Objective: Data are scarce regarding the sociodemographic predictors of antenatal and postpartum depression. This study investigated whether race/ethnicity, age, finances, and partnership status were associated with antenatal and postpartum depressive symptoms. Setting: 1662 participants in Project Viva, a US cohort study. Design: Mothers indicated mid-pregnancy and six month postpartum depressive symptoms on the Edinburgh postpartum depression scale (EPDS). Associations of sociodemographic factors with odds of scoring .12 on the EPDS were estimated. Main results: The prevalence of depressive symptoms was 9% at mid-pregnancy and 8% postpartum. Black and Hispanic mothers had a higher prevalence of depressive symptoms compared with nonHispanic white mothers. These associations were explained by lower income, financial hardship, and higher incidence of poor pregnancy outcome among minority women. Young maternal age was associated with greater risk of antenatal and postpartum depressive symptoms, largely attributable to the prevalence of financial hardship, unwanted pregnancy, and lack of a partner. The strongest risk factor for antenatal depressive symptoms was a history of depression (OR = 4.07; 95% CI 3.76, 4.40), and the strongest risk for postpartum depressive symptoms was depressive symptoms during pregnancy (6.78; 4.07, 11.31) or a history of depression before pregnancy (3.82; 2.31, 6.31). Conclusions: Financial hardship and unwanted pregnancy are associated with antenatal and postpartum depressive symptoms. Women with a history of depression and those with poor pregnancy outcomes are especially vulnerable to depressive symptoms during the childbearing year. Once these factors are taken in account, minority mothers have the same risk of antenatal and postpartum depressive symptoms as white mothers.

The space-time scan statistic is often used to identify incident disease clusters. We introduce a method to adjust for naturally occurring temporal trends or geographical patterns in illness. The space-time scan statistic was applied to reports of lower respiratory complaints in a large group practice. We compared its performance with unadjusted populations from: (1) the census, (2) group-practice membership counts, and on adjustments incorporating (3) day of week, month, and holidays; and (4) additionally, local history of illness. Using a nominal false detection rate of 5%, incident clusters during 1 year were identified on 26, 22, 4 and 2% of days for the four populations respectively. We show that it is important to account for naturally occurring temporal and geographic trends when using the space-time scan statistic for surveillance. The large number of days with clusters renders the census and membership approaches impractical for public health surveillance. The proposed adjustment allows practical surveillance.

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BackgroundThe spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically significant as there is no known way to calculate the null distribution analytically. In Monte Carlo hypothesis testing, simulated random data are generated multiple times under the null hypothesis, and the p-value is r/(R + 1), where R is the number of simulated random replicates of the data and r is the rank of the test statistic from the real data compared to the same test statistics calculated from each of the random data sets. A drawback to this powerful technique is that each additional digit of p-value precision requires ten times as many replicated datasets, and the additional processing can lead to excessive run times.ResultsWe propose a new method for obtaining more precise p-values with a given number of replicates. The collection of test statistics from the random replicates is used to estimate the true distribution of the test statistic under the null hypothesis by fitting a continuous distribution to these observations. The choice of distribution is critical, and for the spatial and space-time scan statistics, the extreme value Gumbel distribution performs very well while the gamma, normal and lognormal distributions perform poorly. From the fitted Gumbel distribution, we show that it is possible to estimate the analytical p-value with great precision even when the test statistic is far out in the tail beyond any of the test statistics observed in the simulated replicates. In addition, Gumbel-based rejection probabilities have smaller variability than Monte Carlo-based rejection probabilities, suggesting that the proposed approach may result in greater power than the true Monte Carlo hypothesis test for a given number of replicates.ConclusionsFor large data sets, it is often advantageous to replace computer intensive Monte Carlo hypothesis testing with this new method of fitting a Gumbel distribution to random data sets generated under the null, in order to reduce computation time and obtain much more precise p-values and slightly higher statistical power.

Monitoring ongoing processes of illness to detect sudden changes is an important aspect of practical epidemiology and medicine more generally. Most commonly, the monitoring has been restricted to a unidimensional stream of data over time. In such situations, analytic results from the industrial process monitoring have suggested optimal approaches to monitor the data streams. Data streams including spatial location as well as temporal sequence are becoming available. Monitoring methods that incorporate spatial data may prove superior to those that ignore it. However, analytically, optimal methods for spatial surveillance data may not exist. In the present article, we introduce and discuss evaluation metrics that can be used to compare the performance of statistical methods of surveillance. Our general approach is to generalize receiver operating characteristic (ROC) curves to incorporate the time of detection in addition to the usual test characteristics of sensitivity and specificity. In addition to weighting ordinary ROC curves by two measures of timeliness, we describe three three-dimensional generalizations of ROC curves that result in timeliness-ROC surfaces. Working in the context of surveillance of cases of disease to detect a sudden outbreak, we demonstrate these in an artificial example and in a previously described simulation context and show how the metrics differ. We also discuss the differences and under which circumstances one might prefer a given method.

ICH is common in elderly fallers presenting to the ED without focal findings. Anticoagulation alone did not appear to increase the risk of ICH, and aspirin was found to be protective, but prospective studies are needed to better assess this relationship.

Bioterrorism surveillance systems can be assessed using modeling to simulate real-world attacks.

As increasing numbers of older adults are discharged to postacute care facilities, they face high-risk care transitions. Extension for Community Healthcare Outcomes-Care Transitions (ECHO-CT) facilitates interdisciplinary communication between hospital and postacute care providers, who normally have minimal interaction. Preliminary data suggests that ECHO-CT may improve the transitions of care processes between these sites.