Getting less of what you want: reductions in statistical power and increased bias when categorizing medication adherence data

Temporal effect of imatinib adherence on time to remission in chronic myeloid leukemia patients

INTRODUCTION

Adherence to imatinib in chronic myeloid leukemia (CML) patients is estimated to be as low as 70% despite its clinical benefit, and our understanding of the impact of nonadherence in this population is limited. This study presents a novel application of the Alternating Conditional Estimation (ACE) algorithm in newly diagnosed CML patients to map the full dose-response curve (DRC) and determine how the strength of this curve varies over time.

METHODS

We applied the ACE algorithm alongside a backward elimination procedure to detect the presence of time dependence and nonlinearity in the relationship between imatinib adherence and time-to-remission. An extended Cox model allowing for the flexible modeling of identified effects via unpenalized B-splines was subsequently fit and assessed.

RESULTS

The substantial improvement in model fit associated with the extended Cox approach suggests that traditional Cox proportional hazards model assumptions do not hold in this setting. Results indicate that the DRC for imatinib is non-linearly increasing, with an attenuated effect above a 74% adherence rate. The strength of this effect on remission varied over time and was strongest in the initial months of treatment, reaching a peak around 90 days post-initiation (log hazard ratio: 2.12, 95% confidence interval: 1.47 to 2.66).

CONCLUSION

Most patients that achieved remission did so by 4 months (120 days) with consistently high adherence, suggesting that this could be a critical time and duration for realizing treatment benefit and patient monitoring. Findings regarding the relationship between adherence and remission can additionally help guide the design of future studies.

Medication Adherence and Healthcare Costs in Chronically Ill Patients Using German Claims Data

BACKGROUND AND OBJECTIVE

Despite the importance of medication adherence for chronically ill patients and the vast literature on its relationship to costs, this field suffers from methodological limitations. These are caused, amongst others, by the lack of generalizability of data sources, varying definitions of adherence, costs, and model specification. We aim to address this with different modeling approaches and to contribute evidence on the research question.

METHODS

We extracted large cohorts of nine chronic diseases (n = 6747-402,898) from German claims data of stationary health insurances between 2012 and 2015 (t0-t3). Defined as the proportion of days covered by medication, we examined the relationship of adherence using several multiple regression models at baseline year t0 with annual total healthcare costs and four sub-categories. Models with concurrent, and differently time-lagged measurements of adherence and costs were compared. Exploratively, we applied non-linear models.

RESULTS

Overall, we found a positive association between the proportion of days covered by medication and total costs, a weak association with outpatient costs, positive with pharmacy costs, and frequently negative with inpatient costs. There were major differences by disease and its severity but little between years, provided adherence and costs were not measured concurrently. The fit of linear models was mainly not inferior to that of non-linear models.

CONCLUSIONS

The estimated effect on total costs differed from most other studies, which highlights concerns about generalizability, although effect estimates in sub-categories were as expected. Comparison of time lags indicates the importance of avoiding concurrent measurement. A non-linear relationship should be considered. These methodological approaches are valuable in future research on adherence and its consequences.

Further psychometric evaluation of the Structured Multidisciplinary Work Evaluation Tool (SMET) questionnaire: Practical implications in healthcare settings

BACKGROUND

Scientific research has identified a lack of psychometrically well-tested methods for evaluation of the work environment in healthcare settings. The Structured Multidisciplinary Work Evaluation Tool (SMET) questionnaire has been evaluated and has shown good content validity, as well as intra-rater and test-retest reliability. There are, however, still unknowns regarding the psychometric properties. If the SMET questionnaire is to be used in practical occupational health service (OHS) work and scientific research in healthcare settings, further psychometric evaluation is needed.

OBJECTIVE

The aim of this study was to gain further understanding of the psychometric properties of the SMET questionnaire when used in research and clinical OHS practice in healthcare settings.

METHODS

The psychometric evaluation was conducted using classical test theory (Cronbach's alpha, explorative factor analysis) and Rasch analysis (measurement targeting, category threshold order, person separation index) on data previously collected in development projects within the healthcare sector.

