€¦ · Web viewManchester Centre for Health Economics, Division of Population Health, Health...
Transcript of €¦ · Web viewManchester Centre for Health Economics, Division of Population Health, Health...
Development of a resource allocation formula for substance misuse treatment
services
Andrew Jones 1, Karen P Hayhurst 2, Will Whittaker 3, Thomas Mason 3, Matt Sutton 3
Andrew Jones, Research Fellow: [email protected]
Karen P Hayhurst, Research Fellow: [email protected]
Will Whittaker, Lecturer in Health Economics: [email protected]
Thomas Mason, Research Associate: [email protected]
Matt Sutton, Professor of Health Economics: [email protected]
1 Centre for Epidemiology, Division of Population Health, Health Services Research and Primary Care, School
of Health Sciences, University of Manchester, Manchester, M13 9PL, UK
2 Centre for Mental Health and Safety, Division of Psychology and Mental Health, School of Health Sciences,
University of Manchester, Manchester, M13 9PL, UK
3 Manchester Centre for Health Economics, Division of Population Health, Health Services Research and
Primary Care, School of Health Sciences, The University of Manchester, Manchester, M13 9PL, UK.
* Corresponding Author: Karen P Hayhurst, Centre for Mental Health and Safety, Division of Psychology and
Mental Health, University of Manchester, Manchester, M13 9PL, UK.
Email: [email protected] Tel: +44 (0) 161 275 8365. Fax: +44 (0) 161 275 1668.
1
Development of a resource allocation formula for substance misuse treatment
services
Abstract
Background Funding for substance misuse services comprises one third of Public Health spend in England. The
current allocation formula contains adjustments for actual activity, performance and need, proxied by the
Standardised Mortality Ratio for under-75s (SMR<75). Additional measures, such as deprivation, may better
identify differential service need.
Methods We developed an age-standardised and an age-stratified model (over-18s, under-18s), with the
outcome of expected/actual cost at postal sector/Local Authority level. A third, person-based model
incorporated predictors of costs at the individual level. Each model incorporated both needs and supply
variables, with the relative effects of their inclusion assessed.
Results Mean estimated annual cost (2013/14) per English Local Authority area was £5,032,802 (sd 3,951,158).
Costs for drug misuse treatment represented the majority (83%) of costs. Models achieved adjusted R-squared
values of 0.522 (age-standardised), 0.533 (age-stratified over-18s), 0.232 (age-stratified under-18s) and 0.470
(person-based).
Conclusion Improvements can be made to the existing resource allocation formulae to better reflect population
need. The person-based model permits inclusion of a range of needs variables, in addition to strong predictors of
cost based on the receipt of treatment in the previous year. Adoption of this revised person-based formula for
substance misuse would shift resources towards more deprived areas.
2
Introduction
Where local public services are funded through central government taxation, the distribution of local allocations
presents a significant challenge.1 In England, the healthcare budget is split into sectors of care, each with a
resource allocation formula. In 2012, public health responsibilities were transferred to Local Authorities (LA);
regional bodies responsible for the delivery of a range of local services.2 There was no previous formula in place
for the allocation of public health funds; a preliminary formula was generated to allocate LA public health
budgets (£2.5bn in 2013-14; 2.6% of total health care expenditure).3,4 The Department of Health commissioned
research in 2014 to derive needs-based resource allocation formulae for drug and alcohol services (representing
31.5% of total public health expenditure), sexual health services (22.3%), and other public health services
(46.2%). This paper describes the approach taken to inform the formula for drug and alcohol services.
Most healthcare systems aim to provide services based on the need for care. Reducing inequality in access
and/or health has therefore become a key policy commitment.5 Distributing resources on the basis of need
maximises the efficiency of allocations.1,6 A key requirement of this approach is the ability to identify the need
for healthcare. Utilization of healthcare can be used as a proxy for need, but this can be problematic, as use of
services is influenced by issues of access (availability of services; patient acceptability of care; affordability).
