Explore the vast range of titles available.

Dengue is a common and growing problem worldwide, with an estimated 70-140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼ 36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

For nearly a decade, interest groups, from politicians to economists to physicians, have touted digitization of the nation’s health information. One frequently mentioned benefit is the transmission of information electronically from laboratories to public health personnel, allowing them to rapidly analyze and act on these data. Switching from paper to electronic laboratory reports (ELRs) was thought to solve many public health surveillance issues, including workload, accuracy, and timeliness. However, barriers remain for both laboratories and public health agencies to realize the full benefits of ELRs. The New York City experience highlights several successes and challenges of electronic reporting and is supported by peer-reviewed literature. Lessons learned from ELR systems will benefit efforts to standardize electronic medical records reporting to health departments.

Dengue is a common and growing problem worldwide, with an estimated 70-140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ~36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

Background: Exposure to cold temperature, air pollution and influenza are important determinants of cardiovascular disease (CVD) mortality; however, it is unclear whether these exposures interact in determining the risk of death from CVD. The objective of this study is to understand the role of influenza-like illness (ILI) as a modifier of the relationship between cold temperatures, air pollution particulate matter 2.5 (PM2.5) and daily CVD mortality. Methods: In this study, we estimated influenza-like illness incidence from Google Flu Trends (GFT) after validating use by comparison to ILI data was first validated with other available ILI datasets. I considered ILI, temperature, and PM2.5 in relation to daily CVD mortality counts in 67 United States metropolitan areas, from the fall of 2003 through 2006. In the first stage, I used Poisson time-series regression modeling to estimate the effects of temperature, at its lowest quintile in each city and each year during the cold season (October – March), at lag 0 through 3 days and by distributed lag, while adjusting for temporal trends and day of week. I found that the cold season PM2.5 analysis did not have enough statistical power to investigate effect modification. In a second-stage mixed-effects model, we included the annual ILI mean intensity estimates from GFT database for each city and each winter. To measure other potential city level modifiers of CVD risk, I included average temperature, population density, the percent over age 65, percent poverty, latitude and PM2.5. Results: In the main analysis, of the effect lags considered, the 2-day lagged coldest quintile of temperature showed the strongest significant association with increased CVD mortality, with an excess risk of 3.9% (95% confidence interval: 2.92, 5.00%). Winter influenza intensity modified the effect of temperature on CVD mortality; an increase in ILI of one inter-quartile range resulted in an increase of 31% in the temperature CVD mortality risk by 31% (95% confidence interval 5.07, 56.22%). No significant effects were seen in the other potential effect modifiers. Conclusions: My research suggests that influenza-like illness (ILI) severity modifies the effects of cold temperature on cardiovascular disease (CVD) mortality. An understanding of factors that modify temperature related health effects is increasingly important in the face of climate change.