RESULTS

The results support the use of the SMET questionnaire as a psychometrically well-tested method for evaluation of the work environment in healthcare settings. They support the use of the initial 1-10 scale since all 10 steps are used. The results also support the trichotomization procedure since the trichotomized scale captures the construct of the work environment with good measurement targeting and good category threshold order.

CONCLUSION

The results of this study support the use of the SMET questionnaire as a psychometrically well-tested method for a broad multifactorial evaluation of the work environment in healthcare settings.

Medication Adherence and Cardiometabolic Control Indicators Among American Indian Adults Receiving Tribal Health Services: Protocol for a Longitudinal Electronic Health Records Study

BACKGROUND

American Indian adults have the highest prevalence of type 2 diabetes (T2D) in any racial or ethnic group and experience high rates of comorbidities. Uncontrolled cardiometabolic risk factors-insulin resistance, resulting in impaired glucose tolerance, dyslipidemia, and hypertension-increase the risk of mortality. Mortality is significantly reduced by glucose- and lipid-lowering and antihypertensive medication adherence. Medication adherence is low among American Indian adults living in non-Indian Health Service health care settings. Virtually nothing is known about the nature and extent of medication adherence among reservation-dwelling American Indian adults who primarily receive their medications without cost from Indian Health Service or tribal facilities. Electronic health records (EHRs) offer a rich but underused data source regarding medication adherence and its potential to predict cardiometabolic control indicators (C-MCIs). With the support of the Choctaw Nation of Oklahoma (CNO), we address this oversight by using EHR data generated by this large, state-of-the-art tribal health care system to investigate C-MCIs.

OBJECTIVE

Our specific aims are to determine, using 2018 EHR data, the bivariate relationships between medication adherence and C-MCIs, demographics, and comorbidities and each C-MCI and demographics and comorbidities; develop machine learning models for predicting future C-MCIs from the previous year's medication adherence, demographics, comorbidities, and common laboratory tests; and identify facilitators of and barriers to medication adherence within the context of social determinants of health (SDOH), EHR-derived medication adherence, and C-MCIs.

METHODS

Drawing on the tribe's EHR (2018-2021) data for CNO patients with T2D, we will characterize the relationships among medication adherence (to glucose- and lipid-lowering and antihypertensive drugs) and C-MCIs (hemoglobin A1c ≤7%, low-density lipoprotein cholesterol <100 mg/dL, and systolic blood pressure <130 mm Hg); patient demographics (eg, age, sex, SDOH, and residence location); and comorbidities (eg, BMI ≥30, cardiovascular disease, and chronic kidney disease). We will also characterize the association of each C-MCI with demographics and comorbidities. Prescription and pharmacy refill data will be used to calculate the proportion of days covered with medications, a typical measure of medication adherence. Using machine learning techniques, we will develop prediction models for future (2019-2021) C-MCIs based on medication adherence, patient demographics, comorbidities, and common laboratory tests (eg, lipid panel) from the previous year. Finally, key informant interviews (N=90) will explore facilitators of and barriers to medication adherence within the context of local SDOH.

RESULTS

Funding was obtained in early 2022. The University of Florida and CNO approved the institutional review board protocols and executed the data use agreements. Data extraction is in process. We expect to obtain results from aims 1 and 2 in 2024.

CONCLUSIONS

Our findings will yield insights into improving medication adherence and C-MCIs among American Indian adults, consistent with CNO's State of the Nation's Health Report 2017 goal of reducing T2D and its complications.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/39193.

Identification of target groups and individuals for adherence interventions using tree-based prediction models

Background: In chronically ill patients, medication adherence during implementation can be crucial for treatment success and can decrease health costs. In some populations, regression models do not show this relationship. We aim to estimate subgroup-specific and personalized effects to identify target groups for interventions.

Methods: We defined three cohorts of patients with type 1 diabetes (n = 12,713), type 2 diabetes (n = 85,162) and hyperlipidemia (n = 117,485) from German claims data between 2012 and 2015. We estimated the association of adherence during implementation in the first year (proportion of days covered) and mean total costs in the three following years, controlled for sex, age, Charlson’s Comorbidity Index, initial total costs, severity of the disease and surrogates for health behavior. We fitted three different types of models on training data: 1) linear regression models for the overall conditional associations between adherence and costs, 2) model-based trees to identify subgroups of patients with heterogeneous adherence effects, and 3) model-based random forests to estimate personalized adherence effects. To assess the performance of the latter, we conditionally re-estimated the personalized effects using test data, the fixed structure of the forests, and fixed effect estimates of the remaining covariates.

