In managed care systems, the identification of most diseases relies on electronic diagnosis coding, such as ICD-9 codes applied in the context of health care visits. The sensitivity of ICD-9 diagnosis coding for new onset autoimmune conditions following vaccination, however, cannot be taken for granted. Because the initial presentation of autoimmune conditions often involves general symptoms, there is often a lag between initial symptom onset and the correct assignment of diagnosis. To address this, several strategies were employed in this study: (1) broad, highly sensitive case identification criteria were used, (2) expert panels were employed to confirm the diagnosis and date of disease onset, (3) a 180-d risk period was used to accommodate lag time for clinical work-up, and (4) only females with 12-mo health plan membership prior to vaccination were included to allow reasonable assessment by the expert panels for onset prior to vaccination. We expanded the initial case identification criteria beyond diagnosis codes to also include abnormal laboratory test results related to the autoimmune conditions of interest, such as rheumatoid factors for rheumatoid arthritis. We also used an expanded list of ICD-9 codes (such as type 2 diabetes codes to capture type 1 diabetes, see Table S1
), in addition to the use of the original disease ICD-9 codes. We then submitted possible cases for detailed case review by expert physician panels. They manually reviewed medical records to confirm the incident diagnosis. With these approaches, we found that half of the potential Hashimoto’s disease cases identified by laboratory tests alone (i.e., without a diagnostic code) were confirmed to be new-onset cases. In fact, incident cases identified by lab results accounted for half of the total confirmed new on-set cases. The expanded ICD-9 codes also captured 40% of all confirmed new on-set cases of the autoimmune conditions of interest, with 24% of those identified by expanded ICD-9 codes were confirmed as new-onset cases. These findings emphasize the limited sensitivity of disease-specific ICD-9 diagnosis code based approaches for capturing new onset cases for at least some autoimmune conditions. In our study, we were able to improve the capture of Hashimoto’s thyroiditis with the lab results and with expanded ICD-9 codes for most autoimmune conditions.
The in-depth case review was also very important. We found that among the potential new-onset cases for all autoimmune conditions identified electronically, 30% were actually pre-existing cases. These subjects had symptom onset or a diagnosis in the chart note prior to the electronic coding for the disease. Of note, an “unmasking” phenomenon was discovered,2
in which a diagnostic lab test was drawn on the day of vaccination, leading to the disease diagnosis at the first time in the days following vaccination. The “unmasking” refers to the fact that vaccination visit may have provided the opportunity for the evaluation of symptoms that may not have been pursued due to less regular health care visits in comparison to the pediatric population with routine well-child visit. This unmasking phenomenon was illustrated in details using the example of Grave’s disease in another manuscript.2
These data again emphasize the limitation of ICD-9 diagnosis code based approaches for assessing the timing of the onset of autoimmune conditions, and highlight the need for in-depth medical record review in vaccine autoimmune safety studies based on electronic health information.
Another consideration for detecting new-onset autoimmune condition is the length of the risk window, or follow-up period allowed in the study. To adopt an appropriate length of risk period, it is necessary to consider the induction time as well as the lead time for the disease of interest. The induction time of various autoimmune conditions has not been well characterized. Experimental data are available for autoimmune disease in murine models, although it is unclear how this applies to the disease course in humans. In general, it is believed that drug induced immune-mediated events can start 10–14 d after exposure. In the case of GBS, the disease may develop in the course of days or up to 3–4 weeks.3
In addition, an extended lead time can be expected for autoimmune conditions for which the initial presentation often involves general symptoms. This leads to a delay in seeking medical attention, as well as a delay in the assignment of the correct diagnosis by the physician. It is, however, somewhat feasible to characterize the lead time for autoimmune diagnosis with epidemiologic studies. In our study, based on 41 confirmed new-onset cases, the distribution of days between first symptom onset (as noted in physician chart notes) and electronic diagnosis coding for autoimmune disease ranges from 0 to 1,210 d, with a median of 23 d and 25th and 75th percentile of 2, 59 d. Five cases (12%) had a lag between symptom onset and clinical diagnosis coding greater than 180 d, suggesting that a risk window of more than 6 mo may be needed to fully capture all true incident cases. Consideration of the length of risk windows for the completeness of case capture is therefore to be evaluated against the need for the timeliness of safety study results.
The accumulation of data such as these could be helpful to characterize the diagnostic course of the autoimmune conditions for various study populations, as this may vary by age, sex and race. For example, one study found that the use of primary health care services was greater among adolescent females compared with adolescent males.4
This data suggest that our “lead time” derived among adolescent females for autoimmune conditions may not be applicable to males. A longer risk window may be needed to capture the majority of incident autoimmune cases developed after vaccination, as well as to rule out prevalent cases for adolescent males. The relatively long lead time for the diagnosis of autoimmune conditions is also relevant for the validity of safety signal evaluation. Given that “lead time” (which includes the clinical undetectable phase and the delay in diagnosis) may be related to health care seeking behavior, the distribution of lead time may be different in groups defined by vaccination status.