Regional variations in health care supply and their potential impact on health care use

Transcription

1 Regional variations in health care supply and their potential impact on health care use Verena Vogt Department of HealthCare Management, Berlin Centre of Health Economics Research, Technische Universität Berlin 5. Juli

2 Agenda 1. What do locational factors contribute to the explanation of regional variation in office-based physicians? 2. Is there a relation between regional variation in office-based physicians and the use of cancer screening in German 5. Juli

3 1. The contribution of locational factors to regional variations in office-based physicians in Germany 5. Juli

4 Background The regional distribution of physicians is a persistent policy concern in many health care systems There are a number of public policies that aim aimed at attracting physicians to high need regions and discourage physicians from locating in over-supplied areas There is considerable literature showing that the regional distribution of office-based physicians is not only explained by varying health care needs of the population It is also determined by factors such as employment opportunities for spouses, quality of education or the financial attractiveness of a region 4

5 Key factors in physicians choice of practice location Individual characteristics(u.a. Daniels et al. 2007; Dussault et al. 2012) Age at graduation Sex Education Professional factors(u.a. Bildeau& Leduc 2003) Specialisation Working hours Regional factors(u.a. Van Lerberghe et al. 2003; Kazanjian et al. 1996) Conditions of employment(also for spouses) Economic incentives(income potential) Quality of school education Cultural factors Need (morbidity, population with PHI) It remains unclear, however, how much of the variation in physician density is explained by each of these determinants. 5

6 Methods 6

7 Regression-based decomposition 7

8 Regression-based decomposition Vgl. Fields A. (2004): Regression-Based Decompositions: A new Tool for Managerial Decision-Making 8

9 Variables Variables Mean(SD) Data source(year) Need for health care DemographicfactorfortheGerman Federal needs-based planning Financial attractiveness 1 (0,01-0,06) Own calculations Share of PHI insured residents(%) 13,56 (4,30) BVA (2010) Urbanity Number of major regional centres(n) 0,40 (0,57) INKAR (2012) Degree of rurality 29,93 (30,02) INKAR (2012) Labour and educational opportunities GDP per capita(in 1000 ) 31,23 (12,49) INKAR (2012) Share of school leavers with higehr education entrance qualification(%) Quality of leisure 32,36 (9,76) INKAR (2012) Number of tourist accomodations(per sqm) 0,20 (0,19) INKAR (2012) Border crossing Proportion of commuters(%) 37,27 (14,87) INKAR (2012) 9

10 Regional distribution of physicians GPs per inhabitants Specialists per inhabitants 10

11 Distribution of the PHI-insured 11

12 Gini-Index and Lorenz curve cumulative proportion of physicians GPs Gini-Coefficients cumulative proportion of physicians Urologists Psychologists Orthopaedists Neurologists Dermatologists ENT-Specialists Gynaecologists Ophthalmologists GPs Specialists Gini-Index: 0,059 cumulative proportion of population Gini-Index: 0,254 cumulative proportion of population 12

13 Results of linear regression models VARIABLES Urologists Psychologists Orthopaedists Neurologists Dermatologists ENT- Specialists Gynaecologists Ophthalmologists GPs PHI share ** 0.090*** 0.056* 0.066*** * 0.083** 0.600*** Regional centres 0.629*** 6.034*** 1.149*** 1.417*** 0.844*** 0.605*** 1.700*** 1.360*** 3.049*** Rurality * *** * ** ** BIP 0.019** *** 0.035** 0.019* 0.034*** 0.074*** 0.052*** High education 0.013* 0.355*** *** 0.027*** 0.025*** 0.061*** 0.035*** Tourist accommodations *** 2.897** 1.887* 1.224* *** Recreational area 0.100*** 1.170*** *** *** 0.233*** 0.126* Commuter 0.026*** 0.186*** 0.072*** 0.056*** 0.043*** 0.032*** 0.077*** 0.068*** 0.064** Demographic factor 6.00*** *** *** *** *** *** *** *** Constant *** *** *** * *** *** *** *** *** R-squared *** p<0.01, ** p<0.05, * p<0.1 13

