· Web viewDoes pooling health & social care budgets reduce hospital use and lower costs? Dr...

Does pooling health & social care budgets reduce hospital use and lower costs?Dr Jonathan Stokes (Corresponding author) a

[email protected]

Dr Yiu-Shing Lau a

[email protected]

Dr Søren Rud Kristensen a,b

[email protected]

Prof Matt Sutton a

[email protected]

a. Health Organisation, Policy, and Economics, Centre for Primary Care and Health Services Research, University of Manchester, Manchester, UK

b. Faculty of Medicine, Institute of Global Health Innovation, Imperial College London, London, UK

Keywords: Pooled budgets; Integrated care; Better Care Fund; Multimorbidity; Health financing; Health policy; Organisation of care

Highlights

Pooled budgets can theoretically provide an added incentive to integrate care. There appears to be a small additional effect of pooling on outcomes. Effect is not in intended direction, but fits with integrated care literature.

1

1

2

3456789

10111213141516171819202122

23

242526

27

28

29303132

Abstract

An increasing burden of chronic disease and multimorbidity has prompted experimentation

with new models of care delivery that aim to improve integration across sectors and reduce

overall costs through decreased use of secondary care. One approach to stimulate this

change is to pool health and social care budgets to incentivise care delivery in the most

efficient location. The Better Care Fund is a large pooled funding initiative gradually taken up

by local areas in England between 2014 and 2015. We exploit this variation in timing of

uptake to examine the short- (1 year) and intermediate-term (up to 2 years) effects of the

Better Care Fund on seven measures of hospital use and costs from a cohort of 14.4 million

patients constructed using national Hospital Episode Statistics. We test for differential

effects on people with multimorbidity. We find no effects of budget pooling on secondary

care use for the whole population. For a multimorbid patients the use of bed days increased

in the short-term by 0.164 (4.9%) per patient per year. In the short- to intermediate-term,

pooling health and social care budgets does not reduce hospital use nor costs. However,

pooling funds does appear to stimulate additional integration activity.

2

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

1. Introduction

Long-term management of patients with chronic conditions, particularly those with

multimorbidity, requires co-ordination between multiple professionals, and across multiple

sectors (e.g. primary, secondary, tertiary and social care). Yet, these sectors have

traditionally been operating independently, with separate commissioners of services,

separate funding sources and payment mechanisms (Struckmann et al., 2017). New forms of

‘integrated care’ attempt to cross these traditional boundaries with the aim of providing

more effective and efficient care in the right place and time (NHS England, 2014; World

Health Organization, 2015).

The main metric for measuring the success of integrated care in the UK has been reducing

hospital activity (Monitor, 2013). Sustainability of current healthcare spending levels have

been questioned, and there is an assumption (although not fully tested) that primary and

social care is cheaper than secondary care. Reducing hospital activity, therefore, might be an

indication of a desired care substitution, or even a care prevention (i.e. increasing health)

effect. However, shrinking the hospital sector to the benefit of a growing primary and social

care sector is likely to be difficult unless incentives are aligned.

One perceived barrier to integrating care, then, is an incentives problem (Struckmann et al.,

2017). Multiple sector providers contribute in some way to a joint patient health outcome.

For example, a patients’ probability of having an emergency admission is affected by

previous activity in all of primary, secondary, and social care (Mason et al., 2015). However,

this interdependency between providers efforts and outcomes is not accounted for with

3

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

sector-specific payments and budgets meaning each provider is incentivised separately for

different activity/outcomes (Stokes et al., 2018b).

Pooling of budgets with all partners contributing to a shared fund for spending on agreed

projects or services is a mechanism that could theoretically ease this transition (Mason et

al., 2015). With shared financial resources, sectors are incentivised to make allocation

decisions in partnership towards achieving shared outcomes, accounting for

interdependency. In theory, this should lead to better integrated service delivery, and

reduced secondary care activity should be a measurable outcome of success. (Stokes et al.,

2018a)(Mason et al., 2015).

The Better Care Fund (BCF) is a large (£5.3 billion in 2015/16, and £5.8 billion 2016/17)

pooled health and social care funding scheme, mandated in England from April 2015. The

BCF was designed to generate measurable effects within one year of implementation,

principally reducing demand for hospital services, e.g. emergency admissions, and delayed

transfers of care. While the scheme was mandated within the 2015/16 financial year, a

number of local areas (Health and Wellbeing Boards) implemented the pooled budget

mechanism from 2014/15 (NHS England, 2016a).

We provide rigorous quasi-experimental evidence on the effects of pooled health and social

care funding. We exploit the gradual roll-out of the BCF in geographical space and over time

and examine effects on secondary care utilisation and costs using a dataset recording all

secondary care use in England.

4

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

1.1 Existing evidence on pooled funding

A recent systematic review analysed the international literature on the effects of integrating

funds for health and social care. They identified 38 schemes from eight countries. However,

the findings were inconsistent, with 33% of the schemes showing no effects on secondary

care costs or utilisation, 9% significantly lower utilisation, and the remaining 58% showing

mixed or unclear evidence. None of the evidence isolated the effect of pooled funding

alone; instead assessing impact of pooled funding plus simultaneous service delivery and

organisational changes (Mason et al., 2015).

Additionally, there are a number of more recent examples of integrated care that have

implemented some pooled budgeting. For example, Gesundes Kinzigtal in Germany has

implemented a ‘virtual’ pooled budget to incentivise multiple providers through shared

savings opportunities. An evaluation found that the programme led to increased hospital

admissions, but decreased length of stay, and an overall decrease in costs per patient per

year (Busse & Stahl, 2014). However, again it was not clear to which extent this was related

to the pooling of budgets as it was part of a package of changes.

In the US, Accountable Care Organisations (ACOs) have combined multiple independent

providers into a single provider (which might alone act to incentivise shared outcomes,

accounting for interdependency) plus used pooled budgets (sometimes termed ‘global

budgets’) and pay-for-performance to shift care priorities. Overall, ACOs found savings of

2.8 percent over two years follow-up (1.9 percent in year one and 3.3 percent in year two)

compared to non-ACOs (Z. Song et al., 2012). Those formed by independent primary care

5

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

groups gained consistently greater savings than hospital-integrated groups (McWilliams et

al., 2016). In the early cohort, differential reductions in spending were greater for high risk

patients than low risk (but with similar relative reductions in both groups), whereas in those

ACOs entering later differential reductions in spending and admissions were almost entirely

among low risk patients (McWilliams et al., 2017). A study examining longer term impacts of

an ACO in Massachusetts found lower spending growth and generally greater quality

improvements after four years, although factors beyond pooled budgets were thought to

have contributed, particularly in the later part of the study period (Zirui Song et al., 2014).

In summary, these previous studies have been unable to separate the impacts of the pooled

budgeting arrangements from the other service delivery and organisational changes that

occurred simultaneously. The BCF offers a unique opportunity to study the impact of pooled

budgets over and above other integration activity.

There have been only two studies of the BCF. An investigation by the government spending

watchdog suggested that the expected reductions in secondary care activity had not been

met (National Audit Office, 2017). However, the analysis compared realised activity rates

with the predictions that had been made for the programme in advance of its

implementation. Our analysis offers a more robust research design, exploiting the gradual

roll-out of the reform.

Another recent report used aggregated data at the hospital Trust-quarter level, and

exploited differences in the amount spent on BCF activity in the two years following the

mandated implementation (Forder et al., 2018). They identified a small reduction in delayed

6

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

transfers of care (0.073% for a 1% increase in BCF expenditure per capita), but no effect on

emergency admissions. This analysis focuses on the effects of variations in the extent to

which the BCF is used rather than its introduction.

We build on the previous literature by examining the effect of pooled funding over and

above other integration activity. We use individual-level data so that we can conduct

detailed risk adjustment and estimate effects for multimorbid patients who are most likely

to benefit from integrated care.

1.2 The Better Care Fund initiative

150 local Health and Wellbeing Boards were created in 2012. They brought social care

commissioners in Local Authorities together with representatives of the NHS (Clinical

Commissioning Groups - local healthcare commissioners), public health, and patients to plan

how to meet the health needs of their local population. In 2015, the BCF required these

Health and Wellbeing Boards to pool a proportion of their health and social care budgets

(National Audit Office, 2017). The BCF was not new or additional money, but a re-allocation

of Clinical Commissioning Group and Local Authority funds (Bennett & Humphries, 2014).

Therefore, any effects associated with the introduction of the BCF will be as a result of the

new pooling mechanism rather than additional funding.

Pooled budgets are intended to deliver their effects through added stimulation of integrated

care activity. The 2012 Health & Social Care Act in England mandated all Clinical

Commissioning Groups (CCGs) to ‘promote integration’ (Department of Health, 2012).

Therefore, all local areas might be expected to be integrating care in some form prior to the

BCF. There are various types of integrated care activity, but the most ubiquitous is case

7

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

management. This consists of individualised care planning and on-going intensive

management in primary care, frequently involving a multidisciplinary team and targeting

patients at high risk of secondary care utilisation (Stokes et al., 2016a). Indeed, case

management of high risk patients was financially incentivised nationally from financial year

2013, cementing its position as the dominant form of integrated care (NHS England, 2013).

Some areas were also funded to do additional integration efforts, for example through the

national ‘Pioneer’ and ‘Vanguard’ programmes (Vanguards were funded from 2015/16,

Pioneers in two waves from 2013/14 and 2015/16). These programmes have tended to build

on their case management activity with additional activity (e.g. health coaching) covering

the broader population and wider organisational changes (NHS England, 2016b, c). In our

analysis we control for this additional integration activity to capture the effect of pooling

funds over and above existing integrated care activity.

In 2015/16, £1 billion (nearly 20%) of the Fund was ring-fenced for out-of-hospital spending.

Each local area was also asked to target reduced emergency admissions and keep an

amount equal to the value of those admissions in a pay-for-performance pot, with areas

then able to spend these funds in line with their BCF plans depending on performance. In

the first year, local areas planned to reduce emergency admissions by 106,000 saving

potentially £171m, and planned to reduce delayed transfers of care by 293,000 days,

potentially saving £90m (National Audit Office, 2017).

8

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

2. Methods

2.1 Data

Secondary care use data

We used the Hospital Episode Statistics dataset containing details of all utilisation at NHS

hospitals in England (NHS Digital, 2017). We constructed a cohort of all patients having any

planned or emergency hospital admission in the two financial years prior to the first

adoption of the BCF (1st April 2011 to 31st March 2013). This ‘high risk’ cohort is targeted for

relatively standardised integrated care service delivery activity nationally in the form of case

management. Therefore, any effects associated with the BCF will reflect its role in

stimulating sites to ‘do more’ integration.

We created an individual-level dataset over seven financial years (annually, between 1st

April 2009 to 31st March 2016), counting all hospital admissions, outpatient visits and

emergency department attendances for each cohort patient per year. We costed each

measure of utilisation using the national tariff applicable in that year (NHS Improvement,

2017), and calculated the total cost of secondary care for each individual in each year.

Activity and costs were set to zero for any year with no recorded utilisation to make a

balanced panel. We used a pseudonymised patient identifier to link in mortality data from

the Office for National Statistics (ONS, 2017). We constructed a dummy variable equal to

one if a patient died in or out of hospital within the year and exited the sample (giving us a

legitimately unbalanced panel). Co-variates were set at the last non-missing observation for

each individual.

9

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

We created seven outcome measures: (i) number of emergency admissions for ambulatory

care sensitive conditions (see Appendix for list of codes); (ii) probability of re-admission

within 30 days of discharge; (iii) total bed-days; (iv) number of days of delayed discharge; (v)

number of ED attendances; (vi) number of outpatient visits; and, (vii) total cost of secondary

care.

Intervention status data

Individuals in our cohort were assigned to Health and Wellbeing Boards based on their

Lower Layer Super Output Area (LSOA) of residence. Missing Health and Wellbeing Board

(LSOA was missing for 5% of the sample) was updated based on GP practice (NHS England,

2017b). We obtained the list of GP practices that were Vanguard’s directly from NHS

England, and we created a dummy for Pioneer status based on CCG and information

available in the literature (Erens et al., 2015; Monitor, 2015). We included dummy variables

for Vanguard and Pioneer status.

Multimorbidity status

We assigned each individual to a multimorbidity class based on all of the ICD-10 codes

recorded for that patient on hospital admissions in the two years of data on which we

constructed the cohort. We drew on a list of 30 long-term conditions used in the previous

literature (see Appendix) (Tonelli et al., 2015). We constructed: 1) a dummy variable

identifying those with two or more conditions; and 2) a categorical variable identifying those

with (a) less than two conditions, (b) those with two or more conditions (only physical

health conditions, or only mental health conditions) and (c) those with two or more

10

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

conditions (including at least one mental health condition and at least one physical health

condition).

2.3 Analysis

Our full dataset included seven years of annual, individual-level data. Models were

estimated on a pool of 14,363,471 patients that had non-missing co-variates, including age,

sex, GP practice’s Vanguard status, Pioneer status, and Health and Wellbeing Board (local

area identifier). There were 1.35 million deaths in the sample between 2012/13 and

2015/16. We have five years of data in the pre-intervention period, with the intervention

introduced in the sixth of seven years. The intervention began in the 2014/15 financial year

when seventy-five percent of the Health and Wellbeing Boards pooled health and social care

funding under the BCF intervention (see Appendix). In 2015/16, all Health and Wellbeing

Boards had implemented the intervention. As our primary analysis, we report estimates

from an ordinary least squares (OLS) regression with high-definition fixed effects (Correia,

2016).

Short-term effects

Our primary analysis uses six years of data (85,073,282 observations), including up to the

2014/15 financial year when there is a single post intervention period where seventy-five

percent of Health and Wellbeing Boards implemented the BCF intervention and remaining

Health and Wellbeing Boards are the control group. We use a standard difference-in-

differences approach:

11

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

(1 ) y ijt=β0+ β1BC F j+β2POS T t+β3BC F j∗POST t+β4mult ii+x i γ+HWB j+Vanguard j+Pioneer j+Year t+ϵ ijt

In which y ijt is the outcome for individual i in Health and Wellbeing Board area j at time t ,

BCF jis a dummy for intervention status, POST tis a dummy for whether the observation is

in the post-intervention period (i.e. 2014/15), mult ii is a dummy indicating whether the

patient is multimorbid, vector x i is a set of indicators for categories of age group (19 five-

year bands to 85+) and gender, HWB j is a set of Health and Wellbeing Board fixed effects,

Vanguard j and Pioneer j are sets of Vanguard status fixed effects controlling for ‘other

integrated care activity’, Year t a set of time fixed effects and εijt is the idiosyncratic error

term. The coefficient β3 measures the effect of pooled payment within the first year of

uptake.

Intermediate-term effects

Our intermediate-term analysis deviates from the traditional difference-in-differences

approach and uses one additional year of post intervention period (98,361,352

observations). We include a binary indicator allowing for the gradual uptake of the

intervention across areas (where POOLjt is a dummy taking the value 1 for the area and time

where the pooled funds are introduced, and 0 otherwise) combined with Health and

Wellbeing Board area and time fixed effects:

(2 ) y ijt=β0+ β1 POOL jt+β2mult ii+x i γ+HWB j+Vanguard j+Pioneer j+Yeart+ϵ ijt

The coefficient β1 measures the effect of pooled payment. The effect estimate in this model

is a weighted average of both possible two-by-two difference-in-difference estimators,

12

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

where units serve as controls for each other during periods when their intervention status

stays constant (Goodman-Bacon, 2018). The estimate relies on a proportion of sites

switching intervention status over each time period, so we are not able to examine effects

beyond two years when BCF coverage is universal.

