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Table 5 Policy impact estimation on demand for PRCS hospitals (Multinomial logit - margins), different periods

From: Co-payments and equity in care: enhancing hospitalisation policy for Palestine refugees in Lebanon

 

PRCS demand

 

(1)

(2)

(3)

(4)

Policy - Jan

 

0.112***

0.112***

0.109***

  

(0.020)

(0.020)

(0.016)

Policy - Jun

0.011

0.034**

0.035**

0.037***

 

(0.014)

(0.017)

(0.015)

(0.012)

Age

0.003***

0.003

0.002

0.002***

 

(0.001)

(0.002)

(0.002)

(0.000)

Age2

-0.000***

-0.000*

-0.000*

-0.000***

 

(0.000)

(0.000)

(0.000)

(0.000)

Woman

0.017

0.023***

0.010*

0.010*

 

(0.012)

(0.006)

(0.006)

(0.005)

Ramadan

0.020

-0.006

-0.013

0.001

 

(0.018)

(0.014)

(0.012)

(0.017)

Distance

0.036***

0.035

0.031

0.031***

 

(0.003)

(0.059)

(0.059)

(0.001)

CLA

0.489***

0.452

0.406

0.406***

 

(0.040)

(0.341)

(0.328)

(0.014)

Visit

-0.007

0.001

-0.001

-0.000

 

(0.007)

(0.010)

(0.006)

(0.002)

Surgery

0.055***

0.038

0.031

0.031***

 

(0.015)

(0.035)

(0.034)

(0.007)

Month FE

   

Yes

Observations

6,961

17,621

38,562

38,562

  1. 1*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors in parentheses
  2. 2(1) 2 months pre and post policy
  3. (2) 5 months pre and post policy
  4. 1(3) Full sample - January 2016 to October 2017
  5. 1(4) Full sample with month FE - January 2016 to October 2017
  6. 3Note: The dependent variables are binary variables with the value 1 if the patient is at each hospital type and 0 otherwise. Note that all patients get treatment, thus for each observation at least one option must be selected. Coefficients show average marginal effects for multinomial logit regression results. Policy is a dummy variable that indicates the period after the last policy change (from June 2016 onwards). These model specifications control for individual and hospital specific variables
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BMC Health Services Research

ISSN: 1472-6963

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