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- 1. Is higher primary care quality associated with lower hospital admissions for people with serious mental illness? Rowena Jacobs*, Nils Gutacker, Anne Mason, Simon Gilbody, Maria Goddard, Hugh Gravelle, Tony Kendrick, Rachel Richardson, June Wainwright *Email: rowena.jacobs@york.ac.uk
- 2. Acknowledgement This project is funded by the National Institute for Health Services & Delivery Research programme (project number 10/1011/22). These are emerging findings. The views and opinions expressed are those of the authors and do not necessarily reflect those of the HS&DR programme, NIHR, NHS or the Department of Health. HS&DR Project - 10/1011/22 http://www.netscc.ac.uk/hsdr/projdetails.php?ref=10 -1011-22
- 3. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 4. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 5. Serious Mental Illness (SMI) • Serious mental illness (SMI) – schizophrenia, psychosis or bipolar disorder – carries a high disease burden • Prevalence • Bipolar disorder 1-2% • Bipolar spectrum disorder 8% • Schizophrenia 0.7% • At greater risk of chronic physical illnesses • Side effects of treatment • Unhealthy lifestyle choices • System barriers to provision • Life expectancy 16- 25 years less than general population • Little empirical work on processes of care for patients with SMI
- 6. Role of primary care • Primary care is central in care of people with SMI • Large proportion of SMI patients are seen only in primary care • 57% for schizophrenia • 38% for bipolar disorder • SMI patients at higher risk of hospitalisations • Key indicator of quality of primary care is potential to reduce ‘unplanned hospital admissions’
- 7. Quality & Outcomes Framework (QOF) • Pay for performance scheme introduced in 2004/05 • Offers financial rewards to GP practices for good quality care • SMI is one of the clinical domains in QOF • Has potential to reduce ‘unplanned admissions’ • Evidence from other clinical areas on impact of reducing admissions is mixed
- 8. Evidence of QOF on admissions Study Clinical area Methodology Results Downing et Asthma, Cancer, 2004/05 (2 PCTs) Small and inconsistent al (2007) COPD, CHD, Diabetes, Stroke Bottle et al CHD (coronary 2004/05 No association (2008) angioplasty and CABG) Bottle et al Diabetes 2004/05 Significant, but weak (2008) negative association (patients over 60) Purdy et al CHD (angina and MI) 2005/06 No association CHD (2011) (negative association angina) Dusheiko et Diabetes 2004/05 – 2006/07 Significant negative al (2011) association Soljak et al Stroke (transient QOF 2008/09, Small negative association (2011) ischaemic attack) Admissions 2006/07 – (cholesterol) 2008/09
- 9. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 10. Research questions • Admissions: • Is better quality of GP care for people with SMI / bipolar disorder associated with lower rates of emergency hospital admissions for mental health or physical care? • Is better quality of GP care for people with SMI / bipolar disorder associated with higher rates of elective hospital admissions for physical care? • Resource use: • Is better quality of GP care for people with SMI / bipolar disorder associated with reduced resource use in terms of: i. frequency of admissions? ii. average length of stay? iii. secondary care costs?
