The Economic Cost of Frailty

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1 The Economic Cost of Frailty Ambulatory Care Costs Assessment using French Data Nicolas Sirven (Ph.D), Thomas Rapp (Ph.D) LIRAES (EA 4470) & Chaire AGEINOMIX Université Paris Descartes Sorbonne- Paris- Cité Séminaire de recherche MODAPA 11 janvier 2016, Ecole d économie de Paris

2 IntroducTon Popula;on ageing and Health Care Expend. (HCE) Age & Time- to- death = Red Herring HCE = f (health = chronic, functonal limitatons, frailty, ) Frailty predicts independent adverse health outcomes Frailty is assumed increase HC demand H1: Frailty as a consequence of medical treatment o Weakened health a]er heavy treatment (cancer ) o Shortened hospital stays (actvity- based funding hospitals) H2: Frailty as perceived health & undiagnosed conditon

3 IntroducTon Popula;on ageing and Health Care Expend. (HCE) Age & Time- to- death = Red Herring HCE = f (health = chronic, functonal limitatons, frailty, ) Frailty predicts independent adverse health outcomes Frailty is assumed increase HC demand H1: Frailty as a consequence of medical treatment o Weakened health a]er heavy treatment (cancer ) o Shortened hospital stays (actvity- based funding hospitals) H2: Frailty as perceived health & undiagnosed conditon Source : Fried et al, 2001.

4 IntroducTon Popula;on ageing and Health Care Expend. (HCE) Age & Time- to- death = Red Herring HCE = f (health = chronic, functonal limitatons, frailty, ) Frailty predicts independent adverse health outcomes Frailty is assumed to increase HC demand H1: Frailty as a consequence of medical treatment o Weakened health a]er heavy treatment (cancer ) o Shortened hospital stays (actvity- based funding hospitals) H2: Frailty as perceived health & undiagnosed conditon

5 How Much Does Frailty Contribute to HCE? Needed to define priority to actons for healthy aging Despite the interest, scarce evidence in Europe o Frail elders have larger primary care consumptons o But, the cost of frailty has not been assessed à lack of data! A Unique Dataset at Hand IntroducTon ESPS Survey from o Health, Social, and Economic QuesTonnaire o Frailty Module in 2012 (Fried s Phenotype) Matched with AdministraTve records o NaTonal Health Insurance Claims o Ambulatory Care (GP+SP visits, Drugs, ER without hospital stays) o LTC and Hospital care recorded through alternatve informaton system

6 IntroducTon How Much Does Frailty Contribute to HCE? Needed to define priority to actons for healthy aging Despite the interest, scarce evidence in Europe o Frail elders have larger primary care consumptons o But, the cost of frailty has not been assessed à lack of data! Assessment Based on a Unique Dataset! ESPS General PopulaTon Survey o Health, Social, and Economic QuesTonnaire o Frailty Module in 2012 (Fried s Phenotype) Matched with AdministraTve records o NaTonal Health Insurance Claims o Ambulatory Care (GP+SP visits, Drugs, ER without hospital stays) o LTC and Hospital care recorded through alternatve informaton system

7 IntroducTon How Much Does Frailty Contribute to HCE? Needed to define priority to actons for healthy aging Despite the interest, scarce evidence in Europe o Frail elders have larger primary care consumptons o But, the cost of frailty has not been assessed à lack of data! Assessment Based on a Unique Dataset! ESPS General PopulaTon Survey o Health, Social, and Economic QuesTonnaire o Frailty Module in 2012 (Fried s Phenotype) Matched with AdministraTve records o NaTonal Health Insurance Claims o Ambulatory Care (GP+SP visits, Drugs, ER without hospital stays) o LTC and Hospital care recorded through alternatve informaton system