Subgroup (multimorbidity) analysis

Our subgroup analysis (both short- and intermediate-term run separately) adds additional

interaction terms to examine any differential effects by multimorbidity status. For example:

(3 ) y ijt=β0+β1BCF j+ β2 POST t+β3BC F j∗POS T t+ β4mult ii+mult ii∗( β¿¿5 BCF j+ β6 POST t+β7BC F j∗POS T t)+x i γ+HWB j+Vanguard j+Pioneer j+Year t+ϵijt ¿

The coefficient for the triple interaction term, β7, measures the differential effect of pooled

payment on multimorbid patients compared to non-multimorbid patients. We estimate this

regression separately for the two specifications of multimorbidity.

Robustness tests

The estimated effect is identified by the earlier adoption of the BCF in some areas. We test

for selection on observable characteristics by predicting early adoption of the BCF using a

logistic regression model at the Health and Wellbeing Board level. We use data from our

cohort from 2009/10-2013/14 on all measured outcomes, number of deaths, age and sex

profile, average morbidity status, average list size and Vanguard status as predictors.

One of the underlying assumptions of the difference-in-differences methodology is that the

trend in the control group is a suitable counterfactual for the trend in the intervention

13

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

group in the absence of the intervention. To examine this assumption we test whether the

trends of the intervention and control groups are statistically significantly different in the

pre-intervention period, i.e. whether outcomes follow parallel trends. We tested this

assumption by interacting the early-adopter intervention dummy with continuous time in

the pre-intervention period (i.e. 2009/10-2013/14).

Effects might be non-linear and occur at the top of the distribution (i.e. those with non-zero

utilisation). We also run each of the above analyses using two alternative models, 1)

regression on the inverse hyperbolic sine (IHS) transformed outcome variable, and; 2) a two-

part model, first a linear probability model predicting a non-zero outcome, followed by a

regression on the log-transformed outcome variable conditional on a non-zero outcome.

We cluster standard errors at the level of the intervention, the Health and Wellbeing Board.

We additionally use the mortality data to examine the influence of death as a competing risk

to utilisation of services, attempting to predict any differential change in death rate by BCF

group. We also drop all persons who die at any point over our follow-up period as an

additional check.

3. Results

3.1 Descriptive statistics

In Table 1 below, we compare descriptive variables for early and late intervention adopters

in our analysed dataset.

14

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

Table 1: Descriptive variable statistics for analysed data, based on 2014/15 intervention status.

[insert Table 1]

Both groups show similar unadjusted trends in the outcomes over time. As expected with

regression to the mean, patients have the highest hospital use and costs during the two

years upon which the cohort was constructed based on their admissions (see Appendix).

3.2 Pre-trends

Health and Wellbeing Boards with more ACSC admissions in the pre-period were more likely

to be early BCF adopters. All other covariates were not statistically significant predictors of

early adoption at the p<0.05 level.

The pre-trends analysis also suggested a significant difference for ACSC admissions (+0.01

admissions per patient per year for early-adopters in the pre-period). We found no

statistically significant differences in pre-trends for all other outcomes. However, graphical

analysis also suggested caution in interpreting the delayed discharges outcome (see

Appendix).

3.3 Overall effects of the BCF

Table 2 summarises the overall results for all seven outcomes considered. We found no

statistically significant effects of areas implementing the pooled budget at the 5%

significance level. At the 10% significance level, total bed days per patient may have

increased very slightly (+0.06 days per patient per year, 4% of the pre-intervention mean).

15

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

Our two-part model (see Appendix) suggests this increase was driven by those patients

already utilising admitted care services (i.e. those with a non-zero value).

Table 2: Overall pooled funding results.

[insert Table 2]

3.4 Multimorbidity subgroup results

Binary multimorbidity status

Table 3 summarises the results for the first measure of multimorbidity.

Table 3: Multimorbidity dummy subgroup results.

[insert Table 3]

For multimorbid patients we identified small increases in total bed days by 0.164 (4.9% of

the mean) per multimorbid patient per year in the short-term. The estimate was only

significant at the 10% level in the intermediate-term, 0.134 (4%).

Physical & mental comorbidity

Table 4: Physical mental co-morbidity subgroup results.

16

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

[insert Table 4]

For multimorbid patients, the differential increases in bed days were for those with only-

physical or only-mental health conditions in the short-term. The estimate for those with

both a physical and mental health condition was larger, but not statistically significant

(except in the inverse-hyperbolic sine model where the increase was significant at the 10%

level for this group in both the short- and intermediate-term – see Appendix).

3.5 Robustness of results across model specifications

The overall findings of a null effect were stable across the majority of robustness checks (see

Appendix).

For multimorbidity subgroups the estimates are qualitatively similar to the OLS model, but

the statistical significance does not always hold (see Appendix). For example, the increase in

bed days for multimorbid patients holds across the inverse-hyperbolic sine model, but only

at the 10% significance level.

Analysis of death as a competing risk showed no significant differential change in deaths by

early/late BCF adopters. When we excluded all patients who died, results were qualitatively

the same as reported above (see Appendix).

