Dataset Information

Determining a cutoff score for the family burden interview schedule using three statistical methods.

ABSTRACT:

Background

While it is widely acknowledged that family burden can be ameliorated with effective psycho-social interventions, how to measure family burden and define a valid cutoff to identify family caregivers in need of such interventions remains a key question. The purpose of the present study was to determine a statistically valid cutoff score for the Family Burden Interview Schedule (FBIS), using the cutoff scores of the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7) as the reference.

Methods

The FBIS, PHQ-9, and GAD-7 were administered to a representative community sample of 327 family caregivers of schizophrenia patients. A FBIS cutoff score was determined using three different statistical methods: tree-based modeling, K-means clustering technique and linear regression. Contingency analysis was conducted to compare the FBIS cutoff with depression and anxiety scale scores.

Results

Findings proposed a cutoff score of 23 for the FBIS, with sensitivity being 76% for PHQ-9 and 74% for GAD-7, specificity being 68% for PHQ-9 and 67% for GAD-7.

Conclusion

This cutoff score would enable health care providers to assess family caregivers at risk and provide necessary interventions to improve their quality of life.

SUBMITTER: Yu Y

PROVIDER: S-EPMC6505248 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determining a cutoff score for the family burden interview schedule using three statistical methods.

Yu Yu Y Liu Zi-Wei ZW Zhou Wei W Zhao Mei M Tang Bing-Wei BW Xiao Shui-Yuan SY

BMC medical research methodology 20190508 1

<h4>Background</h4>While it is widely acknowledged that family burden can be ameliorated with effective psycho-social interventions, how to measure family burden and define a valid cutoff to identify family caregivers in need of such interventions remains a key question. The purpose of the present study was to determine a statistically valid cutoff score for the Family Burden Interview Schedule (FBIS), using the cutoff scores of the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiet ...[more]

PMID: 31068131

Similar Datasets

Project description:BackgroundIn the absence of clinical trials, data on the safety of medicine exposures in pregnancy are dependent on observational studies conducted after the agent has been licensed for use. This requires an accurate history of antenatal medicine use to determine potential risks. Medication use is commonly determined by self-report, clinician records, and electronic pharmacy data; different data sources may be more informative for different types of medication and resources may differ by setting. We compared three methods to determine antenatal medicine use (self-report, clinician records and electronic pharmacy dispensing records [EDR]) in women attending antenatal care at a primary care facility in Cape Town, South Africa in a setting with high HIV prevalence.MethodsStructured, interview-administered questionnaires recorded self-reported medicine use. Data were collected from clinician records and EDR on the same participants. We determined agreement between these data sources using Cohen's kappa and, lacking a gold standard, used Latent Class Analysis to estimate sensitivity, specificity and positive predictive value (PPV) for each data source.ResultsBetween 55% and 89% of 967 women had any medicine use documented depending on the data source (median number of medicines/participant = 5 [IQR 3-6]). Agreement between the datasets was poor regardless of class except for antiretroviral therapy (ART; kappa 0.6-0.71). Overall, agreement was better between the EDR and self-report than with either dataset and the clinician records. Sensitivity and PPV were higher for self-report and the EDR and were similar for the two. Self-report was the best source for over-the-counter, traditional and complementary medicines; clinician records for vaccines and supplements; and EDR for chronic medicines.ConclusionsMedicine use in pregnancy was common and no single data source included all the medicines used. ART was the most consistently reported across all three datasets but otherwise agreement between them was poor and dependent on class. Using a single data collection method will under-estimate medicine use in pregnancy and the choice of data source should be guided by the class of the agents being investigated.

Project description:The etiology of immune-related diseases or traits is often complex, involving many genetic and environmental factors and their interactions. While methodological approaches focusing on an outcome measured at one time point have succeeded in identifying genetic factors involved in immune-related traits, they fail to capture complex disease mechanisms that fluctuate over time. It is increasingly recognized that longitudinal studies, where an outcome is measured at multiple time points, have great potential to shed light on complex disease mechanisms involving genetic factors. However, longitudinal data require specialized statistical methods, especially in family studies where multiple sources of correlation in the data must be modeled. Using simulated data with known genetic effects, we examined the performance of different analytical methods for investigating associations between genetic factors and longitudinal phenotypes in twin data. The simulations were modeled on data from the Québec Newborn Twin Study, an ongoing population-based longitudinal study of twin births with multiple phenotypes, such as cortisol levels and body mass index, collected multiple times in infancy and early childhood and with sequencing data on immune-related genes and pathways. We compared approaches that we classify as (1) family-based methods applied to summaries of the observations over time, (2) longitudinal-based methods with simplifications of the familial correlation, and (3) Bayesian family-based method with simplifications of the temporal correlation. We found that for estimation of the genetic main and interaction effects, all methods gave estimates close to the true values and had similar power. If heritability estimation is desired, approaches of type (1) also provide heritability estimates close to the true value. Our work shows that the simpler approaches are likely adequate to detect genetic effects; however, interpretation of these effects is more challenging.

Dataset Information

Determining a cutoff score for the family burden interview schedule using three statistical methods.

Background

Methods

Results

Conclusion

Publications

Determining a cutoff score for the family burden interview schedule using three statistical methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets