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Our pregnancy identification algorithm builds off the work of others. Manson et al.

developed and evaluated an approach to detect pregnancies and pregnancy markers using a health maintenance organization database.162 This approach was adapted to the Value Added Medical Products based GPRD by Hardy et al. 172 Manson’s general approach of identifying a pregnancy outcome and looking backward a fixed number of days for PCMs has been used by other researchers in a variety of data sets.195, 196 Because of the similarities of our

approach and our data source to that of Hardy’s, consistencies in both results should be noted. For example, the distribution of the number of pregnancies among identified women was similar. Specifically, both studies found that 66.5 percent of women had only one pregnancy and similar distributions for other pregnancy frequencies were observed. Additionally, the mean number of weeks between the first PCM and a live birth was 30 weeks in our data compared to approximately 31-35 weeks in Hardy’s study.

Our pregnancy identification algorithm and the pregnancies that it identified in the GPRD offer several strengths. We identified a large number of pregnancies in a 17.5 year period of the GPRD. These pregnancies have a rich assortment of medical care data associated with them. At least one pregnancy care record was available for 88 percent of all pregnancies, and 78 percent of the identified pregnancies had records accessible going back at least 300 days from the pregnancy outcome. With these records, researchers can identify treatments and care delivered during critical windows of fetal development and throughout the pregnancy. Additionally, over 78 percent of the complete pregnancy profiles had medical history records going back at least 180 days before the first PCM. With records within the six months prior

to the first PCM, details on chronic conditions, health-services utilization and medication orders are available.

This rich dataset also gives researchers the ability to estimate the last menstrual period (LMP) date associated with each pregnancy. The LMP date is important for determining the timing of medication exposure during very early gestation, but is often estimated because it is generally not available in an electronic database. It is possible to estimate LMP using data developed by Manson et al. for the Kaiser-Permanente database. They found that the LMP was on average 40 days prior to the first PCM in the case of fetal deaths, and within 57 days of the first PCM for live births.162 We evaluated our data using the time points from the Manson et al. study to determine the number of pregnancies for which we could estimate an LMP date. Among the aggregated stillbirths, elective terminations and spontaneous

terminations, we found that ninety-three percent of those with a complete pregnancy profile had at least 40 days of medical records prior to the first PCM. Of the live births and

deliveries with complete pregnancy profiles, eighty-five percent had at least 57 days of records prior to the first PCM. If we defined an LMP date as 57days prior to the first PCM regardless of outcome type, we would be able to estimate the LMP date for 86 percent of the 330,153 pregnancies with complete profiles.

An additional strength of our algorithm is its ability to reduce the chance of selecting an indeterminate pregnancy outcome as the final EOP code by detecting outcomes that were out of order in the mother’s record. Because we were able to detect specific codes (i.e. those for a stillbirth or an elective termination) even when they were not the first in a series of codes, we did not have unknown outcome codes. For example, if a fetal death or stillbirth was recorded after a code broadly applied in most patients (e.g. normal delivery or birth details),

the outcome was categorized as a fetal death and rather than an unknown outcome. While this approach relies upon accurate recording of fetal deaths and stillbirths, the reliability of recording by the GP has been shown to be excellent.198-201

Finally, a strength of both the algorithm presented here and the GPRD is the ability to identify recorded spontaneous abortions. Spontaneous abortions composed approximately fifteen percent of the identified EOP outcomes. This conforms with estimates of twelve to fifteen percent from other sources.202, 203 Because we are able to identify multiple pregnancy outcome types, particularly spontaneous abortions, pharmacoepidemiologic studies using the GPRD data will not be restricted to pregnancies with full-term outcomes.

However, researchers should use caution when including these spontaneous abortions. There will be an unknown number of spontaneous abortions that are not detected by the mother or the GP. Many go unnoticed or are mistaken as part of a woman’s normal menstrual cycle. Because of these unidentified spontaneous abortions, developing specific rates for this outcome would be ill advised; however, the GPRD still offers information about spontaneous abortions that is not commonly available in other large electronic databases and these data can be useful for addressing other objectives.

There are several limitations to our pregnancy algorithm and the identifiable pregnancies in the GPRD. The first is the potential for the incomplete ascertainment of pregnancies. In addition to the unrecorded spontaneous abortions mentioned above, the GPRD may not contain records of all elective terminations. These procedures occur at health care facilities other than the GP office and may not be recorded in the medical record, either by omission or at the woman’s request.

evaluating maternal data for evidence of diagnostic and screening tests. We found that only 15 percent of all pregnancies had any record of a possible 149 common diagnostic or

screening tests codes relevant for pregnancy. This would limit the database utility in examining pregnancy complications as outcomes. The Royal College of Obstetricians and Gynecologists and the National Institute for Clinical Excellence,132 have established guidelines for pregnancy diagnostic and screening tests. The NHS has also put in place financial incentives to ensure that these tests are done.204 For this reason, we believe that these tests are likely being performed but do not exist in the discrete data portion of the mother’s electronic health record. The GP has the opportunity to record information as free text comments and this may be where they place information on pregnancy diagnosis and screening tests. We did not search this free text information as the costs involved were outside of the planned budget of this project.

Additionally, at the time of patient registration with the GP, information on pregnancies that occurred prior to the patient’s registration is frequently incomplete. Even though many of these pregnancies are often recorded in the GPRD as historical data, we did not include them in our analysis. There is also the potential that a woman could leave a physician’s practice prior to delivery. We found that there were 13,812 women with a PCM within 90 days of transferring out of the practice and without an accompanying EOP event record. Because not all GP practices are part of the GPRD and records are not linked from one GP practice to the next, it was not feasible to track these outcomes.

Another limitation is the potential for misclassification of our pregnancy outcomes. The correct classification of pregnancies was an expressed goal of our algorithm. Although we believe misclassification was minimized through the use of recognized pregnancy codes,

physicians have the ability to use codes as they judge appropriate. The potential lack of consistency within and across GPs and the reality that many codes within the OXMIS and Read Coding dictionaries may have multiple uses complicate any attempt to avoid

misclassification. Our approach of ranking pregnancy categories and selecting the final outcome using a hierarchical approach, rather than identifying the first available pregnancy outcome code and excluding all others within a fixed time period, should minimize

misclassification. In our analyses, 12,834 pregnancies with the potential of being

misclassified were ultimately identified and removed using a hierarchical pregnancy category approach. We will continue to refine this approach and hope to continue to minimize

misclassification.

We believe that the algorithm for identifying pregnancies described here gives researchers the opportunity to utilize the GPRD for pharmacoepidemiologic research projects. Electronic medical records databases, such as the GPRD, allow researchers to conduct case-control surveillance studies while avoiding the potential limitations from recall bias that can occur with maternal interviews.161 Because of the ability to link details of a mother to her offspring, including information on potential exposures in a mother prior to and during all stages of pregnancy, and potential pregnancy outcomes not limited to live births, the GPRD can now provide detailed records on a sample of

pregnancies large enough to detect rare events This resource should prove valuable for future research on pregnancy outcomes.

C. Validation Of NTD In The GPRD