17

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

4. Discussion

We found no overall effects on seven outcomes for HWBs that implemented the pooled

health and social care funding intervention. Overall health system effects on hospital use

and cost are likely to be limited, at least in the short to intermediate-term.

However, we identified differential effects for the subgroup of individuals with

multimorbidity, likely to be most sensitive to the effects of integration of care. Those with

multimorbidity experienced small increases in total bed days in the short-term compared to

patients without multimorbidities. Despite not being in the intended direction, pooled

budgeting does seem to be having some additional effect of stimulating integration activity.

Limitations

This paper primarily sets out to examine the effects of pooled health and social care funding

as part of the BCF intervention roll-out. The effect of such pooling will act through

incentivising integration activity. The responses are likely to be heterogeneous but

unfortunately this activity is not measured in national datasets. There have been additional

pooled funding arrangements in certain localities (Humphries & Wenzel, 2015), but these

are not recorded in national datasets. We have controlled for integrated care Vanguard and

Pioneer status to try and alleviate this concern. In doing so, we assume that these areas,

recognised as leaders in the advancement of new models of integrated care (NHS England,

2016b), are those most likely to have additional pooled funding arrangements and be

delivering additional integration activity.

18

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

The BCF only pooled a small amount of total health service funding but at a time when social

care budgets were experiencing substantial cuts (Erens et al., 2015). Therefore, the results

may not be generalisable to pooling of larger amounts of funding or situations when both

sectors are experiencing funding growth. The policy assumed significant savings and

reduced utilisation within a short period of time (one year). We used a very large dataset

capable of detecting small effects of the intervention, but we were not able to detect any

reduced utilisation or costs of secondary care in the short or intermediate-term (up to two

years). We were able to identify differential subgroup effects for multimorbid patients,

where we expect any effects of integration to be most pronounced, with small increases in

utilisation of bed days in the short-term. The magnitude of results might differ depending on

the extent of pooling, and different effects across the distribution. Unfortunately,

information on the extent of pooling was not available for the BCF in the first year to test

this (2014/15). In robustness checks, we sought to identify different distributional effects.

Due to the way the BCF was rolled out, we were able to record differential uptake effects

only over a single year before coverage was mandated nationally. Therefore, our primary

results relate to short-term effects of pooled health and social care funding, where any

additional effects might take longer to emerge. We have tried to maximise use of available

data by estimating an intermediate-term effect using the gradual-uptake model. However,

this model has the added limitation that it assumes an instantaneous and constant effect

over time, so averages any treatment effect heterogeneity (Goodman-Bacon, 2018).

Additionally, we had a limited selection of outcome measures in the data available to us,

where effects of integrated care might be more easily captured with measures of patient

experience, for example (Ouwens et al., 2005). We included delayed discharge as an

19

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

outcome because it was a specific policy aim, but there are criticisms of its coding in HES

data, NHS Digital outlines that due to a submission error the field contains incorrect values

(NHS Digital, 2019). Consequently, we also look at total bed days because this does not

depend on whether the hospital stay is coded as a delayed discharge.

The voluntary early adoption of the intervention that we exploit in this analysis brings the

potential for selection bias. We analysed whether early adoption was related to local area

characteristics and identified few correlations though we may underestimate any beneficial

effect on ACSC admissions. There may also have been anticipation effects in areas that were

late-adopters, biasing the results towards null. However, we again expect any anticipation

effects to have been small and equally spread across both groups, as informal evidence from

policymakers suggests that the policy was not widely expected before its introduction.

Our multimorbidity measure was constructed from ICD-10 codes present in the inpatient

records of our cohort. We constructed the measure in the pre-intervention period and held

it constant to prevent any concerns of multimorbidity as a “bad control” (Angrist & Pischke,

2008) as we might expect the intervention to act to prevent further health deterioration

(including development of new disease, and so potentially classification of multimorbidity).

We would have preferred to have constructed these measures instead from primary care

data (or a combination of the two), where recording is likely to be better for chronic

conditions (Sigfrid et al., 2006). We would also have preferred total health system costs as a

dependant variable, rather than total costs of secondary care. Unfortunately, this linked

data was not available to us. We would therefore expect our multimorbidity measure to

20

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

underestimate the prevalence of multimorbidity in our cohort (Tonelli et al., 2015), and we

are unable to estimate effects on costs in other sectors, for example primary care.

Interpretation in context of the wider literature

Previous analysis of the BCF by the National Audit Office concluded that “the Better Care

Fund did not achieve its principal financial or service targets over 2015-16” and that

estimates of effects of the BCF and integration more generally have been “over optimistic”

(National Audit Office, 2017). Our analysis likewise shows that any cost-saving expectations

from the intervention are unlikely, especially in the short-term. The report also highlighted,

however, that 90% of local areas “agreed or strongly agreed that the delivery of the Better

Care Fund plans had a positive impact on integration locally” (National Audit Office, 2017).

Likewise, the fact that we find any significant effect at all suggests that the pooled funding

has some effect on ability to integrate.

A system-level evaluation of the BCF found that those sites spending on activities classified

as ‘intermediate care’ and ‘prevention activities’ also appeared to be more effective than

other forms (Forder et al., 2018). So, there may be more effective ways of implementing the

intervention than others.

A recent systematic review found that a number of pooled funding schemes reported

improved access to care. Some of these schemes reported increased total costs as a result

of identifying substantial levels of unmet need (Mason et al., 2015). Our results would fit

with these findings, showing increased utilisation of bed days in the short-term for the most

complex patients.

21

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

The mechanism of effect for a pooled health and care funding intervention is via

incentivising additional integrated care activity. As outlined in the introduction, in England,

to date, this has been primarily through case management. The evidence for effectiveness

of case management likewise shows that increased integration could increase utilisation and

cost, particularly for the highest risk patients (McWilliams & Schwartz, 2017; Roland & Abel,

2012; Snooks et al., 2018; Stokes et al., 2016b; Stokes et al., 2017; Stokes et al., 2015). The

current evidence for integrated care more widely also matches with these results (Baxter et

al., 2018; Nolte & Pitchforth, 2014).

Implications for policy and practice

It is an unsurprising finding, then, that incentivising integration activity through the BCF

leads to similar results as we find for the integration activity itself. However, our results

show that pooled funding does appear to provide an additional effect (i.e. might help drive

more integrated care activity). The most common aim of integrated care (at least as

identified in UK-based policy) is reducing demand and costs (Hughes, 2017). There is,

therefore, the need for policymakers to identify and recommend interventions that are able

to achieve these aims.

However, current policy speculation is that to achieve increased efficiency, wider

organisational change is needed, with particular emphasis on formation of single provider

organisations, ACOs (most recently re-named integrated care systems in the NHS) (NHS

England, 2019). The BCF, or a similar pooled funding approach, might be a necessary step to

22

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

allow these ACOs to function as intended, and so ultimately allow the outcomes to be

achieved in the long-term. There is the possibility, for instance, that pooled funding could be

more useful for incentivising more preventative care with this population health

management-based approach (NHS England, 2017a), and/or other service delivery

interventions that might affect outcomes differently. However, the early evidence on ACOs

from the USA has so far been mixed (McClellan et al., 2015; McWilliams et al., 2016). Where

savings have occurred, they have not been attributed to better co-ordination but rather to

reducing waste (McWilliams, 2016).

Current results might reflect current trends in integrated care delivery, therefore, but might

allow for improvement in future if better models can be stimulated by the pooled funding

itself.

There do not appear to be beneficial overall effects of pooled health and social care funding

through the BCF. There appear to be some differential effects by multimorbidity subgroup,

with findings in line with the integrated care interventions that pooled funding currently

incentivises. In the short term, pooling health and social care budgets alone does not appear

to reduce hospital use nor costs but does appear to additionally stimulate integration

activity.

23

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

References

Angrist, J.D., & Pischke, J.-S. (2008). Mostly harmless econometrics: An empiricist's companion: Princeton university press.

Baxter, S., Johnson, M., Chambers, D., Sutton, A., Goyder, E., & Booth, A. (2018). The effects of integrated care: a systematic review of UK and international evidence. BMC Health Services Research, 18, 350.

Bennett, L., & Humphries, R. (2014). Making best use of the Better Care Fund: spending to save. London: The Kings Fund.

Busse, R., & Stahl, J. (2014). Integrated care experiences and outcomes in Germany, the Netherlands, and England. Health Affairs, 33, 1549-1558.

Correia, S. (2016). A feasible estimator for linear models with multi-way fixed effects. Duke University Preliminary Version. URL: www. scorreia. com/research/hdfe. pdf.

Department of Health. (2012). Health and Social Care Act 2012. London, UK: Department of Health.

Erens, B., Wistow, G., Mounier-Jack, S., Douglas, N., Jones, L., Manacorda, T., et al. (2015). Early evaluation of the Integrated Care and Support Pioneers Programme. London, UK: Policy Innovation Research Unit.

Forder, J., Caiels, J., Harlock, J., Wistow, G., Malisauskaite, G., Peters, M., et al. (2018). A system-level evaluation of the Better Care Fund: Final Report. pp. 1-140).

Goodman-Bacon, A. (2018). Difference-in-differences with variation in treatment timing. National Bureau of Economic Research.

24

541

542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574

Hughes, G. (2017). New models of care: the policy discourse of integrated care. People Place and Policy, 11, 72-89.

Humphries, R., & Wenzel, L. (2015). Options for integrated commissioning: Beyond Barker.

Mason, A., Goddard, M., Weatherly, H., & Chalkley, M. (2015). Integrating funds for health and social care: an evidence review. J Health Serv Res Policy, 20, 177-188.

McClellan, M., Kocot, S., & White, R. (2015). Early evidence on medicare acos and next steps for the medicare aco program (updated). Health Affairs Blog.

McWilliams, J.M. (2016). Cost Containment and the Tale of Care Coordination. New England Journal of Medicine, 375, 2218-2220.

McWilliams, J.M., Chernew, M.E., & Landon, B.E. (2017). Medicare ACO Program Savings Not Tied To Preventable Hospitalizations Or Concentrated Among High-Risk Patients. Health Aff (Millwood), 36, 2085-2093.

McWilliams, J.M., Hatfield, L.A., Chernew, M.E., Landon, B.E., & Schwartz, A.L. (2016). Early Performance of Accountable Care Organizations in Medicare. New England Journal of Medicine, 374, 2357-2366.

McWilliams, J.M., & Schwartz, A.L. (2017). Focusing on High-Cost Patients — The Key to Addressing High Costs? New England Journal of Medicine, 376, 807-809.

