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Background:The first author undertook the Perinatal Global Health Internship from May to July 2016 in Kampala, Uganda as part of the Canadian Queen Elizabeth II Diamond Jubilee Scholarship Program funded by the Community Foundations of Canada. The internship was carried out in partnership with the University of Calgary, Faculty of Nursing and University of Calgary International, and the Aga Khan University – School of Nursing and Midwifery with technical support from Universities Canada. Aim:In this paper, we explain the role of nursing in global health, explore the first author's learning in the area of perinatal health, and invite other nursing students to engage in global health work. Discussion of Stories:A reflective commentary is used to describe the first author's experience in a government hospital in Kampala as she learned to recognize the implications of perinatal distress, socio-ecological conditions, and resource-poor settings on the health of mothers and premature neonates. In the commentary, the first author also describes the development of an Early Childhood Development resource and the value of partnership in relation to this experience. Reflection: The first author reflects on the benefit of the internship in developing key competencies and attributes for global health work, the need for cultural competency, and barriers to creating effective change to address complex issues. Conclusion:The first author summarizes key learning from the practice, teaching, and research components of the internship. She describes growth, two-way learning, and recommendations for the internship.

Abstract Background Geophagy, a form of pica, is the deliberate consumption of soil and is relatively common across Sub-Saharan Africa. In Tanzania, pregnant women commonly eat soil sticks sold in the market ( pemba ), soil from walls of houses, termite mounds, and ground soil ( kichuguu ). The present study examined geophagy practices of pregnant women in a gold mining area of Geita District in northwestern Tanzania, and also examined the potential for exposure to chemical elements by testing soil samples. Method We conducted a cross sectional study using a convenience sample of 340 pregnant women, ranging in age from 15–49 years, who attended six government antenatal clinics in the Geita District, Tanzania. Structured interviews were conducted in June-August, 2012, to understand geophagy practices. In addition, soil samples taken from sources identified by pregnant women practicing geophagy were analysed for mineral element content. Results Geophagy was reported by 155 (45.6%) pregnant women with 85 (54.8%) initiating the practice in the first trimester. A total of 101 (65%) pregnant women reported eating soil 2 to 3 times per day while 20 (13%) ate soil more than 3 times per day. Of 155 pregnant women 107 (69%) bought pemba from local shops, while 48 (31%) consumed ground soil kichuguu . The estimated mean quantity of soil consumed from pemba was 62.5 grams/day . Arsenic, chromium, copper, iron, manganese, nickel and zinc levels were found in both pemba and kichuguu samples . Cadmium and mercury were found only in the kichuguu samples. Based on daily intake estimates, arsenic, copper and manganese for kichuguu and copper and manganese for pemb a samples exceed the oral Minimum Risk Levels designated by the U.S. Agency for Toxic Substance and Disease Registry. Conclusion Almost 50% of participants practiced geophagy in Geita District consistent with other reports from Africa. Both pemba and kichuguu contained chemical elements at varying concentration, mostly above MRLs. As such, pregnant women who eat soil in Geita District are exposed to potentially high levels of chemical elements, depending upon frequency of consumption, daily amount consumed and the source location of soil eaten.

IntroductionPooling data from cohort studies can be used to increase sample size. However, individual datasets may contain variables that measure the same construct differently, posing challenges in the usefulness of combined datasets. Variable harmonization (an effort that provides comparable view of data from different studies) may address this issue.
Objectives and ApproachThis study harmonized existing datasets from two prospective pregnancy cohort studies in Alberta Canada (All Our Families (n=3,351) and Alberta Pregnancy Outcome and Nutrition (n=2,187)). Given the comparability of the characteristics of the two cohorts and similarities of the core data elements of interest, data harmonization was justifiable. Data harmonization was performed considering multiple factors, such as complete or partial variable matching regarding question asked/responded, the response coded (value level, value definition, data type), the frequency of measurement, the pregnancy time-period of measurement, and missing values. Multiple imputation was used to address missing data resulting from the data harmonization process.
ResultsSeveral variables such as ethnicity, income, parity, gestational age, anxiety, and depression were harmonized using different procedures. If the question asked/answered and the response recorded was the same in both datasets, no variable manipulation was done. If the response recorded was different, the response was re-categorized/re-organized to optimize comparability of data from both datasets. Missing values were created for each resulting unmatched variables and were replaced using multiple imputation if the same construct was measured in both datasets but using different ways/scales. A scale that was used in both datasets was identified as a reference standard. If the variables were measured in multiple times and/or different time-periods, variables were synchronized using pregnancy trimesters data. Finally, harmonized datasets were then combined/pooled into a single dataset (n=5,588).
Conclusion/ImplicationsVariable harmonization is an important aspect of conducting research using multiple datasets. It provides an opportunity to increase study power through maximizing sample size, permitting more sophisticated statistical analyses, and to answer novel research questions that could not be addressed using a single study.

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies– the All Our Families and the Alberta Pregnancy Outcomes and Nutrition.
Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were synchronized across the datasets considering the frequency of measurement, the timing of measurement, and response options. Variables that were completely unmatching could not be harmonized into a single variable.
The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.