CONTEXT: The COVID-19 pandemic exposed governmental public health's outdated information technology and insufficient data science and informatics workforce capacity. The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program (PHIFP) is well positioned to strengthen public health data science and informatics workforce capacity. PROGRAM: Established in 1996, PHIFP is a 2-year, full-time, on-the-job training program. PHIFP includes a didactic curriculum, applied learning through informatics projects completed at the assigned host site, short-term technical assistance projects, and a final capstone project. EVALUATION: Fellows have learned from and bolstered host site informatics capacity through the development or enhancement of information systems, evaluations, data integration, data visualization, and analysis. Among recent graduates, 54% are employed at Centers for Disease Control and Prevention and 16% are employed at other public health organizations, including local health departments. DISCUSSION: Fellowships such as PHIFP, which recruit and train promising scientists in public health informatics, are important components of efforts to strengthen public health workforce capacity.

OBJECTIVE: Opioid use disorder (OUD) affects approximately 5.6 million people in the United States annually, yet rates of the use of effective medication for OUD (MOUD) treatment are low. We conducted an observational cohort study from August 2017 through May 2021, the MOUD Study, to better understand treatment engagement and factors that may influence treatment experiences and outcomes. In this article, we describe the study design, data collected, and treatment outcomes. METHODS: We recruited adult patients receiving OUD treatment at US outpatient facilities for the MOUD Study. We collected patient-level data at 5 time points (baseline to 18 months) via self-administered questionnaires and health record data. We collected facility-level data via questionnaires administered to facility directors at 2 time points. Across 16 states, 62 OUD treatment facilities participated, and 1974 patients enrolled in the study. We summarized descriptive data on the characteristics of patients and OUD treatment facilities and selected treatment outcomes. RESULTS: Approximately half of the 62 facilities were private, nonprofit organizations; 62% focused primarily on substance use treatment; and 20% also offered mental health services. Most participants were receiving methadone (61%) or buprenorphine (32%) and were predominately non-Hispanic White (68%), aged 25-44 years (62%), and female (54%). Compared with patient-reported estimates at baseline, 18-month estimates suggested that rates of abstinence increased (55% to 77%), and rates of opioid-related overdoses (7% to 2%), emergency department visits (9% to 4%), and arrests (15% to 7%) decreased. CONCLUSIONS: Our results demonstrated the benefits of treatment retention not only on abstinence from opioid use but also on other quality-of-life metrics, with data collected during an extended period. The MOUD Study produced rich, multilevel data that can lay the foundation for an evidence base to inform OUD treatment and support improvement of care and patient outcomes.

Background To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to demonstrate its robustness and ability to track disease trends.Methods The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya’s domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback.Results Over 95% of trained domestic animal officers downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 10-fold increase in number of disease reports when compared the previous year (p<0.05), and reports were more spatially distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife.Conclusions This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.Authors Summary Taking advantage of a recently developed freely downloadable disease reporting application in the United States, we customized it for android smartphones to collect and submit domestic and wild animal disease data in real-time in Kenya. To enhance user friendliness, the Kenya Animal Biosurveillance System (KABS) was installed with disease reporting tools currently used by the animal sector and tailored to collected data on transboundary animal disease important for detecting zoonotic endemic and emerging diseases. The KABS database was housed by the government of Kenya, providing important assurance on its security. The application had a feedback module that performed basics analysis to provide feedback to the end-user in real-time. Rolling out of KABS resulted in >70% of domestic and wildlife disease surveillance officers using it to report, resulting in exponential increase in frequency and spatial distributions of reports regions. Utility of the system was demonstrated by successful detected a Rift Valley fever outbreak in livestock in 2018, resulting in early response and prevention of widespread human infections. For the wildlife sector in Eastern Africa, the application provided the first disease surveillance system developed. This open-source system is ideal for rolling out in other countries in sub-Saharan Africa to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.

BACKGROUND: To improve early detection of emerging infectious diseases in sub-Saharan Africa (SSA), many of them zoonotic, numerous electronic animal disease-reporting systems have been piloted but not implemented because of cost, lack of user friendliness, and data insecurity. In Kenya, we developed and rolled out an open-source mobile phone-based domestic and wild animal disease reporting system and collected data over two years to investigate its robustness and ability to track disease trends. METHODS: The Kenya Animal Biosurveillance System (KABS) application was built on the Java® platform, freely downloadable for android compatible mobile phones, and supported by web-based account management, form editing and data monitoring. The application was integrated into the surveillance systems of Kenya's domestic and wild animal sectors by adopting their existing data collection tools, and targeting disease syndromes prioritized by national, regional and international animal and human health agencies. Smartphone-owning government and private domestic and wild animal health officers were recruited and trained on the application, and reports received and analyzed by Kenya Directorate of Veterinary Services. The KABS application performed automatic basic analyses (frequencies, spatial distribution), which were immediately relayed to reporting officers as feedback. RESULTS: Of 697 trained domestic animal officers, 662 (95%) downloaded the application, and >72% of them started reporting using the application within three months. Introduction of the application resulted in 2- to 14-fold increase in number of disease reports when compared to the previous year (relative risk = 14, CI 13.8-14.2, p<0.001), and reports were more widely distributed. Among domestic animals, food animals (cattle, sheep, goats, camels, and chicken) accounted for >90% of the reports, with respiratory, gastrointestinal and skin diseases constituting >85% of the reports. Herbivore wildlife (zebra, buffalo, elephant, giraffe, antelopes) accounted for >60% of the wildlife disease reports, followed by carnivores (lions, cheetah, hyenas, jackals, and wild dogs). Deaths, traumatic injuries, and skin diseases were most reported in wildlife. CONCLUSIONS: This open-source system was user friendly and secure, ideal for rolling out in other countries in SSA to improve disease reporting and enhance preparedness for epidemics of zoonotic diseases.