Results: 1) our simple linear regression model estimated a positive adherence effect, that is an increase in total costs of 10.73 Euro per PDC-point and year for diabetes type 1, 3.92 Euro for diabetes type 2 and 1.92 Euro for hyperlipidemia (all p ≤ 0.001). 2) The model-based tree detected subgroups with negative estimated adherence effects for diabetes type 2 (-1.69 Euro, 24.4% of cohort) and hyperlipidemia (-0.11 Euro, 36.1% and -5.50 Euro, 5.3%). 3) Our model-based random forest estimated personalized adherence effects with a significant proportion (4.2%–24.1%) of negative effects (up to -8.31 Euro). The precision of these estimates was high for diabetes type 2 and hyperlipidemia patients.

Discussion: Our approach shows that tree-based models can identify patients with different adherence effects and the precision of personalized effects is measurable. Identified patients can form target groups for adherence-promotion interventions. The method can also be applied to other outcomes such as hospitalization risk to maximize positive health effects of an intervention.

Intensity of and Adherence to Lipid-Lowering Therapy as Predictors of Major Adverse Cardiovascular Outcomes in Patients With Coronary Heart Disease

Background The effectiveness of lipid-lowering therapy (LLT) is affected by both intensity and adherence. This study evaluated the associations of LLT intensity, adherence, and the combination of these 2 aspects of LLT management with the risk of major adverse cardiovascular events (MACE) in people with coronary heart disease. Methods and Results This is an observational study of all adults who suffered a myocardial infarction or had coronary revascularization during 2012 to 2018 and initiated LLT in Stockholm, Sweden. Study exposures were LLT adherence (proportion of days covered), LLT intensity (expected reduction of low-density lipoprotein cholesterol), and the combined measure of adherence and intensity. At each LLT fill, adherence and intensity during the previous 12 months were calculated. The primary outcomes were MACE (nonfatal myocardial infarction or stroke and death); secondary outcomes were low-density lipoprotein cholesterol goal attainment and individual components of MACE. We studied 20 490 patients aged 68±11 years, 75% men, mean follow-up 2.6±1.1 years. Every 10% increase in 1-year adherence, intensity, or adherence-adjusted intensity was associated with a lower risk of MACE (hazard ratio [HR], 0.94 [95% CI, 0.93-0.96]; HR, 0.92 [95% CI, 0.88-0.96]; and HR, 0.91 [95% CI, 0.89-0.94], respectively) and higher odds of attaining low-density lipoprotein cholesterol goals (odds ratio [OR],1.12 [95% CI, 1.10-1.15]; OR, 1.42 [95% CI, 1.34-1.51], and OR, 1.16 [95% CI, 1.19-1.24], respectively). Among patients with good adherence (≥80%), the risk of MACE was similar with low-moderate and high-intensity LLT despite differences in the low-density lipoprotein cholesterol goal attainment with the treatment intensities. Discontinuation ≥1 year increased the risk markedly (HR,1.66 [95% CI, 1.23-2.22]). Conclusions In routine care, good adherence to LLT was associated with the greatest benefit for patients with coronary heart disease. Strategies that improve adherence and use of intensive therapies could substantially reduce cardiovascular risk.

Antiseizure medication adherence trajectories in Medicare beneficiaries with newly treated epilepsy

OBJECTIVE

This study was undertaken to characterize trajectories of antiseizure medication (ASM) adherence in adults with newly treated epilepsy and to determine predictors of trajectories.

METHODS

This was a retrospective cohort study using Medicare. We included beneficiaries with newly treated epilepsy (one or more ASM and none in the preceding 2 years, plus International Classification of Diseases codes) in 2010-2013. We calculated the proportion of days covered (proportion of total days with any ASM pill supply) for 8 quarters or until death. Group-based trajectory models characterized and determined predictors of trajectories.

RESULTS

We included 24 923 beneficiaries. Models identified four groups: early adherent (60%), early nonadherent (18%), late adherent (11%), and late nonadherent (11%). Numerous predictors were associated with being in the early nonadherent versus early adherent group: non-White race (e.g., Black, odds ratio [OR] = 1.7, 95% confidence interval [CI] = 1.5-1.8), region (e.g., South vs. Northeast: OR = 1.2, 95% CI = 1.1-1.4), and once daily initial medication (OR = 1.1, 95% CI = 1.0-1.3). Predictors associated with decreased odds of being in the early nonadherent group included older age (OR = .9 per decade, 95% CI = .9-.9), female sex (OR = .9, 95% CI = .8-1.0), full Medicaid eligibility (OR = .6, 95% CI = .4-.8), neurologist visit (OR = .6, 95% CI = .6-.7), and initial older generation ASM (OR = .6, 95% CI = .6-.7).

SIGNIFICANCE

We identified four ASM adherence trajectories in individuals with newly treated epilepsy. Whereas risk factors for early nonadherence such as race or geographic region are nonmodifiable, our work highlighted a modifiable risk factor for early nonadherence: lacking a neurologist. These data may guide future interventions aimed at improving ASM adherence, in terms of both timing and target populations.

Comparing Continuous and Binary Group-based Trajectory Modeling Using Statin Medication Adherence Data

BACKGROUND

Of 58 medication adherence group-based trajectory modeling (GBTM) published studies, 74% used binary and 26% used continuous GBTM. Few studies provided a rationale for this choice. No medication adherence studies have compared continuous and binary GBTM.

OBJECTIVE

The objective of this study was to assess whether continuous versus binary GBTM: (1) impacts adherence trajectory shapes; and (2) results in the differential classification of patients into adherence groups.

METHODS

Patients were prevalent statin users with myocardial infarction hospitalization, 66+ years old, and continuously enrolled in fee-for-service Medicare. Statin medication adherence was measured 6 months prehospitalization using administrative claims. Final GBTM specifications beyond default settings were selected using a previously defined standardized procedure and applied separately to continuous and binary (proportion of days covered ≥0.80) medication adherence measures. Assignment to adherence groups was compared between continuous and binary models using percent agreement of patient classification and the κ coefficient.

RESULTS

Among 113,296 prevalent statin users, 4 adherence groups were identified in both models. Three groups were consistent: persistently adherent, progressively nonadherent, and persistently nonadherent. The fourth continuous group was moderately adherent (progressively adherent in the binary model). When comparing patient assignment into adherence groups between continuous and binary trajectory models, only 78.4% of patients were categorized into comparable groups (κ=0.641; 95% confidence interval: 0.638-0.645). The agreement was highest in the persistently adherent group (∼94%).

CONCLUSIONS

Continuous and binary trajectory models are conceptually different measures of medication adherence. The choice between these approaches should be guided by study objectives and the role of medication adherence within the study-exposure, outcome, or confounder.

Title: Functional seizures across the adult lifespan: female sex, delay to diagnosis and disability

OBJECTIVE

The clinical characteristics of functional seizures may vary based on age-of-onset or age-of-presentation. Description of age-related differences has focused on three categories: pediatric, young-adult, and older-adult. We evaluated how factors continuously varied based on age-of-presentation across the adult lifespan.

METHODS

Based on cross-sectional data from 365 adult (18 to 88 years old) patients with documented diagnoses of functional seizures, we evaluated how the quantity and prevalence of specific ictal behaviors, historical factors, and comorbidities varied based on patient age-of-presentation using sequential weighted averages.

RESULTS

Four factors changed prominently with age-of-presentation: female predominance decreased at two inflection points - ages 35 and 62; the prevalence of work disability was higher until age-of-presentation 30 then plateaued at 80%; there was greater delay to diagnosis in older patients; and comorbidities was higher with age-of-presentation, starting from early adulthood. The proportion of patients who presented with functional seizures decreased after 50. Ictal behavior did not substantially vary with age-of-presentation.