- 11. SMI Indicators in the QOF Indicators Definition Thresholds [points] MH4 % patients on lithium therapy 40 - 90% Serum creatinine & TSH check <15mths [1] MH5 % patients on lithium therapy 40 - 90% Lithium within range, <6mths [2] MH6 % patients on the register 25-50% comprehensive care plan documented in the records [6 ] MH7 % SMI patients who do not attend the practice for the 40 - 90% annual review who are identified and followed up by [3] the practice team within 14 days of non attendance MH9 % patients with schizophrenia, bipolar affective 40 - 90% disorder and other psychoses [23] review recorded <15 mths
- 12. Our hypotheses • H0: no association between primary care quality and admissions • Preventive care could lower emergency admissions • H1: lower rates of emergency hospital admissions • Regular screening could increase elective admissions • H1: higher rates of elective admissions for physical conditions Reason for admission All people People with with SMI bipolar disorder Mental health - unplanned - - Physical health - unplanned - - Physical health - planned + +
- 13. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 14. Modelling approach Admissions for SMI / bipolar Achievement Practice Patient Local area Access to (QOF) + characteristics + population + characteristics + services characteristics • Level of analysis is GP practice Admissions for physical
- 15. Admissions • Study period 2006-2010 (8,469 GP practices) Admissions for SMI / bipolar • Number of SMI / bipolar patients aged 18 or (HES) over who are admitted at least once within a Practice level 2006-2010 year per GP practice • All SMI and bipolar admissions are considered emergency Admissions for physical (HES) • Physical care admissions – all diagnoses Practice level 2001-2010 other than mental health or unknown diagnosis • For physical care, SMI diagnosis may not be recorded – track patient records retrospectively from 2001
- 16. Diagnosis codes used to define SMI • GP practices use READ codes • Hospitals use ICD-10 codes • Map READ codes to ICD-10 codes ICD-10 code Description F20 Schizophrenia F21 Schizotypal disorder F22 Persistent delusional disorders F23 Acute and transient psychotic disorders F24 Induced delusional disorder F25 Schizoaffective disorders F28 Other nonorganic psychotic disorders F29 Unspecified nonorganic psychosis F30 Manic episode F31 Bipolar affective disorder
- 17. Total number of admissions over time
- 18. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 19. QOF Achievement • Clinical domain in QOF is SMI Admissions for SMI / bipolar • Number of patients on SMI (HES) register = number of patients at Practice level 2006-2010 Achievement risk of admission (QOF) • Practice achievement = Number Practice level 2006-2010 of patients for which indicator met Admissions for physical / All patients on SMI register or All (HES) patients on lithium therapy Practice level 2001-2010 • Exception reporting – remove inappropriate patients from achievement calculations (logistical reasons, clinical reasons, patient informed dissent)
- 20. QOF achievement Registered as SMI (D) Registered as bipolar (D) Not monitored Monitored (N): Monitored (N) : MH6 (% patients comprehensive care plan); Exception MH4 (% patients Exception MH9 (% patients reviewed reported with lithium record); reported in preceding 15 months) (E) MH5 (% patients in (E) therapeutic range)
- 21. QOF achievement rates, 2010/11 0.95 (0.12) 0.83 (0.22) 0.75 (0.17) 0.81 (0.13)
- 23. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 24. Other explanatory variables Admissions for SMI / bipolar (HES) Practice level 2006-2010 Achievement Practice (QOF) + characteristics Practice level 2006-2010 Admissions for physical (HES) • Average practice list size (6,708) Practice level 2001-2010 • Average age of GPs (48) • Proportion of male GPs (61%) • Proportion foreign-trained GPs (33%) • Single-handed practices (16%) • Contracted under PMS (45%)
- 25. Other explanatory variables Admissions for SMI / bipolar (HES) Practice level 2006-2010 Achievement Practice Patient (QOF) + characteristics + population characteristics Practice level 2006-2010 Admissions for physical (HES) • Average age practice population (39) Practice level 2001-2010 • Proportion male patients (50%) • Prevalence of CHD (3%) • Prevalence of diabetes (4%) • Prevalence of COPD (2%) • Prevalence obese patients (8%)