Service use, alone, may not reflect underlying need.7,8
Resource allocation formulae have been used in healthcare in England since the 1970s. Originally, geographic
allocations were driven by historical expenditure. Carr-Hill and colleagues proposed utilization-based models,
with costed utilization modelled against a range of potential needs measures.9 The resulting needs estimates
informed needs indices, used to generate weighted population-based budget shares. Methodological advances
have addressed the extent to which utilization data reflect access to healthcare, rather than need for healthcare.
Later developments introduced regional-level indicators to control for differences in supply (access) and non-
need variables to control for differentially-met need (‘unmet need’).10 Subsequent refinements proposed one-
stage stratified models, allowing the effects of need variables to vary across age categories and for the effects of
age to be estimated, conditional on variation in supply.11 More recently, person-based allocation methods,
preferred where appropriate data are available, have been used for acute hospital12 and mental health services.13
The current substance misuse resource allocation formula3 is based on actual activity (76% weighting in 2014-
15), need (proxied by the Standardised Mortality Ratio (SMR) in under 75 year olds; 24% weighting), and
performance (20% weighting in 2013-14, 0% after). Treatment activity figures are partitioned into opiate and/or
crack cocaine users (OCU) and non-OCUs. This may be improved as: SMR is a narrow measure of need - other
factors, e.g. deprivation, may better identify need for drug and alcohol services; supply-side and unmet-need
biases were not accounted for; and analyses were conducted at an aggregate level. We aimed to develop needs-
based formulae for distributing resources for substance misuse services. This entailed:
(1) Expanding measures of need;
(2) Accounting for supply-side influences on service use;
(3) Estimating relative need using person-level data.
Methods
3
Data sources
Utilization data were sourced from the National Drug Treatment Monitoring System (NDTMS). NDTMS
routinely collects data on the use of structured community-based or residential treatment for drug or alcohol
misuse in England. The most recent available data (2013/14) were used, relating to c. 319,000 individuals
engaging in c. 413,000 treatment episodes. Area of residence was defined by the combination of postcode sector
and Local Authority (LA), creating the most granular area of residence available within this dataset (n=10 039
areas). Multiple areas were created where more than one sector was contained within a LA or where sector and
LA were not coterminous. Mid-year population estimates by age, sex and postcode were obtained from the
Office for National Statistics (ONS) and converted into the Post Sector/LA level areas.
A literature search informed potential needs variables. Drug treatment clients are characterised by male gender,
white ethnicity, involvement in crime, poor mental and physical health, unstable accommodation,
unemployment, receipt of welfare benefits and previous drug treatment episodes.14-16 Drug and alcohol misuse in
the community has been associated with additional factors, including population density,17 urbanicity,18
neighbourhood instability,19 homelessness,20 low socio-economic status,18 disadvantaged background,21 poor
school performance,21 teenage pregnancy22 and single parenting.18 These were sourced from area-level 2011
Census data, Index of Multiple Deprivation (IMD 2010) and Neighbourhood Statistics and included the
following rates: benefit claimants; lone parents; unemployment; 16+ population with no qualifications;
population turnover; under-18 conceptions; and homelessness, in addition to SMR, ethnicity, average household
size and indicators of population density. Data were required to be readily available, routinely updated and
statistically robust with coverage across England.
The needs of young people attending substance misuse services can differ from those of adult clients. In
particular, young people are more likely to present with problematic cannabis use than adult clients, which may
be associated with different predictors. Younger people are also less likely than adults to present with
problematic use of alcohol.23
Where data points were missing because of changes to geographical output areas, 2011 area data were
calculated using ONS best-fit lookup tables.24 Remaining missing data points were calculated using the mean
score of neighbouring areas. LSOA-, MSOA- (middle-layer super output area, n=6,791) and LA-level (n=151)
predictor variables were mapped into post sector/LA areas by weighting on postcode populations.
Supply variables (extracted from NDTMS) included mean waiting times, the distance (km) from post sector
centroid to postcode of the nearest drug treatment service, and the proportion of substitute prescribing accounted
for by GPs. As a source of prescribing not sourced via public health budgets, the latter measure acted as a proxy
of additional capacity and treatment choice.
