Varenicline versus nicotine replacement therapy for long-term smoking cessation: an observational study using the Clinical Practice Research Datalink.

BACKGROUND: Smoking is the leading avoidable cause of illness and premature mortality. The first-line treatments for smoking cessation are nicotine replacement therapy and varenicline. Meta-analyses of experimental studies have shown that participants allocated to the varenicline group were 1.57 times (95% confidence interval 1.29 to 1.91 times) as likely to be abstinent 6 months after treatment as those allocated to the nicotine replacement therapy group. However, there is limited evidence about the effectiveness of varenicline when prescribed in primary care. We investigated the effectiveness and rate of adverse events of these medicines in the general population. OBJECTIVE: To estimate the effect of prescribing varenicline on smoking cessation rates and health outcomes. DATA SOURCES: Clinical Practice Research Datalink. METHODS: We conducted an observational cohort study using electronic medical records from the Clinical Practice Research Datalink. We extracted data on all patients who were prescribed varenicline or nicotine replacement therapy after 1 September 2006 who were aged ≥ 18 years. We investigated the effects of varenicline on smoking cessation, all-cause mortality and cause-specific mortality and hospitalisation for: (1) chronic lung disease, (2) lung cancer, (3) coronary heart disease, (4) pneumonia, (5) cerebrovascular disease, (6) diabetes, and (7) external causes; primary care diagnosis of myocardial infarction, chronic obstructive pulmonary disease, depression, or prescription for anxiety; weight in kg; general practitioner and hospital attendance. Our primary outcome was smoking cessation 2 years after the first prescription. We investigated the baseline differences between patients prescribed varenicline and patients prescribed nicotine replacement therapy. We report results using multivariable-adjusted, propensity score and instrumental variable regression. Finally, we developed methods to assess the relative bias of the different statistical methods we used. RESULTS: People prescribed varenicline were healthier at baseline than those prescribed nicotine replacement therapy in almost all characteristics, which highlighted the potential for residual confounding. Our instrumental variable analysis results found little evidence that patients prescribed varenicline had lower mortality 2 years after their first prescription (risk difference 0.67, 95% confidence interval -0.11 to 1.46) than those prescribed nicotine replacement therapy. They had similar rates of all-cause hospitalisation, incident primary care diagnoses of myocardial infarction and chronic obstructive pulmonary disease. People prescribed varenicline subsequently attended primary care less frequently. Patients prescribed varenicline were more likely (odds ratio 1.46, 95% confidence interval 1.42 to 1.50) to be abstinent 6 months after treatment than those prescribed nicotine replacement therapy when estimated using multivariable-adjusted for baseline covariates. Patients from more deprived areas were less likely to be prescribed varenicline. However, varenicline had similar effectiveness for these groups. CONCLUSION: Patients prescribed varenicline in primary care were more likely to quit smoking than those prescribed nicotine replacement therapy, but there was little evidence that they had lower rates of mortality or morbidity in the 4 years following the first prescription. There was little evidence of heterogeneity in effectiveness across the population. FUTURE WORK: Future research should investigate the decline in prescribing of smoking cessation products; develop an optimal treatment algorithm for smoking cessation; use methods for using instruments with survival outcomes; and develop methods for comparing multivariable-adjusted and instrumental variable estimates. LIMITATIONS: Not all of our code lists were validated, body mass index and Index of Multiple Deprivation had missing values, our results may suffer from residual confounding, and we had no information on treatment adherence. TRIAL REGISTRATION: This trial is registered as NCT02681848. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 9. See the NIHR Journals Library website for further project information.

Smoking is the number one avoidable cause of ill health and death. Experiments suggest that more smokers will quit after being given the drug varenicline than with any other smoking cessation treatment. However, most of the experiments used to license varenicline had a relatively short follow-up (< 1 year) and did not necessarily recruit participants who were representative of smokers seen in a general practice in the UK, who tend to be older, are sicker and more likely to have neuropsychiatric illnesses. In this study, we investigated the outcomes of 287,079 patients prescribed varenicline or nicotine replacement therapy (e.g. nicotine patches and gum). We followed each patient for up to 4 years after they received their prescriptions and matched their data to information on deaths from the Office for National Statistics and hospital admissions. We investigated how often these patients subsequently attended their general practitioner, and how often they received a diagnosis of myocardial infarction, chronic obstructive pulmonary disease, depression or anxiety in primary care. We found that patients who were prescribed varenicline were much more likely to quit smoking up to 4 years after they received treatment and subsequently attended their general practitioner less frequently. These findings were robust across the three different analysis methods we used. We also found that patients prescribed varenicline were much less likely to be ill or to die than those prescribed nicotine replacement therapy. However, these results may be because the patients who were prescribed varenicline were much healthier before they received the prescription. Therefore, these differences in health are unlikely to be caused by taking varenicline or quitting smoking. In conclusion, varenicline helped patients quit smoking, but there was little causal evidence that prescribing patients varenicline causally reduced rates of mortality or morbidity compared with prescribing nicotine replacement therapy.

