Introduction:

While CBT has been found to be an effective treatment for anxious older children, it has not been empirically validated in children younger than 8 years. In this study we report on an open pilot trial to establish whether a modified form of CBT can benefit young children.

Methods:

Participants were 37 anxious children aged 37–89 months attending a university anxiety specialty clinic. Symptom severity and functioning were assessed before and after treatment by independent evaluators. Feasibility and acceptability of the intervention were high. Parents attended part of each treatment session and were considered part of the treatment team.

Results:

Patients exhibited significant improvement from pre – to post-treatment assessments after an average of 8.3 treatment sessions, using the Strengths and Difficulties Questionnaire (SDQ) and the Global Assessment of Functioning Scale (GAF) ratings.

Conclusions:

A modified form of CBT with active parent involvement may be a useful tool in treating anxiety disorders in preschool and early school aged children.

Background

Thrombosis in neonates is a rare but serious occurrence, usually associated with central catheterization. The objective of this study was to investigate the risk factors associated with catheter related thrombosis in very low birth weight (VLBW) infants.

Procedure

The present retrospective study was performed using data from a randomized trial of duration of umbilical venous catheters (UVC) placement among infants <1250 g birth weight. Twenty-two cases of UVC-associated thrombosis were identified in this sample. The remaining study sample (n=188) served as the comparison group. Data on thrombosis, platelets, gestational age, birth weight, hematocrit, serum sodium, maternal preeclampsia, blood group, infant of diabetic mother and demographic factors were collected using database and record review.

Results

Among the total subjects (n=210), 112 (53%) were males and 126 (60%) were Caucasians, with mean gestational age of 27.7 ± 2.1 weeks (standard deviation) and mean birth weight of 923 ± 195 grams. Bivariate analysis revealed significant association of thrombosis with hematocrit >55% in the first week (odds ratio [OR] 5.4; 95% confidence interval [CI] 2.0-14.6; p=0.0003), being small for gestational age (OR, 2.9; 95% CI, 1.2-7.4; p=0.02) and maternal preeclampsia (OR, 3.97; 95% CI, 1.6-9.84; p=0.0017). In multivariate logistic regression analysis, only hematocrit >55% was independently associated with thrombus (OR, 3.7; 95% CI 1.1-11.8; p=0.03).

Conclusions

This study demonstrates a significant, independent association between elevated hematocrit and development of UVC-associated thrombosis. Careful monitoring for catheter-associated thrombosis may be indicated in VLBW infants who have hematocrit >55% in the first week of life.

Summary

In this article we consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times. We propose a marginally specified latent class pattern mixture model. The marginal mean is assumed to follow a generalized linear model, whereas the mean conditional on the latent class and random effects is specified separately. Because the dimension of the parameter vector of interest (the marginal regression coefficients) does not depend on the assumed number of latent classes, we propose to treat the number of latent classes as a random variable. We specify a prior distribution for the number of classes, and calculate (approximate) posterior model probabilities. In order to avoid the complications with implementing a fully Bayesian model, we propose a simple approximation to these posterior probabilities. The ideas are illustrated using data from a longitudinal study of depression in HIV-infected women.

Objective

To examine facility variation in data quality of the level of pain documented in the minimum data set (MDS) as a function of level of hospice enrollment in nursing homes (NHs).

Data Source

Clinical assessments on 3,469 nonhospice residents from 178 NHs were merged with On-line Survey Certification and Reporting data of 2000, Medicare Claims data of 2000 and the MDS of 2000–2002.

Study Design

Using the same assessment protocol, NH staff and study nurses independently assessed 3,469 nonhospice residents. Study nurses' assessments being gold standard, we quantified and compared quality of NH staff's pain rating across NHs with high, medium, or low hospice use. Multilevel models were built to assess the effect of NH hospice use levels on the occurrence of false positive (FP) and false negative (FN) errors in NH-rated “severe pain.”

Principal Findings

Of 178 NHs, 25 had medium and 41 high hospice use. NHs with higher hospice use had lower sensitivities. In multilevel analysis, we found a significant facility-level variation in the probability of FP and FN errors in facility-rated “severe pain.” Resident characteristics only explained 4 and 0 percent of the facility variation in FP and FN, respectively; characteristics and locations (state) of NHs further explained 53 and 52 percent of the variance. After controlling for resident and NH characteristics, staff in NHs with medium or high hospice use were less likely to have FP or FN errors in their MDS documentation of pain than were staff in NHs with low or no hospice use.

Conclusions

The examination of data quality of pooled MDS data from multiple NHs is insufficient. Multilevel analysis is needed to elucidate sources of heterogeneity in the quality of MDS data across NHs. Facility characteristics, e.g., hospice use or NH location, are systematically associated with overrated/underrated pain and may bias pain quality indicator (QI) comparisons. To ensure the integrity of QI comparison in the NH setting, the government may need to institute regular audits of MDS data quality.

Background

In the US, Quality Indicators (QI's) profiling and comparing the performance of hospitals, health plans, nursing homes and physicians are routinely published for consumer review. We report the results of the largest study of inter-rater reliability done on nursing home assessments which generate the data used to derive publicly reported nursing home quality indicators.

Methods

We sampled nursing homes in 6 states, selecting up to 30 residents per facility who were observed and assessed by research nurses on 100 clinical assessment elements contained in the Minimum Data Set (MDS) and compared these with the most recent assessment in the record done by facility nurses. Kappa statistics were generated for all data items and derived for 22 QI's over the entire sample and for each facility. Finally, facilities with many QI's with poor Kappa levels were compared to those with many QI's with excellent Kappa levels on selected characteristics.

Results

A total of 462 facilities in 6 states were approached and 219 agreed to participate, yielding a response rate of 47.4%. A total of 5758 residents were included in the inter-rater reliability analyses, around 27.5 per facility. Patients resembled the traditional nursing home resident, only 43.9% were continent of urine and only 25.2% were rated as likely to be discharged within the next 30 days.

Results of resident level comparative analyses reveal high inter-rater reliability levels (most items >.75). Using the research nurses as the "gold standard", we compared composite quality indicators based on their ratings with those based on facility nurses. All but two QI's have adequate Kappa levels and 4 QI's have average Kappa values in excess of .80. We found that 16% of participating facilities performed poorly (Kappa <.4) on more than 6 of the 22 QI's while 18% of facilities performed well (Kappa >.75) on 12 or more QI's. No facility characteristics were related to reliability of the data on which Qis are based.

Conclusion

While a few QI's being used for public reporting have limited reliability as measured in US nursing homes today, the vast majority of QI's are measured reliably across the majority of nursing facilities. Although information about the average facility is reliable, how the public can identify those facilities whose data can be trusted and whose cannot remains a challenge.