Faculty Bibliography

OBJECTIVE: To develop an expandable knowledge base of reusable knowledge related to self-management of diabetes that can be used as a foundation for patient-centric decision support tools. MATERIALS AND METHODS: The structure and components of the knowledge base were created in participatory design with academic diabetes educators using knowledge acquisition methods. The knowledge base was validated using scenario-based approach with practicing diabetes educators and individuals with diabetes recruited from Community Health Centers (CHCs) serving economically disadvantaged communities and ethnic minorities in New York. RESULTS: The knowledge base includes eight glycemic control problems, over 150 behaviors known to contribute to these problems coupled with contextual explanations, and over 200 specific action-oriented self-management goals for correcting problematic behaviors, with corresponding motivational messages. The validation of the knowledge base suggested high level of completeness and accuracy, and identified improvements in cultural appropriateness. These were addressed in new iterations of the knowledge base. DISCUSSION: The resulting knowledge base is theoretically grounded, incorporates practical and evidence-based knowledge used by diabetes educators in practice settings, and allows for personally meaningful choices by individuals with diabetes. Participatory design approach helped researchers to capture implicit knowledge of practicing diabetes educators and make it explicit and reusable. CONCLUSION: The knowledge base proposed here is an important step towards development of new generation patient-centric decision support tools for facilitating chronic disease self-management. While this knowledge base specifically targets diabetes, its overall structure and composition can be generalized to other chronic conditions.

BACKGROUND: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. OBJECTIVE: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. METHODS: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. RESULTS: The analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. CONCLUSIONS: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. OBJECTIVE: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. METHODS: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. RESULTS: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. CONCLUSIONS: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

OBJECTIVE: There is evidence that pregnancy-specific stress is associated with preterm birth. The purpose of this study is to examine the association between change in pregnancy-specific stress over the course of pregnancy and birth outcomes (i.e., preterm birth and gestational age) in an understudied but vulnerable group using a theoretically derived model. METHODS: Multivariate linear and logistic regression techniques were used to examine the association between pregnancy-specific stress (measured in second and third trimester) and length of gestation (i.e., preterm birth and gestational age) among a sample of 920 Black and/or Latina adolescent and young women. RESULTS: Second trimester pregnancy-specific stress was not associated with preterm birth or gestational age. Third trimester pregnancy-specific stress was associated with preterm birth but not with gestational age. Change in pregnancy-specific stress between second and third trimester was significantly associated with increased likelihood of preterm delivery and shortened gestational age, even after controlling for important biological, behavioral, psychological, interpersonal, and sociocultural risk factors. CONCLUSIONS: Findings emphasize the importance of measuring pregnancy-specific stress across pregnancy, as the longitudinal change from second to third trimester was significantly associated with length of gestation measured both as a dichotomous variable (preterm birth) and a continuous variable (gestational age). Furthermore, this is the first study to observe the association of pregnancy-specific stress with length of gestation in this understudied population-unique in age, race, and ethnicity.

The field of mHealth has made significant advances in a short period of time, demanding a more thorough and scientific approach to understanding and evaluating its progress. A recent review of mHealth literature identified two primary research needs in order for mHealth to strengthen health systems and promote healthy behaviors, namely health outcomes and cost-benefits (Mechael et al., 2010 ). In direct response to the gaps identified in mHealth research, the aim of this paper is to present the study design and highlight key observations and next steps from an evaluation of the mHealth activities within the electronic health (eHealth) architecture implemented by the Millennium Villages Project (MVP) by leveraging data generated through mobile technology itself alongside complementary qualitative research and costing assessments. The study, funded by the International Development and Research Centre (IDRC) as part of the Open Architecture Standards and Information Systems research project (OASIS II) (Sinha, 2009 ), is being implemented on data generated by 14 MVP sites in 10 Sub-Saharan African countries including more in-depth research in Ghana, Rwanda, Tanzania, and Uganda. Specific components of the study include rigorous quantitative case-control analyses and other epidemiological approaches (such as survival analysis) supplemented by in-depth qualitative interviews spread out over 18 months, as well as a costing study to assess the impact of mHealth on health outcomes, service delivery, and efficiency.