CONCLUSION

The time from onset to diagnosis increased with age-of-presentation, which may be related to increased comorbidities and the misconception that FS do not start in older age. The female predominance decreased nonlinearly with age. By age 30, most patients' seizures already had substantial association with unemployment. These findings emphasize that patients can develop functional seizures at any age. The rapid development of disability relatively early in life, which then stays at a high prevalence rate, demonstrates the need for prompt referral for definitive diagnosis and treatment.

Patients' adherence to smartphone apps in the management of bipolar disorder: a systematic review

Background

Despite an increasing number of available mental health apps in the bipolar disorder field, these tools remain scarcely implemented in everyday practice and are quickly discontinued by patients after downloading. The aim of this study is to explore adherence characteristics of bipolar disorder patients to dedicated smartphone interventions in research studies.

Methods

A systematic review following PRISMA guidelines was conducted. Three databases (EMBASE, PsychInfo and MEDLINE) were searched using the following keywords: "bipolar disorder" or "mood disorder" or “bipolar” combined with “digital” or “mobile” or “phone” or “smartphone” or “mHealth” or “ehealth” or "mobile health" or “app” or “mobile-health”.

Results

Thirteen articles remained in the review after exclusion criteria were applied. Of the 118 eligible studies, 39 did not provide adherence characteristics. Among the selected papers, study length, sample size and definition of measures of adherence were strongly heterogeneous. Activity rates ranged from 58 to 91.6%.

Conclusion

The adherence of bipolar patients to apps is understudied. Standardised measures of adherence should be defined and systematically evaluated in future studies dedicated to these tools.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00224-6.

Patterns of transitions between relapse to and remission from heavy drinking over the first year after outpatient alcohol treatment and their relation to long-term outcomes

OBJECTIVES

Studying clinical course after alcohol use disorder (AUD) treatment is central to understanding longer-term recovery. This study's two main objectives were to (a) replicate a recent study that identified heterogeneity in patterns of remission from/relapse to heavy drinking during the first year after outpatient treatment in an independent data set and (b) extend these recent findings by testing associations between patterns of remission/relapse and long-term alcohol-related and functioning outcomes.

METHOD

Latent profile analyses were conducted using data from Project MATCH (N = 952; M age = 38.9; 72.3% female) and COMBINE (N = 1,383; M age = 44.4; 69.1% male). Transitions between heavy and nonheavy drinking within consecutive 2-week periods over a 1-year posttreatment period were characterized for each participant. From this, latent profiles were identified based on participants' initial 2-week heavy drinking status, the number of observed transitions between 2-week periods of relapse and remission, and the average duration of observed remission/relapse episodes.

RESULTS

In both MATCH and COMBINE, we identified six profiles: (a) "continuous remission," 25.3% of COMBINE sample/25.3% of MATCH sample; (b) "transition to remission," 19.6%/9.6%; (c) "few long transitions," 15.9%/33.7%; (d) "many short transitions," 13.2%/13.6%; (e) "transition to relapse," 7.2%/7.1%; and (f) "continuous relapse," 18.8%/10.5%. Profiles 1 and 2 had the best long-term outcomes, Profiles 5 and 6 had the worst, and Profiles 3 and 4 fell between these groups.

CONCLUSIONS

That many individuals can remit from heavy drinking following one or more relapses to heavy drinking may be of direct interest to individuals in recovery from AUD. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Neurodevelopmental effects of low dose ionizing radiation exposure: A systematic review of the epidemiological evidence

BACKGROUND

The neurodevelopmental effects of high doses of ionizing radiation (IR) in children are well established. To what extent such effects exist at low-to-moderate doses is unclear. Considering the increasing exposure of the general population to low-to-moderate levels of IR, predominantly from diagnostic procedures, the study of these effects has become a priority for radiation protection.

OBJECTIVES

We conducted a systematic review of the current evidence for possible effects of low-to-moderate IR doses received during gestation, childhood and adolescence on different domains of neurodevelopment.

DATA SOURCES

Searches were performed in PubMed, Scopus, EMBASE and Psychinfo on the 6th of June 2017 and repeated in December 2018.

STUDY ELIGIBILITY CRITERIA

We included studies evaluating the association between low-to-moderate IR doses received during gestation, childhood and adolescence, and neurodevelopmental functions.

STUDY APPRAISAL AND SYNTHESIS METHODS

Studies were evaluated using the Cochrane Collaboration's risk of bias tool adapted to environmental sciences. A qualitative synthesis was performed.

RESULTS

A total of 26 manuscripts were finally selected. Populations analyzed in these publications were exposed to the following sources of IR: atomic bomb (Hiroshima and Nagasaki), diagnostic/therapeutic radiation, and Chernobyl and nuclear weapon testing fallout. There was limited evidence for an association between low-to-moderate doses of IR and a decrease in general cognition and language abilities, that is, a causal interpretation is credible, but chance or confounding cannot not be ruled out with reasonable confidence. Evidence for a possible stronger effect when exposure occurred early in life, in particular, during the fetal period, was inadequate. Evidence for an association between IR and other specific domains, including attention, executive function, memory, processing speed, visual-spatial abilities, motor and socio-emotional development, was inadequate, due to the very limited number of studies found.

LIMITATIONS, CONCLUSIONS, AND IMPLICATIONS OF KEY FINDINGS

Overall, depending on the domain, there was limited to inadequate evidence for an effect of low-to-moderate IR doses on neurodevelopment. Heterogeneity across studies in terms of outcome and exposure assessment hampered any quantitative synthesis and any stronger conclusion. Future research with adequate dosimetry and covering a range of specific neurodevelopmental outcomes would likely contribute to improve the body of evidence.

SYSTEMATIC REVIEW REGISTRATION NUMBER

The systematic review protocol was registered in PROSPERO (registration number CRD42018091902).

Geriatric pharmacotherapy: Appraising new drugs for neurologic disorders in older patients

New drug development for neurologic disorders has one of the highest attrition rates of all clinical drug developments. This is problematic when, with innovative technology available in so many aspects of life, modern societies expect to have effective treatments for neurodegenerative disorders and mental health conditions that provide something beyond simple symptomatic relief-the expectation is treatment that impacts and mitigates fundamental mechanisms that drive these disorders. The disease burden of neurologic disorders remains extremely high, whereas the proportion of patients receiving effective therapy is relatively low, demonstrating a sizeable unmet medical need. Whether for novel breakthrough therapies or for drugs considered successful, deciding on the basis of clinical trial data whether a particular treatment will be effective for a specific patient is always a leap of faith. However, expertise at reading trial results combined with knowledge of the patient and of his or her disease, together with an understanding of the effect of age on drug pharmacokinetics and pharmacodynamics, the effect of age on the patient's condition, and the effect of age on the patient's life and outlook will ensure the landing is safe. The focus of this article is to provide such knowledge and thereby optimize this expertise.

Impact of Medication Adherence on Health Services Utilization in Medicaid

OBJECTIVE

To examine the impact of adherence to chronic disease medications on health services utilization among Medicaid enrollees.

SUBJECTS

Eligibility, claims, and encounter data from the Medicaid Analytic Extract files from 10 states (Alabama, California, Florida, Illinois, Indiana, Louisiana, New Hampshire, New Mexico, New York, and Virginia) were used to construct a 3-year (2008-2010), longitudinal dataset of Medicaid recipients 18-64 years of age, including 656,646 blind/disabled individuals and 704,368 other adults. Patients were classified as having ≥1 of 7 chronic conditions: (1) congestive heart failure; (2) hypertension; (3) dyslipidemia; (4) diabetes; (5) asthma/chronic obstructive pulmonary disease; (6) depression; and (7) schizophrenia/bipolar.

METHODS

Poisson regression was used to estimate associations between medication adherence [continuous and categorical proportion of days covered (PDC)] and 3 dependent variables: number of inpatient hospitalizations, emergency department visits, and outpatient physician/clinic visits.

RESULTS

Full adherence was associated with 8%-26% fewer hospitalizations and 3%-12% fewer emergency department visits among those with congestive heart failure, hypertension, diabetes, and schizophrenia/bipolar. In all analyses, full adherence was associated with up to 15% fewer outpatient physician/clinic visits. Moreover, low and moderate levels of adherence were also related to less health care use.