8 Main Explana;ve Variable A frailty module o Self- reported questons on five dimensions o Slowness, shrinking, exhauston, weakness, and low physical actvity o Frailty Phenotype: (0) Robust (1-2) Pre- frail (3-5) Frail Other Drivers of HCE Data Health covariates o Chronic conditons (14 diag.) + ADL LimitaTons o Time- to- death (last year of life death in 2013) o MCA Health index (SRH, mental health LT illness, limitatons, etc.) Individual Controls o Complementary Health Insurance, Time- preference, Risk- aversion o Socio- Demo (age, sex, marital status) o NS: income, financial diff. over the life- course, educaton, social capital

9 Main Explana;ve Variable A frailty module o Self- reported questons on five dimensions o Slowness, shrinking, exhauston, weakness, and low physical actvity 40 o Frailty Phenotype: (0) Robust (1-2) Pre- frail (3-5) Frail Other Drivers of HCE Health covariates o Chronic conditons (14 diag.) + ADL LimitaTons o Time- to- death (last year of life death in 2013) 10 o MCA Health index (SRH, mental health LT illness, limitatons, etc.) Individual Controls Data Distribution of Frailty Phenotype France 2012 Respondents 65+ in the community Percent of the Population o Complementary Health Insurance, Time- preference, Risk- aversion o Socio- Demo (age, sex, marital status) Nb of Frailty Characteristics o NS: income, financial diff. over the life- course, educaton, social capital Men Women Source: IRDES - ESPS Survey Note: Individual weights applied.

10 Main Explana;ve Variable A frailty module o Self- reported questons on five dimensions o Slowness, shrinking, exhauston, weakness, and low physical actvity o Frailty Phenotype: (0) Robust (1-2) Pre- frail (3-5) Frail Other Drivers of HCE Data Health covariates o Chronic conditons (14 reported diag.) + ADL LimitaTons (5 items) o Time- to- death (last year of life death in 2013) o MCA Health index (SRH, mental health, LT illness, limitatons, etc.) Individual Controls o Complementary Health Insurance, Time- preference, Risk- aversion o Socio- Demo (age, sex, marital status) o NS: income, financial diff. over the life- course, educaton, social capital

11 Main Explana;ve Variable A frailty module o Self- reported questons on five dimensions o Slowness, shrinking, exhauston, weakness, and low physical actvity o Frailty Phenotype: (0) Robust (1-2) Pre- frail (3-5) Frail Other Drivers of HCE Data Health covariates o Chronic conditons (14 reported diag.) + ADL LimitaTons (5 items) o Time- to- death (last year of life death in 2013) o MCA Health index (SRH, mental health, LT illness, limitatons, etc.) Individual Controls o Complementary Health Insurance, Time- preference, Risk- aversion o Socio- Demo (age, sex, marital status) o NS: income, financial diff. over the life- course, educaton, social capital

12 Health Care Expenditures & Prevalence of Frailty by Age Class France 2012 Ambulatory Care Frailty phenotype: Robust Pre-frail Frail Population 60% 50% 40% 30% 20% 10% 0% Median Expenditures in Age class Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Individual weights applied.

13 Health Care Expenditures Total Ambulatory Costs in France Frailty phenotype: Robust Pre-frail Frail Density Log scale Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more.

Health Care Expenditures Total Ambulatory Costs in France 2012 0.6 Frailty phenotype: Robust Pre-frail Frail 0.5 Density 0.4 0.3 0.2 Only 1.

14 Health Care Expenditures Total Ambulatory Costs in France Frailty phenotype: Robust Pre-frail Frail 0.5 Density Only 1.8% of the sample has no recorded HCE (24 observatons) Log scale Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more.

15 Methods Es;ma;on using GLM E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) o More accurate No Re- transformaton is required o Great Flexibility in the choice of g(.) and V(.) o Valid assumptons are essental Popular choice is log- gamma model o Link tests: Goodness- of- Fit tests for models comparison o Variance test: Modified Park Test Model specificaton o RelaTve contributon of all health covariates (best set of health controls) o Models comparison with and without Frailty (LR- Test) Robustness checks using semi- parametric GLM (EEE) Parameters estmates provide guidance for o Link functon: Power (box- cox) vs. Log. o Variance: Power vs. QuadraTc

