Background

The incidence of both gestational diabetes mellitus and preeclampsia is on the rise; however, these pregnancy complications may not be systematically reported. This study aimed to examine differences in reporting of preeclampsia and gestational diabetes between hospital records and birth certificate data, and to determine if such differences vary by maternal socioeconomic status indicators.

Methods

We obtained over 70,000 birth records from 2001 to 2006 from the perinatal research database of the Memorial Care system, a network of four hospitals in Los Angeles and Orange Counties, California. Memorial birth records were matched to corresponding state birth certificate records and analyzed to determine differential rates of reporting of preeclampsia and diabetes. Additionally, the influence of maternal socioeconomic factors on the reported incidence of such adverse pregnancy outcomes was analyzed. Socioeconomic factors of interest included maternal education levels, race, and type of health insurance (private or public).

Results

It was found that the birth certificate data significantly underreported the incidence of both preeclampsia (1.38% vs. 3.13%) and diabetes (1.97% vs. 5.56%) when compared to Memorial data. For both outcomes of interest, the degree of underreporting was significantly higher among women with lower education levels, among Hispanic women compared to Non-Hispanic White women, and among women with public health insurance.

Conclusion

The Memorial Care database is a more reliable source of information than birth certificate data for analyzing the incidence of preeclampsia and diabetes among women in Los Angeles and Orange Counties, especially for subpopulations of lower socioeconomic status.

Background

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.

Methods

Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.

Results

Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.

Conclusions

The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.