- 26. Other explanatory variables Admissions for SMI / bipolar (HES) Practice level 2006-2010 Achievement Practice Patient Local area (QOF) + characteristics + population + characteristics characteristics Practice level 2006-2010 Admissions for physical (HES) • Overall Index of Multiple Deprivation Practice level 2001-2010 • Proportion claiming incapacity benefits for mental health disorders • Ethnicity (% non-whites) • Rurality (% living in urban areas)
- 27. Other explanatory variables Admissions for SMI / bipolar (HES) Practice level 2006-2010 Achievement Practice Patient Local area Access to (QOF) + characteristics + population + characteristics + services characteristics Practice level 2006-2010 Admissions for physical (HES) • Distance - GP practice to nearest acute Practice level 2001-2010 (8km) and mh (14km) provider • NHS comm psych beds per 1000 pop • 48-hour access to GP practices (84%) • Population providing informal care • CRHT teams – PCT level fixed effects
- 28. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 29. Empirical approach 𝐸[𝑎𝑑𝑚𝑖𝑡 𝑖𝑡 ] = 𝑉[𝑎𝑑𝑚𝑖𝑡 𝑖𝑡 ] = 𝑟𝑖𝑠𝑘 𝑖𝑡 ∗ 𝛾 𝑖 ∗ exp(𝜃 + 𝑞 ′ 𝑖𝑡 + 𝑥 ′ 𝑖𝑡 𝛽 + 𝜅 𝑡 ) Variable Definition admit number of patients in GP practice i = 1…I admitted to hospital ≥ 1 within the year t = 1….Ti risk number of SMI or bipolar patients at risk of admission γ GP practice-specific effect that captures unobserved, time-invariant differences between practices in terms of their admission propensity θ common intercept q' assessment of GP practice quality as measured by the QOF x’ vector of covariates that capture differences in: practice, patient population, local area characteristics, supply of and access to mental health resources (including PCT fixed effects) Include pre-sample baseline admissions (2003 and 2004) κ' vector of time dummy variables
- 30. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 31. Emerging results • …suggest a positive and significant association between QOF performance & unplanned hospital admission • ...confirm a priori expectation of positive and significant association for elective admissions
- 32. Coefficient estimates for bipolar MH4 Variable IRR T-stat Pre-sample baseline bipolar admissions (2003 and 2004) 1.08 11.72*** Year 2 1.17 8.61*** Year 3 1.17 8.46*** Year 4 1.25 10.69*** Year 5 1.22 8.73*** List size 1.00 -5.14*** Proportion of foreign trained GPs 1.07 2.24* Average age practice population 0.98 -4.46*** Proportion male patients 1.01 2.03* Proportion claiming incapacity benefits mental health cat2 1.04 1.08 Proportion claiming incapacity benefits mental health cat3 1.14 2.92** Proportion claiming incapacity benefits mental health cat4 1.23 3.88*** Proportion claiming incapacity benefits mental health cat5 1.26 3.58*** Proportion living in urban areas 1.13 3.18** Ethnicity (% non-whites) 2.24 5.24*** Prevalence of CHD 0.02 -2.06* NHS community psychiatric beds per 1000 population 0.95 -2.47* 48-hour access to GP practices 0.83 -2.54*
- 33. Estimation results • All exclusions are deemed invalid, since we don’t know if all patients admitted have been excluded or not • QOF measure Achievemen t (N) SMI / bipolar Register (D E) Indicator IRR T-stat IRR T-stat IRR T-stat SMI / Bipolar Physical emergency Physical elective SMI patients MH 6 1.16 4.90*** 1.18 5.09*** 1.19 6.89*** MH 9 1.25 6.52*** 1.30 7.11*** 1.23 7.33*** Bipolar patients MH 4 1.15 2.19* 1.29 4.45*** 1.23 4.13*** MH 5 1.14 3.11** 1.20 4.96*** 1.20 6.84***
- 34. Estimation results • All exclusions are deemed valid, which is how GPs are actually reimbursed Achievemen t (N) • QOF measure (SMI / bipolar Register - Exceptions ) (D) Indicator IRR T-stat IRR T-stat IRR T-stat SMI / Bipolar Physical emergency Physical elective SMI patients MH 6 1.02 0.70 1.03 0.77 1.04 1.76 MH 9 1.06 1.20 1.20 3.35*** 1.03 0.92 Bipolar patients MH 4 1.29 2.86** 1.28 2.85** 1.25 3.20** MH 5 1.17 3.20** 1.12 2.77** 1.16 4.53***
- 35. Sensitivity analysis – exception reporting
- 36. Average exception reporting Indicator Mean Median Max SD Numbers MH 6 6 3 239 7.9 MH 9 6.6 3 235 8.9 Rate MH 6 0.12 0.09 1 0.11 MH 9 0.13 0.097 1 0.12 • Reasons for exclusions (e.g. MH9): • Patient unsuitable 47% (very high by QOF standards) • Logistical 22% • Informed dissent 25% • Unknown 6%
