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

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

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%

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

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

Disease classification (2) DRG-setDRG (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 Pregnancy related problems 15A and 15B

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 – Δ ɛ = difference between insured and uninsured

DRG-group ɛiɛi ɛuɛu ΔɛΔɛ 1. Traumatology & Burns39,9627,84+43,53% 2. Cancer33,1329,51+12,29% 3. Diabetes37,5427,31+37,49% 4. Tuberculosis40,2530,32+32,75% 5. HIV/AIDS35,6827,53+29,63% 6. Cardiovascular diseases32,7822,65+44,72% 7. Pneumonia29,1121,74+33,89% 8. Digestive diseases38,2426,53+44,15% 9. Malaria29,5023,32+26,50% 10. Genital-urinary diseases35,6729,01+22,95% 11. Pregnancy related problems33,1329,74+11,39% All DRGs 35,34 n=14313 SD=61,12 28,08 n=1512 SD=42,56 +25,87% P<0.001

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 Δ ɛ stable per DRG between 2009 and 2012 in spite of changing insured patients demographics (more poor people insured)

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

Hospital bound mortality (2) DRG-groupΔmortality 1. Traumatology & Burns-52,70% * 2. Cancer-44,81% * 3. Diabetes-55,44% * 4. Tuberculosis3,61% 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%

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 Δ ɛ

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 & Δ ɛ constant in spite of important shift from non- insured to insured (mainly poorer population groups) Δmortality & Δ ɛ very significant for non-communicable chronic diseases Cancer, hypertension, stroke & diabetes Growing importance in sub-Saharan region: NHI cost impact!