16 Methods Es;ma;on using GLM E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) o More accurate No Re- transformaton is required o Great Flexibility in the choice of g(.) and V(.) o Valid assumptons are essental Popular choice is log- gamma model o Link tests: Goodness- of- Fit tests for models comparison o Variance test: Modified Park Test Model specificaton o RelaTve contributon of all health covariates (best set of health controls) o Models comparison with and without Frailty (LR- Test) Robustness checks using semi- parametric GLM (EEE) Parameters estmates provide guidance for o Link functon: Power (box- cox) vs. Log. o Variance: Power vs. QuadraTc

17 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Distance to death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

18 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Distance to death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

19 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Distance to death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

20 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Distance to death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

21 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

22 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

23 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

24 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

25 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

26 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

27 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

28 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

29 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

30 Results Dep. Var. is Individual Ambulatory Expenditures Model (1) Model (2) Model (3) Model (4) without with without with without with without with frailty frailty frailty frailty frailty frailty frailty frailty Age 23.44* * * Female Married/Living as a couple * ** * Compl. health insur * * Time preference * * * Risk seeking *** ** *** ** Nb. Chronic illnesses *** *** *** *** Nb. ADL Limitations *** *** *** *** Robust Ref. Ref. Ref. Ref. Pre-Frail *** *** *** *** (194.82) (193.59) (171.04) (171.17) Frail *** *** *** *** (441.30) (371.76) (423.90) (417.52) Time-to-death (months) *** *** *** *** Bad Health (MCA score) *** *** *** *** N Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) LR-Test (H0: Without Frailty) VIF Kappa (condit. number) Legend: * p<0.1, ** p<0.05, *** p<0.01. Individual sampling weights applied.

31 Robustness checks Conditional mean E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) Link function g(.) = (µ i λ 1) / λ log(µ i ) Variance distribution Power: V(µ i ) = θ1 µ i θ 2 Quadratic: V(µ i ) = θ1 µ i + θ 2µ i 2 Gamma: V(µ i ) = µ i 2 Model Name -4BII- EEE with Power variance -4BIII- EEE with Quadratic variance -4B- Reference model: GLM Log-Gamma Link & variance parameters Semi-parametric estimation Estimate p-val (1) Estimate p-val (1) w. Power variance Fixed parameters w. Quadratic variance λ θ θ Goodness-of-fit P-value P-value P-value Link function tests Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) Variance function test Park (H0: Gamma) N. Obs Legend: * p<0.1, ** p<0.05, *** p<0.01. Note: (1) Test of H0: Estimated value in Models 4BII & 4BIII equals the corresponding fixed value in Model 4B. (2) Dependent variable (y i ) is individual ambulatory care expenditures in 2012 in France for individuals aged 65+. Individual survey sample weights applied.

32 Robustness checks Conditional mean E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) Link function g(.) = (µ i λ 1) / λ log(µ i ) Variance distribution Power: V(µ i ) = θ1 µ i θ 2 Quadratic: V(µ i ) = θ1 µ i + θ 2µ i 2 Gamma: V(µ i ) = µ i 2 Model Name -4BII- EEE with Power variance -4BIII- EEE with Quadratic variance -4B- Reference model: GLM Log-Gamma Link & variance parameters Semi-parametric estimation Estimate p-val (1) Estimate p-val (1) w. Power variance Fixed parameters w. Quadratic variance λ θ θ Goodness-of-fit P-value P-value P-value Link function tests Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) Variance function test Park (H0: Gamma) N. Obs Legend: * p<0.1, ** p<0.05, *** p<0.01. Note: (1) Test of H0: Estimated value in Models 4BII & 4BIII equals the corresponding fixed value in Model 4B. (2) Dependent variable (y i ) is individual ambulatory care expenditures in 2012 in France for individuals aged 65+. Individual survey sample weights applied.