Costs
Average costs per day of individual structured drug and alcohol interventions were obtained from Public Health
England (PHE). Costs were derived from a 2008/09 survey of treatment agencies (PHE unpublished data) to
4
which inflationary uplifts were applied. Total costs, accounted for by NDTMS-recorded interventions, in
2013/14 were £920 million.
Costs which could not be assigned to an area (5% of total), as client area of residence was not recorded, were
assigned geographically, according to the distribution of known cases. Where postcode sector was not recorded,
costs were redistributed within the LA, weighted by known treatment costs by age group within that LA. Where
LA was missing, costs were redistributed nationally, weighted by known treatment costs by age group
nationally.
Analysis
Our analytical approach was to replicate approaches that have been used previously for other elements of health
funding in England. Three approaches have been used: (i) age-standardised models, (ii) age stratified models,
and (iii) person-based models.
A series of models used both needs and supply variables to predict area-level cost of substance misuse service
provision. The inclusion of supply measures reduces confounding on the needs estimates where correlations
exist between the supply and needs measures, and supply and costed use.
A series of stepwise regressions determined inclusion of needs variables in the final models with the order of
inclusion determined by the relative coefficient size of associations between covariate z-scores and the
dependent variable. Covariates with a high degree of collinearity with other variables (VIF > 5) were removed.
The resulting covariate set was entered into linear regression models, accounting for Upper Tier Local Authority
(UTLA), the administrative area responsible for commissioning drug and alcohol treatment provision.
The coefficients of the needs variables were used to generate the needs index. This adds needs-specific
“weights” to areas at the commissioning level (UTLA). The inclusion of supply measures in the regression
analysis but exclusion in the needs index serves to ‘sterilise’ the formula from reflecting supply-side access bias
due to the removal of observable supply-side confounding on the needs estimates.25
Analyses used SPSS version 20 and STATA version 13.
Model 1: age-standardised model
A model was developed to examine predictors of small-area variation in the age-standardised ratio of actual cost
to expected cost (1-year period). This was the approach used prior to the CARAN (Combining Age Related and
Additional Needs) review.9 Areas with a small population size (n < 30, 7%, n=673) were excluded to avoid
undue leverage on the model by extreme cost ratio values. Expected cost was obtained by calculating national
cost per capita for eight age bands (<15, 15-19, 20-24, 25-29, 30-44, 60-64, 65+) and applying these costs to
each area, using age-specific 2011 population data. Analyses were weighted by expected costs for each area.
Since the need for drug services and alcohol services may differ we estimated three models: all service use, drug
services only, and alcohol services only.
Model 2: age-stratified model
Model 1 assumes the effects of the needs measures are the same across all age groups. Model 2 relaxes this
assumption by analysing predictors of small-area variation in the ratio of actual to expected cost separately for
5
different age groups. This was the econometric approach taken in both the CARAN and the RAMP (Resource
Allocation for Mental health and Prescribing) review.26 Younger clients in treatment may differ from over-18s.
Separate analyses were therefore performed for over-18s and under-18s. Areas with small (n < 30) adult (18+)
populations (n=760) and under-18 populations (n=1333) were excluded.
Model 3: person-based model
A model was developed to investigate predictors of costs at individual level using previous year supply-
independent risk markers to predict costs in the current year. This was the approach used in the PRAM (Person-
based Resource Allocation formula for Mental Health) review.13 Measures of past-year treatment utilization
(days of treatment received, whether treatment was completed and receipt of substitute prescribing) were
incorporated into the model and used to predict 2013/14 expenditure at the individual level. We also included
area-level needs variables stratified by age group. Cases with known receipt of treatment but unknown area of
residence were excluded. Non-users of services were included with zero costs, with cases aggregated by age
group and area. The model was weighted by area population, set to one for individual case data.
Results
Mean recorded annual (2013/14) cost of treatment per LA area was £5,032,802 (sd 3,951,158). Drug misuse
costs comprised the majority (83%) of substance misuse treatment costs (mean £4,107,650, sd 3,398,499) with
costs of alcohol misuse treatment considerably lower (mean £108,042, sd 694,558). Costs for under-18s
represented 2% of total costs.
