now focus on patient-reported access to primary care, namely patient experience or perception of access: how easy patients believe it is to access their GP practice [12]. This includes factors such as convenience of GP surgery opening hours and ease of making appointments [13]. Policies to improve access to health care have included schemes to extend operating hours in GP practices, including GP appointments in the evenings or at weekends, measures to reduce inequity in access, and a target that every patient should be able to see a GP within 48 h [14,15].
Perceived access and quality of care are interrelated as higher patient satisfaction and engagement are linked to better self-care and clinical outcomes [16,17]. Indeed patient experience can be conceived as an aspect of quality of care, with the 2008 NHS Next Stage Review identifying patient experience as one dimension of quality, together with clinical effectiveness and patient safety [18]. More specifically in terms of avoidable hospitalisations, recent studies (e.g., [19]) find a negative relation between such hospitalisations and improved access, as measured by responses to the GP Patient Survey.
Impacts of primary quality of care and access on adverse outcomes, such as emergency admissions, may, however, be confounded by socioeconomic differences and health behaviours [20,21,22]. Thus, [23] mention that positive associations between ambulatory sensitive admissions and area deprivation may be related to socio-economic inequalities in health care provision; for example, see [24] regarding diabetes.
Impacts of deprivation on adverse outcomes may be partly mediated by variations in quality of care and access linked to socioeconomic deprivation. Thus, [25] found that poorly performing practices (in terms of diabetes care) tended to have deprived populations, and variations in access have been linked to characteristics of patient populations [11]. Links between access and deprivation may be partly an indirect expression of inequities in the supply of GPs with several studies showing mismatch between GP supply and health need (e.g., [26]).
Impacts of quality of care and access, as measured in regression or other statistical analysis, may also be affected by the methods used to derive summary indices. Quality of care and access cannot be measured by any single indicator and may, instead, be better regarded as latent quantities indirectly measured by a set of manifest observed indicators. Whereas the official method used to obtain quality scores under the QOF scheme does not adjust for overlapping correlation between input variables, multivariate techniques, such as factor analysis and structural equation methods, may seek instead to derive scores adjusted for multicollinearity [27,28].
The particular focus in the present paper is on the measurement of quality of care for diabetes, and assessing impacts of care quality and access on diabetes related emergency admissions. A factor score (latent indicator) method for measuring care quality and perceived access is proposed, with impacts on diabetes related emergency admissions assessed as part of a broader structural equation model (SEM). Manifest indicators of diabetes care (from which the latent indicator is derived) are taken from the Quality Outcomes Framework, while manifest access indicators are from a national survey of patients. A Bayesian inference approach is adopted, using Markov Chain Monte Carlo (MCMC) sampling [29], via the rjags program in R [30,31].

2. Case Study

A case study involves 90 GP practices in two London Clinical Commissioning Groups (Havering CCG, and Barking and Dagenham CCG), which are agencies coordinating provision of primary care. Patient populations are drawn predominantly from two north London boroughs—Havering, and Barking and Dagenham. Around 96% of the residents of these two boroughs have primary care provided by GP practices affiliated to the two CCGs.
As observed measures of care quality, the study uses four clinical indicators of diabetes care from the Quality Outcomes Framework for the financial year 2013–2014. The choice of indicators (see Table 1) is motivated by relevance in predicting risk of hospitalisation or complication, and also to avoid the duplication present in the official QOF indicators, such as overlapping measures of HbA1c and blood pressure.
Regarding blood pressure, cholesterol, and blood glucose control (the first three indicators in Table 1), several studies confirm their importance in preventing complications and hospitalisation [21,32,33]. The National Audit Office [34] (Section 2.9) mentions that “the risk of developing diabetic complications can be minimised by early detection and management of high levels of blood glucose (measured using HbA1c), blood pressure and cholesterol”. Regarding influenza immunisation, U.S. data [35] show patients with diabetes are six times more likely to be hospitalized from complications of influenza or pneumonia than those in the general population, while a UK study [36] demonstrated that influenza vaccination significantly reduced hospital admissions by diabetic patients.
As discussed in the Introduction, access to primary care is defined in terms of patient experience or perception of access, as drawn from NHS Patient Surveys of primary care experience and satisfaction. The indicators used in the present study (see Table 1) are from the 2014 patient survey and refer to patients (a) dissatisfied with surgery opening hours; (b) stating that appointments were not convenient; (c) stating their overall experience of the surgery as poor; and (d) reporting that waiting times at surgery were too long.
Studies such as [13] find that access to care may depend on GP supply or GP practice list size. These are incorporated in the analysis as causes of, as opposed to indicators of, varying access—following the multiple indicators-multiple causes terminology in the SEM literature (e.g., [37]).
As well as deriving composite quality and access scores, we seek to assess how far quality of care and access affect emergency hospital admissions by GP practices in 2013/2014, where diabetes is among the contributing diagnoses, either as the primary diagnosis or as the leading subsidiary diagnosis [38,39].
Hospital use may be affected by morbidity and demographic factors, including variations in socio-economic status [40]. Levels of diabetes morbidity are measured by practice level age standardised rates for diagnosed diabetes. Regarding socio-economic conditions, the study region shows wide differences: 10 of its 260 neighbourhoods (Census lower super output areas, abbreviated as LSOAs) are in the most affluent decile of such neighbourhoods across England, while at the other extreme, 13 neighbourhoods are among the most deprived 10% of all English neighbourhoods.

3. Methods

Some studies have used official QOF achievement scores (intended to measure quality of care) in assessing impacts on patient outcomes, for example in regression analysis of emergency admission rates [41,42]. However, these scores do not adjust for overlapping correlations between the input indicators used to obtain overall achievement scores within clinical domains such as diabetes. The official scores also take percentage indicators of achievement as known without error, regardless of the size of the patient population denominator (which may vary considerably between indicators and GP practices) and, therefore, do not allow for sampling variability (i.e., varying precision) in the percentage indicators based on binomial data.

3.1. Quality Scores in a Structural Equation Model

We wish to take account of sampling variability and control for correlations between measured input indicators, and obtain a composite measure of care quality with loadings on input indicators which are optimal in relation to potential outcomes (e.g., emergency admissions). For example, some indicators may have greater impact on emergency admissions.
Accordingly we define a structural equation model with a measurement model in which quality of care as a latent variable, denoted F, is measured by a set of observed input indicators Z, and an outcome model relating diabetes related emergency admissions Y to quality of care F (see Appendix A for a formal development). There are I = 90 GP practices and J = 4 manifest (observed) clinical indicators, Zij (i = 1, …, I; j = 1, …, J), to define the latent quality scale. These are in the form of patient totals achieving a given performance target, and are from the Quality Outcomes Framework for 2013–2014.
We wish to represent stochastic uncertainty in the clinical indicators. Thus, let Zij denote the number of diabetic patients in practice i for whom a particular clinical threshold is attained (e.g., diabetic patients with blood pressure reading below 150/90 mmHg), and Nij denote the relevant patient denominator (e.g., number of registered diabetes patients). A binomial likelihood model with unknown performance attainment probabilities is adopted for the observations (Zij, Nij) (see Appendix A), with the likelihood allowing for overdispersion.
It is assumed that variability in performance attainment probabilities is explained by normally-distributed latent quality scores Fi. Regarding these scores, alternative distribution assumptions might be considered, for example, assuming Fi to be Student’s t-distributed rather than as normal. This might be relevant if there were distinct outlier practices with unduly high/low QOC or access.
The goal is not only to summarize quality of care in a composite index, but assess how far quality of care affects adverse hospital outcomes. In principle there may be more than one such outcome, but here there is a single outcome, the total of diabetes related emergency admissions, Yi. In the baseline model (model 1) relative risks of emergency admission νi are predicted from a log-link regression including only quality of care:
log(νi) = γ0 + γ1Fi
As Fi is a positive measure of quality, one would expect γ1 to be significantly negative if high QOC significantly reduces unplanned admissions. From the Bayesian estimation process, one can the estimated probability Pr(γ1 < 0|Y,Z) that the coefficient is negative. This provides an alternative method of assessing the direction of effect to the 95% credible interval [43].
A second model (model 2) allows for the impact on emergency admissions of a score measuring area socioeconomic deprivation, Di. This is the 2015 index of income deprivation [44], calculated for GP practice populations. This is used instead of the main Index of Multiple Deprivation (IMD), which includes standardised emergency admission rates in the constituent variables [40].
A subsidiary analysis, not reported on in detail, assessed sensitivity to deprivation impacts of using the employment deprivation score [44] rather than income deprivation, throughout models 2 and 3, as considered below. This shows a slight diminution in goodness of fit as the employment domain had a lower impact on emergency diabetes admissions.
We further consider the impact on emergency admissions of diabetes morbidity (Mi), namely, the age-standardised rate at mid-2013 of diagnosed diabetes—see Equation (A8). Model 2 also extends the model for the quality scores so that deprivation can also potentially affect quality of care, as indicated by several studies [45,46,47].

3.2. Access to Care

As mentioned above, unplanned hospitalisation may also be linked to various aspects of access to primary care. In common with a number of UK recent studies, perceived patient access to primary care is based on indicators from the GP Patient Survey [48]. Just as for quality of care, access is conceived as a latent quality, not measurable by any single indicator.
Thus, the structural equation model (model 3) now has a measurement model in which quality of care as a latent variable F is measured by a set of observed input indicators Z, another measurement model in which poor access is a latent variable G measured by a set of observed indicators W, and an outcome model relating diabetes related emergency admissions Y to quality of care F and access G.
Let Wik (k = 1, …, K) be patient totals rating access as poor, and Mik be denominator populations (i.e., all patients surveyed, regardless of their rating). The indicators used are as in Table 1. Since access and quality of care may potentially be interrelated [13], it is assumed that Fi and Gi are bivariate normal, with covariance ∑ and correlation ρ.
Furthermore, we allow care quality and care access to both depend on deprivation (e.g., [49]), with respective coefficients β1 and β2. Access to care may also depend on GP supply, namely full time equivalent GPs per 1000 patients, S1i, and GP practice list size, S2i [13]. These are included as additional potential causal influences on access, with respective coefficients β3 and β4; see Equation (A20).

4. Analysis and Results

Analyses are carried out using the rjags package in R, with detailed assumptions, model checks and fit measures described in Appendix B. Table 2 shows satisfactory predictive checks for all three models, all between 0.1 and 0.9. The WAIC fit criteria (Appendix B) show a gain in moving from model 1 to model 2, but no change in fit between models 2 and 3.

