Achieving geographic equity in the Portuguese hospital financing system Monica Duarte Correia de Oliveira Department of Operational Research, London School of Economics and Political Science Thesis submitted to the University of London for the Degree of Doctor of Philosophy April, 2003 UMI Number: U61583B All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dissertation Publishing UMI U615833 Published by ProQuest LLC 2014. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 ^ Of A POLITICAL A N D ruj 4 M cS o gU 1037* 7)1 To my parents and sister ii ABSTRACT The Portuguese health care system is based on a national health service structure. The Portuguese government has with various statements over time shown that it is seeking some kind of geographical equity but this has never been clearly defined. There are wide inequalities in the distribution o f hospital resources in Portugal with marked concentration in urban coastal areas and little information. The objective o f the research described in this thesis is to develop methods to inform the allocation of resources to Portuguese hospitals so that this can be made more equitable in both current and capital spending. The methods used are a combination of methods already used in other countries and new methods to address two questions. First, to measure inequities in hospital care in terms of capital, finance and utilisation using capitation formulas. These formulas are constructed using: a multiplicative model to measure need for hospital care; a multilevel model to estimate unavoidable costs and to disentangle allocative inefficiencies o f hospital care; and a flow demand model to predict hospital geographical utilisation and to compute cross-boundary flows. Second, to indicate how redistribution of hospital supply will best improve equity of utilisation and access, using location-allocation models that were designed to consider alternative policy objectives and account for patients’ choice of hospitals. ACKNOWLEDGMENTS I am deeply indebted to Professor Gwyn Bevan for being an extremely attentive and committed supervisor. Without all his effort and patience, all the progress and learning process throughout the last three and a half years would not have been both as challenging and pleasant as it has been. His intellectual contribution and guidance on how to carry out research have greatly marked my academic attitude. This research has been funded by the scholarship of Fundaqao para a Ciencia e Tecnologia, Ministerio da Ciencia e Ensino Superior , Portugal (schoolarship co-funded by the European Social Fund, Structural Fund III; grant BD 19972/99). I would also like to express my gratitude to Professor Pedro Pita Barros, Mr. Jose Fernandez, Dr. Elias Mossialos, Professor Joao Antonio Pereira and Professor Carlos Gouveia Pinto for their availability, help and insightful comments. I am thankful to others who have always been helpful at different stages o f the research: Professor Walter Holland, Ms. Ceu Mateus, Mr. David McDaid, Professor Alistair McGuire, Ms. Brenda Mowlam, Dr. Adam Oliver, Ms. Filomena Parra, Dr. Susan Powell and Ms. Ceu Valente. I am grateful to the LSE Health and Social Care and to the Operational Research Department at the LSE for providing me with excellent working conditions and a very intellectual and stimulating environment in which I could develop my research. I acknowledge the financial support of the UK Health Economics Study Group, which allowed me to attend its meetings. I thank the Portuguese National Institute of Statistics and the Financial Institute for Informatics and Management of the Portuguese Ministry of Health who provided data. I am grateful to the participants of the seminars and conferences where various studies iji t of this thesis were presented . I specially thank Dr. Zaid Chalabi and Dr. Andrew Street for their detailed comments as discussants of the papers presented in the meetings o f the UK Health Economic Study Group. Parts of this thesis have been published elsewhere: working paper Oliveira, M. and G. Bevan (2001). Measuring geographic inequalities in the Portuguese health care system: An estimation of hospital care need. Lisboa, Associa 9ao Portuguesa de Economia da Saude: 35; Oliveira, M. (2002); and discussion p ap ers flow demand model to predict hospital utilisation. London, LSE Health and Social Care: 57. An adapted version of Chapter 5 o f this thesis was accepted for publication in the Health Policy Journal (March 2003). I thank Dina Davaki for accompanying and supporting me throughout most of this journey. I especially thank Leandro Rothmuller for giving me strength during the last stage of the thesis. I especially thank Sofia Silva for all her support in the difficult times. I thank my friends Dimitris Boucas, Ana Carvalho, Carmen Constan 9a, Martin Cumpa, Filipe Garcia, Carsten Holbraad, Balazc Kotnyek, Heather Lannin, Peter Lutz, Yannis Mourtos, Lidia Oliveira, Pedro Oliveira, John Tan, Andrea Volfova and the Berloquenhos for their positive energy and encouragement. * Parts of this thesis were presented in the following conferences and seminars: 7th National Meeting of Health Economics, organised by the Portuguese Association of Health Economics, Lisboa, November 2000; LSE Health and Social Care seminar series, May 2001; LSE Health and Social Care informal seminar series, December 2001; 60th Meeting of the UK Health Economics Study Group, Norwich, January 2002; London and South East Operational Research Society, London, March 2002; 22nd Meeting of the Spanish Health Economics Association, organised by the Spanish Association of Health Economics, Pamplona, May 2002; 4th European Conference on Health Economics, organised by the College des Economistes de la Sante, Paris, July 2002; LSE Health and Social Care informal seminar, November 2002; 62nd Meeting of the UK Health Economics Study Group, Leeds, January 2003. v Cada qual com seu igual. Each one with his equal. A Portuguese expression La premiere egalite, c ’est I’equite. The first equality is equity. Victor Hugo, Les Miserables Las majestueuse egalite des lois, qui interdit au riche comme au pauvre de coucher sous les ponts, de mendier dans les rues et de voler du pain. The majestic equality of the law forbids the rich as well as the poor to sleep under bridges, to beg in the streets and to steal bread. Anatole France, Le Lys rouge ABBREVIATIONS AIC: Aikeke Information Criteria ASMR: Age Specific Mortality Rates CBF: Cross-Boundary Flows DBM: Distance Based Model DEA: Data Envelopment Analysis DHA: District Health Service Authority DRG: Diagnostic Related Groups EM: Entropy Model EU: European Union FCE: Finished Consultant Episode FDM: Flow Demand Model GDP: Gross Domestic Product GLM: Generalised Linear Model GLS: Generalised Least Squares GP: General Practitioner HFEM: Hierarchical Fixed Effects Model IGIF: Institute for Financial and Informational Management LOS: Length o f Stay MLM: Multilevel Model with random intercepts and slopes MoH: Ministry of Health, Portugal MP: Mathematical Programming (model) n/a: not available NF: Net Flow method NHS: National Health Service NUTS: Statistical Nomenclature of Territorial Units OECD: Organisation for Economic Cooperation and Development OLS: Ordinary Least Squares PC: Primary Care PIDDAC: Program of Investments and Expenditure for Development o f the Central Administration, Portugal PF: Proportional Flow method PPI: Purchasing Power Index PPP: Purchasing Power Parities PYLL: Potential Years of Life Lost RAWP: Resource Allocation Working Party RHA: Regional Health Authority RMI: Relative Mortality Index SFM: Stochastic Frontier Model SIM: Spatial Interaction Model SMR: Standardised Mortality Ratio SSMs: Sample Selection Models TI: Treatment Intensity method TPM: Two-Part Model UBM: Utilisation Based Model UC: Unavoidable Costs UFBM: Utilisation Flows Based Model UK: United Kingdom USD: United States Dollar US: United States VHI: Voluntary Health Insurance NOTATION Notation Interpretation r r is a geographic district unit (district; for Portugal, r =1,2,..18). cap _indexr Relative capitation index for district r , accounting for all the selected adjustments of the capitation formula. Pr , P Resident population in district r and total resident population. h r Age and additional need index for district r . h r CBFs index for district r . h r UC index for district r . District _ share _ \ r Share of need for hospital care for district r . District _ share _ 2 r Share of need for hospital care, adjusted by CBFs for district r . District _ share _ 3r Share of need for hospital care, adjusted by CBFs and UCs for district r . a Age groupa . * \a Age (and sex) cost for age (and sex) group a . d^ar Number of deaths in area r from the age groupa . Par Resident population of the age group a in area r . far Death rate in area r from the age group a, which corresponds to the definition of age specific mortality rates for area r and for age group a . ra National death rate for age group a . cutoff Age reference used in the computation of the potential years of life lost index. It is related to life expectancy. h Mid-age point of age group a (required to compute the potential years of life lost index). SMRr Standardised mortality ratio index for district r . ASMRar Age specific mortality ratio index for age group a and for district r . PYLLr Potential years of life lost index for district r . RMIr Relative mortality index for district r . h , h' Hospital identifier (h * h ')- c Types of hospital in the administrative (and hierarchical) classification (for Portugal: c = general central, specialised central, district, level I).