MRC/UVRI and LSHTM Uganda research Unit PhD – Quantitative Social Science Jobs

Project 1:

Title: Assessing educational outcomes in a school-based menstrual health intervention trial in Uganda

Eligibility

MSc in Medical Statistics, Epidemiology, Social statistics, data science or economics.
Supervisors: Professor Helen Weiss (Department of Infectious Disease Epidemiology, LSHTM), Professor

John Jerrim (Institute of Education, UCL)
Aim: Poor menstrual health can affect girls’ school attendance and possibly educational
attainment.

The aim of this PhD is to analyse the impact of a comprehensive menstrual health
intervention on educational outcomes in a school-randomised trial in Uganda, and to compare and validate methods of measuring school attendance and educational attainment in this context.

To do this will require the use of statistical techniques for the analysis of longitudinal cluster-randomised trial data.
Data: This project will explore baseline and endline measures of school attendance and educational attainment using data from i) 5000 girls and 5000 boys in 48 Ugandan secondary schools enrolled in a cluster-randomised trial of a menstrual health intervention; and ii) a nested cohort of 1920 girls randomly selected from study participants who will complete a daily diary recording school attendance
and menstrual patterns. Data collection will take place from Sep 2021 (pre-intervention) to March 2023 (post-intervention), and the student will provide input into data collection and measurement validation strategies, as well as analysis.

Statistical Methods

This project will include

a systematic review of school-based health intervention trials with educational outcomes;

Analysis of longitudinal binary and continuous outcomes to investigate the impact of the intervention on school absenteeism, and associations of absenteeism with menstruation adjusting for within-participant variability;

Design and analysis of a nested validation
study of different methods of assessing school attendance (diary, unannounced spot checks, assessment completion), and

Impact of the intervention on educational outcomes using random-effects logistic
and continuous regression.

Outcomes: Findings from the PhD will address an evidence-gap in valid methods to collect data on school attendance and attainment in sub-Saharan Africa, and the impact of a school-based menstrual health intervention on educational outcomes. Findings will be dissemination to national, regional and
international educational and health policy makers.

Project 2:

Title

Applying causal mediation analysis to evaluate complex school-based interventions to improve menstrual health

Eligibility

MSc in Medical Statistics or Epidemiology
Supervisors: Dr Clare Tanton (Department of Global Health & Development; LSHTM); Professor Elizabeth Allen (Department of Medical Statistics, LSHTM); Dr Jenny Renju (Department of Population Health,
LSHTM)

Aim

Causal mediation analysis can be applied to better understand the mechanisms through which
complex (multi-component) interventions affect change. However, these mediation analyses are often conducted post-hoc, without due consideration of the statistical constraints that need to be considered and using data that were not specifically collected for these analyses, thereby limiting their utility.

This PhD will build on previous work

conducted by the team in planning for effective causal mediation analysis, in the design stage of two studies of complex interventions. Specifically, this project will determine how best to operationalise logic models for change (theories of change models) in order to
inform data collection and conduct rigorous causal mediation analyses on two complex menstrual health (MH) interventions.

The objectives of this project are to:

Assess the theory of change models for two complex MH studies to determine data collection
requirements for a causal mediation analysis.

Develop specific guidance on how to construct directed acyclic graphs from logic models to inform both appropriate data collection and the development of rigorous statistical models to explore hypothesised causal associations.

Conduct causal mediation analyses on data from two complex MH interventions in order to
quantify the contribution of different intervention components to the study outcomes.
Data: The PhD will use data from two MH intervention studies. The first is a phase 3 cluster-randomised controlled trial of a multi-component intervention (MENISCUS) to improve menstrual health in 48 schools in Uganda. The intervention addresses both the social (education, attitudes, stigma) and physical
(menstrual products, improved water, sanitation and hygiene facilities, pain management) barriers.

The primary objective of the trial is to evaluate whether the intervention improves educational attainment and reduces mental health symptoms. The second is a pilot intervention study which aims to build a sustainable and scalable menstrual, sexual and reproductive health programme within government
secondary schools in Tanzania. An existing intervention package addressing barriers around education, pain management, menstrual products and improving parental support, is currently being enhanced and refined.

This will later be piloted in 4 schools. For each study, the PhD will help quantify the contribution of different components to the study outcomes.

The data collection timings for both these studies is aligned and suitable for the timing of this PhD programme.

Methods

Using the theory of change models developed for these two studies, the candidate will
identify what data need to be collected for a causal mediation analysis and design appropriate data collection tools. At the end of the studies, the candidate will then use causal mediation analysis to understand the causal mechanisms for these interventions.

How to Apply

For more information and job application details, see; MRC/UVRI and LSHTM Uganda research Unit PhD – Quantitative Social Science Jobs

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