- 37. Results • Results suggest positive and significant association between primary care quality and admissions • Reject H0 but fail to accept H1 • Direction of association is contrary to initial expectations for emergency admissions and contrary to evidence in other clinical domains • Results confirm a priori expectation of positive association for elective admissions • Accept H1
- 38. Outline • Background • Research questions • Modelling admissions • Modelling quality • Other explanatory variables • Empirical approach • Results • Discussion
- 39. Discussion • Association, not causality • Possible explanations for results: • GPs provide care after admission and discharge → reverse causality (timing of events) • High achieving practices attract /retain hard-to-reach patients → incomplete practice risk adjustment • Practices with more admissions / severe patients better at recording QOF → reporting bias • Others...
- 40. Discussion • To test hypotheses we need patient level data from GP practices • We don’t know whether patients admitted are patients for whom QOF achieved • Address timing of events • Improve individual patient risk adjustment
- 41. Further refinements & research questions • Explore inclusion of lags to try to disentangle causal effect • lagged achievement rates on admissions • achievement rates on lagged admissions • Examine QOF indicators simultaneously in analysis • MH4 and MH5 • MH6 and MH9 • Is better QOF associated with reduced resource use: • frequency of admissions • average length of stay • secondary care costs
- 42. Any questions? Rowena Jacobs rowena.jacobs@york.ac.uk HS&DR Project - 10/1011/22 http://www.netscc.ac.uk/hsdr/projdetails.php?ref=10- 1011-22 Thank you!
Notas do Editor
- Patients with schizophrenia are more likely to suffer from preventable physical illnesses like obesity, hypertension and smoking-related diseases and are at increased risk of diabetes. Antipsychotic medications cause weight gain. Poor compliance with medication is well recognised, and this may lead to relapse, poorer outcomes, and admissions. Smoking-related diseases, heart disease and premature death are more common in people with SMI who smoke. Disenfranchised, marginalised, stigma, doesn’t get same priority as other chronic disease areas
- GPs oversee care, prescribe medication and provide both mental and physical health services. In Europe and the United States there has been a general trend towards decreasing lengths of hospital stays in favour of short-term pharmacological stabilization in hospital, followed by longer multidisciplinary follow-up in the community or primary care setting.- SMI patients at higher risk of hospitalisationsPreventive care could lower emergency admissionsRegular screening could increase elective admissions
- Offers financial rewards to GP practices for meeting targets on clinical, organisational and patient experience indicators
- - Evidence is mixed:No association found for CHD, asthma, COPDNegative association found for diabetes, stroke
- Preventive care could lower emergency / unplanned admissionsRegular screening could increase elective / planned admissions
- MH7 – did consider this, but problems with the indicator. In the review there should be evidence that the patient has been offered routine health promotion and prevention advice appropriate to their age, gender and health statusUpper threshold another mechanism to protect patients from coercive care, so practices don’t have to achieve targets for all patients to receive maximum payment. Practices awarded points based on proportion of appropriate patients (not exception reported) for whom targets achieved, between lower threshold and upper threshold. 2008/09 each point earned £126, adjusted for prevalence of disease and size of practice population.
- Preventive care could lower emergency / unplanned admissionsRegular screening could increase elective / planned admissions
- The aim of our empirical analysis is to relate the number of patients admitted to hospital froma GP practice to the practice's quality performance, controlling for other factors that may drive admissions but are unrelated to the quality of primary care provided.