4.1. Regression Findings

Table 3 shows estimated regression coefficients under the three models for the emergency admissions regression. Table 4 sets out summaries for λj and κk, the loadings in the measurement model(s) for care quality and care access, together with β coefficients relating these constructs to deprivation and supply variables.
Table 3 shows that under model 1, the impact of care quality on emergency admissions has 95% credible interval (−0.50, 0.01) [50] (p. 1063). casts doubt on converting such an interval (which just straddles zero) into evidence for a null effect. Under a Bayesian approach, whether quality has a negative impact can be assessed via the posterior probability Pr(γ1 < 0|Y,Z) [43] (Chapter 5). We find that there is a 97% probability that the coefficient is negative.
The measurement model coefficients (Table 4) for model 1 show broadly similar relevance of each of the observed indicators (see Table 1) in defining quality of care. Posterior means on the loadings λj range from 0.59 to 1.29.
Under model 2, the impacts of deprivation and morbidity on emergency admissions are both highly significant. This model also allows part of the impact of deprivation to be indirect, in that quality of care depends on deprivation; see Equation (A12) in Appendix A. In fact, this indirect effect is negative, as expected, but not pronounced: we find a probability of 0.73 that Pr(β1 < 0|Y,Z), where a negative coefficient β1 means that higher practice deprivation is associated with lower quality of care.
The impact of care quality itself on emergency admissions remains significant in model 2, as assessed by a probability Pr(γ1 < 0|Y,Z) of 0.974. Figure 1 plots the posterior means (under model 2) of the quality score Fi against those for emergency admission relative risk νi, together with a LOWESS plot (locally weighted scatterplot smoothing). This plot suggests a threshold effect, with greater impacts of high care quality in reducing emergency admissions.
Model 3, the full structural model, involves access as well as quality of care. Models 2 and 3 have very similar fit measures, and statistical criteria may be supplemented by substantive considerations, since on the basis of accumulated evidence from other studies, model 3 may be regarded as providing a more complete description of the interrelated processes. Regarding the measurement model for access, the highest loading κk (with posterior mean approaching 2) is for the variable overall poor experience of the surgery.
Of particular interest in the estimates for model 3 are positive impacts of deprivation on perceived poor access to care (the β2 coefficient), and a negative correlation ρ between poor access and quality of care. The respective 95% credible intervals are (0.23, 0.68) and (−0.61, −0.12). There is also a significant positive impact on poor access of larger GP list sizes (the population total served by a GP practice), and a mostly negative impact of GP supply, with 95% interval (−0.82, 0.07). A list size effect on access (i.e., perceived access better in small practices) is also reported by [13].
The impact of poor access on emergency admissions has a posterior density concentrated on positive values, with a 95% credible interval (−0.10, 0.46), and a 90% probability of a positive effect. Figure 2 plots the posterior means of the poor access score Gi against those for emergency admission relative risk νi, with LOWESS smooth included.
It is of interest to assess how the QOC scores (the Fi) compare with the official diabetes QOF attainment scores. The correlation between the posterior mean F from model 3 and the QOF attainment scores is 0.84, and their interrelationship is shown in Figure 3. It is apparent that the modelled QOC scores provide extra discrimination in measuring quality of care at the highest levels of the official attainment score, namely, 23 practices with official attainment scores between 105 and the maximum possible 107.

4.2. Sensitivity Analysis

The above analysis may be affected by distributional assumptions. The derivation of latent factors for care quality and access is central to the proposed approach, and so effects were assessed of adopting a Student’s t distribution of factor scores rather than a normal density, as this heavier tailed density may be more robust to any outlier practices with unduly high/low quality of care or access.
A bivariate Student’s t density for the factor scores in model 3 was achieved using a scale mixture approach [51] (p. 138), with a default degrees of freedom set at four, as advocated by [52] (p. 449). This provides a fourth model, results from which are also included in Table 2, Table 3 and Table 4. This shows no marked change in fit, but slightly enhances the impact of quality of care on emergency admissions, with a 93% probability that the impact is negative.

4.3. Implications for Geographic Differences in Access

As noted above, around 96% of residents in the case study region have their primary care provided by GP practices in the two CCGs (which have the same names as the boroughs, namely Barking and Dagenham CCG, and Havering CCG). Hence, GP practice differences in access and care quality (measured by the G and F scores) can be translated into implied geographic differences in access and care quality. These are obtained using cross-reference files of populations classified both by LSOA and by GP practice (hence of dimension 260 by 90). LSOA weighted averages for access or care quality are based on proportions of each neighbourhood population cared for by each of the 90 GP practices.
Figure 4 accordingly maps out access scores for the 260 neighbourhoods (LSOAs) using the posterior mean G scores for GP practices, and shows clear geographic clustering of poor access. An accompanying map (Figure 5) shows LSOA scores for income deprivation, and associations between the geographic patterns of deprivation and poor access are apparent.

5. Conclusions

This paper has developed a structural equation model for quality of primary care and perceived access to primary care, and assessed their impacts on unplanned hospital admissions for diabetes, while allowing also for socio-economic deprivation, morbidity, and supply indicators. This methodology is potentially applicable to other long term conditions, and relevant when care quality and access cannot be measured directly and are better regarded as latent variables. Methodology for measuring quality of care and access may affect findings regarding their impacts on outcomes, such as unplanned admissions.
In that regard, a number of studies report insignificant or negligible effects of quality of care on adverse hospital outcomes, such as avoidable or ambulatory sensitive hospital admissions. For example [22] report small associations between official QOF scores and emergency admissions, whilst impacts of socio-economic deprivation were much stronger. [40] also obtain a strong effect of deprivation on potentially avoidable emergency admissions, and mention that this is in part because deprivation is correlated with morbidity.
In the case study region of the current paper, primary care population register data for diagnosed diabetes (standardized prevalence rates in 2013) show wide variation between GP practices, and there is a 0.70 correlation between diabetes prevalence and income deprivation. This inter-correlation is controlled for in models 2 and 3, but a strong deprivation effect remains.
The analysis also suggests a strong indirect effect of deprivation on emergency admissions through access, though not a strong indirect impact through quality of care. The positive association between income deprivation and perceived poor access under model 3 can be represented in terms of implied neighbourhood access variations (as shown by Figure 4). This pattern is consistent with evidence that care access in deprived, as compared to affluent areas, may be related to variations in primary care provision, especially provision that matches health care need, with continuing evidence of an inverse care law [53,54].
Some potential extensions of the model framework and analysis of this paper may be proposed. Thus, an additional dimension that has been shown in the literature to have a substantial effect on quality of care is continuity of care, “in the sense of a patient repeatedly consulting the same doctor and forming a therapeutic relationship” [55]. For example, [56] in a Korean study derive different indices of continuity from detailed patient consultation histories. One could potentially form a composite index of continuity based on combining different indices, but a UK application is impeded by non-availability of patient primary care consultation data.
Interpretation of quality and access effects on adverse outcomes has preoccupied several recent studies. After accounting for the impact of deprivation and morbidity in model 2—which fits as well as the full model to the hospitalisation and quality indicators—the effect of quality of care on unplanned admissions remains significant: a 97% probability for a negative impact. For the full model, quality of care has a 91.8% probability of a negative impact, and poor access has a 90% probability of a positive impact. The latter two impacts are increased slightly under model 4 with a bivariate Student’s t model for correlated quality and access.
These probabilities may be interpreted from a Bayesian perspective in terms of marginal Bayes factors [57]. The prior on the regression coefficients is neutral with regard to the direction (positive or negative) of the coefficient, so a 91.8% posterior probability of a negative impact of quality on emergency admissions implies a marginal Bayes factor of 11.2. Using standard guidelines [58], this counts as strong evidence of an effect.
In such terms, the analysis of the present study is not consistent with a null finding regarding impacts of quality of care and access on potentially avoidable emergency admissions. Rather the regression evidence is weighted towards negative impacts of higher primary care quality and improved access on unplanned admissions. Other aspects of the model are also important in healthcare terms, such as poor access being greater for deprived practice populations.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A. Formal Aspects of Statistical Models

For quality of care indices, let Zij denote the number of diabetic patients in practice i for whom a particular clinical threshold is attained (e.g., diabetic patients with blood pressure reading below 150/90 mmHg), and Nij denote the relevant patient denominator (e.g., number of registered diabetes patients). One has binomial sampling:
Zij ~ Bin(Nijij), j = 1, …, J
with performance attainment probabilities πij that are unknown parameters to be estimated.
In the simplest model (model 1) it is assumed that variability in attainment probabilities πij is explained by a normally-distributed common quality factor Fi, and random residual effects uij, accounting for over-dispersion, which are also normally distributed. Thus with a logit link, and intercepts ςj, one has:
logit(πij) = ςj + λjFi + uij,
uij ~ N(0,ψj),
Fi ~ N(0,θ).
For identifiability, the loading λ1 = 1 and so the variance θ of the factor scores is an unknown [59]. The remaining loadings are constrained to be positive in line with a quality of care interpretation for the F scores, and for computational stability, to avoid label switching during MCMC updating.
In model 1, hospitalisation risks are also related to the latent QOC score. Denoting observed emergency admissions as Yi and expected admissions as Ei, it is assumed that the Yi are negative binomial, with overdispersion parameter α, Yi ~ NB(Eiνi,α), and likelihood:
( 1 1 + α μ i ) 1 / α ( α μ i 1 + α μ i ) y i Γ ( y i + 1 / α ) Γ ( 1 / α ) y i !
where μi = Eiνi are predicted emergency admission totals, and conditional variances are var(Yi) = μ i ¯ + α μ i 2 . Relative risks of emergency admission νi are predicted from a log-link regression including only quality of care:
log(νi) = γ0 + γ1Fi.
Model 1 is a joint likelihood over Y and Z data, represented by Equations (A1)–(A6).
In model 2, we additionally consider the impacts on emergency admissions of a socioeconomic deprivation index, Di, and also of diabetes morbidity (Mi). Thus:
Yi ~ NB(Eiνi,α)
log(νi) = γ0 + γ1Fi + γ2Di + γ3Mi.
Model 2 also allows for an indirect impact of deprivation on hospitalisations, via quality of care. This leads to a multiple indicator, multiple cause form of structural equation model. Thus with deprivation scores Di, centred for identifiability, one has:
Zij ~ Bin(Nijij),
logit(πij) = ςj + λjFi + uij,
uij ~ N(0,ψj),
Fi ~ N(β1Di,θ).
The joint likelihood under the second model is represented by Equations (A7)–(A12).
In model 3, the latent variable model encompasses both poor access (denoted G) and quality of care (denoted F). Then:
Wik ~ Bin(Mikik), k = 1, …, K
logit(ξik) = ζj + κkGi + eik,
eik ~ N(0,ϑk),
Zij ~ Bin(Nijij), j = 1, …, J
logit(πij) = ςj + λjFi + uij,
uij ~ N(0,ψj),
(Fi,Gi) ~ N2i,Σ),
i1i2) = (β1Di, β2Di + β3S1i + β4S2i)
with N2 denoting a bivariate normal density. Causes of poor access are deprivation and supply indicators, as in (A20). For identifiability, κ1 = 1, with remaining κ loadings constrained to be positive in line with a poor access interpretation for the G scores. The model for emergency admissions includes access, as well as deprivation and morbidity, in view of evidence such as [8]:
Yi ~ NB(Eiνi,α)
log(νi) = γ0 + γ1Fi + γ2Di + γ3Gi + γ4Mi
The joint likelihood under the third model is defined by Equations (A13)–(A22). Appendix C contains the code for the bivariate normal model 3.
A bivariate Student t density for quality and access factors is obtained by a scale mixture, namely:
(Fi,Gi) ~ N2i,Σ/ϕi),
ϕi ~ Gamma(0.5δ,0.5δ),
where δ is the Student’s t degrees of freedom. Following [52] (p. 449), a robust analysis is obtained by taking a preset degrees of freedom δ = 4.

Appendix B. Prior Densities and Fit Measures

We assume gamma priors, with shape 1 and index 0.01, on inverse variance parameters (1/ψj, 1/ϑk, 1/θ) and the negative binomial parameter 1/α, and Normal priors with mean zero and precision 0.001 on fixed effects (such as regression intercepts and slopes). The precision matrix ∑ in model 3 is assigned a Wishart prior, with identity scale matrix and two degrees of freedom. The unknown loadings λj and κk in the factor score models are assigned exponential priors with mean 1. Estimates are based on the second halves of two chain runs of 20,000 iterations, with convergence assessed using Brooks-Gelman-Rubin diagnostics [60].
Fit is assessed using the WAIC information criterion [61], with WAIC obtained only on the Y and Z data in models 1 and model 2, and for the Y, Z, and W data in model 3. Posterior predictive checks [62,63] are also applied. For the emergency admissions model, these are based on predicted emergency admissions Ynew,i sampled from the relevant posterior predictive density, namely p(Ynew|Y,Z) in models 1 and 2, and p(Ynew|Y,Z,W) in models 3 and 4. Posterior predictive checks for the clinical and access indicators are based on predicted clinical responses Znew, and predicted access indicators Wnew.
With R and Rnew denoting fit measures using observations and predictions respectively, posterior predictive p-values are estimated as Pr(Rnew > R|Y,Z) or Pr(Rnew > R|Y,Z,W), the proportion of iterations where Rnew > R. Extreme p-values (under 0.05 or over 0.95) indicate model discrepancies. A chi-square fit measure is used, with RY = ∑i(Yi−μi)2i, and RY,new = ∑i(Ynew,i−μi)2i for the Y data. For the clinical indicators, RZj = ∑i(Zij−Nijπij)2/[Nijπij] and RZj,new = ∑i(Znew,ij−Nijπij)2/[Nijπij]. For the access indicators, RWk = ∑i(Wik−Mikξik)2/[Mikξik] and RWk,new = ∑i(Wnew,ik−Mikξik)2/[Mikξik].

