Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa

Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors

Application of handheld devices to field research among underserved construction worker populations: a workplace health assessment pilot study

This is the final version of the article. Available from BioMed Central via the DOI in this record.BACKGROUND: Novel low-cost approaches for conducting rapid health assessments and health promotion interventions among underserved worker groups are needed. Recruitment and participation of construction workers is particularly challenging due to their often transient periods of work at any one construction site, and their limited time during work to participate in such studies. In the present methodology report, we discuss the experience, advantages and disadvantages of using touch screen handheld devices for the collection of field data from a largely underserved worker population. METHODS: In March 2010, a workplace-centered pilot study to examine the feasibility of using a handheld personal device for the rapid health assessment of construction workers in two South Florida Construction sites was undertaken. A 45-item survey instrument, including health-related questions on tobacco exposure, workplace safety practices, musculoskeletal disorders and health symptoms, was programmed onto Apple iPod Touch® devices. Language sensitive (English and Spanish) recruitment scripts, verbal consent forms, and survey questions were all preloaded onto the handheld devices. The experience (time to survey administration and capital cost) of the handheld administration method was recorded and compared to approaches available in the extant literature. RESULTS: Construction workers were very receptive to the recruitment, interview and assessment processes conducted through the handheld devices. Some workers even welcomed the opportunity to complete the questionnaire themselves using the touch screen handheld device. A list of advantages and disadvantages emerged from this experience that may be useful in the rapid health assessment of underserved populations working in a variety of environmental and occupational health settings. CONCLUSIONS: Handheld devices, which are relatively inexpensive, minimize survey response error, and allow for easy storage of data. These technological research modalities are useful in the collection and assessment of environmental and occupational research data.This study was supported in part by the National Institute for Occupational Safety and Health (NIOSH)'s Deep South Educational Research Center at the University of Alabama (sub-contract: 288477-10) as a Graduate Student Pilot Grant Award; the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant F31AR057687 and the National Institute for Occupational Safety and Health (NIOSH) grant R01 OH003915

First experiences in the implementation of biometric technology to link data from Health and Demographic Surveillance Systems with health facility data

BACKGROUND: In developing countries, Health and Demographic Surveillance Systems (HDSSs) provide a framework for tracking demographic and health dynamics over time in a defined geographical area. Many HDSSs co-exist with facility-based data sources in the form of Health Management Information Systems (HMIS). Integrating both data sources through reliable record linkage could provide both numerator and denominator populations to estimate disease prevalence and incidence rates in the population and enable determination of accurate health service coverage. OBJECTIVE: To measure the acceptability and performance of fingerprint biometrics to identify individuals in demographic surveillance populations and those attending health care facilities serving the surveillance populations. METHODOLOGY: Two HDSS sites used fingerprint biometrics for patient and/or surveillance population participant identification. The proportion of individuals for whom a fingerprint could be successfully enrolled were characterised in terms of age and sex. RESULTS: Adult (18-65 years) fingerprint enrolment rates varied between 94.1% (95% CI 93.6-94.5) for facility-based fingerprint data collection at the Africa Centre site to 96.7% (95% CI 95.9-97.6) for population-based fingerprint data collection at the Agincourt site. Fingerprint enrolment rates in children under 1 year old (Africa Centre site) were only 55.1% (95% CI 52.7-57.4). By age 5, child fingerprint enrolment rates were comparable to those of adults. CONCLUSION: This work demonstrates the feasibility of fingerprint-based individual identification for population-based research in developing countries. Record linkage between demographic surveillance population databases and health care facility data based on biometric identification systems would allow for a more comprehensive evaluation of population health, including the ability to study health service utilisation from a population perspective, rather than the more restrictive health service perspective

An ontology for factors affecting tuberculosis treatment adherence behavior in sub-Saharan Africa

Olukunle Ayodeji Ogundele,1 Deshendran Moodley,1 Anban W Pillay,1 Christopher J Seebregts1,2 1UKZN/CSIR Meraka Centre for Artificial Intelligence Research and Health Architecture Laboratory, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, KwaZulu-Natal, 2Jembi Health Systems NPC, Cape Town, South Africa Purpose: Adherence behavior is a complex phenomenon influenced by diverse personal, cultural, and socioeconomic factors that may vary between communities in different regions. Understanding the factors that influence adherence behavior is essential in predicting which individuals and communities are at risk of nonadherence. This is necessary for supporting resource allocation and intervention planning in disease control programs. Currently, there is no known concrete and unambiguous computational representation of factors that influence tuberculosis (TB) treatment adherence behavior that is useful for prediction. This study developed a computer-based conceptual model for capturing and structuring knowledge about the factors that influence TB treatment adherence behavior in sub-Saharan Africa (SSA).Methods: An extensive review of existing categorization systems in the literature was used to develop a conceptual model that captured scientific knowledge about TB adherence behavior in SSA. The model was formalized as an ontology using the web ontology language. The ontology was then evaluated for its comprehensiveness and applicability in building predictive models. Conclusion: The outcome of the study is a novel ontology-based approach for curating and structuring scientific knowledge of adherence behavior in patients with TB in SSA. The ontology takes an evidence-based approach by explicitly linking factors to published clinical studies. Factors are structured around five dimensions: factor type, type of effect, regional variation, cross-dependencies between factors, and treatment phase. The ontology is flexible and extendable and provides new insights into the nature of and interrelationship between factors that influence TB adherence. Keywords: tuberculosis, treatment adherence behavior, influencing factor, conceptual model, ontolog