Model 1: age-standardised model
Figure 1 shows the age specific per-capita costs used to calculate area-level expected costs for drug and alcohol
misuse treatment. Costs peaked within the 30-44 age group; more sharply for drug services. The age-
standardised model for combined drug and alcohol misuse costs had an adjusted R-squared of 0.522 (see Table
1). Significant predictors of the ratio of actual to expected cost were SMR, the IMD domains of Crime,
Environment, Income, population turnover, proportion male and proportion white British. Significant supply
variables were the proportion of GP prescribing and distance to nearest service. The inclusion of needs
variables (in addition to SMR) reduced the coefficient associated with SMR by 40% and increased the adjusted
R-squared substantively (from 0.470 to 0.517). Despite significant associations, the addition of supply variables
made little difference to the overall explanatory power of the model, increasing the R-squared from 0.517 to
0.522.
Predictors common to both the drugs misuse and the alcohol misuse model were SMR, IMD Crime, IMD
Environment, population turnover, proportion male and proportion white British. A significant predictor in the
drugs model but not the alcohol model was IMD Income. The final alcohol model contained the IMD mood and
anxiety indicator; not a significant predictor in the drugs model. The final models achieved an adjusted R-
squared of 0.513 for the drugs cost ratio but performed less well for the alcohol cost ratio (adjusted R-squared =
0.334).
6
Model 2: age-stratified model
Table 1 also shows the age-stratified model, applied separately to young people (under-18s) and adults (18+).
The removal of the under-18s from the all-age group increased the adjusted R-squared statistic from 0.522 to
0.533. The model performed less well in the under-18s group (adjusted R-squared = 0.232. Variables which
were significant predictors of the over-18s group were not significant in the under-18s (IMD Income, IMD
Environment, proportion male, proportion GP prescribing and distance to nearest service). Mean waiting time
was not significant in the over-18s but was in the under-18s. Additional potential predictor variables, such as
proportion in full-time education, were explored to determine whether a better-performing model could be
obtained for the under-18s but an adjusted R-squared of higher than 0.232 could not be achieved.
Within a drugs-only model a higher adjusted R-squared (0.526) was achieved for over-18s but was decreased (to
0.169) for under-18s. Within an alcohol-only model, a lower adjusted R-squared was achieved for both over-18s
(0.327) and under-18s (0.160).
Model 3: person-based model
Table 2 presents the results of the person-based model for drug and alcohol misuse combined. The best-
performing predictors in the model were: received prescribing in the past year; days treated in the past year; and
whether treatment was completed in the previous year. These three variables together (plus age dummy
variables) explained 46.9% of the variance in expenditure. The addition of other needs variables (SMR,
population turnover and proportion male) and supply variables did not add substantially to the final adjusted R-
squared statistic (0.470). When aggregated to area level for the purposes of comparison with other models, an R-
squared statistic of 0.560 was achieved.
Separate person-based models for drug and alcohol misuse services were considered. As with the combined
model, the three best explanatory variables in both the drugs misuse and the alcohol misuse models were:
received prescribing in the past year; days treated in the past year; and whether treatment was completed in the
previous year. An adjusted R-squared of 0.490 was obtained in the drug misuse model and a much lower
statistic (0.021) in the alcohol misuse model.
Impact of adoption of revised formula on target share
Figure 2 summarises the impact that adoption of the revised person-based formula for substance misuse would
have on existing target share. This shows the size of the impact plotted against a measure of overall deprivation
(IMD 2010) and the proportion of young adults (18-30 year olds) for each LA. Adoption of the revised formula
would have a net effect of redistributing more resources towards more deprived areas (higher IMD 2010 score).
There is no corresponding shift in resources toward areas with a higher or lower proportion of young people.27
Discussion
We developed age-standardised, age-stratified and person-based models of substance misuse treatment costs by
Local Authority in England, incorporating both need and supply variables to predict area-level variation in the
costs of service provision. These were designed to better inform the allocation of substance misuse-specific
public health budgets.