OBJECTIVE: The aim of this study was to develop and to assess the validity and reliability of two brief questionnaires for assessing patient experiences with hospital and outpatient care in a low-income setting. DESIGN: Using literature review and data from focus groups (n = 14), we developed questionnaires to assess patient experiences with inpatient (I-PAHC) and with outpatient (O-PAHC) care in a low-income setting. Questionnaires were administered in person by trained interviewers. Construct validity was assessed with factor analysis; convergent validity was assessed by correlating summary scores for each scale with overall patient evaluations, and reliability was assessed with Cronbach's alpha coefficients. SETTING: Eight health facilities in Ethiopia. PARTICIPANTS: Patients >18 years old who had a hospital stay >1 day (n = 230), and patients who received outpatient care (n = 486). MAIN OUTCOME MEASURES: Patient evaluations of health care experiences. RESULTS: The factor analysis revealed 12 items that loaded on five factors for the I-PAHC questionnaire. The O-PAHC showed similar results with 13 items that loaded on four factors. Summary scores for nearly all factors were significantly associated (P-value < 0.05) with the patient's overall evaluation score. The measure of reliability, Cronbach's alpha coefficients, showed good to excellent internal consistency for all scales. CONCLUSIONS: The I-PAHC on O-PAHC questionnaires can be useful in assessing patients' evaluations of care delivery in low-income settings. The questionnaires are brief and can be integrated into health systems strengthening efforts with the support of leadership at the health facility and the country levels.

OBJECTIVES: Strong evidence exists to support an intergenerational cycle of adolescent fatherhood, yet such a cycle has not been studied. We examined whether paternal adolescent fatherhood (i.e., father of study participant was age 19 years or younger when his first child was born) and other factors derived from the ecological systems theory predicted participant adolescent fatherhood. METHODS: Data included 1496 young males who were interviewed annually from the National Longitudinal Survey of Youth 1997. Cox regression survival analysis was used to determine the effect of paternal adolescent fatherhood on participant adolescent fatherhood. RESULTS: Sons of adolescent fathers were 1.8 times more likely to become adolescent fathers than were sons of older fathers, after other risk factors were accounted for. Additionally, factors from each ecological domain-individual (delinquency), family (maternal education), peer (early adolescent dating), and environment (race/ethnicity, physical risk environment)-were independent predictors of adolescent fatherhood. CONCLUSIONS: These findings support the need for pregnancy prevention interventions specifically designed for young males who may be at high risk for continuing this cycle. Interventions that address multiple levels of risk will likely be most successful at reducing pregnancies among partners of young men.

Mobile phone text messaging is a potentially powerful tool for behavior change because it is widely available, inexpensive, and instant. This systematic review provides an overview of behavior change interventions for disease management and prevention delivered through text messaging. Evidence on behavior change and clinical outcomes was compiled from randomized or quasi-experimental controlled trials of text message interventions published in peer-reviewed journals by June 2009. Only those interventions using text message as the primary mode of communication were included. Study quality was assessed by using a standardized measure. Seventeen articles representing 12 studies (5 disease prevention and 7 disease management) were included. Intervention length ranged from 3 months to 12 months, none had long-term follow-up, and message frequency varied. Of 9 sufficiently powered studies, 8 found evidence to support text messaging as a tool for behavior change. Effects exist across age, minority status, and nationality. Nine countries are represented in this review, but it is problematic that only one is a developing country, given potential benefits of such a widely accessible, relatively inexpensive tool for health behavior change. Methodological issues and gaps in the literature are highlighted, and recommendations for future studies are provided.

Low birth weight (LBW) is a risk factor for infant mortality, morbidity, growth retardation, poor cognitive development, and chronic diseases. Maternal exposure to diseases such as malaria, HIV, and syphilis has been shown to have a significant impact on birth weight (BW). This study was aimed at determining whether there was a difference in rates of LBW in areas of varying malaria transmission intensity in Korogwe, Tanzania. Retrospective data for one year (June 2004-May 2005) in three maternal and child health (MCH) clinics in the district were analysed. Villages were stratified into three strata: lowlands-semi urban (average altitude of 320m), lowlands-rural (below 600m) and highlands (> or =600m). There was a significant decreasing trend of rate of LBW from rural lowlands to highlands (chi2trend = 7.335, P=0.007). Adjusting for covariates, women in parity-two were at reduced risk of delivering LBW babies compared to first parity women (OR=0.44, 95% CI 0.19-0.98, P=0.045). Similarly, the risk of LBW was higher in women who had delayed MCH gestational booking and in women who conceived during high malaria transmission seasons. There was high degree of preference of digits ending with 0/5 in reporting BW in the studied MCHs. In conclusion, a rate of LWB was high in rural lowlands where malaria is also endemic, and was associated with high malaria transmission seasons.