Comparison of three levels of ascertainment of antenatal medication use at Gugulethu Midwife Obstetric Unit

van Der Hoven, Jani

Comparison of three levels of ascertainment of antenatal medication use at Gugulethu Midwife Obstetric Unit

Master Thesis

2022

Abstract

Background The use of medicines and/or remedies among pregnant women is common. Pregnant women are generally excluded from clinical trials due to ethical reasons. There are therefore minimal data available about the safety of most drugs during pregnancy by the time they come to the market, and post-marketing evaluation of medicine use during pregnancy is required. In South-Africa, with mass treatment campaigns for Tuberculosis (TB) and Human Immunodeficiency Virus (HIV), the introduction of new therapeutic agents and frequent self-medication, it is important for reliable methods to determine medicine exposures, including the frequency and timing of use, to support such evaluations. Databases about medication exposures are promising resources for pharmaco-epidemiological investigations, however the optimal method of ascertainment of medicine use during pregnancy is uncertain. Different data sources could also be better for different types of medication. To improve the quality of data, a combination of data sources may be ideal but time-consuming and expensive. By looking at and comparing three data sources: 1) self-report, 2) clinician records and 3) electronic dispensing systems, we aimed to identify the optimal method of ascertainment of antenatal medicine use for multiple medication types. Methods The aim of this investigation was to provide a more comprehensive reflection of the drug exposures during pregnancy and to make recommendations to strengthen routine clinical data capturing to improve maternity case reporting. The data of 988 pregnant women seeking antenatal care at Gugulethu Midwife Obstetric Unit (GMOU) in Cape Town between 2016 and 2018 were used. The three data sources consisted of self-reports gathered by an interviewer administered questionnaire at up to three antenatal visits to the GMOU; clinical records as recorded in the Pregnancy Exposure Registry (PER); and linked electronic dispensing data obtained from the Provincial Health Data Centre (PHDC) of the Western Cape Department of Health. Medication exposure data were coded using the Anatomical Therapeutic Chemical (ATC) Classification system, an internationally acknowledged system to classify medicine maintained by the WHO. ATC codes were assigned to active ingredients, depending on the therapeutic indication. The three data sources were then assessed in terms of missing or overlapping information and evaluated on the level of agreement between sources using Spearman's rank coefficient and Cohen's Kappa. Results According to the Spearman rank test, the PER and PHDC datasets as a whole showed the highest correlation both at 1st and 5th ATC level. The overlaps between the datasets were poor and the Kappa agreement between the sources was low for most therapeutic classes, except for HIV treatments. An “almost perfect” Kappa agreement existed between anti-diabetic medication (ATC A10) reported in the self-report and PHDC datasets. Traditional, herbal, complementary and home remedies were only reported in the self-report dataset. Conclusion We found an overall poor agreement between data sources, with one alone not able to effectively capture all data. The datasets should thus be used in conjunction to ensure accurate and reliable record of exposure. Self-report was the best data source for traditional, home, herbal and complementary medicine exposures while the PER provided a better and more complete reflection of influenza vaccines and vitamins. The best method of ascertaining antenatal medicine exposure therefore depends on the type of medicine being investigated, and choice of data source depends on the objectives of the investigation. This study suggests that PER, PHDC and self-report should ideally be used together since each is critical to ensure accurate, reliable and effective exposure data, although this will have resource and cost implications.

Keywords

Clinical Pharmacology

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