Epistaxis occurs more commonly during the winter and in older patients. AR, CRS, coagulopathy, HHT, hematologic malignancy, and HTN are associated with increased epistaxis incidence.

A growing body of literature has shown that environmental exposures in the period around conception can affect the sex ratio at birth through selective attrition that favors the survival of female conceptuses. Glucose availability is considered a key indicator of the fetal environment, and its absence as a result of meal skipping may inhibit male survival. We hypothesize that breakfast skipping during pregnancy may lead to a reduction in the fraction of male births. Using time use data from the United States we show that women with commute times of 90 minutes or longer are 20 percentage points more likely to skip breakfast. Using U.S. census data we show that women with commute times of 90 minutes or longer are 1.2 percentage points less likely to have a male child under the age of 2. Under some assumptions, this implies that routinely skipping breakfast around the time of conception leads to a 6 percentage point reduction in the probability of a male child. Skipping breakfast during pregnancy may therefore constitute a poor environment for fetal health more generally. IntroductionBreakfast skipping is a common and growing phenomenon among American women. Approximately 40 percent of women of childbearing age are attempting to lose weight (Cohen and Kim 2009), and rates of breakfast skipping have grown steadily over time, especially for adolescent women (Haines, Guilkey, and Popkin 1996;Siega-Riz, Popkin, and Carson 1998). Holtzman (2010) finds that 75 percent of women aged 18 to 26 skip breakfast at least one day a week, 58 percent skip breakfast at least four days a week, and 29 percent skip breakfast every day. Using detailed time use data we find that on a given day, 51 percent of employed women between the ages of 15 and 45 report not eating between the hours of 5 a.m. and 10 a.m. 1 Even among pregnant women, 24 percent report skipping meals (Siega-Riz et al. 2001).Given the vast and growing literature documenting the profound and lasting effects of the prenatal environment on long-term health and socioeconomic success (e.g., Almond and Currie 2011), one might be concerned that the rising incidence of breakfast skipping among women of childbearing age might have important intergenerational ramifications. Pregnant women who extend the overnight fast by skipping breakfast experience a sharp drop in glucose levels and other associated biochemical changes referred to as "accelerated Address correspondence to Bhashkar Mazumder, Federal Reserve Bank of Chicago, 230 S. La Salle Street, Chicago, IL 60604, USA. E-mail: bhash.mazumder@gmail.com 1 This is based on the Eating and Health module subsample of the data, which includes all primary and secondary activities.Color versions of one or more of the figures in the article can be found online at www. tandfonline.com/hsbi. 188 B. Mazumder and Z. Seeskinstarvation" (Metzger et al. 1982). Declines in glucose levels as a result of overnight fasts of 10 to 12 hours in length have been observed in women as early as the sixth to tenth weeks of pregnancy (Mills et al. 1998)....

The question of whether to carry out a quinquennial Census is faced by national statistical offices in increasingly many countries, including Canada, Nigeria, Ireland, Australia, and South Africa. We describe uses and limitations of cost-benefit analysis in this decision problem in the case of the 2016 Census of South Africa. The government of South Africa needed to decide whether to conduct a 2016 Census or to rely on increasingly inaccurate postcensal estimates accounting for births, deaths, and migration since the previous (2011) Census. The cost-benefit analysis compared predicted costs of the 2016 Census to the benefits of improved allocation of intergovernmental revenue, which was considered by the government to be a critical use of the 2016 Census, although not the only important benefit. Without the 2016 Census, allocations would be based on population estimates. Accuracy of the postcensal estimates was estimated from the performance of past estimates, and the hypothetical expected reduction in errors in allocation due to the 2016 Census was estimated. A loss function was introduced to quantify the improvement in allocation. With this evidence, the government was able to decide not to conduct the 2016 Census, but instead to improve data and capacity for producing post-censal estimates.

This paper investigates the utility of a commercial property tax data from CoreLogic, Inc. (CoreLogic) aggregated from county and township governments from across the country, for use to improve American Community Survey (ACS) estimates of property tax amounts for single-family homes. Particularly, the research uses linkages of the CoreLogic file to the 2010 ACS to evaluate the use of CoreLogic data directly to replace survey responses for estimation of property tax amounts, potentially reducing measurement error and respondent burden. I find that the coverage of CoreLogic data varies among geographic areas across the U.S., as does the correspondence between ACS and CoreLogic property taxes. Large differences between CoreLogic and ACS property taxes in some instances may reflect conceptual differences between what is collected in the two data sources for certain counties. This research draws attentions to the challenges of using non-survey data sources that are aggregated from many state or local agencies with different practices for data collection and curation.

In the United States, state and local agencies administering government assistance programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical for agencies to conduct such analyses and to improve their data for future use. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality. We describe our efforts in developing tools that can be used to assess data quality as well as the challenges encountered in constructing these tools. The toolkit focuses on critical dimensions of quality for analyzing an administrative dataset, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest. State and local administrative databases often include a longitudinal component which our toolkit also aims to exploit to help evaluate data quality. In addition, we incorporate data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern. While we seek to develop general tools for common data quality analyses, most administrative datasets have particularities that can benefit from a customized analysis building on our toolkit.