7
Main findings of this study
The age-standardised model performed well in predicting variation in the ratio of expected to actual cost by area
(R-squared=0.522). A separate model for alcohol misuse performed less well than that for drug misuse. The
age-stratified model performed well for over-18s (R-squared=0.533) but poorly for under-18s (R-
squared=0.232). The person-based model permits the use of a wider range of predictor variables, in particular,
very strong predictors based on past-year treatment history. Predictive power was high for a model based on
individual-level data (R-squared=0.470) and conversion to area-level data for comparison purposes identified
this model as the strongest of the three. For these reasons, we prefer the use of a person-based model of drug
and alcohol misuse services.
What is already known on this topic
Resource allocation formulae have been used in healthcare in England since the 1970s. Needs estimates were
generally amalgamated into a needs index, used to generate weighted populations to inform budget share.
Developments introduced regional-level indicators to control for differences in supply (access) and non-need
variables to control for differentially-met need.10 Subsequent refinements incorporated age-stratification. More
recently, person-based models have been developed where individual-level data are available. The existing
substance misuse allocation formula3 is based on actual activity, need (SMR<75), and performance. However,
SMR is a narrow measure of need, supply-side and unmet-need biases were not accounted for and analyses were
conducted at an aggregate level.
What this study adds
We aimed to develop a needs-based formula for distributing resources for substance misuse services that:
expands the measures of need over that used currently; accounts for supply-side influences on the use of
services; and estimates relative need using individual-level data.
We expanded on current measures of previous year service use data (incorporating days treated, treatment
completion, receipt of prescription) and these were found to be strong predictors of current service use.
Expanded measures of need, including population turnover and proportion male were also significant predictors
of cost. The R-squared statistic achieved by our person-based model (0.470) was higher than that seen in similar
work (0.123) to develop formulae to allocate resources for hospital care.12
Our analysis has shown that it is possible to generate statistical models that predict the costs of substance misuse
services using measures of population need and supply characteristics. This included approaches to reduce
confounding due to observed supply-side provision differences. A range of needs measures were consistently
found to predict service use: SMR, the proportion of males in an area, age, and population turnover. The ability
to identify previous service use substantially improves explanatory power.
These models can be used to generate budgets that reflect variations in need across areas and therefore direct
limited resources where they are needed most. Internationally, public health policies have the central aim of
tackling inequalities in health. 5 Socioeconomic factors, such as income and education, together with physical
and social environmental factors, such as crime, are the main determinants of health inequalities. 5 Of note, three
IMD domains of deprivation, Income (models 1 and 2), Environment (models 1 and 2) and Crime (model 1)
8
were important predictors of the ratio of actual to expected cost in our models, demonstrating the pivotal role of
deprivation in describing the need for drug and alcohol misuse services. Adoption of our revised person-based
formula for substance misuse would shift resources toward more deprived areas, thereby tackling health
inequalities. At the time of writing, these models had not been adopted by the English Department of Health.
Limitations of this study
The available geographic level of analysis (postcode sector/ LA combination) did not directly match that of
available socio-demographic data, requiring the use of weighted population estimates.
Separate tariffs for alcohol or young people’s services were not available; costs were estimated based on adult
drug service estimates. Explanatory power was lower for alcohol and young people’s models. There may be
other predictors of these services that are not routinely collected. Although the alcohol model was not as strong,
the best alcohol misuse model was highly correlated with the drug misuse model (r = 0.994); a combined model
for drug and alcohol misuse was therefore recommended.
Whilst we were able to model observed confounding, there remains the possibility of unobserved confounding
due to excluded supply-side factors that are correlated with our needs variables. There may also be measures of
need that we have failed to source but that are valid and strong predictors of service use.
