Predictive Model Development: Identifying high-risk patients in primary care attendance Financed with the help PI 06/1122 (FIS) of ISCIII Antonio Sarría Santamera Mª Auxiliadora Martín Martínez Mª del Rocío Carmona Alférez Pilar Gallego Berciano AGENCY EVALUATION OF HEALTH TECHNOLOGIES INSTITUTO DE SALUD CARLOS III

Sandín Vázquez M Conde Espejo P de Bustos Guadaño M Asunsolo del Barco A Riesgo Fuertes R Garrido Elustondo S Cabello Ballesteros ML Escortell Mayor ME Sanz Cuesta T Calvo Parra I Villaitodo Villén P Bartolomé Casado MS Jiménez Carramiñana J Casado López M Parralejo Buendía M Basanta López M Bonache Blay M Martínez-Toledano Olaya P Rico Blázquez M Utilization of Primary Health Care Research Group

 High frequency of attendance is one of the main problems in PC.  Excessive use has been linked to a limited number of patients who have been called frequent user.  Who are they? How many are they?  High frequency of attendance is one of the main problems in PC.  Excessive use has been linked to a limited number of patients who have been called frequent user.  Who are they? How many are they? INTRODUCTION

 There is no agreement on the definition of frequent users.  We propose that the level of use should be linked to the patient risk profile of patients.  Overuse will suppose that patient have a higher number of visits than expected given their risk profile.  There is no agreement on the definition of frequent users.  We propose that the level of use should be linked to the patient risk profile of patients.  Overuse will suppose that patient have a higher number of visits than expected given their risk profile. INTRODUCTION

1. To identify factors associated with the use of visits to primary care physicians. 2. To assess the frequency of over-use. OBJECTIVES

METHODOLOGY Design. location and sources of information  Design: transversal. observational and ecological.  Location : 6 health areas in the Community of Madrid.  Sources of information: - Electronic medical records of Primary Care (OMI-AP). - Institute of Statistics of the Community of Madrid. Design. location and sources of information  Design: transversal. observational and ecological.  Location : 6 health areas in the Community of Madrid.  Sources of information: - Electronic medical records of Primary Care (OMI-AP). - Institute of Statistics of the Community of Madrid. Health areas: 1, 3, 7, 8, 9 y 10

METHODOLOGY  Study patients: INCLUSION CRITERIA:  Registration in one of the 6 Health Areas  > 24 years in 2006  At least one visit to the clinic in 2006 EXCLUSION CRITERIA:  Physician assigned to the “traditional model”  Study period: January 1, December 3, 2007  Study patients: INCLUSION CRITERIA:  Registration in one of the 6 Health Areas  > 24 years in 2006  At least one visit to the clinic in 2006 EXCLUSION CRITERIA:  Physician assigned to the “traditional model”  Study period: January 1, December 3, 2007

Flowchart of patient selection PATIENTS OVER 24 YEARS WITH AT LEAST ONE VISIT TO THE CENTRE OF HEALTH IN 2006 IN 6 HEALTH AREAS ( ) PATIENTS INCLUDED IN STUDY ( ) DUPLICATE PATIENTS (7.307) METHODOLOGY

PATIENT VARIABLES Socio-demographic 1. Age 2. Sex 3. Nationality 4. User Type 5. Social Problems Morbidity 1. Diseases (CIAP-1) 2. Temporary Disability 3. Patient Overview 4. Protocols 5. Drugs prescribed Use of Health Services 1. Total medical PC consultations (2006 and 2007). 2. Total nursing PC consultations (2006 and 2007). 3. Analytics 4. Radiology tests 5. Referrals to specialists PRIMARY CARE TEAM VARIABLES Organizational Characteristics and Health System Capacity 1. Location of the PCT 2. Type of primary health care team 3. Time schedule 2. Average PC doctor workload 3. Average PC nursing workload 4. Percentage of patients aged ≥ 65 years of primary care team Socioeconomic 1. Educational level of the Basic Health Zone (Population and Housing Census 2001 of the CM) 2. Per capita Gross Disposable Income of the Basic Health Zone (Statistical Institute of the CM)