How to sell a condom? The impact of demand creation tools on male and female condom sales in resource limited settings.

Despite condoms being cheap and effective in preventing HIV, there remains an 8billion shortfall in condom use in risky sex-acts. Social marketing organisations apply private sector marketing approaches to sell public health products. This paper investigates the impact of marketing tools, including promotion and pricing, on demand for male and female condoms in 52 countries between 1997 and 2009. A static model differentiates drivers of demand between products, while a dynamic panel data estimator estimates their short- and long-run impacts. Products are not equally affected: female condoms are not affected by advertising, but highly affected by interpersonal communication and HIV prevalence. Price and promotion have significant short- and long-run effects, with female condoms far more sensitive to price than male condoms. The design of optimal distribution strategies for new and existing HIV prevention technologies must consider both product and target population characteristics.

The role of common genetic variation in educational attainment and income: evidence from the National Child Development Study.

We investigated the role of common genetic variation in educational attainment and household income. We used data from 5,458 participants of the National Child Development Study to estimate: 1) the associations of rs9320913, rs11584700 and rs4851266 and socioeconomic position and educational phenotypes; and 2) the univariate chip-heritability of each phenotype, and the genetic correlation between each phenotype and educational attainment at age 16. The three SNPs were associated with most measures of educational attainment. Common genetic variation contributed to 6 of 14 socioeconomic background phenotypes, and 17 of 29 educational phenotypes. We found evidence of genetic correlations between educational attainment at age 16 and 4 of 14 social background and 8 of 28 educational phenotypes. This suggests common genetic variation contributes both to differences in educational attainment and its relationship with other phenotypes. However, we remain cautious that cryptic population structure, assortative mating, and dynastic effects may influence these associations.

What are the effects of varenicline compared with nicotine replacement therapy on long-term smoking cessation and clinically important outcomes? Protocol for a prospective cohort study.

INTRODUCTION: Smoking is a major avoidable cause of ill-health and premature death. Treatments that help patients successfully quit smoking have an important effect on health and life expectancy. Varenicline is a medication that can help smokers successfully quit smoking. However, there are concerns that it may cause adverse effects, such as increase in the occurrence of depression, self-harm and suicide and cardiovascular disease. In this study we aim to examine the effects of varenicline versus other smoking cessation pharmacotherapies on smoking cessation, health service use, all-cause and cause-specific mortality and physical and mental health conditions. METHODS: In this project we will investigate the effects of varenicline compared to nicotine replacement therapies on: (1) long-term smoking cessation and whether these effects differ by area level deprivation; and (2) the following clinically-important outcomes: rate of general practice and hospital attendance; all-cause mortality and death due to diseases of the respiratory system and cardiovascular disease; and a primary care diagnosis of respiratory illness, myocardial infarction or depression and anxiety. The study is based on a cohort of patients prescribed these smoking cessation medications from the Clinical Practice Research Datalink (CPRD). We will use three methods to overcome confounding: multivariable adjusted Cox regression, propensity score matched Cox regression, and instrumental variable regression. The total expected sample size for analysis will be at least 180,000. Follow-up will end with the earliest of either an 'event' or censoring due to the end of registration or death. ETHICS AND DISSEMINATION: Ethics approval was not required for this study. This project has been approved by the CPRD's Independent Scientific Advisory Committee (ISAC). We will disseminate our findings via publications in international peer-reviewed journals and presentations at international conferences.

Keep it simple? Predicting primary health care costs with clinical morbidity measures.