There are limitations of the existing approaches. First, the age-standardised approach may lead to biased age
estimates – should these be correlated with the additional needs measures they could over- or under-represent
the effects of age. In addition, such models impose a restriction that the additional needs measures have the
same proportionate effect for all age groups. Age stratified models alleviate this issue to an extent, enabling
additional needs measures and their estimated effects to vary across age groups. The recent transition towards a
person-based approach enabled historic risk information for each individual captured in prior diagnoses to be
factored into the formula. There is a concern that this information may contain non-need influences, such as
supply factors that influence the probability of diagnosis. As a consequence, it is recommended to include a rich
set of supply variables and include risk markers that are as independent as possible from unmeasured supply
factors.28
In common with previous approaches to estimating resource allocation formulae for health care services in
England, we have analysed variations in cost-weighted utilisation and assumed that, once the effects of supply
variables have been controlled for, the effects of population characteristics reflect differences in need. If there is
systematic unmet need for some population groups, or differences in quality and effectiveness across population
groups, this assumption is not met. The adoption of utilisation to measure equity has been disputed for many
years, 29,30 but remains the preferred practical compromise in the absence of less imperfect, comprehensive
measures of the need for resources.31
9
Disclosure of potential conflicts of interest
None
Funding
This work was supported by the UK Department of Health (DH) Policy Research Programme (grant number
085/0010). The views expressed are the sole responsibility of the authors and do not necessarily represent those
of TAG, ACRA or the Department of Health.
Acknowledgements
We are grateful to members of the DH Technical Advisory Group (TAG) and the DH Advisory Committee on
Resource Allocation (ACRA) for advice and comments and specifically would like to thank Michael Chaplin,
Stephen Lorrimer and Keith Derbyshire from the Department of Health and Virginia Musto of Public Health
England for their guidance and advice.
Disclaimer
This report relates to independent research commissioned and funded by the Department of Health Policy
Research Programme.The views expressed in this publication are those of the author(s) and not necessarily
those of the Department of Health, ‘arms’ length bodies and other government departments.
References
1. Smith PC. Formula Funding of Public Services. Routledge, 2007.
2. UK Health and Social Care Act, 2012. http://www.legislation.gov.uk/ukpga/2012/7/notes/contents.
Accessed 2 December 2016.
3. Department of Health. Exposition Book Public Health Allocations 2013-14 and 2014-15: Technical Guide.
London, Department of Health, 2013.
4. NHS. NHS allocations for 2013/14. 2014. https://www.england.nhs.uk/allocations-2013-14/ .
5. Crombie IK, Irvine L, Elliott L, Wallace H. Closing the health inequalities gap: an international
perspective. World Health Organization, 2005.
http://www.euro.who.int/__data/assets/pdf_file/0005/124529/E87934. pdf. Accessed 9 February 2016.
6. Whittaker W. Resource Allocation Funding Formulae, Efficiency of. In: Culyer AJ (ed.) Encyclopedia of
Health Economics 2014;3:256-266.
7. Gulliford M, Figueroa-Munoz J, Morgan M et al. What does 'access to health care' mean? J Health Serv
Res Policy 2002;7:186-188.
8. McIntyre D, Thiede M, Birch S. Access as a policy-relevant concept in low- and middle-income
countries. Health Econ Policy and Law 2009;4:179-193.
9. Carr-Hill RA, Hardman G, Martin S, Peacock S, Sheldon T, Smith P. A formula for distributing NHS
revenues based on small area use of hospital beds. York, Centre for Health Economics, University of
York, 1994.
10. Sutton M, Gravelle H, Morris S et al. Allocation of resources to English areas: Individual and small area
determinants of morbidity and use of healthcare resources. Report to the Department of Health.
Edinburgh, Information and Statistics and Division, 2002.
11. Morris S, Carr-Hill R, Dixon P et al. Combining Age Related and Additional Needs: 2007 Review of the
Needs Formulae for Hospital Services and Prescribing Activity in England (CARAN Report). Report to the
Department of Health, 2008.
12. Dixon J, Smith P, Gravelle H et al. A person based formula for allocating commissioning funds to general
practices in England: development of a statistical model. BMJ 2011;343:d6608 doi: 10.1136/bmj.d6608.
13. Sutton M, Kristensen SR, Lau Y et al. Developing the Mental Health Funding Formula for Allocations to
General Practices. Report for the Department of Health, 2012.