- Dataset of around 40,000 practice-yearsHES – patient level
- Exception reporting - mechanism to protect patients from coercive care, respect patients choice to refuse intervention. Use clinical judgement to remove inappropriate patients from achievement calculations. - Logistical (recent registration of patient with practice, recent diagnosis) - Recently registered patients or recent diagnosis automatically excepted.- Clinical (contraindication to treatment or drug intolerance), patient unsuitable (treatment clinically inappropriate, extreme frailty, patient received at least 3 invitations for a review during preceding 12 months but not attended), Patient informed dissent (not agreeing to investigation or treatment – doctors required to make contact with patient and record patient’s reasons for rejecting intervention). Drawback of exception reporting – allows practices to receive max remuneration without necessarily providing required care for eligible patients. If applied to readily or inappropriately, high achievement can mask suboptimal care.
- - Number of patients on SMI register = number of patients at risk of admission- Practice achievement = Number of patients for which indicator met ÷ All patients on SMI or bipolar register- Exception reporting – remove inappropriate patients from achievement calculations (logistical reasons, clinical reasons, patient informed dissent) Exception reporting - mechanism to protect patients from coercive care, respect patients choice to refuse intervention. Use clinical judgement to remove inappropriate patients from achievement calculations. Logistical (recent registration of patient with practice, recent diagnosis) - Recently registered patients or recent diagnosis automatically excepted.- Clinical (contraindication to treatment or drug intolerance), patient unsuitable (treatment clinically inappropriate, extreme frailty, patient received at least 3 invitations for a review during preceding 12 months but not attended), Patient informed dissent (not agreeing to investigation or treatment – doctors required to make contact with patient and record patient’s reasons for rejecting intervention). Drawback of exception reporting – allows practices to receive max remuneration without necessarily providing required care for eligible patients. If applied too readily or inappropriately, high achievement can mask suboptimal care.
- Mean (SD) - pooledThis is N/(D+E)If we remove exclusions, distribution shifts to the right (higher achievement)The highest level of full achievement is observed for MH4, where 67% of practices report a score of 1. In contrast, only about 5% of practices report full achievement on MH6 or MH9.
- (GMS)
- (QOF,GMS)
- (ONS, ADS)
- HES, GP Survey, ONS
- The number of admissions per GP practice is a non-negative integer or count variable and we estimate mixed effects count models that acknowledge the data generating process. We estimate separate models for each QOF indicator.GP effects drawn from gamma distribution, uncorrelated with regressorsEquidispersion assumedconditional mean, E[admit] and variance, V [admit] constrained to be equal Bootstrap to allow for over- or underdispersed data
- Significant factors: - deprivation (MH claim) - time- Ethnicity (% non-white) has large impact on probability of admission- NHS psychiatric residential housing
- N/D = 0.927; N/(D+E) = 0.809Significant and positive increase in admissions for physical care of 29% for bipolar patients, holding all else constant.Statistically significant associations between QOF achievement and admissions for the general care indicators (MH6, MH9)and the two lithium indicators (MH4, MH5). Association is positive, implying that better QOF performance is associated with more admissions, not fewer.IRR - means that the number of expected admissions per year, i.e. admission rate increases by a factor of 1.16 when the QOF increases by one unit, while holding all other variables in the model constant.
- N/D = 0.927; N/(D+E) = 0.809
- Fewer patients excepted for indicators perceived to be less challenging to achieve. Propensity to legitimately except patients or thoroughness in documenting exceptions is related to practice characteristics. Larger practices tend to be better organised, better at identifying patients who should be excepted. Practices with larger disease register for given list size may detect more patients with less severe disease who may be less likely to meet exception reporting criteria. Doran (BMJ 2012) – financial gains from exception reporting varied substantially with deprivation. Practices in more deprived areas tended to have lower achievement rates for clinical indicators and so more likely to achieve below upper payment thresholds and therefore benefit from excepting patients.