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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