Caroline Jeffery

Caroline Jeffery
University of Liverpool | UoL

PhD

About

31
Publications
3,690
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451
Citations
Introduction
Current Research Interest: statistical methods for public health, public health surveillance, low resource settings. Methods: spatial methods, Lot Quality Assurance Sampling, Respondent Driven Sampling Working on now: combining administative and survey data to improve estimates
Additional affiliations
March 2010 - present
Liverpool School of Tropical Medicine
Position
  • Research Associate in Biostatistics

Publications

Publications (31)
Article
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Objective Investigating attitudes accepting two categories of violence against women and girls (VAWG) (intimate partner violence—IPV—and other expressions of VAWG) and their association with seven demographic/social determinants and health-seeking behaviours in South Sudan. Design Cross-sectional study using data from the South Sudan National Hous...
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Objective Combine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error. Setting This study is set in Bihar, the fifth poorest state in India, where half the population liv...
Article
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Background Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for district...
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Introduction Is achievement of Sustainable Development Goal (SDG) 16 (building peaceful societies) a precondition for achieving SDG 3 (health and well-being in all societies, including conflict-affected countries)? Do health system investments in conflict-affected countries waste resources or benefit the public’s health? To answer these questions,...
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Background: National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely avail...
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While countries now have national and regional measures of HIV prevalence, sub-regional (district) and sub-district level information is sparse. Growing demand to fill this gap with health facility testing data, in addition to other HIV testing data requires understanding the comparability of these various data sources. We analysed the 2011 Uganda...
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Health systems resilience (HSR) is defined as the ability of a health system to continue providing normal services in response to a crisis, making it a critical concept for analysis of health systems in fragile and conflict-affected settings (FCAS). However, no consensus for this definition exists and even less about how to measure HSR. We examine...
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Objective Global monitoring of maternal, newborn and child health (MNCH) programmes use self-reported data subject to recall error which may lead to incorrect decisions for improving health services and wasted resources. To minimise this risk, samples of mothers of infants aged 0–2 and 3–5 months are sometimes used. We test whether a single sample...
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Lymphatic filariasis (LF) elimination as a public health problem requires the interruption of transmission by administration of preventive mass drug administration (MDA) to the eligible population living in endemic districts. Suboptimal MDA coverage leads to persistent parasite transmission with consequential infection, disease and disability, and...
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Significance Over the last 2 decades, low- and middle-income countries have moved from fragmented paper-based systems to electronic health information systems (HIS). Although these are a major advance, they are not well suited to drive operational policy decisions and detect gaps in service coverage. To avoid overreliance on administrative estimate...
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Background: It is well known that safe delivery in a health facility reduces the risks of maternal and infant mortality resulting from perinatal complications. What is less understood are the factors associated with safe delivery practices. We investigate factors influencing health facility delivery practices while adjusting for multiple other fac...
Article
Beginning in 2003, Uganda used Lot Quality Assurance Sampling (LQAS) to assist district managers collect and use data to improve their human immunodeficiency virus (HIV)/AIDS program. Uganda's LQAS-database (2003-2012) covers up to 73 of 112 districts. Our multidistrict analysis of the LQAS data-set at 2003-2004 and 2012 examined gender variation a...
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Objectives: Two common methods used to measure indicators for health program monitoring and evaluation are the Demographic and Health Surveys (DHS) and Lot Quality Assurance Sampling (LQAS); each one has different strengths. We report on both methods when utilized in comparable situations. Methods: We compared 24 indicators in Southwest Uganda,...
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Suboptimal sexual and reproductive health (SRH) increases morbidity, mortality, and gender inequity and slows development. In Uganda, youths represent 20% of the population, and the burden of sexually transmitted infections (STIs), including human immunodeficiency virus (HIV), is substantial. We analyzed survey data collected using the lot quality...
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Objectives: This study estimates the proportion of Orphans and Vulnerable Children (OVC) attending school in 89 districts of Uganda from 2011 - 2013 and investigates the factors influencing OVC access to education among this population. Methods: This study used secondary survey data from OVCs aged 5 - 17 years, collected using Lot Quality Assura...
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A major strategy for preventing transmission of HIV and other STIs is the consistent use of condoms during sexual intercourse. Condom use among youths is particularly important to reduce the number of new cases and the national prevalence. Condom use has been often promoted by the Uganda National AIDS Commission. Although a number of studies have e...
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Spatial data on cases are available either in point form (e.g. longitude/latitude), or aggregated by an administrative region (e.g. zip code or census tract). Statistical methods for spatial data may accommodate either form of data, however the spatial aggregation can affect their performance. Previous work has studied the effect of spatial aggrega...
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Background There has been no sound evidence on the status and dynamics of Libya’s HIV-epidemic, which is urgently needed to inform near-term policy making while the window of opportunity to act is still open. With funding from the European Union we therefore aimed to assess HIV prevalence and related risk factors among populations most vulnerable t...
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Publications on Libya's HIV epidemic mostly examined the victims of the tragic nosocomial HIV outbreak in the 1990s and the related dispute about the detention of foreign medical workers. The dispute resolution in 2003 included an agreement with the European Union on humanitarian cooperation and the development of Libya's first National HIV Strateg...
Data
Table S1 – Socio-demographic characteristics and prevalence of HIV and other infections among MSM in Tripoli, Libya, 2010. Table S2 – Socio-demographic characteristics and prevalence of HIV and other infections among FSW in Tripoli, Libya, 2010. Table S3 – Sexual behaviour and risk factors for HIV infection among MSM in Tripoli, Libya, 2010. Table...
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: In this article, we consider the problem of comparing the distribution of observations in a planar region to a pre-specified null distribution. Our motivation is a surveillance setting where we map locations of incident disease, aiming to monitor these data over time, to locate potential areas of high/low incidence so as to direct public health a...
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Objective Uncertainty regarding the location of disease acquisition, as well as selective identification of cases, may bias maps of risk. We propose an extension to a distance-based mapping method (DBM) that incorporates weighted locations to adjust for these biases. We demonstrate this method by mapping potential drug-resistant tuberculosis (DRTB)...
Article
Background: Libya had one of the world's largest nosocomial HIV outbreaks in the late 1990 s leading to the detention of 6 foreign medical workers. They were released in 2007 after the Libyan Government and the European Union agreed to humanitarian cooperation that included the development of Libya's first National HIV Strategy and the research re...
Article
In most countries with large drug resistant tuberculosis epidemics, only those cases that are at highest risk of having MDRTB receive a drug sensitivity test (DST) at the time of diagnosis. Because of this prioritized testing, identification of MDRTB transmission hotspots in communities where TB cases do not receive DST is challenging, as any obser...
Article
Spatio and/or temporal surveillance systems are designed to monitor the ongoing appearance of disease cases in space and time, and to detect potential disturbances in either dimension. Patient addresses are sometimes reported at some level of geographic aggregation, for example by ZIP code or census tract. While this aggregation has the advantage o...
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Background Aggregation of spatial data is intended to protect privacy, but some effects of aggregation on spatial methods have not yet been quantified. Methods We generated 3,000 spatial data sets and evaluated power of detection at 12 different levels of aggregation using the spatial scan statistic implemented in SaTScan v6.0. Results Power to d...
Chapter
We have shown in this chapter not only how to look at the number of patients entering into the surveillance system, but also to consider from whence they came. The statistic used to measure the goodness-of-fit of the spatial distribution we consider is the M-statistic. This statistic has the advantage that its distribution, for large numbers of pat...
Chapter
IntroductionMotivationDistance-based statistics for surveillanceSpatio-temporal surveillance: an exampleLocating clustersConclusion Acknowledgments

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