14. Jones A, Weston S, Moody A et al. The Drug Treatment Outcomes Research Study (DTORS): Baseline
Report. London, Home Office, 2007.
15. Jones A, Hayhurst KP, Pierce M, Millar T, Dunn G, Donmall M. Short-term outcomes for opiate and crack
users accessing treatment: the effects of criminal justice referral and crack use. Eur Add Res 2016;22:145-
152.
16. Gossop M, Marsden J, Stewart D et al. Substance use, health and social problems of service users at 54
drug treatment agencies. Intake data from the National Treatment Outcome Research Study. Br J
Psychiatry 1998;173:166-171.
17. Adger H. Problems of alcohol and other drug use and abuse in adolescents. J Adolesc Health 1991;12:606-
613.
18. Galea S, Nandi A, Vlahov D. The Social Epidemiology of Substance Use. Epidemiol Rev 2004;26:36-52.
19. Stone AL, Becker LG, Huber AM, Catalano RF. Review of risk and protective factors of substance use and
problem use in emerging adulthood. Addict Behav 2012;37:747–775.
20. Bravender T, Knight JR. Recent patterns of use and associated risks of illicit drug use in adolescents. Curr
Opin Pediatr 1998;10:344-349.
21. Degenhardt L, Hall W. Extent of illicit drug use and dependence, and their contribution to the global
burden of disease. Lancet 2012;379:55-70.
22. Pumariega AJ, Rodriguez L, Kilgus MD. Substance abuse among adolescents. Current perspectives. Addict
Disord Their Treat 2004;3:145–155.
23. Sinha R, Easton C, Kemp K. Substance abuse treatment characteristics of probation-referred young adults
in a community-based outpatient program. Am J Drug Abuse 2003;29:585-597.
24. ONS. Changes to output areas and super output areas in England and Wales, 2001 to 2011. London, Office
for National Statistics.
25. Gravelle H, Sutton M, Morris S et al. Modelling supply and demand influences on the use of health care:
implications for deriving a needs-based capitation formula. Health Economics 2003;12:985-1004.
26. Sutton M, Whittaker W, Morris S et al. Report of the Resource Allocation for Mental health and
Prescribing (RAMP) Project. Report for the Department of Health, 2010.
27. Department of Health. Public health grant: proposed target allocation formula for 2016/17. London,
Department of Health, 2015. https://www.gov.uk/government/consultations/public-health-formula-for-
local-authorities-from-april-2016
28. Dixon P, Dusheiko M, Gravelle H, Martin S, Rice N, Smith P (2009) Developing a person-based resource
allocation formula for allocations to general practice in England.
http://nuffield.dh.bytemark.co.uk/sites/files/nuffield/document/Developing_a_person-
based_resource_allocation_formula_REPORT.pdf . Accessed 21st September 2017
29. Culyer AJ, van Doorslaer E, Wagstaff A. Utilisation as a measure of equity by Mooney, Hall, Donaldson
and Gerard. Journal of Health Economics 1992;11:93-98.
30. Mooney G, Hall J, Donaldson C, Gerard K. Reweighing heat: Response to Culyer, van Doorslaer and
Wagstaff. Journal of Health Economics 1992;11:199-205.
31. Vallejo-Torres L, Morris S, Carr-Hill R, Dixon P, Law M, Rice N, Sutton M. Can regional resource shares
be based only on prevalence data? An empirical investigation of the proportionality assumption. Soc Sci
Med 2009;69:1634-1642.
Fig. 1 Mean annual costs per capita for drug and alcohol misuse treatment by age group (2013-14 data).
