Globally, 2.6 million stillbirths and 2.5 million neonatal deaths occur annually, with 98 % of cases occurring in low- and middle-income countries. Accurate neonatal mortality and stillbirth rates are essential to track countries’ progress toward achieving the global development targets. They are also important to inform global, national and subnational policymaking and resource allocation to improve newborn survival. Despite their high burden of stillbirths and neonatal deaths, low- and middle-income countries generally don’t have accurate neonatal and stillbirth mortality estimates, mainly due to insufficient empirical data. High-quality civil registration and vital statistics systems are usually lacking. As an alternative, estimates of neonatal and stillbirth mortality are computed using periodically conducted population surveys, such as the Demographic and Health Surveys and the Multiple Indicator Cluster Surveys. However, accurate measurement of stillbirths and neonatal deaths is often threatened by misclassification errors in such household surveys.
The purpose of the IMPROVE – Tanzania misclassification analysis is to assess the extent, directionality, and correlates of misclassification among stillbirths and early neonatal deaths. Utilizing linked data from the 2016 Demographic and Health Survey and the subsequent 2017 Verbal Autopsy and Social Autopsy (VASA), this analysis will look at the potential relationship between various sociodemographic, maternal, newborn, and survey characteristics and the outcome of misclassification. We’ll also explore the option of including the cause-of-death analysis among misclassified cases. The results from this analysis will build upon a growing body of misclassification research by highlighting potential adjustments to DHS-based neonatal mortality estimates and by being the first study, to our knowledge, to detect and assess two-way misclassification.
This position will lead the work on Tanzania: measuring the misclassification of neonatal deaths and stillbirths. The candidate will be responsible for data collection, analysis, interpretation, and reporting. Perform literature searches, lead the development, submission, revision and publication of a manuscript, support potential forthcoming peer-reviewed revision and provide overall project management support.
Specific Duties & Responsibilities
Utilize knowledge of statistical and quantitative analysis to perform data analysis and interpretation, based on topic by:
Revising and expanding the analysis plan and methodology by using contraceptive calendar data coding an updated Stata .do file.
Executing the data analysis, as represented by the production of a full Stata log file
Analyzing and visualizing results, including developing all necessary tables and figures
Exploring the option of including the cause-of-death analysis among misclassified cases
Execute an updated literature search, including an in-depth search of recent peer-reviewed and programmatic evidence relevant to the study topic.
Develop an annotated matrix of relevant articles to inform the manuscript introduction and discussion sections.
Lead the development, submission, revision and publication of a manuscript in a peer-reviewed journal, including the writing of the introduction, methods, results, discussion, and conclusion sections in collaboration with all relevant co-authors.
Support potential forthcoming peer-reviewed revisions (including potential conference revisions such as PAA (an extended abstract of the study has been submitted to the 2021 PAA)) and/or additional sub-analyses, as needed.
Provide overall project management support to ensure the analysis is fully executed, including organizing weekly meetings with PI and other collaborators as needed, updating the research plan and analysis, etc.
Bachelor’s degree in related discipline (biostatistics, statistics, epidemiology or related quantitative field). Three years of experience with prior research study data collection, development & analysis.
Additional education (Master's level) may substitute for required experience, to the extent permitted by the JHU equivalency formula:
JHU Equivalency Formula: 30 undergraduate degree credits (semester hours) or 18 graduate degree credits may substitute for one year of experience.
Proficient with data analyses programs (e.g. Stata, SAS, SPSS, and R). Prior work experience with data sets creating programs and generating tables and graphs. Knowledge of basic epidemiology biostatistics. Familiar with working on summary reports, grants & manuscripts.
Special Knowledge, Skills, and Abilities
Excellent organizational, communication, and writing skills.
Detail oriented, able to work independently and multi-task.
Classified Title: Research Data Analyst Working Title: Research Data Analyst Role/Level/Range: ACRP/04/MC Starting Salary Range: $21.72 - $29.90; Commensurate with experience Employee group: Casual / On Call Schedule: Monday-Friday, 8:30am-5:00pm; Hours Vary, Up to 27 hrs/ week Exempt Status: Exempt Location: 05-MD:School of Public Health Department name: 10001147-Institute for International Prgms IIP Personnel area: School of Public Health
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