David Scales

Syrian refugees, between rocky crisis in Syria and hard inaccessibility to healthcare services in Lebanon and Jordan

Around 3% of the world’s population (n = 214 million people) has crossed international borders for various reasons. Since March 2011, Syria has been going through state of political crisis and instability resulting in an exodus of Syrians to neighbouring countries. More than 1 million Syrian refugees are residents of Lebanon, Jordan, Turkey, Egypt and North Africa. The international community must step up efforts to support Syrian refugees and their host governments.

Preventing pandemics via international development: a systems approach.

Tiffany Bogich and colleagues find that breakdown or absence of public health infrastructure is most often the driver in pandemic outbreaks, whose prevention requires mainstream development funding rather than emergency funding.

Entry and exit screening of airline travellers during the A(H1N1) 2009 pandemic: a retrospective evaluation

OBJECTIVE To evaluate the screening measures that would have been required to assess all travellers at risk of transporting A(H1N1)pdm09 out of Mexico by air at the start of the 2009 pandemic. METHODS Data from flight itineraries for travellers who flew from Mexico were used to estimate the number of international airports where health screening measures would have been needed, and the number of travellers who would have had to be screened, to assess all air travellers who could have transported the H1N1 influenza virus out of Mexico during the initial stages of the 2009 A(H1N1) pandemic. FINDINGS Exit screening at 36 airports in Mexico, or entry screening of travellers arriving on direct flights from Mexico at 82 airports in 26 other countries, would have resulted in the assessment of all air travellers at risk of transporting A(H1N1)pdm09 out of Mexico at the start of the pandemic. Entry screening of 116 travellers arriving from Mexico by direct or connecting flights would have been necessary for every one traveller at risk of transporting A(H1N1)pdm09. Screening at just eight airports would have resulted in the assessment of 90% of all air travellers at risk of transporting A(H1N1)pdm09 out of Mexico in the early stages of the pandemic. CONCLUSION During the earliest stages of the A(H1N1) pandemic, most public health benefits potentially attainable through the screening of air travellers could have been achieved by screening travellers at only eight airports.

Quantifying the effect of media limitations on outbreak data in a global online web-crawling epidemic intelligence system, 2008-2011.

Background This is the first study quantitatively evaluating the effect that media-related limitations have on data from an automated epidemic intelligence system. Methods We modeled time series of HealthMaps two main data feeds, Google News and Moreover, to test for evidence of two potential limitations: first, human resources constraints, and second, high-profile outbreaks “crowding out” coverage of other infectious diseases. Results Google News events declined by 58.3%, 65.9%, and 14.7% on Saturday, Sunday and Monday, respectively, relative to other weekdays. Events were reduced by 27.4% during Christmas/New Years weeks and 33.6% lower during American Thanksgiving week than during an average week for Google News. Moreover data yielded similar results with the addition of Memorial Day (US) being associated with a 36.2% reduction in events. Other holiday effects were not statistically significant. We found evidence for a crowd out phenomenon for influenza/H1N1, where a 50% increase in influenza events corresponded with a 4% decline in other disease events for Google News only. Other prominent diseases in this database – avian influenza (H5N1), cholera, or foodborne illness – were not associated with a crowd out phenomenon. Conclusions These results provide quantitative evidence for the limited impact of editorial biases on HealthMaps web-crawling epidemic intelligence.

Forecasting High-Priority Infectious Disease Surveillance Regions: A Socioeconomic Model

BACKGROUND Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. METHODS We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organizations Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. RESULTS Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. CONCLUSIONS Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

Toward a county-level map of tuberculosis rates in the U.S

Active tuberculosis (TB) is a reportable communicable disease in all 50 states, but nationwide, county-level data are not released publicly. The CDC’s Online Tuberculosis Information System (OTIS) provides public surveillance data only by state. Owing to an agreement with the states, the CDC cannot publicly release TB data at the county level, precluding the development of publicly available, county-level maps of TB cases and incidence rates. The lack of a more granular nationwide data set has limited the study of TB trends and socioeconomic risk factors to states,1 Metropolitan Statistical Areas,2 or census tracts within a single state.3 A nationwide county-level data set of TB rates provides opportunities to examine TB-related trends across multiple states, metropolitan areas, and across counties with similar demographic characteristics, such as the number of people deemed to be at high risk.4