Models of the determinants of individuals' primary care costs can be used to set capitation payments to providers and to test for horizontal equity. We compare the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs and examine capitation payments based on them. The measures were derived from four morbidity descriptive systems: 17 chronic diseases in the Quality and Outcomes Framework (QOF); 17 chronic diseases in the Charlson scheme; 114 Expanded Diagnosis Clusters (EDCs); and 68 Adjusted Clinical Groups (ACGs). These were applied to patient records of 86,100 individuals in 174 English practices. For a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power. The EDC measures performed best followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Comparisons of predictive power for different morbidity measures were similar for linear and exponential models, but the relative predictive power of the models varied with the morbidity measure. Capitation payments for an individual patient vary considerably with the different morbidity measures included in the cost model. Even for the best fitting model large differences between expected cost and capitation for some types of patient suggest incentives for patient selection. Models with any of the morbidity measures show higher cost for more deprived patients but the positive effect of deprivation on cost was smaller in better fitting models.

Implications of comorbidity for primary care costs in the UK: a retrospective observational study.

BACKGROUND: Comorbidity is increasingly common in primary care. The cost implications for patient care and budgetary management are unclear. AIM: To investigate whether caring for patients with specific disease combinations increases or decreases primary care costs compared with treating separate patients with one condition each. DESIGN: Retrospective observational study using data on 86 100 patients in the General Practice Research Database. METHOD: Annual primary care cost was estimated for each patient including consultations, medication, and investigations. Patients with comorbidity were defined as those with a current diagnosis of more than one chronic condition in the Quality and Outcomes Framework. Multiple regression modelling was used to identify, for three age groups, disease combinations that increase (cost-increasing) or decrease (cost-limiting) cost compared with treating each condition separately. RESULTS: Twenty per cent of patients had at least two chronic conditions. All conditions were found to be both cost-increasing and cost-limiting when co-occurring with other conditions except dementia, which is only cost-limiting. Depression is the most important cost-increasing condition when co-occurring with a range of conditions. Hypertension is cost-limiting, particularly when co-occurring with other cardiovascular conditions. CONCLUSION: Three categories of comorbidity emerge, those that are: cost-increasing, mainly due to a combination of depression with physical comorbidity; cost-limiting because treatment for the conditions overlap; and cost-limiting for no apparent reason but possibly because of inadequate care. These results can contribute to efficient and effective management of chronic conditions in primary care.

Economic instruments for obesity prevention: results of a scoping review and modified Delphi survey.

BACKGROUND: Comprehensive, multi-level approaches are required to address obesity. One important target for intervention is the economic domain. The purpose of this study was to synthesize existing evidence regarding the impact of economic policies targeting obesity and its causal behaviours (diet, physical activity), and to make specific recommendations for the Canadian context. METHODS: Arksey and O'Malley's (2005) methodological framework for conducting scoping reviews was adopted for this study and this consisted of two phases: 1) a structured literature search and review, and 2) consultation with experts in the research field through a Delphi survey and an in-person expert panel meeting in April 2010. RESULTS: Two key findings from the scoping review included 1) consistent evidence that weight outcomes are responsive to food and beverage prices. The debate on the use of food taxes and subsidies to address obesity should now shift to how best to address practical issues in designing such policies; and 2) very few studies have examined the impact of economic instruments to promote physical activity and clear policy recommendations cannot be made at this time. Delphi survey findings emphasised the relatively modest impact any specific economic instrument would have on obesity independently. Based on empirical evidence and expert opinion, three recommendations were supported. First, to create and implement an effective health filter to review new and current agricultural polices to reduce the possibility that such policies have a deleterious impact on population rates of obesity. Second, to implement a caloric sweetened beverage tax. Third, to examine how to implement fruit and vegetable subsidies targeted at children and low income households. CONCLUSIONS: In terms of economic interventions, shifting from empirical evidence to policy recommendation remains challenging. Overall, the evidence is not sufficiently strong to provide clear policy direction. Additionally, the nature of the experiments needed to provide definitive evidence supporting certain policy directions is likely to be complex and potentially unfeasible. However, these are not reasons to take no action. It is likely that policies need to be implemented in the face of an incomplete evidence base.

Identification of causal effects on binary outcomes using structural mean models.

Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomized controlled trials affected by nonignorable noncompliance. It has already been established that SMMs can identify these causal effects in randomized placebo-controlled trials under fairly weak assumptions. SMMs are now being used to analyze other types of study where identification depends on a no effect modification assumption. We highlight how this assumption depends crucially on the unknown causal model that generated the data, and so is difficult to justify. However, we also highlight that, if treatment selection is monotonic, additive and multiplicative SMMs do identify local (or complier) causal effects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMMs by means of an application and a simulation study.

The cost of relapse for patients with a manic/mixed episode of bipolar disorder in the EMBLEM study.

BACKGROUND: Bipolar disorder (BD) is characterized by episodes of mania and depression. The debilitating symptoms during an acute episode require intensive treatment, frequently leading to inpatient psychiatric care, which places significant demands on health and social care systems and incurs substantial costs. However, no study to date has estimated the economic impact of relapse. OBJECTIVES: To estimate the direct costs associated with relapse in the treatment of BD following an acute manic or mixed episode over a 21-month follow-up period in routine clinical practice in Europe, using data from a large, prospective, observational study. METHODS: EMBLEM was a prospective, observational study on the outcomes of patients with a manic/mixed episode of BD conducted in 14 European countries. Patients eligible for analysis were those enrolled in the 21-month maintenance phase of the study, following the 3-month acute phase. Relapse was defined as achieving any one of the following criteria: (i) at least a one-point increase in Clinical Global Impression - Bipolar Disorder (CGI-BP) overall score from the previous visit, with a final rating of > or =4; (ii) inpatient admission for an acute episode of BD; or (iii) psychiatrists' confirmation of relapse. Data on healthcare resource use were recorded retrospectively for the four respective periods (3-6, 6-12, 12-18 and 18-24 month visits). Multivariate analyses were performed to compare the cost of resource use (inpatient stay, day care, psychiatrist visits and medication) for those who relapsed during the 21-month maintenance phase and those who never relapsed. A sensitivity analysis was also conducted to examine the 6-month costs during relapse. The analyses were adjusted for patient characteristics and took account of non-Normality of the cost data by using a log link function. UK unit costs were applied to resource use. The analysis was repeated after multiple imputation for missing data. All costs were presented as year 2007/08 values. RESULTS: A total of 1379 patients completed all visits during the maintenance phase and were eligible for inclusion in the present analysis. Of these, over half (54.3%) experienced relapse during this period. A total of 792 patients without any missing data were eventually included in the final cost model. Costs incurred by patients who relapsed during the 21-month maintenance phase were approximately double those incurred by patients who never relapsed (pounds sterling 9140 vs pounds sterling 4457; p < 0.05). Of the cost difference, 80.3% was accounted for by inpatient stay. Estimates on the economic impact were higher (pounds sterling 11,781 vs pounds sterling 4789; p < 0.05) in the additional analysis with imputed missing data. The impact of relapse was even greater in the 6-month cost comparison. The average 6-month costs for patients who relapsed were found to be about three times higher than for those who did not relapse (pounds sterling 4083 vs pounds sterling 1298; p < 0.05). CONCLUSIONS: Our findings confirm the significant economic impact of relapse in BD patients after an acute manic or mixed episode, even when considering direct costs only. Such costs were dominated by inpatient stay. Nevertheless, the use of UK unit costs requires caution when interpreting this costing in the context of a specific country, as resource use and the associated costs will differ by country.

The cost of relapse in patients with schizophrenia in the European SOHO (Schizophrenia Outpatient Health Outcomes) study.

BACKGROUND: Relapse in schizophrenia is one of the greatest burdens of the illness. AIMS: To estimate the costs associated with relapse in a pan-European naturalistic setting. METHOD: The SOHO study is a 3-year, prospective, observational study of 10,972 outpatients with schizophrenia across 10 European countries. The cost of resource use (inpatient stay, day care, psychiatrist visits and medication) for those who ever relapsed in three years was compared to those who never relapsed. One-year costs for both groups were also compared for a more stringent comparison. The analyses were adjusted for patient characteristics and took account of non-normality of the cost data by using a log-link function. UK unit costs were applied to resource use. The analysis was repeated after multiple imputation for missing data. RESULTS: Costs incurred by patients who ever relapsed ( pound14,055) during three years were almost double to those incurred by patients who never relapsed ( pound7417). 61% of the cost difference was accounted for by hospital stay. The impact of relapse was even greater in the 1-year cost comparison. Results from the additional analysis with imputed missing data remained largely consistent. CONCLUSIONS: Our findings confirm the significant economic burden of relapse, and show such costs were mainly due to hospital stay. Nevertheless, the use of UK unit costs requires caution when interpreting this costing in the context of a specific country, as resource use and their associated costs will differ by country.

