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Developing appropriate risk messages during challenging situations like public health outbreaks is complicated. The focus of this paper is on how First Nations and Metis people in Manitoba, Canada, responded to the public health management of pandemic H1N1, using a focus group methodology (n = 23 focus groups). Focus group conversations explored participant reactions to messaging regarding the identification of H1N1 virus risk groups, the H1N1 vaccine and how priority groups to receive the vaccine were established. To better contextualize the intentions of public health professionals, key informant interviews (n = 20) were conducted with different health decision makers (e.g., public health officials, people responsible for communications, representatives from some First Nations and Metis self-governing organizations). While risk communication practice has improved, ‘one size’ messaging campaigns do not work effectively, particularly when communicating about who is most ‘at-risk’. Public health agencies need to pay more attention to the specific socio-economic, historical and cultural contexts of First Nations and Metis citizens when planning for, communicating and managing responses associated with pandemic outbreaks to better tailor both the messages and delivery. More attention is needed to directly engage First Nations and Metis communities in the development and dissemination of risk messaging. [ABSTRACT FROM AUTHOR]

Introduction: Sex workers have endured a high burden of HIV infection in and across HIV epidemics. A comprehensive, community empowerment-based HIV prevention intervention emphasizes sex worker organization and mobilization to address HIV risk and often includes community-led peer education, condom distribution, and other activities. Meta-analysis of such interventions suggests a potential 51% reduction in inconsistent condom use. Mathematical modeling exercises provide theoretical insight into potential impacts of the intervention on HIV incidence and burden in settings where interventions have not yet been implemented. Methods: We used a deterministic model, Goals, to project the impact on HIV infections when the community empowerment interventions were scaled up among female sex workers in Kenya, Thailand, Brazil, and Ukraine. Modeling scenarios included expansion of the comprehensive community empowerment-based HIV prevention intervention from baseline coverage over a 5-year period (5–65% in Kenya and Ukraine; 10–70% in Thailand and Brazil), while other interventions were held at baseline levels. A second exercise increased the intervention coverage simultaneously with equitable access to ART for sex workers. Impacts on HIV outcomes among sex workers and adults are observed from 2012–2016 and, compared to status quo when all interventions are held constant. Results: Optimistic but feasible coverage (65%–70%) of the intervention demonstrated a range of impacts on HIV: 220 infections averted over 5 yrs. among sex workers in Thailand, 1,830 in Brazil, 2,220 in Ukraine, and 10,800 infections in Kenya. Impacts of the intervention for female sex workers extend to the adult population, cumulatively averting 730 infections in Thailand to 20,700 adult infections in Kenya. Impacts vary by country, influenced by HIV prevalence in risk groups, risk behaviors, intervention use, and population size. Discussion: A community empowerment approach to HIV prevention and access to universal ART for female sex workers is a promising human rights-based solution to overcoming the persistent burden of HIV among female sex workers across epidemic settings. [ABSTRACT FROM AUTHOR]

Background: Health inequities in developing countries are difficult to eradicate because of limited resources. The neglect of adult mortality in Sub-Saharan Africa (SSA) is a particular concern. Advances in data availability, software and analytic methods have created opportunities to address this challenge and tailor interventions to small areas. This study demonstrates how a generic framework can be applied to guide policy interventions to reduce adult mortality in high risk areas. The framework, therefore, incorporates the spatial clustering of adult mortality, estimates the impact of a range of determinants and quantifies the impact of their removal to ensure optimal returns on scarce resources. Methods: Data from a national cross-sectional survey in 2007 were used to illustrate the use of the generic framework for SSA and elsewhere. Adult mortality proportions were analyzed at four administrative levels and spatial analyses were used to identify areas with significant excess mortality. An ecological approach was then used to assess the relationship between mortality “hotspots” and various determinants. Population attributable fractions were calculated to quantify the reduction in mortality as a result of targeted removal of high-impact determinants. Results: Overall adult mortality rate was 145 per 10,000. Spatial disaggregation identified a highly non-random pattern and 67 significant high risk local municipalities were identified. The most prominent determinants of adult mortality included HIV antenatal sero-prevalence, low SES and lack of formal marital union status. The removal of the most attributable factors, based on local area prevalence, suggest that overall adult mortality could be potentially reduced by ∼90 deaths per 10,000. Conclusions: The innovative use of secondary data and advanced epidemiological techniques can be combined in a generic framework to identify and map mortality to the lowest administration level. The identification of high risk mortality determinants allows health authorities to tailor interventions at local level. This approach can be replicated elsewhere. [ABSTRACT FROM AUTHOR]

Background: Timely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark. Methods: A manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score  = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes. Results: 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20–70%, while retaining sensitivities of 58–75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value. Conclusion: Specialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity. [ABSTRACT FROM AUTHOR]

Objective: Pneumocystis jirovecii pneumonia (PCP), the commonest opportunistic infection in HIV-infected patients in the developed world, is less commonly described in tropical and low and middle income countries (LMIC). We sought to investigate predictors of PCP in these settings. Design: Systematic review and meta-regression. Methods: Meta-regression of predictors of PCP diagnosis (33 studies). Qualitative and quantitative assessment of recorded CD4 counts, receipt of prophylaxis and antiretrovirals, sensitivity and specificity of clinical signs and symptoms for PCP, co-infection with other pathogens, and case fatality (117 studies). Results: The most significant predictor of PCP was per capita Gross Domestic Product, which showed strong linear association with odds of PCP diagnosis (p<0.0001). This was not explained by study design or diagnostic quality. Geographical area, population age, study setting and year of study also contributed to risk of PCP. Co-infection was common (444 episodes/1425 PCP cases), frequently with virulent organisms. The predictive value of symptoms, signs or simple tests in LMIC settings for diagnosis of PCP was poor. Case fatality was >30%; treatment was largely appropriate. Prophylaxis appeared to reduce the risk for development of PCP, however 24% of children with PCP were receiving prophylaxis. CD4 counts at presentation with PCP were usually <200×103/ml. Conclusions: There is a positive relationship between GDP and risk of PCP diagnosis. Although failure to diagnose infection in poorer countries may contribute to this, we also hypothesise that poverty exposes at-risk patients to a wide range of infections and that the relatively non-pathogenic P. jirovecii is therefore under-represented. As LMIC develop economically they eliminate the conditions underlying transmission of virulent infection: P. jirovecii, ubiquitous in all settings, then becomes a greater relative threat. [ABSTRACT FROM AUTHOR]