Marsha Wilcox

Alpha-2 macroglobulin is genetically associated with Alzheimer disease

Alpha-2-macroglobulin (α-2M; encoded by the gene A2M) is a serum pan-protease inhibitor that has been implicated in Alzheimer disease (AD) based on its ability to mediate the clearance and degradation of Aß, the major component of ß-amyloid deposits. Analysis of a deletion in the A2M gene at the 5 splice site of exon II of the bait region (exon 18) revealed that inheritance of the deletion (A2M-2 ) confers increased risk for AD (Mantel-Haenzel odds ratio = 3.56, P = 0.001). The sibship disequilibrium test (SDT) also revealed a significant association between A2M and AD (P = 0.00009). These values were comparable to those obtained for the APOE-ε4 allele in the same sample, but in contrast to APOE-ε4, A2M-2 did not affect age of onset. The observed association of A2M with AD did not appear to account for the previously published linkage of AD to chromosome 12, which we were unable to confirm in this sample. A2M, LRP1 (encoding the α-2M receptor) and the genes for two other LRP ligands, APOE and APP (encoding the amyloid ß-protein precursor), have now all been genetically linked to AD, suggesting that these proteins may participate in a common neuropathogenic pathway leading to AD.

Assessing behavioral health outcomes in outpatient programs: reliability and validity of the BASIS-32.

The Behavior and Symptom Identification Scale (BASIS-32) was developed to assess mental health outcomes among patients with severe illness treated on inpatient programs. However, its applicability and utility to those treated in outpatient programs has not been determined. The objective of this study was to assess reliability, validity, and sensitivity to change of the BASIS-32 among mental health consumers treated in outpatient programs. A total of 407 outpatients completed the BASIS-32 and the Short Form Health Status Profile (SF-36) at the beginning of a treatment episode and again 30 to 90 days later. Outpatients reported less difficulty at intake than did inpatients, and the BASIS-32 detected statistically significant changes 30 to 90 days after beginning outpatient treatment. Factor structure and construct validity were partially confirmed on this sample of outpatient consumers. Analyses of data from a wide range of facilities and samples would add to validation efforts and to further refinement of the BASIS-32.

Assessing consumer perceptions of inpatient psychiatric treatment: the perceptions of care survey.

BACKGROUNDnConsumer perceptions of behavioral health care are widely recognized as important quality indicators. This article reports the development and use of the Perceptions of Care (PoC) survey, a standardized public domain measure of consumer perceptions of the quality of inpatient mental health or substance abuse care. The goals were to develop a low-cost, low-burden survey that would address important quality domains, allow for interprogram comparisons and national benchmarks, be useful for quality improvement purposes, and meet accreditation and payer requirements.nnnMETHODSnThe sample was composed of 6,972 patients treated in 14 inpatient behavioral health or substance abuse treatment programs. The PoC survey was given to patients by program staff in the 24-hour period before discharge.nnnRESULTSnAggregate reports and ratings of care identified areas that are highly evaluated by consumers, as well as areas that provide opportunities for quality improvement. Factor analysis identified four domains of care, and a 100-point score was developed for each domain. Regression analyses identified significant predictors of perceptions of care for use in computing risk-adjusted scores. Unadjusted and adjusted scores were presented to demonstrate the impact of risk adjustment on quality of care scores and relative ranking of programs. Examples were given of how programs used survey results to improve the quality of care.nnnDISCUSSIONnResults demonstrated that the PoC survey is sensitive to detecting differences among inpatient behavioral health programs and can be useful in guiding quality improvement efforts. However, risk adjustment is important for appropriate interpretation of results.

The Sib Transmission/Disequilibrium Test is a Mantel-Haenszel Test

To the Editor:Spielman and Ewenss (1998xA sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Spielman, RS and Ewens, WJ. Am J Hum Genet. 1998; 62: 450–458Abstract | Full Text | Full Text PDF | PubMed | Scopus (508)See all References1998) proposed extension of the transmission/disequilibrium test (TDT), using discordant sibships, provides a simple and elegant way to apply the TDT in instances in which parents are not available. It is easy to see that the adapted test, called the “sib TDT” (S-TDT), is numerically equivalent to a Mantel-Haenszel test of trend, also known as the “Mantel extension test” (Rosner 1995xRosner, B. See all References1995).The original Mantel-Haenszel test is used routinely in matched case-control studies, to test for association between disease and exposure. When exposure is expressed as a quantitative risk factor with C levels, the Mantel extension test allows the investigator to obtain a 1-df test against the alternative of a monotone trend. For each matched set, a 2×C table classifying subjects according to disease and exposure status is formed. The statistic is determined by assigning the columns quantitative values corresponding to exposure level. The statistic also may be derived as a score test for no association within each matched set, by use of a model for the log odds of disease, which is linear in exposure level.To obtain the S-TDT by use of the Mantel extension test, sibship is used as the stratifying variable, and for each sibship a 2×3 table cross-classifying sibs on the basis of disease status and genotype is formed. The quantitative value of exposure that yields the S-TDT assigns to each genotype the number of putative disease-associated alleles that a sib has (i.e., 2, 1, or 0) for genotypes AA, AB, or BB.The advantages of viewing the S-TDT as a Mantel extension test are threefold. First, the test is already widely available on commercial software. For example, SAS currently implements the Mantel extension test as part of its Cochran-Mantel-Haenszel procedure. This version of the test allows user-specified scores for the levels of the quantitative variable but does not provide a continuity correction. Another program, StatXact, provides an exact P value for the Mantel extension test, as well as the asymptotic P value.Second, it immediately is obvious how to use the test with other genetic models. For example, for an arbitrary genetic model, an investigator may want to use the 2-df Mantel-Haenszel test, which makes no assumption about how risk varies with number of A alleles. To test a dominant model, a value of 1 would be assigned to genotype AA or AB and a value of 0 to genotype BB; to test a recessive model, exposure values of 1 for AA and of 0 for all other genotypes would be used.Third, if the marker is actually a candidate gene, the investigator may wish to estimate risk ratios. Collapsing over sibships and estimating risk ratios by use of the 2×3 margin results in biased estimates, which may be confounded because sibships may come from different populations with different disease risks and allele distributions. Instead, to estimate risk ratios, a Mantel-Haenszel estimate of odds ratio—or, in the general case, conditional logistic regression (Breslow and Day 1980xBreslow, NE and Day, NE. See all References1980)—should be used.

Comparison of the QTDT analysis for IgE in the CSGA data set.

Over the past few years at least 13 transmission/disequilibrium test (TDT)‐based tests have been developed for quantitative (Q) traits for the assessment of association or linkage in the presence of the other. A total of six of these QTDT methods were used to analyze log10IgE in the Collaborative Study on the Genetics of Asthma data set. Only moderate agreement was found between the tests. The results of the QTDT analyses were only slightly affected by the use of gender and age as covariates. Results from analysis of IgE and log10IgE were inconsistent. Our conclusion is that there is only modest agreement among the QTDT methods examined, covariates should be used even if they have a small effect, and that data should be normalized before analysis.