Cost-utility analysis of treatment with olanzapine compared with other antipsychotic treatments in patients with schizophrenia in the pan-European SOHO study.

OBJECTIVE: To determine the cost utility of treating schizophrenic patients with olanzapine compared with other antipsychotics in a naturalistic outpatient setting. METHODS: The pan-European SOHO study is a 3-year, prospective, outpatient, observational study of outcomes associated with antipsychotic treatment, focusing on olanzapine, in ten European countries. For the cost-utility analysis, healthcare resource use (inpatient care, day care, outpatient psychiatric consultations and antipsychotic and concomitant medication use) and EQ-5D data were collected at baseline and at 3, 6 and 12 months. The perspective was that of the health service payer. UK healthcare unit costs (year 2004 values) were applied to the resource use data for the ten countries. UK population tariffs were applied to the EQ-5D data to determine utility values.An Epoch analysis was used to analyze the longitudinal data. Multivariate regression analyses that adjusted for baseline covariates were used to estimate the incremental cost and utility gains for patients treated with olanzapine compared with each of the other antipsychotics (risperidone, quetiapine, amisulpride, clozapine and oral or depot typical antipsychotics). RESULTS: A total of 10 972 patients were enrolled at baseline, of which 9107 completed the 12-month study period. Treatment with olanzapine was more effective in terms of QALYs gained than all of the other antipsychotic treatments. Treatment with olanzapine dominated quetiapine and amisulpride. The incremental cost for olanzapine compared with risperidone was pound sterling 226 per patient over 12 months and the incremental cost per QALY gained was pound 5156, with bootstrap analyses showing 100% of the replications falling below a pound sterling 30 000 per QALY gained threshold. Compared with treatment with clozapine, olanzapine was found to be marginally more effective, at an additional cost of pound sterling 13 per patient over 12 months and to have an incremental cost per QALY gained of pound sterling 775. Bootstrap analyses showed that 81% of replications fell below a pound sterling 30 000 per QALY gained threshold. Comparing olanzapine with oral and depot typical antipsychotics, the incremental cost was pound sterling 849 and pound sterling 1106 per patient over 12 months and the incremental cost per QALY gained was pound sterling 15 696 and pound sterling 23 331, respectively. Bootstrap analyses showed that 98% of the replications fell below a pound sterling 30 000 per QALY gained threshold for the comparison with oral typical antipsychotics, and 79% of replications for the comparison with depot preparations. CONCLUSIONS: Among SOHO patients, if a funding threshold of pound sterling 30 000 per QALY gained is assumed, this analysis suggests that olanzapine has a high probability of being the most cost-effective treatment compared with other antipsychotic treatments. However, comparison of olanzapine with clozapine and typical depot antipsychotics should be viewed with caution because clozapine is a second-line treatment and depot treatment is used for patients who do not adhere to their oral medication.

Methodological approach for assessing the cost-effectiveness of treatments using longitudinal observational data: the SOHO study.

OBJECTIVES: The objective of this study was to develop a method to allocate treatment effects when patients switch medication frequently in longitudinal observational studies and apply the approach to assess the cost-effectiveness of treatments in the Schizophrenia Outpatient Health Outcomes (SOHO) study. METHODS: Data were collected on patients at entry to the SOHO study at 3, 6, and 12 months. The 12-month follow-up period was considered as three epochs: 0-3 months, 3-6 months, and 6-12 months. Patients who switched treatment at 3 months had their new treatment considered as a new baseline observation, as these two 3-month observations provide two sets of information on the cost-effectiveness of a drug in the first 3 months after initiation. Multivariate regression analysis was used to adjust for baseline covariates. The model allowed for flexible functional forms, and the cost data were modeled using an exponential mean function. Bootstrapping assessed the uncertainty of the estimated parameters and incremental cost-effectiveness analysis decision rule. RESULTS AND CONCLUSIONS: We show the feasibility of the epoch analysis approach using data from the SOHO study comparing two antipsychotics. Estimates for the incremental cost and effectiveness per epoch over the full 12-month period are presented. Using the estimates of 200 bootstrap samples, we demonstrate how one drug is cost-effective compared with another.

