1. Dr. Frank Verbeke Vrije Universiteit Brussel Kigali Health Institute
Secondary use of electronic health records Measuring the impact of health insurance status on health services consumption and in-hospital mortality
Dr. Frank Verbeke
Vrije Universiteit Brussel
Kigali Health Institute

2. Health insurance and health outcomes
Many studies suggest a relationship between health insurance and outcomes (such as mortality)
Most research coming from industrialized countries
Few studies in sub-Saharan region
Different medical therapeutics
Different demography of insured patients
Purpose of this study:
Evaluate usability of routinely registered electronic patient data for documenting relationship between health insurance status, health services consumption and mortality in a tertiary reference hospital in Rwanda

3. Health insurance coverage in Rwanda
More than 90% of the population covered by some form of health insurance in 2012
Government employees (RAMA)
Military & police (MMI)
Community based health insurance (70%)
Private health insurance companies (1%)
Out of pocket (OOP) payments for insured patients vary between 0% and 15%

4. Study site & data set Study site: Kigali University Teaching Hospital
3rd level national reference hospital
OpenClinic GA HIS (free open source)
Data set
15,825 hospital admissions in the period
Insurance status
Less than 25% OOP payments = insured
n = 14,313 encounters
More than 75% OOP payments = uninsured
n = 1,512 encounters

5. Disease classification
Aggregation of international disease classification codes into DRGs
ICD-10 and ICPC-2 based disease classification available at CHUK since 2006
Development of local DRG system (KPGS) since 2009
Simplified DRG set of 174 codes based on ICD-10
Adapted to sub-Saharan setting
Selection of 11 DRG-collections for analysis of health insurance impact

6. Disease classification (2)
DRG-set
DRG (KPGS) codes
1. Traumatology & Burns
190,19A and 19B
2. Cancer
02A to 02D
3. Diabetes
04B
4. Tuberculosis
01B
5. HIV/AIDS
01M
6. Cardiovascular diseases
09A to 09R
7. Pneumonia
10C
8. Digestive diseases
11A to 11S
9. Malaria
01V
10. Genital-urinary diseases
140
11. Pregnancy related problems
15A and 15B

7. Care consumption
Health care services invoiced to patient and/or insurer (procedures, drugs, consumables...) through the HIS
Clinician-generated weight factor λ between 0 and 1 for each health service per DRG
Consensus document from team of 12 physicians for all DRGs
In case of multi-DRG clinical conditions
Disability weights based distribution of consumed health services over DRGs using CALCO method
Care consumption score ɛ calculated per DRG for insured and uninsured patients
Δɛ = difference between insured and uninsured

8. ɛi ɛu Δɛ 1. Traumatology & Burns 39,96 27,84 +43,53% 2. Cancer 33,13
DRG-group ɛi ɛu Δɛ
1. Traumatology & Burns
39,96
27,84
+43,53%
2. Cancer
33,13
29,51
+12,29%
3. Diabetes
37,54
27,31
+37,49%
4. Tuberculosis
40,25
30,32
+32,75%
5. HIV/AIDS
35,68
27,53
+29,63%
6. Cardiovascular diseases
32,78
22,65
+44,72%
7. Pneumonia
29,11
21,74
+33,89%
8. Digestive diseases
38,24
26,53
+44,15%
9. Malaria
29,50
23,32
+26,50%
10. Genital-urinary diseases
35,67
29,01
+22,95%
11. Pregnancy related problems
29,74
+11,39%
All DRGs
35,34
n=14313
SD=61,12
28,08
n=1512
SD=42,56
+25,87%
P<0.001

9. Care consumption (2)
26% higher health services consumption in group of insured patients
No significant difference in total admission cost per DRG between insured and uninsured
Higher tariffs applied for uninsured patients
Δɛ stable per DRG between 2009 and 2012 in spite of changing insured patients demographics (more poor people insured)

10. Hospital bound mortality
Definition
% of patients treated for DRG x that eventually died in the hospital (not necessarily from DRG x)
Significant 19% lower mortality in the insured patients group compared to the uninsured group
For 7 of the 11 DRG-groups, mortality was more than 40% lower in the insured group

11. Hospital bound mortality (2)
DRG-group
Δmortality
1. Traumatology & Burns
-52,70% *
2. Cancer
-44,81% *
3. Diabetes
-55,44% *
4. Tuberculosis
3,61%
5. HIV/AIDS
-5,34%
6. Cardiovascular diseases
-40,28% *
7. Pneumonia
-52,51% *
8. Digestive diseases
-46,24% *
9. Malaria
-21,98%
10. Genito-urinary diseases
-44,50% *
All DRGs
-19,14%
Hospital bound mortality (2)

12. Discussion Study confirmed for sub-Saharan reference hospital setting
Insured patients get more care for the same clinical conditions
Insured patients have lower mortality for the same clinical conditions
Health service consumption score ɛ
Heavily impacted by weight factor λ for health services
Health service distribution not significantly different between insured and uninsured => λ value has no impact on Δɛ

13. Discussion (2)
EHR data prove to be usefull and convenient for monitoring HI impact
Causal relationship health insurance status  care consumption & mortality?
Many confounding factors
Age & gender: no significant differences
Income, employment status, education, exercise, tobacco & alcohol, body-mass index, marital status...
Δmortality & Δɛ constant in spite of important shift from non-insured to insured (mainly poorer population groups)
Δmortality & Δɛ very significant for non-communicable chronic diseases
Cancer, hypertension, stroke & diabetes
Growing importance in sub-Saharan region: NHI cost impact!