Walking distance for vulnerable populations to public health emergency response points of dispensing in New York City.

During certain public health emergencies, points of dispensing (PODs) may be used to rapidly distribute medical countermeasures such as antibiotics to the general public to prevent disease. Jurisdictions across the country have identified sites for PODs in preparation for such an emergency; in New York City (NYC), the sites are identified based largely on population density. Vulnerable populations, defined for this analysis as persons with income below the federal poverty level, persons with less than a high school diploma, foreign-born persons, persons of color, persons aged ≥65 years, physically disabled persons, and unemployed persons, often experience a wide range of health inequities. In NYC, these populations are often concentrated in certain geographic areas and rely heavily on public transportation. Because public transportation will almost certainly be affected during large-scale public health emergencies that would require the rapid mass dispensing of medical countermeasures, we evaluated walking distances to PODs. We used an ordinary least squares (OLS) model and a geographically weighted regression (GWR) model to determine if certain characteristics that increase health inequities in the population are associated with longer distances to the nearest POD relative to the general NYC population. Our OLS model identified shorter walking distances to PODs in neighborhoods with a higher percentage of persons with income below the federal poverty level, higher percentage of foreign-born persons, or higher percentage of persons of color, and identified longer walking distances to PODs in neighborhoods with a higher percentage of persons with less than a high school diploma. Our GWR model confirmed the findings from the OLS model and further illustrated these patterns by certain neighborhoods. Our analysis shows that currently identified locations for PODs in NYC are generally serving vulnerable populations equitably-particularly those defined by race or income status-at least in terms of walking distance.

Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.

Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.In April 2019, only six weeks after Cyclone Idai devastated Mozambique's central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth's landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.The model prior to Kenneth's arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model's utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.