Methodological aspects in the assessment of treatment effects in observational health outcomes studies.

Prospective observational studies, which provide information on the effectiveness of interventions in natural settings, may complement results from randomised clinical trials in the evaluation of health technologies. However, observational studies are subject to a number of potential methodological weaknesses, mainly selection and observer bias. This paper reviews and applies various methods to control for selection bias in the estimation of treatment effects and proposes novel ways to assess the presence of observer bias. We also address the issues of estimation and inference in a multilevel setting. We describe and compare the use of regression methods, propensity score matching, fixed-effects models incorporating investigator characteristics, and a multilevel, hierarchical model using Bayesian estimation techniques in the control of selection bias. We also propose to assess the existence of observer bias in observational studies by comparing patient- and investigator-reported outcomes. To illustrate these methods, we have used data from the SOHO (Schizophrenia Outpatient Health Outcomes) study, a large, prospective, observational study of health outcomes associated with the treatment of schizophrenia. The methods used to adjust for differences between treatment groups that could cause selection bias yielded comparable results, reinforcing the validity of the findings. Also, the assessment of observer bias did not show that it existed in the SOHO study. Observational studies, when properly conducted and when using adequate statistical methods, can provide valid information on the evaluation of health technologies.

Pharmaceutical promotion and GP prescription behaviour.

The aim of this paper is to empirically analyse the responses by general practitioners to promotional activities for ethical drugs by pharmaceutical companies. Promotion can be beneficial as a means of providing information, but it can also be harmful in the sense that it lowers price sensitivity of doctors and it merely is a means of maintaining market share, even when cheaper, therapeutically equivalent drugs are available. A model is estimated that includes interactions of promotion expenditures and prices and that explicitly exploits the panel structure of the data, allowing for drug specific effects and dynamic adjustments, or habit persistence. The data used are aggregate monthly GP prescriptions per drug together with monthly outlays on drug promotion for the period 1994-1999 for 11 therapeutic markets, covering more than half of the total prescription drug market in the Netherlands. Identification of price effects is aided by the introduction of the Pharmaceutical Prices Act, which established that Dutch drugs prices became a weighted average of the prices in surrounding countries after June 1996. We conclude that GP drug price sensitivity is small, but adversely affected by promotion. Ltd.

Waiting lists, waiting times and admissions: an empirical analysis at hospital and general practice level.

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. Whereas previous empirical analyses have used data aggregated to area level, our analysis focuses on the supply responses of a single hospital and the demand responses of the GP practices it serves, and distinguishes between first outpatient visits, inpatient admissions, day-case treatment and emergency admissions. The results are plausible and in line with the theoretical model. For example: the demand from practices for first outpatient visits is negatively affected by waiting times and distance to the hospital. Increases in waiting times and waiting lists lead to increases in supply; the supply of elective inpatient admissions is affected negatively by current emergency admissions and positively by lagged waiting list and waiting time. We use the empirical results to investigate the dynamic responses to one off policy measures to reduce waiting times and lists by increasing supply.

Modelling supply and demand influences on the use of health care: implications for deriving a needs-based capitation formula.

Many health-care systems allocate funding according to measures of need. The utilisation approach for measuring need rests on the assumptions that use of health care is determined by demand and supply and that need is an important element of demand. By estimating utilisation models which allow for supply it is possible to isolate the socio-economic and health characteristics which affect demand. A subset of these variables can then be identified by a combination of judgement and further analysis as needs variables to inform funding allocations. We estimate utilisation models using newly assembled data on admissions to acute hospitals, measures of supply, morbidity and socio-economic characteristics for 8414 small geographical areas in England. We make a number of methodological innovations including deriving additional measures of specific morbidities at small area level from individual level survey data. We compare models with different specifications for the effect of waiting times and provider characteristics, with total, planned and unplanned hospital admissions, and estimated at small area (ward) and primary care organisation (general practice) level. After allowing for waiting times, distance, capacity and the availability of private health care, measures of mortality, self-reported morbidity, low education and low income increase the use of health care. We find evidence of horizontal inequity with respect to ethnicity and employment and suggest a method for reducing its effects when deriving a needs-based allocation formula.