Appendix C

The core of the rjags code for the bivariate normal factor model 3 is as follows, with terms to extract WAIC elements and predictive checks excluded for brevity.
# N GP practices; J quality indicators; K access indicators
model {for (i in 1:N) {# outcome model
y[i] ~ dnegbin(p.emrg[i],r)
p.emrg[i] <- r/(r+lambda[i])
lambda[i] <- E[i]*nu[i]
# dep.c, deprivation; mrb.c, diabetes prevalence
log(nu[i]) <- gam0+gam[1]*F[1,i]+gam[2]*dep.c[i]
                        +gam[3]*F[2,i]+ gam[4]*mrb.c[i]
# factor model (F1 is quality of care, F2 is poor access)
F[1:2,i] ~ dmnorm(mu[1:2,i],tau.F[,])
# dep.c, deprivation; supp.c, GP supply; lst.c, list size
mu[1,i] <- beta[1]*dep.c[i]
mu[2,i] <- beta[2]*dep.c[i] +beta[3]*supp.c[i]+beta[4]*lst.c[i]
# measurement models
for (j in 1:J) {Z[i,j] ~ dbin(pi[i,j],N[i,j])
                               logit(pi[i,j]) <- omeg.pi[j]+lambda[j]*F[1,i]+u[i,j]
                               u[i,j] ~ dnorm(0,tau.u[j])}
for (k in 1:K) {W[i,k] ~ dbin(xi[i,k],M[i,k])
                            logit(xi[i,k]) <- omeg.xi[k]+kappa[k]*F[2,i]+e[i,k]
                            e[i,k] ~ dnorm(0,tau.e[k])}}
# priors
gam0 ~ dnorm(0,0.001)
for (j in 1:4) {gam[j] ~ dnorm(0,0.001); p.gam[j] <- step(gam[j]);
                             beta[j] ~ dnorm(0,0.001)}
for (j in 1:J) {omeg.pi[j] ~ dnorm(0,0.001); tau.u[j] ~ dgamma(1,0.01)}
for (k in 1:K) {omeg.xi[k] ~ dnorm(0,0.001); tau.e[k] ~ dgamma(1,0.01)}
r ~ dgamma(1,0.01) ; alpha <- 1/r
lambda[1] <- 1; for (j in 2:J) {lambda[j] ~ dexp(1)}
kappa[1] <- 1;    for (j in 2:K) {kappa[k] ~ dexp(1)}
tau.F[1:2 , 1:2] ~ dwish(S[ , ], 2)
# correlation between factors
     sig.F <- inverse(tau.F); cor.F <- sig.F[1,2]/sqrt(sig.F[1,1]*sig.F[2,2]) }