14 Results of the decomposition GPs Ophthalmologistlogists Gynaeco- ENT- Dermato- Neuro- Orthopae Psycho- Urologists Specialists logists logists dists logists ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) PHI share Regional centres Rurality GDP High education Tourist accommodations Recreational area Commuter Demographic factor ( )(Residual) Total Juli

15 5. Juli

16 Discussion Overall, the results suggest that not only need or potential income play a role in explaining specialist density Morbidity/need only play a minor role in explaining the distribution of physicians This confirms the hypotheses that physicians seek to maximise not only their income but rather their overall utility 16

17 Examining regional variation in the use of cancer screening in Germany Vogt, V., Siegel, M., Sundmacher, S. (2014): Examining regional variation in the use of cancer screeningin Germany. SocSciMed110: Juli

18 Background Cancer is the second leading cause of death in most developed countries (WHO 2008): In Germany in ,9% / 22,4% of all deathsarerelatedtocancer amongmen/ women The chances of recovery improve if cancer is detected in its early latent stages, when the cancer has not yet spread and became incurable Strategies for early detection of cancer can reduce the burden of the disease In Germany, screening for breast, colon, cervical, skin and prostate cancerarecoveredbytheshi However, the participation in screening is still too low

19 Determinants of screening uptake Spillover Effects (E.g. informal communication) Predispos sing Enabling Collective Characteristics (E.g. attitudes and knowledge) Compositional Characteristics (E.g. social structure) Accessibility (E.g. physician supply) Prevention Uptake Source: own representation based on Andersen (1995)

20 Aim and hypotheses of the analysis Aim: Identifyingspatialpatternsin screeninguptakeand examiningwhether low screening rates are associated with low accessibility of health services. Hypotheses: a) There are significant regional variations in the use of cancer screening services; b) Accessibility of health care services explains a significant part of the variation in rates, even after controlling for socioeconomic and other regional covariates; c) Rates are clustered regionally due to spillover effects from informal communication and observational learning.

21 Measuringscreeninguptakein Germany Claims data provided by the KBV from 2008 to 2011 Information about claimed services as well as age, sex and residence of the patients Number of patients who used a service at least once within a year / number of patients who were eligible to use these services Directly standardised by age or age and sex Based on the patient s place of residence Level of aggregation: districts (Kreise und Kreisfreie Städte) (n=402)

22 Measuringscreeninguptakein Germany EBM code Screening intervention Eligiblepopulation Prostate cancer Men aged 45 and screening over Pap-test Women aged 20 and (cervical cancer) over Colonoscopy (cancer Women and Men of the colon) aged 55 and over 01745/ Skin cancer screening Women and Men aged 35 and over Mammography Women aged (breast cancer) between 50 and 70 Note: a average rates Period under consideration Mean rate (Std. Dev.) Moran s I (p-value) (0.034) (<0.001) (0.040) (<0.001) a (0.005) (<0.001) a (0.030) (<0.001) a (0.035) (<0.001)

23 Mammography Pap-Test Prostate cancer screening 5,01% - 15,00% 20,31% - 20,81% 24,13% - 37,37% 41,27% - 42,19% 11,4% - 16,7% 21,1% - 22,0% 15,01% - 17,10% 20,82% - 21,90% 37,38% - 38,75% 42,20% - 42,99% 16,8% - 17,8% 22,1% - 22,9% 17,11% - 18,52% 21,91% - 22,66% 38,76% - 39,69% 43,00% - 44,20% 17,9% - 18,8% 23,0% - 24,1% 18,53% - 19,62% 22,67% - 23,79% 39,70% - 40,44% 44,21% - 46,84% 18,9% - 20,1% 24,2% - 25,3% 19,63% - 20,30% 23,80% - 29,24% 40,45% - 41,26% 46,85% - 56,41% 20,2% - 21,0% 25,4% - 31,9%

