CHAPTER 4 ONLINE APPENDIX: ESTIMATED COEFFICIENTS

Transcription

1 CHAPTER 4 ONLINE APPENDIX: ESTIMATED COEFFICIENTS Table 4.1. Estimation for primary care consumption, implementation in SESIM-LEV (neg. bin.). Estimation performed in STATA version 8.1. s static s dynamic I Intercept Male Age Age Health Health Health Upper secondary school education University education # prim. care visits previous year Relative income Cohabiting Born in Sweden lnalpha Table 4.2. Estimation of outpatient care visits age 16-59, implementation in SESIM-LEV. Estimation performed in STATA version s static s dynamic I Intercept Male Age Age Health Health Health OP vis prev. year >1 OP vis. prev. year Cohabiting Baby Upper secondary school education University education Relative income Metropolitan Urban Born in Sweden lnalpha

2 Table 4.3. Estimation of outpatient care visits age 60+, as implemented in SESIM-LEV. Estimation performed in STATA version s static s dynamic I (logit) Intercept Male Age Age Health Health Health ADL OP vis. prev. year >1 OP vis. previous year Special housing Cohabiting Upper secondary school education University education Relative income Metropolitan Urban Born in Sweden lnalpha Table 4.4. Estimation of outpatient care visits, dynamic model II for the group 60+, implementation in SESIM-LEV. Estimation performed in STATA version s dynamic II (inflate) Intercept Age Health Health Health ADL OP vis. prev. year >1 OP vis. prev. year Special housing

3 Table 4.5. Estimation of days of inpatient care, group 16-59, static I and dynamic I, as implemented in SESIM-LEV. Estimation performed in STATA version s static I s dynamic I Intercept Male Age Age Health Health Health OP vis. prev. year >1 OP vis. prev. year IP day prev. year >1 IP day prev. year Cohabiting Baby Upper secondary school education University education Relative income Metropolitan Urban Born in Sweden lnalpha Table 4.6. Estimation of days of inpatient care, group 60+, static I and dynamic I, as implemented in SESIM-LEV. Estimation performed in STATA version s static I s dynamic I Intercept Male Age Health Health Health ADL OP vis. prev. year >1 OP vis. prev. year >0 IP days prev. year Special housing Cohabiting Upper secondary school education University education Relative income Metropolitan Urban Born in Sweden lnalpha

4 Table 4.7. Estimation of days of inpatient care age 16-59, the inflate portion, as implemented in SESIM-LEV. Estimation performed in STATA version s static II inflate s dynamic II inflate Intercept Male Age Health Health Health OP vis. prev. year >1 OP vis. prev. year IP day prev. year >1 IP day prev. year Baby Table 4.8. Estimation of days of inpatient care age 60+, the inflate portion, as implemented in SESIM-LEV. Estimation performed in STATA version s static II inflate s dynamic II inflate Intercept Male Age Age Health Health Health ADL OP vis. prev. year >1 OP vis. prev. year >0 IP days prev. year Special housing

5 Table 4.9. Estimation of consumption of prescribed drugs age 16-59, as implemented in SESIM-LEV. Estimation performed in STATA version s static I Women, logit consume? s static I Men, logit consume? s static II Women and men, linear how much? Intercept Age Age Cohabiting Health Health Health IP day prev. year >1 IP day prev. year OP vis. prev. year >1 OP vis. prev. year Child aged Upper secondary school education Male University education Male * University ed Relative income Metropolitan Urban Born in Sweden A

6 Table Estimation of consumption of prescribed drugs age 60+, as implemented in SESIM-LEV. Estimation performed in STATA version s static I Women, logit consume? s static I Men, logit consume? s static II Women, linear how much? s static II Men, linear how much? Intercept Age Age Cohabiting Health Health Health ADL IP day prev. year >1 IP day prev. year OP vis. prev. year >1 OP vis. prev. year Upper secondary school education University education Relative income Metropolitan Urban Born in Sweden A Table Results, estimation of dementia, implemented in SESIM-LEV. Estimation performed in SAS. The model estimates the probability of the individual not having dementia. Statistical estimation only. Variable Standard error Wald Chi2 p-value Intercept < ADL < Age < Cohabiting Upper secondary school ed < University education Table Results, estimation of ADL dependence, from the BabyBoom project (2006), which has been implemented in SESIM-LEV. Age was classified into four groups: -65 = ageg1, 65-74=ageg2, 75-84=ageg3, 85+=ageg4. Health status was represented at 4 levels where H1=full health H4=severe ill-health. Variable ageg2 * H ageg2 * H ageg2 * H ageg3 * H ageg3 * H ageg3 * H ageg3 * H ageg4 * H ageg4 * H ageg4 * H ageg4 * H female 0.34

7 The following cut-offs were used: -4.42, -6.36, and Table Results, initial estimation of home help service and special housing. Does the individual receive any intervention from the municipality?. Estimated in SAS. Variable Intercept ADL/DEM_thisyear= ADL/DEM_thisyear= ADL/DEM_thisyear= Age Gender Cohabiting Upper secondary school ed University education Table Results, estimation of intervention level, initial estimation. Which type of intervention?. Estimated in SAS. Variable Intercept ADL/DEM_thisyear= ADL/DEM_thisyear= ADL/DEM_thisyear= Age Gender Upper secondary school ed University education Table Results, dynamic estimate of intervention level at current intervention level = 0 ( no intervention ). Estimated in SAS. s To level 1 home help service Intercept ADL/DEM_lag= ADL/DEM_lag= ADL/DEM_lag= Age Gender Cohabiting Upper secondary school ed University education s To level 2 special housing

8 Table Results, dynamic estimate of intervention level at current status = 1 ( home help service ). Estimated in SAS. s p-value s p-value To level 0 no intervention To level 2 special housing Intercept ADL/DEM_lag= < ADL/DEM_lag= ADL/DEM_lag= < Age Gender Cohabiting Upper secondary school ed University education Table s for transition to ADL/DEM = 0 at ADL/DEM = 0 or 2 and intervention level > 0 (home help service or special housing). intercept ADL/DEM_1 = special housing age gender cohabiting medium-length educational career long educational career Table s for transition to ADL/DEM = 1 at ADL/DEM = 0 or 2 and intervention level >0 (home help service and special housing). intercept ADL/DEM_1 = special housing age gender cohabiting medium-length education long education The probability of going to ADL/DEM = 3 was identified by calculating the complement to the summed probability of ADL/DEM =0, ADL/DEM =1 and ADL/DEM =2.1 1 Denominator = 1 + Xbeta(ADL/DEM0)+ Xbeta(ADL/DEM1)+Xbeta(ADL/DEM3) P(ADL/DEM0) = Xbeta(ADL/DEM0)/Denominator P(ADL/DEM1) = Xbeta(ADL/DEM0)/Denominator P(ADL/DEM2) = Xbeta(ADL/DEM0)/Denominator P(ADL/DEM3)= 1 (P(ADL/DEM0)+ P(ADL/DEM1)+ P(ADL/DEM2))

9 Table s for transition to ADL/DEM = 2 at ADL/DEM = 0 or 2 and intervention level > 0 (home help service or special housing). intercept ADL/DEM_1 = special housing age pay cohabiting medium-length educational career long educational career Table s for transition to ADL/DEM = 0 at ADL/DEM = 0 or 2 and intervention level = 0. intercept ADL/DEM_1 = age gender cohabiting medium-length education long education Table s for transition to ADL/DEM = 1 at ADL/DEM = 0 or 2 and intervention level = 0. intercept ADL/DEM_1 = age gender 2.35 cohabiting medium-length educational career long educational career Table s for transition to ADL/DEM = 2 at ADL/DEM = 0 or 2 and intervention level = 0. intercept ADL/DEM_1 = age gender cohabiting medium-length educational career long educational career Table s at ADL/DEM = 1. intercept home help service special housing age gender medium-length or long educational career

10 Table Estimated coefficients for annual mortality risk, without. intercept male age cohabiting Health = Health = Health = Number of OP visits Number of IP days Upper secondary school education University education Table Estimated coefficients for annual mortality risk, mortality model with p-value intercept home help service special housing ADL/DEM = ADL/DEM = ADL/DEM = age gender cohabiting Upper secondary school education University education