References

  1. Dawda, P.; Jenkins, R.; Varnam, R. Quality Improvement in General Practice; The King’s Fund: London, UK, 2010. [Google Scholar]
  2. Dixon, A.; Khachatryan, A. A review of the public health impact of the quality and outcomes framework. Qual. Prim. Care 2010, 18, 133–138. [Google Scholar] [PubMed]
  3. Gunther, S.; Taub, N.; Rogers, S.; Baker, R. What aspects of primary care predict emergency admission rates? A cross sectional study. BMC Health Serv. Res. 2013, 13, 11. [Google Scholar] [CrossRef] [PubMed]
  4. Bankart, M.; Baker, R.; Rashid, A.; Habiba, M.; Banerjee, J.; Hsu, R.; Conroy, S.; Agarwal, S.; Wilson, A. Characteristics of general practices associated with emergency admission rates to hospital: A cross-sectional study. Emerg. Med. J. 2011, 28, 558–563. [Google Scholar] [CrossRef] [PubMed]
  5. Purdy, S.; Griffin, T.; Salisbury, C.; Sharp, D. Ambulatory care sensitive conditions: Terminology and disease coding need to be more specific to aid policy makers and clinicians. Public Health 2009, 123, 169–173. [Google Scholar] [CrossRef] [PubMed]
  6. Kontopantelis, E.; Springate, D.; Ashworth, M.; Webb, R.; Buchan, I.; Doran, T. Investigating the relationship between quality of primary care and premature mortality in England: A spatial whole-population study. BMJ 2015, 350, h904. [Google Scholar] [CrossRef] [PubMed]
  7. Soljak, M.; Majeed, A.; Calderon-Larrannaga, A. [Responses to] Investigating the relationship between quality of primary care and premature mortality in England: A spatial whole-population study. BMJ 2015, 350. Available online: http://0-www-bmj-com.brum.beds.ac.uk/content/350/bmj.h904/rapid-responses (accessed on 1 August 2016). [Google Scholar]
  8. Bindman, A.; Grumbach, K.; Osmond, D.; Komaromy, M.; Vranizan, K.; Lurie, N.; Billings, J.; Stewart, A. Preventable hospitalizations and access to health care. JAMA 1995, 274, 305–311. [Google Scholar] [CrossRef] [PubMed]
  9. Ricketts, T.; Goldsmith, L. Access in health services research: The battle of the frameworks. Nurs. Outlook 2005, 53, 274–280. [Google Scholar] [CrossRef] [PubMed]
  10. Boyle, S.; Appleby, J.; Harrison, A. A Rapid View of Access to Care; The King’s Fund: London, UK, 2010. [Google Scholar]
  11. Ford, J.; Jones, A.; Wong, G.; Clark, A.; Porter, T.; Shakespeare, T.; Swart, A.; Steel, N. Improving access to high-quality primary care for socioeconomically disadvantaged older people in rural areas: A mixed method study protocol. BMJ Open 2015, 5, e009104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. National Health Service (NHS). Primary Care and Community Services: Improving GP Access and Responsiveness; NHS: London, UK, 2009. [Google Scholar]
  13. Kontopantelis, E.; Roland, M.; Reeves, D. Patient experience of access to primary care: Identification of predictors in a national patient survey. BMC Fam. Pract. 2010, 11, 61. [Google Scholar] [CrossRef] [PubMed]
  14. Chapman, J.; Zechel, A.; Carter, Y.; Abbott, S. Systematic review of recent innovations in service provision to improve access to primary care. Br. J. Gen. Pract. 2004, 54, 374–381. [Google Scholar] [PubMed]
  15. National Health Service (NHS). Improving Access, Responding to Patients; NHS Practice Management Network; NHS: London, UK, 2009. [Google Scholar]
  16. Doyle, C.; Lennox, L.; Bell, D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open 2013, 3, e001570. [Google Scholar] [CrossRef] [PubMed]
  17. Raleigh, V.; Frosini, F. Improving GP Services in England: Exploring the Association between Quality of Care and the Experience of Patients; The King’s Fund: London, UK, 2012. [Google Scholar]
  18. Department of Health. High Quality Care for All; DoH: London, UK, 2008. [Google Scholar]
  19. Cowling, T.; Harris, M.; Watt, H.; Soljak, M.; Richards, E.; Gunning, E.; Bottle, A.; Macinko, J.; Majeed, A. Access to primary care and the route of emergency admission to hospital: Retrospective analysis of national hospital administrative data. BMJ Qual. Saf. 2015, 25, 432–440. [Google Scholar] [CrossRef] [PubMed]
  20. Burgdorf, F.; Sundmacher, L. Potentially avoidable hospital admissions in Germany: An analysis of factors influencing rates of ambulatory care sensitive hospitalizations. Deutsch. Ärzteblatt Int. 2014, 111, 215–223. [Google Scholar]
  21. Dusheiko, M.; Doran, T.; Gravelle, H.; Fullwood, C.; Roland, M. Does higher quality of diabetes management in family practice reduce unplanned hospital admissions? Health Serv. Res. 2011, 46, 27–46. [Google Scholar] [CrossRef] [PubMed]
  22. Downing, A.; Rudge, Y.; Cheng, Y.; Tu, Y.; Keen, J.; Gilthorpe, M. Do the UK government’s new quality and outcomes framework (QOF) scores adequately measure primary care performance? A cross-sectional survey of routine healthcare data. BMC Health Serv. Res. 2007, 7, 166. [Google Scholar] [CrossRef] [PubMed]
  23. Tian, Y.; Dixon, A.; Gao, H. Emergency Hospital Admissions for Ambulatory Care-Sensitive Conditions: Identifying the Potential for Reductions; The King’s Fund: London, UK, 2012. [Google Scholar]
  24. Ricci-Cabello, I.; Ruiz-Perez, I.; Olry de Labry-Lima, A. Do social inequalities exist in terms of the prevention, diagnosis, treatment, control and monitoring of diabetes? A systematic review. Health Soc. Care Community 2010, 18, 572–587. [Google Scholar] [CrossRef] [PubMed]
  25. Doran, T.; Fullwood, C.; Kontopantelis, E.; Reeves, D. Effect of financial incentives on inequalities in the delivery of primary clinical care in England: Analysis of clinical activity indicators for the quality and outcomes framework. Lancet 2008, 372, 728–736. [Google Scholar] [CrossRef]
  26. Benzeval, M.; Judge, K. Access to health care in England: Continuing inequalities in the distribution of GPs. J. Public Health Med. 1996, 18, 33–40. [Google Scholar] [CrossRef] [PubMed]
  27. Cinar, A.B.; Schou, L. Interrelation between patient satisfaction and patient-provider communication in diabetes management. Sci. World J. 2014, 2014, 372671. [Google Scholar] [CrossRef] [PubMed]
  28. Amoah, A.O.; Amirfar, S.; Silfen, S.L.; Singer, J.; Wang, J.J. Applied use of composite quality measures for EHR-enabled practices. EGEMS 2015, 3, 1118. [Google Scholar] [CrossRef] [PubMed]
