BACKGROUND: Machine learning has the potential to help researchers better understand and close the gap in HIV care delivery in large metropolitan regions such as Mecklenburg County, North Carolina, USA. OBJECTIVES: We aim to identify important risk factors associated with delayed linkage to care for HIV patients with novel machine learning models and identify high-risk regions of the delay. METHODS: Deidentified 2013-2017 Mecklenburg County surveillance data in eHARS format were requested. Both univariate analyses and machine learning random forest model (developed in R 3.5.0) were applied to quantify associations between delayed linkage to care (>30 days after diagnosis) and various risk factors for individual HIV patients. We also aggregated linkage to care by zip codes to identify high-risk communities within the county. RESULTS: Types of HIV-diagnosing facility significantly influenced time to linkage; first diagnosis in hospital was associated with the shortest time to linkage. HIV patients with lower CD4+ cell counts (<200/ml) were twice as likely to link to care within 30 days than those with higher CD4+ cell count. Random forest model achieved high accuracy (>80% without CD4+ cell count data and >95% with CD4+ cell count data) to predict risk of delay in linkage to care. In addition, we also identified top high-risk zip codes of delayed linkage. CONCLUSION: The findings helped public health teams identify high-risk communities of delayed HIV care continuum across Mecklenburg County. The methodology framework can be applied to other regions with HIV epidemic and challenge of delayed linkage to care.

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

Black and Hispanic children are hospitalized with complications of asthma at much higher rates than white children. The Boston Children's Hospital Community Asthma Initiative (CAI) provides asthma case management and home visits for children from low-income neighborhoods in Boston, Massachusetts, to address racial/ethnic health disparities in pediatric asthma outcomes. CAI objectives were to evaluate 1) case management data by parent/guardian report for health outcomes and 2) hospital administrative data for comparison between intervention and comparison groups. Data from parent/guardian reports indicate that CAI decreased the number of children with any (one or more) asthma-related hospitalizations (decrease of 79% at 12 months) and any asthma-related emergency department visits (decrease of 56% at 12 months) among children served, most of whom were non-Hispanic black or Hispanic. Hospital administrative data also indicate that the number of asthma-related hospitalizations per child significantly decreased among CAI participants compared with a comparison group. The CAI model has been replicated in other cities and states with adaptations to local cultural and systems variations. Health outcome and cost data have been used to contribute to a business case to educate legislators and insurers about outcomes and costs for this enhanced approach to care. Strong partnerships with public health, community, and housing agencies have allowed CAI to leverage its outcomes to expand systemic changes locally and statewide to reduce asthma morbidity.

BACKGROUND: Compared with the general population in the United States (U.S.), Hispanics with hypertension are less likely to be aware of their condition, to take antihypertensive medication, and to adopt healthy lifestyles to control high blood pressure. We examined whether a multi-community intervention successfully increased the prevalence of actions to control hypertension among Hispanics. METHODS: Annual survey from 2009-2012 was conducted in six Hispanic communities in the Racial and Ethnic Approaches to Community Health (REACH) Across the U.S. PROJECT. The survey used address based sampling design that matched the geographies of intervention program. RESULTS: Age- and sex-standardized prevalences of taking hypertensive medication, changing eating habits, cutting down on salt, and reducing alcohol use significantly increased among Hispanics with self-reported hypertension in REACH communities. The 3-year relative percent increases were 5.8, 6.8, 7.9, and 35.2% for the four indicators, respectively. These favorable (healthier) trends occurred in both foreign-born and U.S.-born Hispanics. CONCLUSION: This large community-based participatory intervention resulted in more Hispanic residents in the communities taking actions to control high blood pressure.

PROBLEM: Substantial racial/ethnic health disparities exist in the United States. Although the populations of racial and ethnic minorities are growing at a rapid pace, large-scale community-based surveys and surveillance systems designed to monitor the health status of minority populations are limited. CDC conducts the Racial and Ethnic Approaches to Community Health across the U.S. (REACH U.S.) Risk Factor Survey annually in minority communities. The survey focuses on black, Hispanic, Asian (including Native Hawaiian and Other Pacific Islander), and American Indian (AI) populations. REPORTING PERIOD COVERED: 2009. DESCRIPTION OF SYSTEM: An address-based sampling design was used in the survey in 28 communities located in 17 states (Arizona, California, Georgia, Hawaii, Illinois, Massachusetts, Michigan, New Mexico, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Virginia, West Virginia, and Washington). Self-reported data were collected through telephone, questionnaire mailing, and in-person interviews from an average of 900 residents aged ≥ 18 years in each community. Data from the community were compared with data derived from the Behavioral Risk Factor Surveillance System (BRFSS) for the metropolitan and micropolitan statistical area (MMSA), county, or state in which the community was located and also compared with national estimates. RESULTS: Reported education level and household income were markedly lower in black, Hispanic, and AI communities than that among the general population living in the comparison MMSA, county, or state. More residents in these minority populations did not have health-care coverage and did not see a doctor because of the cost. Substantial variations were identified in self-perceived health status and prevalence of selected chronic conditions among minority populations and among communities within the same racial/ethnic population. In 2009, the median percentage of men who reported fair or poor health was 15.8% (range: 8.3%-29.3%) among A/PI communities and 26.3% (range: 22.3%-30.8%) among AI communities. The median percentage of women who reported fair or poor health was 20.1% (range: 13.3%-37.2%) among A/PI communities, whereas it was 31.3% (range: 19.4%-44.2%) among Hispanic communities. AI and black communities had a high prevalence of self-reported hypertension, cardiovascular disease, and diabetes. For most communities, prevalence was much higher than that in the corresponding MMSA, county, or state in which the community was located. The median percentages of persons who knew the signs and symptoms of a heart attack and stroke were consistently lower in all four minority communities than the national median. Variations were identified among racial/ethnic populations in the use of preventive services. Hispanics had the lowest percentages of persons who had their cholesterol checked, of those with high blood pressure who were taking antihypertensive medication, and of those with diabetes who had a glycosylated hemoglobin (HbA1C) test in the past year. AIs had the lowest mammography screening rate within 2 years among women aged ≥40 years (median: 72.7%; range: 69.4%-76.2%). A/PIs had the lowest Pap smear screening rate within 3 years (median: 74.4%; range: 60.3%-80.8%). The median influenza vaccination rates in adults aged ≥65 years were much lower among black (57.3%) and Hispanic communities (63.3%) than the national median (70.1%) among the 50 states and DC. Pneumococcal vaccination rates also were lower in black (60.5%), Hispanic (58.5%), and A/PI (59.7%) communities than the national median (68.5%). INTERPRETATIONS: Data from the REACH U.S. Risk Factor Survey demonstrate that residents in most of the minority communities continue to have lower socioeconomic status, greater barriers to health-care access, and greater risks for and burden of disease compared with the general populations living in the same MMSA, county, or state. Substantial variations in prevalence of risk factors, chronic conditions, and use of preventive services among different minority populations and different communities within the same racial/ethnic population provide opportunities for public health intervention. These variations also indicate that different priorities are needed to eliminate health disparities for different communities. PUBLIC HEALTH ACTION: These community-level survey data are being used by CDC and community coalitions to implement, monitor, and evaluate intervention programs in each community. Continuous surveillance of health status in minority communities is necessary so that community-specific, culturally sensitive strategies that include system, environmental, and individual-level changes can be tailored to these communities.

From April 2009 through March 2010, during the pandemic (H1N1) 2009 outbreak, approximately 8.2 million prescriptions for influenza neuraminidase-inhibiting antiviral drugs were filled in the United States. We estimated the number of hospitalizations likely averted due to use of these antiviral medications. After adjusting for prescriptions that were used for prophylaxis and personal stockpiles, as well as for patients who did not complete their drug regimen, we estimated the filled prescriptions prevented approximately 8,400-12,600 hospitalizations (on the basis of median values). Approximately 60% of these prevented hospitalizations were among adults 18-64 years of age, with the remainder almost equally divided between children 0-17 years of age and adults >65 years of age. Public health officials should consider these estimates an indication of success of treating patients during the 2009 pandemic and a warning of the need for renewed planning to cope with the next pandemic.

OBJECTIVE: The paucity of evidence and wide variation among communities creates challenges for developing congressionally mandated national performance standards for public health preparedness. Using countermeasure dispensing as an example, we present an approach for developing standards that balances national uniformity and local flexibility, consistent with the quality of evidence available. METHODS: We used multiple methods, including a survey of community practices, mathematical modeling, and expert panel discussion.RESULTS: The article presents recommended dispensing standards, along with a general framework that can be used to analyze tradeoffs involved in developing other preparedness standards. CONCLUSIONS: Standards can be developed using existing evidence, but would be helped immensely by a stronger evidence base.