Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction
Abstract
:1. Introduction
2. Methods
2.1. Data Sources
2.2. Ethics Committee Approval
2.3. Study Population
2.4. Hidden Markov Model and the Transition Rate Matrix
2.5. Statistical Analysis
3. Results
4. Discussion
4.1. Findings from Administrative Data Were Consistent with Randomized Trials
4.2. The Prevalence of MI Was Under-Estimated if Misclassification Is Ignored
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Characteristics | Health States | |||
---|---|---|---|---|
Well | NSTEMI | STEMI | Death | |
All cases | 55.5% | 27.4% | 11.6% | 5.6% |
By sex | ||||
Females | 58.0% | 27.6% | 8.4% | 6.0% |
Males | 54.1% | 27.3% | 13.3% | 5.3% |
p-value of the sex difference | <0.001 | 0.132 | <0.001 | 0.254 |
By ethnicity | ||||
Non-Indigenous | 55.6% | 27.4% | 11.5% | 5.6% |
Indigenous | 53.2% | 29.3% | 13.4% | 4.1% |
p-value of the ethnic difference | 0.735 | 0.035 | 0.044 | 0.163 |
By age groups | ||||
<55 | 56.3% | 26.3% | 16.2% | 1.1% |
55–64 | 57.5% | 26.6% | 13.9% | 2.1% |
64–74 | 56.8% | 27.5% | 10.9% | 4.8% |
75+ | 52.4% | 28.6% | 7.3% | 11.7% |
p-value of differences across age groups | <0.001 | <0.001 | <0.001 | <0.001 |
Age-standardized rates | ||||
Females | 59.0% | 27.3% | 8.6% | 5.1% |
Males | 53.8% | 27.4% | 12.8% | 6.0% |
Non-Indigenous | 55.6% | 27.3% | 11.6% | 5.5% |
Indigenous | 53.4% | 29.0% | 10.6% | 7.0% |
Transitions | Males | Females | Indigenous | Non-Indigenous |
---|---|---|---|---|
Well → NSTEMI | 8.70% | 8.46% | 8.62% | 8.61% |
Well → STEMI | 1.92% | 1.29% | 1.68% | 1.69% |
Well → Dead | 2.83% | 2.82% | 2.81% | 2.83% |
NSTEMI → STEMI | 2.43% | 2.42% | 2.41% | 2.43% |
NSTEMI → Dead | 5.12% | 5.08% | 5.10% | 5.11% |
STEMI → Dead | 4.68% | 4.64% | 4.68% | 4.66% |
Health States | Mean | 95% Credible Set | ||
---|---|---|---|---|
Observed = NSTEMI | True = Well | 0.011 | 0.01 | 0.012 |
Observed = Well | True = NSTEMI | 0.037 | 0.03 | 0.039 |
Observed = STEMI | True = NSTEMI | 0.015 | 0.01 | 0.017 |
Observed = NSTEMI | True = STEMI | 0.027 | 0.02 | 0.030 |
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Nghiem, S.; Williams, J.; Afoakwah, C.; Huynh, Q.; Ng, S.-k.; Byrnes, J. Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction. Int. J. Environ. Res. Public Health 2021, 18, 7385. https://doi.org/10.3390/ijerph18147385
Nghiem S, Williams J, Afoakwah C, Huynh Q, Ng S-k, Byrnes J. Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction. International Journal of Environmental Research and Public Health. 2021; 18(14):7385. https://doi.org/10.3390/ijerph18147385
Chicago/Turabian StyleNghiem, Son, Jonathan Williams, Clifford Afoakwah, Quan Huynh, Shu-kay Ng, and Joshua Byrnes. 2021. "Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction" International Journal of Environmental Research and Public Health 18, no. 14: 7385. https://doi.org/10.3390/ijerph18147385
APA StyleNghiem, S., Williams, J., Afoakwah, C., Huynh, Q., Ng, S. -k., & Byrnes, J. (2021). Can Administrative Health Data Improve the Gold Standard? Evidence from a Model of the Progression of Myocardial Infarction. International Journal of Environmental Research and Public Health, 18(14), 7385. https://doi.org/10.3390/ijerph18147385