Factors Related to Maternal and Early Neonatal Mortality in Ghana
University of Michigan Principal Investigator(s) and unit: Frank Anderson, M.D., Obstetrics & Gynecology; and Timothy Johnson, M.D., Center for Human Growth and Development, Obstetrics & Gynecology
International colleague(s) and unit: TBA
Rationale: In Africa, the lifetime risk of dying in pregnancy is 1 in 21. Maternal mortality is the health indicator with the greatest disparity between developed and developing countries, and is closely associated with neonatal mortality. Early neonatal mortality is directly associated with the care of the mother during pregnancy, labor and delivery.
Anticipated undergraduate student activities on project: In this project, students will work with the faculty and staff in one of the teaching hospitals in Ghana to determine risk factors for and the pregnancy complications that lead to maternal early neonatal mortality, and evaluate the impact of interventions recently put into place to reduce mortality.
Determining the Sickle Cell Disease Phenotype
within the Ghanaian SCD Population:
A Cross-secional Analysis of Pediatric and Adult Patients
University of Michigan Principal Investigator(s) and unit: Andrew Campbell, MD; Department of Pediatric Hematology/Oncology
Sites: Accra, Ghana
1) Princess Memorial Children’s Hospital
MHIRT Mentor: Dr. Eric Sifah
2) Charles Antwi-Boasiako, M.S.
Department of Physiology, University of Ghana Medical School
Background: Sickle Cell Disease (SCD) is caused by a single gene substitution mutation resulting in a chronic hematologic debilitating condition that affects millions worldwide. While West Africa has the highest carrier rate of the sickle gene (1 in 4) and has one of the highest concentrations of patients in the world with 2% of babies born each year with SCD, the clinical phenotype is poorly described. Our previous studies focused primarily on determining the sickle cell disease clinical concordance/discordance rates amongst mostly pediatric Twins and Siblings with sickle cell disease. With new collaborators at University of Ghana and Princess Memorial Children’s Hospital in Accra Ghana, we will study a cross-sectional cohort of pediatric and adult sickle cell disease patients in Ghana.
- Determining clinical phenotypic differences between adults and children with sickle cell disease
- Determining the most common sickle cell disease complications and risk factors associated within adult Ghanaian patients
- Determining prevalence of sickle cell nephropathy in pediatric and adult sickle cell patients by screening for proteinuria in the urine.
- Determining the risk factors associated with sickle cell disease complications (leg ulcers, frequent pain crises, kidney disease/proteinuria) within adult and pediatric patients.
- Compare sickle cell disease clinical disease patterns in Ghana transcontinentally to SCD patients in Europe and United States.
Rationale: Determining the sickle cell disease phenotype in Ghanaian children and adults will allow us to determine the risk factors associated with age-dependent sickle cell disease complications in a cross-sectional manner. It will also allow us to compare disease patterns transcontinentally in Europe and United States.
Study Design: Cross-sectional Cohort Study. Plan to enroll 250 pediatric patients and 250 adult patients from 2012-2017
- We will administer comprehensive questionnaire forms that includes past medical history through our CASIRE International Sickle Cell Research Consortium in an effort to determine what laboratory, environmental and other clinical variables can contribute to sickle cell disease variability in Ghanaian adults and children with SCD.
- We will measure urine microalbumin levels by in pediatric and adult SCD patients to determine the prevalence sickle cell nephropathy in Ghanaian SCD Patients
Anticipated undergraduate/graduate student activities on project:
- Administering the medical history questionnaires with Ghanaian Interpreter
- Review medical records of patients
- Measuring urine microalbumin levels urine test strips
- Entering Data into Microsoft Excel and SPSS