Tuberculosis Infection Control In Rural South Africa: Information, Motivation, And Behavioral Skills

Healthcare-associated tuberculosis (TB) transmission occurs in resource-limited health facilities, putting patients and healthcare workers (HCWs) at risk. TB infection control (IC) can prevent such transmission but is inadequately implemented. We attempted to better characterize HCW TB IC implementation. We hypothesized that TB IC implementation would correlate in the manner predicted by the Information-Motivation-Behavioral skills (IMB) behavioral model. We conducted direct observations of TB IC behavior and staff questionnaires of TB IC IMB and behavior at two district hospitals (Church of Scotland Hospital, COSH, and Charles Johnson Memorial Hospital, CJM) in rural South Africa. Direct observations were conducted on 10-14 consecutive working days. Observed TB IC practices varied greatly by department (natural ventilation: 50.7-97.0%, respirator use: 5.0-100.0%). Questionnaires were completed by 123 HCWs at COSH and 75 at CJM. Information levels were generally high at both hospitals. Motivation responses were generally appropriate, though 29.4% would not be bothered very much by catching TB, and 22.8% thought TB IC was not worth the effort. Behavioral skills assessment indicated that HCWs found the majority of TB IC procedures to be easy to perform, though respondents highlighted several discrete tasks as being relatively difficult, especially those relating to personal HIV testing and relocation to low-risk departments if HIV-positive. When in high-risk TB areas of the hospital, more than half of respondents claimed to always wear a respirator (54.3%), instruct patients on cough hygiene (63.0%) and ensure effective natural ventilation (67.4%). Most (74.0%) knew their HIV status (81.0% at COSH, 63.8% at CJM, p=0.012). Correlations were noted between self-reported TB IC implementation and several IMB variables, particularly those related to social support. A social support Motivational sub-scale correlated with self-reported respirator use (p=0.002), cough hygiene instruction (p=0.001), and natural ventilation (p=0.006). A global model was created to compare IMB variables to aggregated self-reported TB IC behaviors. The only significant global scale variable was Motivation as a covariate of Behavioral skills (p\u3c0.000). IMB models were created for self-reported respirator use, cough hygiene instruction to patients, natural ventilation implementation, and knowledge of personal HIV status. The respirator IMB model performed much better than the others. Information did not vary significantly with other variables in any of the models. Results suggest that rather than focusing on improving staff Information, efforts to increase TB IC implementation should focus on HCW Motivation and Behavioral skills development. TB IC implementation in this study compared favorably to other reports from the developing world. Social support, especially that of colleagues and supervisors, is an important element in ensuring better TB IC implementation, which is crucial to preventing healthcare-associated transmission. Though individual models require refinement, IMB modeling offers a promising avenue for further research and guiding interventions

Friction by Definition : Conflict at Patient Handover Between Emergency and Internal Medicine Physicians at an Academic Medical Center.

INTRODUCTION: Patient handoffs from emergency physicians (EP) to internal medicine (IM) physicians may be complicated by conflict with the potential for adverse outcomes. The objective of this study was to identify the specific types of, and contributors to, conflict between EPs and IM physicians in this context. METHODS: We performed a qualitative focus group study using a constructivist grounded theory approach involving emergency medicine (EM) and IM residents and faculty at a large academic medical center. Focus groups assessed perspectives and experiences of EP/IM physician interactions related to patient handoffs. We interpreted data with the matrix analytic method. RESULTS: From May to December 2019, 24 residents (IM = 11, EM = 13) and 11 faculty (IM = 6, EM = 5) from the two departments participated in eight focus groups and two interviews. Two key themes emerged: 1) disagreements about disposition (ie, whether a patient needed to be admitted, should go to an intensive care unit, or required additional testing before transfer to the floor); and 2) contextual factors (ie, the request to discuss an admission being a primer for conflict; lack of knowledge of the other person and their workflow; high clinical workload and volume; and different interdepartmental perspectives on the benefits of a rapid emergency department workflow). CONCLUSIONS: Causes of conflict at patient handover between EPs and IM physicians are related primarily to disposition concerns and contextual factors. Using theoretical models of task, process, and relationship conflict, we suggest recommendations to improve the EM/IM interaction to potentially reduce conflict and advance patient care

Facility-Based Delivery during the Ebola Virus Disease Epidemic in Rural Liberia: Analysis from a Cross-Sectional, Population-Based Household Survey

Background: The Ebola virus disease (EVD) epidemic has threatened access to basic health services through facility closures, resource diversion, and decreased demand due to community fear and distrust. While modeling studies have attempted to estimate the impact of these disruptions, no studies have yet utilized population-based survey data. Methods and Findings: We conducted a two-stage, cluster-sample household survey in Rivercess County, Liberia, in March–April 2015, which included a maternal and reproductive health module. We constructed a retrospective cohort of births beginning 4 y before the first day of survey administration (beginning March 24, 2011). We then fit logistic regression models to estimate associations between our primary outcome, facility-based delivery (FBD), and time period, defined as the pre-EVD period (March 24, 2011–June 14, 2014) or EVD period (June 15, 2014–April 13, 2015). We fit both univariable and multivariable models, adjusted for known predictors of facility delivery, accounting for clustering using linearized standard errors. To strengthen causal inference, we also conducted stratified analyses to assess changes in FBD by whether respondents believed that health facility attendance was an EVD risk factor. A total of 1,298 women from 941 households completed the survey. Median age at the time of survey was 29 y, and over 80% had a primary education or less. There were 686 births reported in the pre-EVD period and 212 in the EVD period. The unadjusted odds ratio of facility-based delivery in the EVD period was 0.66 (95% confidence interval [CI] 0.48–0.90, p-value = 0.010). Adjustment for potential confounders did not change the observed association, either in the principal model (adjusted odds ratio [AOR] = 0.70, 95%CI 0.50–0.98, p = 0.037) or a fully adjusted model (AOR = 0.69, 95%CI 0.50–0.97, p = 0.033). The association was robust in sensitivity analyses. The reduction in FBD during the EVD period was observed among those reporting a belief that health facilities are or may be a source of Ebola transmission (AOR = 0.59, 95%CI 0.36–0.97, p = 0.038), but not those without such a belief (AOR = 0.90, 95%CI 0.59–1.37, p = 0.612). Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias. Conclusions: We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. Because health facilities never closed in Rivercess County, this estimate may under-approximate the effect seen in the most heavily affected areas. These are the first population-based survey data to show collateral disruptions to facility-based delivery caused by the West African EVD epidemic, and they reinforce the need to consider the full spectrum of implications caused by public health emergencies

Logistic regression models of facility-based delivery among women of child-bearing age in Rivercess County, Liberia, 2011–2015.

<p>Logistic regression models of facility-based delivery among women of child-bearing age in Rivercess County, Liberia, 2011–2015.</p

Proportion of women with facility-based delivery in Rivercess County, Liberia, during 2011–2014.

<p>Years begin in June, such that the 2014 estimate corresponds to the generalized Ebola virus disease epidemic in the country (June 2014–April 2015). Error bars represent 95% confidence intervals.</p

Demographic and social characteristics, Rivercess, Liberia, by births that occurred before and during the Ebola period.

<p>Demographic and social characteristics, Rivercess, Liberia, by births that occurred before and during the Ebola period.</p