Monitor. (2013). Closing the NHS funding gap: how to get better value health care for patients pp. 1-23).

Monitor. (2015). Integrated Care Pioneers.National Audit Office. (2017). Health and social care

integration.NHS Digital. (2017). Hospital Episode Statistics.NHS Digital. (2019). HES data quality notes.

25

575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610

NHS England. (2013). 2013/14 general medical services (GMS) contract - guidance and audit requirements for new and amended enhanced services: Risk profiling and care management scheme. In N. Employers (Ed.).

NHS England. (2014). Five Year Forward View. pp. 1-41).

NHS England. (2016a). Better Care Fund.NHS England. (2016b). New care models - vanguard

sites.NHS England. (2016c). People helping people: Year two

of the pioneer programme. pp. 1-45).NHS England. (2017a). ACOs and the NHS

commissioning system.NHS England. (2017b). Better Care Fund.NHS England. (2019). The NHS Long Term Plan.NHS Improvement. (2017). National tariff payment

system Nolte, E., & McKee, M. (2008). Caring for people with

chronic conditions: a health system perspective. McGraw-Hill International.

Nolte, E., & Pitchforth, E. (2014). What is the evidence on the economic impacts of integrated care.

Omran, A.R. (1971). The epidemiologic transition: a theory of the epidemiology of population change. The Milbank Memorial Fund Quarterly, 49, 509-538.

ONS. (2017). Deaths.Ouwens, M., Wollersheim, H., Hermens, R., Hulscher,

M., & Grol, R. (2005). Integrated care programmes for chronically ill patients: a review of systematic reviews. International Journal for Quality in Health Care, 17, 141-146.

Roland, M., & Abel, G. (2012). Reducing emergency admissions: are we on the right track? BMJ, 345.

Sigfrid, L.A., Turner, C., Crook, D., & Ray, S. (2006). Using the UK primary care Quality and Outcomes Framework to audit health care equity: preliminary

26

611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647

data on diabetes management. Journal of Public Health, 28, 221-225.

Snooks, H., Bailey-Jones, K., Burge-Jones, D., Dale, J., Davies, J., Evans, B., et al. (2018). Predictive risk stratification model: a randomised stepped-wedge trial in primary care (PRISMATIC).

Song, Z., Rose, S., Safran, D.G., Landon, B.E., Day, M.P., & Chernew, M.E. (2014). Changes in Health Care Spending and Quality 4 Years into Global Payment. New England Journal of Medicine, 371, 1704-1714.

Song, Z., Safran, D.G., Landon, B.E., Landrum, M.B., He, Y., Mechanic, R.E., et al. (2012). The ‘Alternative Quality Contract,’ Based On A Global Budget, Lowered Medical Spending And Improved Quality. Health Affairs, 31, 1885-1894.

Stokes, J., Checkland, K., & Kristensen, S.R. (2016a). Integrated care: theory to practice. J Health Serv Res Policy, 21, 282-285.

Stokes, J., Kristensen, S.R., Checkland, K., & Bower, P. (2016b). Effectiveness of multidisciplinary team case management: difference-in-differences analysis. BMJ Open, 6, e010468.

Stokes, J., Kristensen, S.R., Checkland, K., Cheraghi-Sohi, S., & Bower, P. (2017). Does the impact of case management vary in different subgroups of multimorbidity? Secondary analysis of a quasi-experiment. BMC Health Services Research, 17, 521.

Stokes, J., Panagioti, M., Alam, R., Checkland, K., Cheraghi-Sohi, S., & Bower, P. (2015). Effectiveness of Case Management for 'At Risk' Patients in Primary Care: A Systematic Review and Meta-Analysis. PLoS ONE, 10, e0132340.

Stokes, J., Riste, L., & Cheraghi-Sohi, S. (2018a). Targeting the ‘right’ patients for integrated care: stakeholder perspectives from a qualitative study. J Health Serv Res Policy, 23, 243-251.

27

648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684

Stokes, J., Struckmann, V., Kristensen, S.R., Fuchs, S., van Ginneken, E., Tsiachristas, A., et al. (2018b). Towards incentivising integration: A typology of payments for integrated care. Health Policy, 122, 963-969.

Struckmann, V., Quentin, W., Busse, R., & Van Ginneken, E. (2017). How to strengthen financing mechanisms to promote care for people with multimorbidity in Europe? The European Observatory on Health Systems and Policies.

Tonelli, M., Wiebe, N., Fortin, M., Guthrie, B., Hemmelgarn, B., James, M., et al. (2015). Methods for identifying 30 chronic conditions: application to administrative data. BMC Medical Informatics and Decision Making, 15, 31.

Wallace, E., Salisbury, C., Guthrie, B., Lewis, C., Fahey, T., & Smith, S.M. (2015). Managing patients with multimorbidity in primary care. BMJ, 350.

World Health Organization. (2015). WHO global strategy on people-centred and integrated health services: interim report.

Table 2: Descriptive statistics (based on early/late intervention adoption)

Early adopters Late adopters Totaln = (observations) 78,576,826 19,784,526 98,361,352n = (individuals) 11,476,318 2,886,650 14,362,968Multimorbid (observations)

22,480,283 (28.6%)

5,585,771 (28.2%)

28,066,054(28.5%)

Aged over 65 years (observations)

22,319,530(28.4%)

5,418,872(27.4%)

27,738,402(28.2%)

Female (observations) 40,558,603(51.6%)

10,314,756(52.1%)

50,873,359(51.7%)

ACSC admissions (mean[SD]) 0.05 (0.34) 0.04 (0.31) 0.05 (0.33)% zeros 96.52 96.62 96.54

Bed days (mean[SD]) 1.48 (13.05) 1.41 (12.88) 1.46 (13.02)

28

685686687688689690691692693694695696697698699700701702703704705706

707

708

709

710

% zeros 82.66 82.97 82.72Re-admission within 30 days

(mean[SD]) 0.03 (0.18) 0.03 (0.18) 0.03 (0.18)

% zeros 96.71 96.77 96.72Days of delayed discharge (mean[SD]) 0.008 (10.64) 0.006 (9.43) 0.008 (10.41)

% zeros 99.97 99.98 99.98ED attendances (mean[SD]) 0.50 (1.31) 0.53 (1.42) 0.51 (1.33)

% zeros 72.20 71.40 72.04Outpatient visits (mean[SD]) 3.42 (6.42) 3.56 (7.08) 3.44 (6.56)

% zeros 44.23 44.14 44.21Total cost of secondary care

(£, mean[SD]) 1209.89(4125.88)

1205.60(5962.82)

1209.03(4555.27)

% zeros 38.83 38.78 38.82Deaths n (%) 1,089,017

(9.49%)261,770 (9.07%)

1,350,787 (9.40%)

29

Table 2: Difference-in-differences estimates of the effects of pooled funding.

ACSC admissions

Bed days Delayed discharges

ED attendances

Outpatient visits

Total cost secondary care (£)

Probability of re-admission

within 30 days

Short-termBCF*Post 4.95e-05 0.061* 0.005 0.001 -0.173 1.647 -2.4e-05

(0.0004) (0.034) (0.004) (0.009) (0.224) (17.923) (0.0004)

Intermediate-termPooled budget 0.0002 0.049 0.003 0.0004 -0.132 3.311 3.09e-06

(0.0004) (0.031) (0.004) (0.008) (0.198) (16.179) (0.0004)

OLS estimates with cluster robust standard errors in parentheses. All ‘short-term’ models based on 87,073,282 observations, ‘intermediate-term’ models based on 98,361,352 observations. All models include indicators for interactions of sex and age group, multimorbidity status, years, Vanguard status, Pioneer status, and areas. *** p<0.01, ** p<0.05, * p<0.1

30

711712

713714715716

Table 3: Difference-in-differences estimates of the effects of pooled funding. Multimorbidity dummy subgroup.

ACSC admissions


ED attendances

Outpatient visits



within 30 days

Short-termBCF*Post 5.99e-05 0.018 -0.001 -2.81e-05 -0.066 3.192 8.61e-05

(0.0002) (0.020) (0.002) (0.009) (0.110) (7.706) (0.0003)BCF*Post -5.83e-05 0.164** 0.020 0.001 -0.426 -12.960 -0.001*Multimorbid (0.001) (0.079) (0.013) (0.007) (0.452) (44.012) (0.0009)

Intermediate-termPooled budget 0.0001 0.015 -0.001 -0.001 -0.049 2.527 8.55e-05

(0.0002) (0.019) (0.002) (0.008) (0.095) (6.632) (0.0002)Pooled budget 0.0002 0.134* 0.018 0.002 -0.343 -3.826 -0.0005*Multimorbid (0.001) (0.071) (0.011) (0.006) (0.408) (41.055) (0.0008)


31

717718

719720721722

Table 4: Difference-in-differences estimates of the effects of pooled funding. Physical mental co-morbidity subgroup.


ACSC admissions


ED attendances

Outpatient visits



within 30 days

Short-termBCF*Post 6.01e-05 0.018 -0.001 -2.32e-05 -0.066 3.195 8.64e-05

(0.0002) (0.020) (0.002) (0.009) (0.110) (7.709) (0.0003)BCF*Post -0.0004 0.111** 0.021 0.0009 -0.332 -6.596 -0.0005*1.Physical/mental (0.001) (0.055) ( 0.014) ( 0.005) ( 0.414) ( 39.175) (0.0008)BCF*Post 0.001 0.286 0.017 -0.004 -0.671 -35.491 -0.001*2.Physical/mental (0.002) (0.197) (0.027) (0.016) (0.565) (61.414) (0.002)

Intermediate-termPooled budget 0.0001 0.015 -0.001 -0.001 -0.049 2.528 8.56e-05

(0.0002) (0.019) (0.002) (0.008) (0.095) (6.634) (0.0002)Pooled budget -6.92e-05 0.093 0.019 0.002 -0.272 5.151 -0.0004*1.Physical/mental (0.001) (0.049) (0.012) (0.005) (0.373) (37.047) (0.0007)Pooled budget 0.001 0.228 0.015 -0.001 -0.531 -32.255 -0.001*2.Physical/mental (0.002) (0.184) (0.023) (0.014) (0.510) (56.285) (0.002)

32

723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754

755

· Web viewDoes pooling health & social care budgets reduce hospital use and lower costs? Dr...

Documents

Transcript of · Web viewDoes pooling health & social care budgets reduce hospital use and lower costs? Dr...