  29. Gilks, W.; Richardson, S.; Spiegelhalter, D. Markov Chain Monte Carlo in Practice; Chapman and Hall: London, UK, 1996. [Google Scholar]
  30. R Core Team. R: A Language And Environment For Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016. [Google Scholar]
  31. Plummer, M. rjags: Bayesian Graphical Models Using MCMC. 2013. Available online: http://cran.r-project.org/package=rjags (accessed on 1 October 2015).
  32. Menzin, J.; Korn, J.; Cohen, J.; Lobo, F.; Zhang, B.; Friedman, M.; Neumann, P. Relationship between glycemic control and diabetes-related hospital costs in patients with type 1 or type 2 diabetes mellitus. J. Manag. Care Pharm. 2010, 16, 264–275. [Google Scholar] [CrossRef] [PubMed]
  33. Arouz-Pacheco, C.; Parrott, M.; Raskin, P. Treatment of hypertension in adults with diabetes. Diabetes Care 2003, 26 (Suppl. 1), S80–S82. [Google Scholar]
  34. National Audit Office. The Management of Adult Diabetes Services in the NHS; National Audit Office: London, UK, 2012.
  35. CDC. Flu and Pneumonia Vaccinations Decrease Relative Morbidity Risk for People with Diabetes. 2015. Available online: http://www.cdc.gov/diabetes/projects/pdfs/hpNewsletter.pdf (accessed on 22 December 2015). [Google Scholar]
  36. Colquhoun, A.; Nicholson, K.; Botha, J.; Raymond, N. Effectiveness of influenza vaccine in reducing hospital admissions in people with diabetes. Epidemiol. Infect. 1997, 119, 335–341. [Google Scholar] [CrossRef] [PubMed]
  37. Proitsi, P.; Hamilton, G.; Tsolaki, M.; Lupton, M.; Daniilidou, M.; Hollingworth, P.; Archer, N.; Foy, C.; Stylios, F.; McGuinness, B.; et al. A multiple indicators multiple causes (MIMIC) model of behavioural and psychological symptoms in dementia (BPSD). Neurobiol. Aging 2011, 32, 434–442. [Google Scholar] [CrossRef] [PubMed]
  38. Gibbons, D.; Soljak, M.; Millett, C.; Valabhji, J.; Majeed, A. Use of hospital admissions data to quantify the burden of emergency admissions in people with diabetes mellitus. Diabet. Med. 2014, 31, 971–975. [Google Scholar] [CrossRef] [PubMed]
  39. Washington, R.; Andrews, R.; Mutter, R. Healthcare Cost and Utilization Project; Agency for Health Care Policy and Research: Rockville, MD, USA, 2013. [Google Scholar]
  40. O’Cathain, A.; Knowles, E.; Maheswaran, R.; Pearson, T.; Turner, J.; Hirst, E.; Goodacre, S.; Nicholl, J. A system-wide approach to explaining variation in potentially avoidable emergency admissions: National ecological study. BMJ Qual. Saf. 2014, 23, 47–55. [Google Scholar] [CrossRef] [PubMed]
  41. Jacobs, R.; Gutacker, N.; Mason, A.; Goddard, M.; Gravelle, H.; Kendrick, A.; Gilbody, S.; Aylott, L.; Wainwright, J. Do higher primary care practice performance scores predict lower rates of emergency admissions for persons with serious mental illness? An analysis of secondary panel data. Health Serv. Deliv. Res. 2015, 3. [Google Scholar] [CrossRef] [PubMed]
  42. Purdy, S.; Griffin, T.; Salisbury, C.; Sharp, D. Emergency admissions for coronary heart disease: A cross-sectional study of general practice, population and hospital factors in England. Public Health 2011, 125, 46–54. [Google Scholar] [CrossRef] [PubMed]
  43. Ntzoufras, I. Bayesian Modeling Using WinBUGS; Wiley: Hoboken, NJ, USA, 2009. [Google Scholar]
  44. Department of Communities and Local Government (DCLG). The English Indices of Deprivation 2015; DCLG: London, UK, 2015.
  45. Hippisley-Cox, J.; O’Hanlon, S.; Coupland, C. Association of deprivation, ethnicity, and sex with quality indicators for diabetes: Population based survey of 53,000 patients in primary care. BMJ 2004, 329, 1267–1270. [Google Scholar] [CrossRef] [PubMed]
  46. Wright, J.; Martin, D.; Cockings, S.; Polack, C. Overall Quality of Outcomes Framework scores lower in practices in deprived areas. Br. J. Gen. Pract. 2006, 56, 277–279. [Google Scholar] [PubMed]
  47. McLean, G.; Sutton, M.; Guthrie, B. Deprivation and quality of primary care services: Evidence for persistence of the inverse care law from the UK Quality and Outcomes Framework. J. Epidemiol. Community Health 2006, 60, 917–922. [Google Scholar] [CrossRef] [PubMed]
  48. Ipsos-MORI. GP Patient Survey—National Summary Report. 2014. Available online: https://gp-patient.co.uk/surveys-and-reports#july-2014 (accessed on 1 October 2015).
  49. McCormick, B.; Hill, P.; Emmi, P. Are Hospital Services Used Differently in Deprived Areas? Evidence to Identify Commissioning Challenges; Centre for Health Service Economics & Organisation: Oxford, UK, 2012. [Google Scholar]
  50. Rothman, K. Six persistent research misconceptions. J. Gen. Intern. Med. 2014, 29, 1060–1064. [Google Scholar] [CrossRef] [PubMed]
  51. Lunn, D.; Jackson, C.; Best, N.; Thomas, A.; Spiegelhalter, D. The BUGS Book: A Practical Introduction to Bayesian Analysis; CRC Press: Boca Raton, FL, USA, 2013. [Google Scholar]
  52. Gelman, A.; Carlin, J.; Stern, H.; Rubin, D. Bayesian Data Analysis, 2nd ed.; Chapman & Hall/CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
  53. Mercer, S.; Watt, G. The inverse care law: Clinical primary care encounters in deprived and affluent areas of Scotland. Ann. Fam. Med. 2007, 5, 503–510. [Google Scholar] [CrossRef] [PubMed]
  54. Norbury, M.; Mercer, S.; Gillies, J.; Furler, J.; Watt, G. Time to care: Tackling health inequalities through primary care. Fam. Pract. 2011, 28, 1–3. [Google Scholar] [CrossRef] [PubMed]
  55. Freeman, G.; Hughes, J. Continuity of Care and the Patient Experience; The King’s Fund: London, UK, 2010. [Google Scholar]
  56. Cho, K.; Lee, S.; Jun, B.; Jung, B.; Kim, J.; Park, E. Effects of continuity of care on hospital admission in patients with type 2 diabetes: Analysis of nationwide insurance data. BMC Health Serv. Res. 2015, 15, 107. [Google Scholar] [CrossRef] [PubMed]
  57. Ghosh, J.; Ghattas, A. Bayesian variable selection under collinearity. Am. Stat. 2015, 69, 165–173. [Google Scholar] [CrossRef]
  58. Jeffreys, H. Theory of Probability; Clarendon Press: Oxford, UK, 1961. [Google Scholar]
  59. Skrondal, A.; Rabe-Hesketh, S. Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models; Chapman & Hall/CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
  60. Brooks, S.; Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 1998, 7, 434–455. [Google Scholar]
