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PAPERS: Roy A Carr-Hill, James Q Jamison, Dermot O'Reilly, Michael R Stevenson, James Reid, and Barry Merriman Risk adjustment for hospital use using social security data: cross sectional small area analysis BMJ 2002; 324: 390 [Abstract] [Full text]

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Downplaying the role of ecology

Vincent J Lorant, Marie-Laurence Lambert   (23 February 2002)

Correlation leads to counter-intuitive sign in model

David C Lloyd   (27 February 2002)

Rural areas may need more health care resources in England too

Sheena N Asthana, Alex Gibson, Graham Moon, John Dicker, Philip Brigham   (6 March 2002)

Downplaying the role of ecology 23 February 2002
Vincent J Lorant, Lecturer Clos chapelle aux champs 30.41 1200 Bruxelles Belgium, Marie-Laurence Lambert Send response to journal: Re: Downplaying the role of ecology	Editor –Carr Hill et al. propose new socio-economic indicators for allocating public funding to hospital services, using socioeconomic and demographic variables that can be updated between census1. Although they used spatial techniques to reflect the influence of health care supply on usage, we think, however, that they overlooked two critical aspects of space : clustering and contextual factors of health care use. As we have shown, it is incorrect to treat data from contiguous spatial entities (such as wards) as independent observations, because this overlooks the fact that they have similar levels of unobserved socio- economic factors, environmental risks, and health status 2. Overlooking such spatial clustering in the dependent variable (resources use) or independent variables (socio-economic or health status) will lead to over- estimation of parameter precision. It might also bias the results if location can act as a confounding factor, as illustrated by some epidemiological studies 3. Both over-precision and bias will leads to incorrect conclusions , hence, unfair resources allocation. Modern spatial techniques would help tackle such spatial clustering, for example by the way of simultaneous auto-regressive models. Moreover, aggregating the data at ward level conflates different levels of analysis such as the household, ward, and district levels. Being a elderly person living alone may increase health care needs at the household level, but this relation could also be much stronger in rural areas where the impact of isolation is harder to temper than in urban centres. Conversely, urban centres may have poorer environmental standards, making health care use more likely. In a multilevel analysis, Jones and Duncan concluded that “In general, irrespective of individual characteristics, places with a low income or a high deprivation suffer the worst health […]” 4. The allocation of resources would be made fairer by identifying such contextual factors that, for given individual risk factors, decrease or increase the association between socio-economic or health status and health care use. The authors have used dummy variables to control for between-boards differences. This entails ignoring health or socio-economic differences between boards. This is a pity, as the authors have themselves previously shown that up to 44% of the variance of health care use is due to inter-district variation 5. It is certainly a significant improvement to allocate resources with socio-economic indicators that are more up-to-date. But it is a mistake to downplay the role of ecology in health, particularly if this were to lead to resource redistribution from metropolitan boards to rural ones. References 1.Carr-Hill RA, Jamison JQ, O'Reilly D, Stevenson MR, Reid J, Merriman B. Risk adjustment for hospital use using social security data: cross sectional small area analysis. BMJ 2002;324:390. 2.Lorant V, Thomas I, Deliège D, Tonglet R. Deprivation and mortality : implication of spatial autocorrelation . Social Science and Medicine 2001;53:1711-9. 3.Clayton DG, Bernardinelli L, Montomoli C. Spatial correlation in ecological analysis. Int J Epidemiol 1993;22:1193-202. 4.Jones K,.Duncan C. Individuals and their ecologies : analysing the geography of chronic illness within a multilevel modelling framework. Health and Place 1995;1:27-40. 5.Smith P, Sheldon TA, Carr Hill RA, Martin S, Peacock S, Hardman G. Allocating resources to health authorities: results and policy implications of small area analysis of use of inpatient services. BMJ 1994;309:1050-4.
Correlation leads to counter-intuitive sign in model 27 February 2002
