Uncertainties in gender violence epidemiology

This thesis contains 11 papers published in peer reviewed journals between 2006 and 2012. The papers focused on gender violence research methods, the prevalence of risk factors for gender violence, and its association with HIV and maternal morbidity. The accompanying commentary addresses three uncertainties that affect gender violence epidemiology. These are missing data, clustering and unrecognised causal relationships. In this thesis I ask: Can we reduce these three uncertainties in gender violence epidemiology? A systematic review of the intimate partner violence literature over the last decade found that few epidemiological studies manage missing data in gender violence questionnaires in a satisfactory way. Focus groups in Zambia, Nigeria and Pakistan confirmed that missing data lead to underestimation of gender violence prevalence. A partial solution to this problem was to place greater emphasis on interviewer training. In a reanalysis of the data from the published papers I compared different approaches to dealing with clustering in gender violence epidemiology. Generalised linear mixed models and other methods found that clustering potentially plays a causal role. This can be important in interventions that target a community at large, and act throughout the cluster. In a reanalysis of several datasets I show how a history of gender violence influences measurement of many associations related to HIV, possibly due to an unanticipated role of gender violence in the causal pathway with HIV. In conclusion, it is possible to reduce the uncertainties associated with missing data, clustering, and unrecognised causality in gender violence epidemiology.

Name dc:type.qualificationname

phd

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

City University London

Year dc:date.issued

2013

Author dc:creator

• Andersson, Neil

• HA Statistics
• HQ The family. Marriage. Woman