  61. Watanabe, S. A widely applicable Bayesian information criterion. J. Mach. Learn. Res. 2013, 14, 867–897. [Google Scholar]
  62. Berkhof, J.; van Mechelen, I.; Hoijtink, H. Posterior predictive checks: Principles and discussion. Comput. Stat. 2000, 15, 337–354. [Google Scholar] [CrossRef]
  63. Lynch, S.; Western, B. Bayesian posterior predictive checks for complex models. Sociol. Methods Res. 2004, 32, 301–335. [Google Scholar] [CrossRef]
Figure 1. Quality of care and emergency admission relative risk.
Figure 1. Quality of care and emergency admission relative risk.
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Figure 2. Poor access and emergency admission relative risk.
Figure 2. Poor access and emergency admission relative risk.
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Figure 3. Modelled quality of care scores vs. QOF attainment scores.
Figure 3. Modelled quality of care scores vs. QOF attainment scores.
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Figure 4. LSOA averages on posterior mean access scores, model 3.
Figure 4. LSOA averages on posterior mean access scores, model 3.
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Figure 5. LSOA income deprivation scores.
Figure 5. LSOA income deprivation scores.
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Table 1. Indicators for quality of care and access.
Table 1. Indicators for quality of care and access.
ConceptIndicatorNumeratorDenominatorOverall Percent Rate10th Percentile of Practice Rates90th Percentile of Practice Rates
QualityZ1Patients with last blood pressure reading of 150/90 mmHg or lessDiagnosed diabetes patients92.185.897.2
Z2Patients whose last measured total cholesterol 9 of 5 mmol/L or lessDiagnosed diabetes patients79.470.287.7
Z3Patients with last IFCC-HbA1c of 64 mmol/mol or lessDiagnosed diabetes patients74.661.785.6
Z4Patients with influenza immunization in most recent winter period.Diagnosed diabetes patients93.084.399.4
AccessW1Dissatisfied with surgery opening hoursSurveyed Patients at Practice12.64.619.1
W2Last appointment not very convenient or not at all convenientSurveyed Patients at Practice9.73.017.3
W3Overall experience of making an appointment fairly poor or very poorSurveyed Patients at Practice13.11.923.9
W4Waiting times at surgery too longSurveyed Patients at Practice13.42.729.5
Table 2. Model checks and fit measures.
Table 2. Model checks and fit measures.
Variable TypeDescriptionPosterior Predictive Checks
Model 1Model 2Model 3Model 4
Patient outcome (Y)Diabetes related emergency admissions0.420.230.180.19
Clinical Quality Indicators (Z variables)Last blood pressure reading 150/90 mmHg or less0.530.540.530.53
Last total cholesterol 5 mmol/L or less0.560.560.570.56
Last HbA1c 64 mmol/mol or less0.620.620.630.60
Influenza immunization, most recent winter0.510.510.520.51
Access Indicators (W variables)Dissatisfied with opening hours0.620.61
Appointments not convenient0.890.87
Overall experience of surgery poor0.820.82
Waiting times at surgery too long0.570.58
Fit (WAIC) *
Model 1Model 2Model 3Model 4
Emergency admissions530.0501.3501.5502.8
Clinical indicators2248.42247.02246.32248.7
Access Indicators1684.41685.5
* Lower value for better fit.
Table 3. Coefficients for outcome model (emergency diabetes related admissions).
Table 3. Coefficients for outcome model (emergency diabetes related admissions).
SymbolPosterior Summary
MeanSt. Devn.2.5%97.5%Probability Regression Coefficient PositiveProbability Regression Coefficient Negative
Model 1
Interceptγ00.020.05−0.090.120.6240.376
Quality of Careγ1−0.250.13−0.500.010.0290.971
Overdispersion parameterα0.140.040.070.23
Model 2
Interceptγ0−0.020.04−0.100.060.3420.658
Quality of Careγ1−0.200.10−0.410.000.0260.974
Deprivationγ20.310.120.070.560.9950.005
Diabetes morbidityγ30.090.030.040.140.9990.001
Overdispersion parameterα0.040.030.000.10
Model 3
Interceptγ0−0.030.04−0.120.050.2170.783
Quality of Careγ1−0.170.12−0.420.070.0820.918
Deprivationγ20.240.13−0.010.500.9670.033
Poor Accessγ30.180.14−0.100.460.9000.101
Diabetes morbidityγ40.090.020.040.141.0000.000
Overdispersion parameterα0.030.030.000.10
Model 4
Interceptγ0−0.030.04−0.110.060.2780.722
Quality of Careγ1−0.170.12−0.410.060.0700.930
Deprivationγ20.250.13−0.010.510.9690.031
Poor Accessγ30.180.14−0.090.460.9090.091
Diabetes morbidityγ40.090.030.040.140.9990.001
Overdispersion parameterα0.030.030.000.09
Table 4. Measurement model parameters.
Table 4. Measurement model parameters.
SymbolPosterior Summary
Model 1Model 2Model 3Model 4
Mean2.5%97.5%Mean2.5%97.5%Mean2.5%97.5%Mean2.5%97.5%
Clinical Quality
Blood pressure ≤ 150/90 mmHgλ11111
Total cholesterol 5 mmol/L or lessλ20.590.390.830.570.370.820.630.410.880.610.390.89
Last HbA1c 64 mmol/mol or lessλ30.740.511.000.700.490.960.770.531.040.750.511.04
Influenza immunization, most recent winterλ41.290.681.921.260.671.861.330.761.941.260.691.93
Poor Access
Dissatisfied with opening hoursκ111
Appointments not convenientκ21.350.911.901.290.911.71
Overall experience of surgery poorκ31.891.392.541.871.342.45
Waiting times at surgery too longκ41.470.952.111.360.871.90
Correlation between constructsρ−0.38−0.61−0.12−0.34−0.58−0.07
Impacts of Deprivation
On quality of careβ1−0.09−0.380.20−0.08−0.350.19−0.11−0.450.23
On poor accessβ20.450.230.680.450.240.69
Impacts of Practice Factors on Poor Access
GP Supply (FTE GPs per 1000 Patients)β3−0.37−0.820.07−0.36−0.850.09
List Size (in 000s)β40.0530.0220.0870.0470.0140.079

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Congdon, P. Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes. Int. J. Environ. Res. Public Health 2016, 13, 870. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13090870

AMA Style

Congdon P. Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes. International Journal of Environmental Research and Public Health. 2016; 13(9):870. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13090870

Chicago/Turabian Style

Congdon, Peter. 2016. "Assessing Impacts on Unplanned Hospitalisations of Care Quality and Access Using a Structural Equation Method: With a Case Study of Diabetes" International Journal of Environmental Research and Public Health 13, no. 9: 870. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13090870

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