David C Lloyd, Applied Statistician Prescribing Support Unit, Leeds Send response to journal: Re: Correlation leads to counter-intuitive sign in model	Carr-Hill et al present a model and formula using both Family Credit and Income Support data. We know from other work that both these variables are probably correlated with use of in-patient services and so, as the authors say, their appearance in the model seems "intuitively appropriate". However they are also probably correlated with each other which is presumably the reason that Family Credit has a negative coefficient in the model, which is not intuitively appropriate at all. Any attempt to include two highly correlated variables in the same model usually results in one of them having a negative sign even though it is positively correlated with the variable of interest. The solution is to only include only one of the variables or to construct a single variable which combines the two variables using principal components or some similar method.
Rural areas may need more health care resources in England too 6 March 2002
Sheena N Asthana, Principal Lecturer, Social Policy University of Plymouth, Drake Circus, Plymouth, PL4 8AA., Alex Gibson, Graham Moon, John Dicker, Philip Brigham Send response to journal: Re: Rural areas may need more health care resources in England too	EDITOR - Carr-Hill et al have shown that the use of a risk adjustment formula in Northern Ireland that incorporates direct measures of poverty at small area level would move resources from urban to rural areas1. Their inclusion of social security data suggests one way of overcoming the limitations of using indirect census based proxies to assess need for health care. Another is to use existing epidemiological evidence to derive direct estimates of morbidity in different areas. We have modelled the impact of applying a morbidity-based capitation methodology to the allocation of resources for inpatient care for coronary heart disease (CHD)2. We also find that rural areas, particularly those with older demographic profiles, would stand to gain most from the introduction of a direct needs-based approach to resource allocation. We derived morbidity-based allocations for inpatient CHD use for 34 primary care organisations (PCOs) serving 3.54 million patients in 7 health authorities in contrasting locations in England. Age, sex and class adjusted prevalence rates of severe (Grade 2) angina and myocardial infarction (MI) recorded in the Health Survey for England were attributed to PCO populations. An age cost curve was established by dividing historical HRG reference cost expenditure on CHD in the total sample by the number of people in each age cohort who, on the basis of our epidemiological estimates, would be expected to have symptoms of severe angina and/or MI. This was then applied to our estimates at a local level in order to determine a CHD clinical programme budget for each PCO. Percentage differences between morbidity-based allocations and indicative allocations based on the current Hospital and Community Services (HCHS) formula were compared against Townsend's Material Deprivation scores, the percentage of population aged 65+ and the DETR Index of Multiple Deprivation Access domain scores. We found that a morbidity-based capitation methodology resulted in a significant shift of hospital resources for CHD away from PCOs serving deprived areas (r=0.845; p<0.001); towards PCOs serving populations with older demographic profiles (r=0.529; p<0.001); and towards rural PCOs (r=0.847; p<0.001). There is a growing concern that the needs of rural populations are not adequately reflected in the formulae used to make funding allocations to the NHS3,4. The move to PCO allocations is likely to magnify such bias. Serious consideration should thus be given to the fact that, using two very different approaches to capturing 'direct' need, there appears to be case for transferring resources from urban to rural areas. Sheena Asthana, Principal Lecturer, Department of Social Policy, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, sasthana@plymouth.ac.uk Alex Gibson, Senior Lecturer, Department of Geography, University of Exeter, Amory Building, Rennes Drive, Exeter, EX6 4PN, a.gibson@exeter.ac.uk Graham Moon, Professor, Institute for the Geography of Health, University of Portsmouth, Milldam, Burnaby Rd, Portsmouth, PO1 3AS, graham.moon@portsmouth.ac.uk John Dicker, Associate Director, Information Management and Technology, Iechyd Morgannwg Health, 41 High Street, Swansea, SA1 1LT, john.dicker@morgannwg-ha.wales.nhs.uk. Philip Brigham, Senior Research Fellow, Department of Social Policy, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, pbrigham@plymouth.ac.uk 1 Carr-Hill, RA., Jamison, J.Q., O'Rielly, D., Stevenson, M.R., Reid, J., Merriman, B. Risk adjustment for hospital use using social security data: cross sectional small area analysis. BMJ 2002;324:390-2. 2 This research has been funded by the Economic and Social Research Council's Health Variations Programme (Reference L128251031) 3 White, C. Who gets what, where - and why? The NHS allocation system in England is failing rural and disadvantaged areas. Rural Health Forum and University of St Andrews, 2001. 4 Asthana, S., Brigham, P. Gibson, A. Health Resource Allocation in England: What Case can be Made for Rurality? Rural Health Allocations Forum and University of Plymouth, 2002. No competing interest