Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering.

BACKGROUND: Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE: The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS: We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran's I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS: Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS: Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

Family socioeconomic status and self-reported sexually transmitted diseases among black and white american adolescents.

OBJECTIVE: The objective of this study was to assess associations between socioeconomic status (SES) and adolescent sexually transmitted diseases (STDs) stratified by race and gender. STUDY: In cross-sectional analyses of the National Longitudinal Study of Adolescent Health Wave One (1995), unadjusted and adjusted associations between 4 family SES indicators and STD reports for black and white 7th through 12th graders were examined. RESULTS: Lower maternal education and nonprofessional maternal occupations were associated with higher STD reports in all groups except white females. Generally, STD reports were higher for adolescents not living in 2-parent homes, and lower income was only associated for black males. CONCLUSION: Overall, SES is only a weak to moderate marker for adolescent STD risks. The relationship of SES and STDs varies by the SES measure used and differs across race-gender groups. Other individual factors such as risk behaviors or community factors such as income inequality could play a more critical role for adolescent STDs than family SES.