ABSTRACTAuthors examine prevalence of sexual violence and stalking victimization by the same perpetrator, reporting perpetrator types, intimate partner context and impacts for this combination of victimization. Data are from the 20102012 National Intimate Partner and Sexual Violence Survey, a nationally representative adult telephone survey. Analyses examined the characteristics of the victimization, presence of other intimate partner violence by the same perpetrator, and victim impacts (e.g., injury). An estimated 8.1% (9.8 million) of women and 1.6% (1.9 million) of men in the United States were stalked and sexually victimized by the same perpetrator, most often an intimate partner. Over 90% of female and male victims experienced sexual violence, stalking, psychological aggression, and physical violence by the same intimate partner perpetrator. Impacts of both intimate partner and non-intimate partner perpetrated victimization were most commonly fearfulness, concern for safety, and posttraumatic stress disorder symptoms. Sexual violence combined with stalking is common in the context of intimate partner violence. Early prevention efforts (i.e., in youth) addressing the context of intimate partner violence may be helpful in reducing these forms of violence and their impacts.

IMPORTANCE: Suicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making. OBJECTIVE: To estimate weekly suicide fatalities in the US in near real time. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017. EXPOSURES: Longitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017). MAIN OUTCOMES AND MEASURES: Weekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System. RESULTS: Combining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%). CONCLUSIONS AND RELEVANCE: The proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.