33 Robustness checks Conditional mean E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) Link function g(.) = (µ i λ 1) / λ log(µ i ) Variance distribution Power: V(µ i ) = θ1 µ i θ 2 Quadratic: V(µ i ) = θ1 µ i + θ 2µ i 2 Gamma: V(µ i ) = µ i 2 Model Name -4BII- EEE with Power variance -4BIII- EEE with Quadratic variance -4B- Reference model: GLM Log-Gamma Link & variance parameters Semi-parametric estimation Estimate p-val (1) Estimate p-val (1) w. Power variance Fixed parameters w. Quadratic variance λ θ θ Goodness-of-fit P-value P-value P-value Link function tests Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) Variance function test Park (H0: Gamma) N. Obs Legend: * p<0.1, ** p<0.05, *** p<0.01. Note: (1) Test of H0: Estimated value in Models 4BII & 4BIII equals the corresponding fixed value in Model 4B. (2) Dependent variable (y i ) is individual ambulatory care expenditures in 2012 in France for individuals aged 65+. Individual survey sample weights applied.

34 Robustness checks Conditional mean E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) Link function g(.) = (µ i λ 1) / λ log(µ i ) Variance distribution Power: V(µ i ) = θ1 µ i θ 2 Quadratic: V(µ i ) = θ1 µ i + θ 2µ i 2 Gamma: V(µ i ) = µ i 2 Model Name -4BII- EEE with Power variance -4BIII- EEE with Quadratic variance -4B- Reference model: GLM Log-Gamma Link & variance parameters Semi-parametric estimation Estimate p-val (1) Estimate p-val (1) w. Power variance Fixed parameters w. Quadratic variance λ θ θ Goodness-of-fit P-value P-value P-value Link function tests Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) Variance function test Park (H0: Gamma) N. Obs Legend: * p<0.1, ** p<0.05, *** p<0.01. Note: (1) Test of H0: Estimated value in Models 4BII & 4BIII equals the corresponding fixed value in Model 4B. (2) Dependent variable (y i ) is individual ambulatory care expenditures in 2012 in France for individuals aged 65+. Individual survey sample weights applied.

35 Robustness checks Conditional mean E(Y i X i = x i ) = µ i = f (x i β) = g 1 (x i β) Link function g(.) = (µ i λ 1) / λ log(µ i ) Variance distribution Power: V(µ i ) = θ1 µ i θ 2 Quadratic: V(µ i ) = θ1 µ i + θ 2µ i 2 Gamma: V(µ i ) = µ i 2 Model Name -4BII- EEE with Power variance -4BIII- EEE with Quadratic variance -4B- Reference model: GLM Log-Gamma Link & variance parameters Semi-parametric estimation Estimate p-val (1) Estimate p-val (1) w. Power variance Fixed parameters w. Quadratic variance λ θ θ Goodness-of-fit P-value P-value P-value Link function tests Pearson Copas Pregibon Hosmer & Lemeshow Deviance (square root) Variance function test Park (H0: Gamma) N. Obs Legend: * p<0.1, ** p<0.05, *** p<0.01. Note: (1) Test of H0: Estimated value in Models 4BII & 4BIII equals the corresponding fixed value in Model 4B. (2) Dependent variable (y i ) is individual ambulatory care expenditures in 2012 in France for individuals aged 65+. Individual survey sample weights applied.

36 Discussion What Is the Ambulatory Cost of Frailty? Let s keep in mind some rounded figures for 65+ Frailty is a progressive conditon: o Pre- frail o Frail What type of expenditures? What Does Frailty add to HCE Models? Helps reducing omiqed variable bias o Performs beqer than TTD o Provides satsfactorily model fit just with Chronic + ADL o Supports easy data producton à Holly Trinity What about the Hospital Costs of Frailty? Further research is going on

37 Discussion What Is the Ambulatory Cost of Frailty? Let s keep in mind some rounded figures for 65+ Frailty is a progressive conditon: o Pre- frail o Frail What type of expenditures? What Does Frailty add to HCE Models? Helps reducing omiqed variable bias o Performs beqer than TTD o Provides satsfactorily model fit just with Chronic + ADL o Supports easy data producton à Holly Trinity What about the Hospital Costs of Frailty? Further research is going on

38 Ambulatory Health Care Expenditures by Types of Care France Mean Expenditures in Robust Pre-frail Frail Practitionners Auxiliaries Biology Health Resources ER w/t Hospit. Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more.