Table 1 Age-standardised and age-stratified models, drug and alcohol misuse, 2013/14 data
Variable All ages (standardised) 18+ yrs only Under 18 yrs only
SMRa 0.025** 0.015** 0.015** 0.017** 0.006**
[0.001] [0.001] [0.001] [0.001] [0.001]
IMD Crime 0.414** 0.389** 0.451** 0.261**
[0.024] [0.024] [0.027] [0.037]
Population turnover b 0.006** 0.006** 0.005** 0.005**
[0.001] [0.001] [0.001] [0.001]
Proportion white British 0.790** 0.829** 0.535** 1.177**
[0.093] [0.093] [0.111] [0.160]
Proportion male 4.174** 4.189** 6.339** 1.661
[0.861] [0.862] [1.020] [1.151]
IMD Income 0.632** 0.631** 0.731** -0.020
[0.120] [0.119] [0.138] [0.216]
IMD Environment 0.003** 0.003** 0.004** 0.002
[0.001] [0.001] [0.001] [0.001]
Proportion of GP prescribing -0.571** -0.630** 0.148
[0.051] [0.059] [0.107]
Distance to nearest service c -0.01** -0.01** -0.006
[0.002] [0.002] [0.004]
Mean waiting time -0.002 -0.002 -0.008**
[0.001] [0.001] [0.002]
Adj. R-squared 0.470 0.517 0.522 0.533 0.232
Notes: * p < 0.05. ** p < 0.01. a Standardised Mortality Ratio. b Outflow rate all ages: rate per 1000 (2009-10). c Distance (km) from post sector centroid to post code of the nearest treatment service. The dependent variable is the indirectly-standardised cost ratio. Unit of observation is the postcode sector/local authority combination (n=9,366). Robust standard errors in [ ]. All models include indicators for each Upper Tier Local Authority.
Table 2 Person-based model, all ages, 2013/14 costs of drug and alcohol misuse treatment.
i ii iii ivAge
Age (15-19 years) 3.09** 1.06** 0.96** 0.99**
[0.167] [0.101] [0.108] [0.113]
Age (20-24) 7.01** 1.11** 0.59** 0.64**
[0.243] [0.135] [0.141] [0.146]
Age (25-29) 22.31** 4.30** 3.95** 4.04**
[0.421] [0.187] [0.191] [0.197]
Age (30-44) 42.17** 8.14** 8.12** 8.37**
[0.598] [0.165] [0.169] [0.177]
Age (45-59) 16.81** 2.36** 2.55** 2.64**
[0.287] [0.114] [0.119] [0.125]
Age (60-64) 2.93** -0.21* 0.09 0.11
[0.177] [0.103] [0.108] [0.114]
Age (65+) -0.11 -0.65** -0.33** -0.34**
[0.149] [0.077] [0.083] [0.088]
Individual treatment history
Days of treatment previous year 10.31** 10.31** 10.32**
[0.042] [0.042] [0.042]
Completed treatment previous year -1640.84** -1641.03** -1644.97**
[6.475] [6.474] [6.527]
Received prescribing previous year 1316.24** 1315.91** 1319.25**
[13.569] [13.569] [13.658]
Area characteristics
SMR a 0.05** 0.05**
[0.003] [0.003]
Population turnover b 0.03** 0.03**
[0.002] [0.003]
Proportion male 25.92** 30.05**
[3.856] [4.257]
Supply variables
Proportion of GP prescribing -8.55**
[0.351]
Distance to nearest service c -0.06**
[0.012]
Mean waiting time -0.01
[0.009]
Adjusted R-squared 0.025 0.469 0.469 0.470Notes: * p < 0.05. ** p < 0.01. Reference category is age group 1 (under 15). a Standardised Mortality Ratio. b Outflow rate all ages: rate per 1000 (2009-10). c Distance (km) from post sector centroid to post code of the nearest treatment service. The dependent variable is drug and alcohol misuse expenditure for 2013/14. Unit of observation is the individual (n=53,085,707). Robust standard errors in [ ]. All models account for UTLA.
Fig. 2 The impact of changing the substance misuse formula on LA target shares.
Notes: solid circles indicate an increased target share, open circles a reduced target share, with the area of the circle proportional to the impact (London in solid red / bold, other in solid blue / faint). Figure reproduced with permission from: Advisory Committee on Resource Allocation (ACRA) and the Department of Health, London (2015) Public health grant: proposed target allocation formula for 2016/17, https://www.gov.uk/government/consultations/public-health-formula-for-local-authorities-from-april-2016