SOCIO-ECONOMIC DATA Patient Per Capita Gross Disposable Income in 2000 of the basic health area SOCIO-ECONOMIC DATA Patient Per Capita Gross Disposable Income in 2000 of the basic health area METHODOLOGY

 DATA ANALYSIS 1.DESCRIPTIVE ANALYSIS  Frequency analysis  Correlation analysis  Detecting Multicollinearity  Contingency tables  DATA ANALYSIS 1.DESCRIPTIVE ANALYSIS  Frequency analysis  Correlation analysis  Detecting Multicollinearity  Contingency tables METHODOLOGY

 DATA ANALYSIS 2.DEVELOPMENT OF RISK ADJUSTMENT MODELS: MULTILEVEL REGRESSION Characterized by: hierarchical clustering of variables LEVEL 1: PATIENTS LEVEL 2: PC Teams DEPENDENT VARIABLE: Total number of visits to PC physicians in 2007 Total number of visits to PC physicians in 2007 The variability in total of visits to PC physicians are due to differences between patients and differences between PC Teams  DATA ANALYSIS 2.DEVELOPMENT OF RISK ADJUSTMENT MODELS: MULTILEVEL REGRESSION Characterized by: hierarchical clustering of variables LEVEL 1: PATIENTS LEVEL 2: PC Teams DEPENDENT VARIABLE: Total number of visits to PC physicians in 2007 Total number of visits to PC physicians in 2007 The variability in total of visits to PC physicians are due to differences between patients and differences between PC Teams METHODOLOGY

MULTILEVEL MODEL:  INDEPENDENT VARIABLES OF PATIENT MULTILEVEL MODEL:  INDEPENDENT VARIABLES OF PATIENT METHODOLOGY

Sociodemográficas 1.Edad 2. Sexo 3. Problemas Sociales Use of Health Service 1.Total of visits to PC physicians in Total nursing consultations in Analytical 4.Radiology tests 5.Referrals to other specialists Sociodemográficas 1.Edad 2. Sexo 3. Problemas Sociales Use of Health Service 1.Total of visits to PC physicians in Total nursing consultations in Analytical 4.Radiology tests 5.Referrals to other specialists Sociodemográficas 1.Edad 2. Sexo 3. Problemas Sociales Sociodemographic 1.Age 2. Sex 3. Social Problems Morbidity 1.Nonspecific problems 2.Digestive disorders 3.Ophthalmic conditions 4.ENT conditions 5.HBP 6.Phlebitis 7.Heart failure / arrhythmias 8.Ischemic heart disease / Stroke 9.Osteoarthritis / osteoporosis 10.Another bone-joint conditions 11.Headache 12.Vertigo/ dizziness 13.Anxiety / depression 14.COPD / Asthma 15.Allergic rhinitis 16.Other respiratory disease 17.Skin / appendages 18.Diabetes 19.Obesity 20.Lipid disorder 21.Cancer 22.Anemia 23.HIV 24.Pulmonary embolism 25.Peripheral neuropathy 26.Schizophrenia 27.Temporary Disability

MULTILEVEL MODEL :  INDEPENDENT VARIABLES OF PC Team MULTILEVEL MODEL :  INDEPENDENT VARIABLES OF PC Team METHODOLOGY

Características y Capacidad Organizativa del Sistema Sanitario 1. Tipo de EAP: Rural/ Urbano 2. Turno del EAP: Mañana y Tarde/ Mañana o Tarde 3. PAMED 4. PAENF 5. % de pacientes con edad ≥ 65 años del EAP Organizational Characteristics and Health System Capacity 1.Type of PC Team: Rural / Urban 2.Time Team's primary care: Morning and Evening / Morning or Afternoon 3.Pressure care average PC physician PC Team 4.Pressure half of nursing care of PC Team 5.% of patients aged ≥ 65 years of PC Team Socio-economic Per capita disposable income 2000 METHODOLOGY

 DATA ANALYSIS 3.RISK ADJUSTED ATTENDANCE RATIO Estimated values were obtained for each patient according to the risk adjustment model.  DATA ANALYSIS 3.RISK ADJUSTED ATTENDANCE RATIO Estimated values were obtained for each patient according to the risk adjustment model. METHODOLOGY