39 Ambulatory Health Care Expenditures by Types of Care France 2012 Mean Expenditures in Currently working on frailty and medicaton 0 Robust Pre-frail Frail Practitionners Auxiliaries Biology Health Resources ER w/t Hospit. Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more.

40 Discussion What Is the Ambulatory Cost of Frailty? Let s keep in mind some rounded figures for 65+ Frailty is a progressive conditon: o Pre- frail o Frail What type of expenditures? What Does Frailty add to HCE Models? Helps reducing omiqed variable bias o Performs beqer than TTD o Provides satsfactorily model fit just with Chronic + ADL o Supports easy data producton à Holly Trinity What about the Hospital Costs of Frailty? Further research is going on

41 Discussion What Is the Ambulatory Cost of Frailty? Let s keep in mind some rounded figures for 65+ Frailty is a progressive conditon: o Pre- frail o Frail What type of expenditures? What Does Frailty add to HCE Models? Helps reducing omiqed variable bias o Performs beqer than TTD o Provides satsfactorily model fit just with Chronic + ADL o Supports easy data producton à Holly Trinity What about the Hospital Costs of Frailty? Further research is going on

42 What s Next? MCA Coordinates: Active 1 Active 0 Supplt. 1 hospnp FRAIL urg_amb tran psy Axis 2 visitgp1 neu visitsp1 maté chi renonce biol ane hosppr gas car aut prot PREFR phar biol rad orl end rhu dentist oph der ROBUS opti phar Axis 3

43 What s Next? MCA Coordinates: Active 1 Active 0 Supplt. 1 hospnp FRAIL urg_amb tran psy Hospital & Associated Ambulatory care Axis 2 visitgp1 neu visitsp1 maté chi renonce biol ane hosppr gas car aut prot PREFR phar biol rad orl end rhu dentist oph der ROBUS opti Independent Ambulatory care phar Axis 3

44 What s Next? MCA Coordinates: Active 1 Active 0 Supplt. 1 hospnp FRAIL urg_amb tran psy Hospital & Associated Ambulatory care Axis 2 Planned Care visitgp1 neu visitsp1 maté chi renonce biol ane hosppr gas car aut prot PREFR phar biol rad orl end rhu dentist oph der ROBUS opti Independent Ambulatory care phar Axis 3

45 What s Next? MCA Coordinates: Active 1 Active 0 Supplt. 1 Emergency Care hospnp FRAIL urg_amb tran psy Hospital & Associated Ambulatory care Axis 2 Planned Care visitgp1 neu visitsp1 maté chi renonce biol ane hosppr gas car aut prot PREFR phar biol rad orl end rhu dentist oph der ROBUS opti Independent Ambulatory care phar Axis 3

46 What s Next? At Least One Hospital Visit in 2012 France Frailty phenotype: Robust Pre-frail Frail Percent ER without Hosp. Emergency Hosp. Planned Hosp. Total Hosp. Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more. Sampling weights applied.

47 What s Next? Health Care Expenditures Total Hospital Costs in France 2012 Frailty phenotype: Robust Pre-frail Frail Density Log scale Source: IRDES ESPS Survey 2012 & National Health Insurance data. Note: Respondents aged 65 or more.

48 Thank You! Corresponding Author

New evidence from SHARE data. J. Sicsic T. Rapp. Séminaire Modapa, 12 Avril 2018 PRELIMINARY DRAFT. LIRAES, Université Paris Descartes

New evidence from SHARE data. J. Sicsic T. Rapp. Séminaire Modapa, 12 Avril 2018 PRELIMINARY DRAFT. LIRAES, Université Paris Descartes New evidence from SHARE data J Sicsic LIRAES, Université Paris Descartes Séminaire Modapa, 12 Avril 2018 PRELIMINARY DRAFT 1/ 29 Motivations Steady rise of health care expenditures (HCE) in GDP concerns