24 Scin cancer screening Colonoscopy 6,01% - 10,16% 10,17% - 11,44% 11,45% - 12,64% 12,65% - 13,34% 13,35% - 14,13% 14,14% - 14,92% 14,93% - 15,88% 15,89% - 16,75% 16,76% - 17,95% 17,96% - 23,30% 0,49% - 1,09% 1,10% - 1,30% 1,31% - 1,45% 1,46% - 1,57% 1,58% - 1,71% 1,72% - 1,81% 1,82% - 1,95% 1,96% - 2,11% 2,12% - 2,37% 2,38% - 3,21%

25 Methods

26 Spatial autocorrelation Correlation of the values of one district with those of its adjacent districts Knowledge-Spillover Observational Learning Residuals of adjacent areas are not independent COV(e i, e j ) 0 OLS estimates are biased and significance tests invalid Includingspatialdependencein themodelspecificationvia a (binary) standardized spatial weight matrix(w)

27 Spatial Lag Model Y = βx + ρwy + є Y X W ρ Dependent variable (screening uptake rate) Explanatoryvariables Spatial weight matrix Spatial lag coefficient Spatial weight matrix(contiguity matrix): with w ij =1 if the districts iand j are neighbors and w ij =0 if not. we defined neighborhood as a car travel time of 70 minutes or less between the geographic centers of the districts. row-standardized

28 Explanatory variables Variable Description Accessibility Physiciandensity Travel time tophysicians Mammography-centres number of specialists per 10,000 inhabitants who perform the following screenings logarithmizedaverage time (in minutes) patients spend to visit a specialist, based on street maps dummy: 1=screening centre is present in district Socioeconomic factors Income disposable household income per inhabitant in in 2009 Education Psychosocial factors Voter turnout Smokers share of young adults, year old adults who hold a certificate at secondary level II in 2008 based on Germany s national election in 2009 in percent percentage of people who report to smoke tobacco products at least occasionally Other control variables east/west East-west-dummy(western states =1) oral contraceptives number of oral contraceptives prescribed to women aged 18 years old in relation to all women in the district at the same age

29 Results

30 Results I Mammography Pap-test Prostate cancer screening OLS Spatial lag OLS Spatial lag OLS Spatial lag Physician density a ** ** *** Travel time to *** *** * physicians a Mammographycenter 2.165*** 1.912*** Education *** *** *** Income *** * Voter turnout 0.116** *** 0.299*** 0.266*** *** Smoker West dummy *** *** *** *** *** *** Oral Contraceptives 0.088*** 0.071*** Constant *** *** Adjusted R² b b b ρ 0.587*** 0.388*** *** Moran s I 0.164*** *** *** Note: ρ, spatial lag coefficient; *p<0.05; **p<0.005; ***p<0.001

31 Results II Skin cancer screening Colonoscopy OLS Spatial lag OLS Spatial lag Physician density a ** *** *** 0.166*** Travel time to *** ** ** *** physicians a Education *** * Income Voter turnout 0.075** 0.071*** *** 0.048*** Smoker West dummy *** *** Oral Contraceptives Constant Adjusted R² ρ Moran s I b b 0.740*** 0.548*** 0.282*** *** Note: ρ, spatial lag coefficient; *p<0.05; **p<0.005; ***p<0.001

32 Discussion Results demonstrate that cancer screening rates are significantly higher in areas that offer better access to health care Possible mechanisms: reduced economic costs of access and waiting times Regarding the social structure variables, we found no consistent pattern across the different screening examinations. Knowledge-spillover between districts Individual preferences and knowledge regarding cancer screening are transmitted through personal contacts within cross-regional social networks

33 Thanks for your attention! Verena Vogt Department of Health Care Management WHO Collaborating Center for Health Care Systems Research and Management Technische Universität Berlin Straße des 17. Juni 135, H Berlin, Germany Phone: