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Background: The Namibian Ministry of Health and Social Services (MoHSS) piloted the first HIV Project ECHO (Extension for Community Health Outcomes) in Africa at 10 clinical sites between 2015 and 2016. Goals of Project ECHO implementation included strengthening clinical capacity, improving professional satisfaction, and reducing isolation while addressing HIV service challenges during decentralization of antiretroviral therapy. Methods: MoHSS conducted a mixed-methods evaluation to assess the pilot. Methods included pre/post program assessments of healthcare worker knowledge, self-efficacy, and professional satisfaction; assessment of continuing professional development (CPD) credit acquisition; and focus group discussions and in-depth interviews. Analysis compared the differences between pre/post scores descriptively. Qualitative transcripts were analyzed to extract themes and representative quotes. Results: Knowledge of clinical HIV improved 17.8% overall (95% confidence interval 12.2-23.5%) and 22.3% (95% confidence interval 13.2-31.5%) for nurses. Professional satisfaction increased 30 percentage points. Most participants experienced reduced professional isolation (66%) and improved CPD credit access (57%). Qualitative findings reinforced quantitative results. Following the pilot, the Namibia MoHSS Project ECHO expanded to over 40 clinical sites by May 2019 serving more than 140 000 people living with HIV. Conclusions: Similar to other Project ECHO evaluation results in the United States of America, Namibia's Project ECHO led to the development of ongoing virtual communities of practice. The evaluation demonstrated the ability of the Namibia HIV Project ECHO to improve healthcare worker knowledge and satisfaction and decrease professional isolation.

Evolution has conserved “economic” systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions—including the use of arrows that connect pairs of consecutive observations—non-reductionist (spatial–temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

BackgroundDiagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases.MethodsLeukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version.ResultsIn all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed.ConclusionsInfectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies.

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