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ObjectiveWe aim to examine the relationships between substance use disorders and preventable hospitalizations for Ambulatory Care Sensitive Conditions among adult Medicaid beneficiaries.MethodsCross-sectional analysis using de-identified Medicaid claims data in 2012 from 177,568 beneficiaries in Missouri was conducted. Logistic regression models were estimated for the associations of substance use disorder status with Ambulatory Care Sensitive Conditions, demographics, chronic physical and mental illnesses. Zero-inflated negative binomial regressions assessed substance use disorders, hospitalization for Ambulatory Care Sensitive Conditions, and length of hospital stay for Ambulatory Care Sensitive Conditions adjusting for co-morbid physical illnesses, mental illnesses and demographics.ResultsOver 12% of the sample had been diagnosed for substance use disorder. Beneficiaries with substance use disorder were more likely than Nonsubstance use disorder beneficiaries to have admissions for chronic conditions including short/long-term complications of diabetes, uncontrolled diabetes, hypertension, chronic obstructive pulmonary disease/asthma, but not for acute conditions. While substance use disorder beneficiaries were more likely than Nonsubstance use disorder beneficiaries to be hospitalized for any Ambulatory Care Sensitive Conditions; there were no statistical differences between the two groups in terms of length of hospital stays.ConclusionsSubstance use disorder is statistically associated with hospitalizations for most Ambulatory Care Sensitive Conditions but not with length of hospital stay for Ambulatory Care Sensitive Conditions, after adjusting for covariates. The significant associations between substance use disorder and Ambulatory Care Sensitive Condition admissions suggest unmet primary health care needs for substance use disorder beneficiaries and a need for integrated primary/behavioral healthcare.

The readability of a computer program has recently attained a high level of interest deriving in part from its expected close relationship with program maintainability; debugging and modification expenses are understood to account for a large proportion of software costs over the life of the software.A computable measure of readability would therefore be useful to program developers during coding and to those assuming responsibility for maintenance of software developed elsewhere. In a series of Algol 68 programs, analyzer generated (machine-computable) and human-judged program factors were examined. The first two present authors found that program length and reasonable practice concerning identifier length were excellent predictors of judgments of readability. These predictors were chosen from a large set of analyzer-generated predictors including software science measures as defined by Halstead and several others; the analyzer-generated predictors were found to replicably estimate a high proportion (41 percent) of variance in readability in new readability judgments.While an estimate of readability based only on analyzer-generated predictors would be clearly useful, human ratings (such as quality of comments, logicality of control flow, and meaningfulness of identifier names) were examined to determine whether they could add significancy to the quality of estimates of readability. The addition of the rating of well structured control flow to the set of analyzer-generated predictors increased the proportion of replicably estimated variance in new readability judgments from 41 to 72 percent.

The emergence of managed behavioral health care has increased the value of data describing outcomes of mental health treatment. At the same time, increased development of the national information infrastructure and other computer linkage systems has facilitated the flow of information among a wide network of data systems. These two developments create a dynamic tension between the need to share information and the need to protect the privacy of mental health clients and the confidentiality of their computerized records. This problem is exacerbated by the cost associated with potential solutions. Unfortunately, policy development in this area has lagged behind rapid developments in technology. The mental health administrator must balance the three components of this conflict (the increasing need for information transfer, the protection of confidentiality, and cost) without a great deal of guidance. This article offers recommendations that may help the mental health administrator manage this conflict.