 DATA ANALYSIS 3.RISK ADJUSTED ATTENDANCE RATIO For each patient we calculated the difference between the actual number of visits to PC physician in 2007 and the value estimated by the model:  DATA ANALYSIS 3.RISK ADJUSTED ATTENDANCE RATIO For each patient we calculated the difference between the actual number of visits to PC physician in 2007 and the value estimated by the model: Difference = OBSERVED - ESTIMATED OBSERVED – ESTIMATED > 0  PATIENT OVER-USER OBSERVED – ESTIMATED < 0  PATIENT INFRA-USER METHODOLOGY

 DATA ANALYSIS 3. RISK ADJUSTED ATTENDANCE RATIO This approach allowed to obtained the observed/expeced number of visits for each PC Team. The total number of visits to PC in 2007 of these patients and the estimated total number of consultations by the model.  DATA ANALYSIS 3. RISK ADJUSTED ATTENDANCE RATIO This approach allowed to obtained the observed/expeced number of visits for each PC Team. The total number of visits to PC in 2007 of these patients and the estimated total number of consultations by the model. METHODOLOGY PC Team Total visits to PCEstimated number of visits physiciansPC physicians

 DATA ANALYSIS 3. RISK ADJUSTED ATTENDANCE RATIO  DATA ANALYSIS 3. RISK ADJUSTED ATTENDANCE RATIO METHODOLOGY PC Team Total visits to PCEstimated number of visits RAAR = RATIO physiciansPC physicians

CHARACTERISTICS OF PATIENTS AND PC TEAM MEANS.D.% AGE SEX: FEMALE IMMIGRANT TOTALVISITS TO PC PHYSICIANS TOTALVISITS TO PC PHYSICIANS TOTALNOURSING CONSULTATIONS TOTALNOURSING CONSULTATIONS ANALYTICAL TOTAL OF PRESCRIBED DRUGS REFERRAL USER TYPE: ACTIVE / PENSIONER PRESSURE CARE AVERAGE PC PHYSICIAN PC TEAM PRESSURE HALF OF NURSING CARE OF PC TEAM PERCENTAGE OF PATIENTS AGED ≥ 65 YEARS OF PC TEAM Type of PC Team: Rural Time Team's PC: Morning and Evening PER CAPITA INCOME AVAILABLE BASIC HEALTH ZONE RESULTS

VARIABLESCoeff.Z95% CI Age Sex (Ref. Male) Log Total visits to PC Physicians Log Total Noursing Consultations Analytical (Ref. No) Radiology tests (Ref. No) Nonspecific problems (Ref. No) Digestive disorders (Ref. No) Ophthalmic Pathology (Ref. No) ORL Pathology (Re. No) HBP (Ref. No) Phlebitis (Ref. No) Heart failure / arrhythmias (Ref. No) Ischemic heart disease / Stroke (Ref. No) RESULTS

VARIABLESCoeff.Z95% CI Osteoarthritis / osteoporosis (Ref. No) Another osteoarticular pathology(Ref. No) Headache (Ref. No) Vertigo/ dizziness Anxiety / depression (Ref. No) COPD / Asthma (Ref. No) Other respiratory disease (Ref. No) Skin / appendages (Ref. No) Diabetes (Ref. No) Obesity (Ref. No) Lipid disorder (Ref. No) Cancer (Ref. No) Schizophrenia (Ref. No) RESULTS

COEFFICIENTSTANDARD ERROR LOG-LIKELIHOOD Variability between PC Teams Variability between patients ICC 12.6% VARIABLESCoeff.Z95% CI Type of PC Team (Ref. Rural) Pressure Half of Nursing Care of PC Team Income categories (Ref. Low) Intermediate High/ Very High RESULTS

 53% of patients visits their primary care physician more than estimated according to their risk.  40% visits less primary care physicians than expected. RESULTS PATIENT OVER-USER53% INFRA-USER40% n= 1,017,002

 There is a significant amount of visits to PC physician which are not explained by the risk of patients or characteristics of PC Teams.  Overall, the number of visits of over-users exceeds that of infra-users. CONCLUSIONS

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