Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness.

The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow's sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model's output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC=0.66), activity (AUC=0.60), and milking order (AUC=0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame.

Dissatisfaction with medical care among women with HIV: dimensions and associated factors.

Studies have shown that women with HIV/AIDS in the USA are less likely than men to have access to appropriate health care and to utilize services, including the latest antiretroviral drug therapies. One explanation for this underutilization is patient dissatisfaction with medical care. Dissatisfaction with care has been shown to be associated not only with treatment underutilization, but also with discontinuity of care and poor clinical outcomes. Using Patient Satisfaction Questionnaire data from a national cohort of women with HIV, this study examines levels of dissatisfaction across seven established dimensions of care, and uses multivariate analysis to identify patient characteristics associated with these dimensions (N = 1,303). Women were most dissatisfied with access to care and the technical quality of care, and least dissatisfied with financial aspects of care and their providers' interpersonal manner. Women who reported poor health, who had depressive symptomatology, who were not receiving antiretroviral therapy (ART), who had no consistent care providers or who were Hispanic/Latina were more likely to be dissatisfied across most dimensions of care. Implications for enhancing clinical care for women with HIV/AIDS and overcoming barriers to utilization of care and treatment are discussed.