CONCLUSIONS

Substantial reductions in health services utilization and costs may be realized with improved medication adherence in Medicaid. These benefits begin to accrue at adherence levels below the common 0.80 PDC threshold. Therefore, interventions should focus not just on perfecting moderate adherers, but also on encouraging Medicaid patients with chronic conditions to initiate pharmacotherapy.

Housing First Improves Adherence to Antipsychotic Medication Among Formerly Homeless Adults With Schizophrenia: Results of a Randomized Controlled Trial

Adherence to antipsychotic medication is a significant challenge among homeless patients. No experimental trials have investigated the impact of Housing First on adherence among patients with schizophrenia. We investigated whether Housing First in congregate and scattered-site configurations resulted in superior adherence compared to usual care. Adult participants (n = 165) met criteria for homelessness, schizophrenia, and initiation of antipsychotic pharmacotherapy prior to recruitment to an unblinded, 3-arm randomized controlled trial in Vancouver, Canada. Randomization arms were: congregate Housing First (CHF) with on-site supports (including physician and pharmacy services); scattered-site Housing First (SHF) with Assertive Community Treatment; or treatment as usual (TAU) consisting of existing services. Participants were followed for an average of 2.6 years. Adherence to antipsychotic medication was measured using the medication possession ratio (MPR), and 1-way ANOVA was used to compare outcomes between the 3 conditions. Data were drawn from comprehensive pharmacy records. Prior to randomization, mean MPR among participants was very low (0.44–0.48). Mean MPR in the follow-up period was significantly different between study arms (P < .001) and approached the guideline threshold of 0.80 in SHF. Compared to TAU, antipsychotic adherence was significantly higher in SHF but not in CHF. The results demonstrate that further implementation of SHF is indicated among homeless people with schizophrenia, and that urgent action is needed to address very low levels of antipsychotic adherence in this population (trial registration: ISRCTN57595077).

Social Support Moderates Effects of Natural Disaster Exposure on Depression and Posttraumatic Stress Disorder Symptoms: Effects for Displaced and Nondisplaced Residents

Social support is a known protective factor against the negative psychological impact of natural disasters. Most past research has examined how the effects of exposure to traumatic events influences whether someone meets diagnostic criteria for depression and posttraumatic stress disorder (PTSD); it has also suggested sequelae of disaster exposure depends on whether survivors are displaced from their homes. To capture the full range of the psychological impact of natural disasters, we examined the buffering effects of social support on depressive symptoms and cluster-specific PTSD symptoms, with consideration of displacement status. In a survey conducted 18 to 24 months after Hurricane Katrina, 810 adults exposed to the disaster reported the number of Katrina-related traumatic events experienced, perceived social support 2 months post-Katrina, and cluster-specific PTSD and depressive symptoms experienced since Katrina. Analyses assessed the moderating effects of social support and displacement and the conditional effects of displacement status. Social support significantly buffered the negative effect of Katrina-related traumatic events on depressive symptoms, B = -0.10, p = .001, and avoidance and arousal PTSD symptoms, B = -0.02, p = .035 and B = -0.02, p = .042, respectively. Three-way interactions were nonsignificant. Conditional effects indicated social support buffered development of depressive symptoms across all residents; however, the moderating effects of support on avoidance and arousal symptoms only appeared significant for nondisplaced residents. Results highlight the protective effects of disaster-related social support among nondisplaced individuals, and suggest displaced individuals may require more formal supports for PTSD symptom reduction following a natural disaster.

Treatment adherence and illness self-management: introduction to the special issue

The current issue is devoted broadly to research on treatment adherence and chronic illness self-management behavior. As the prevalence of chronic illness increases, the pervasive problem of treatment nonadherence is increasingly viewed as having a major impact on treatment outcomes, public health and healthcare costs, making this issue particularly timely. Sixteen articles spanning an array of topics are presented; articles include empirical studies, statistical simulations, systematic reviews, and theoretical commentaries. Studies conducted with diverse patient populations (e.g., chronic headache, diabetes, end-stage renal disease, HIV, hypertension, severe obesity), samples (e.g., adolescents, ethnic/racial minorities, low-income adults, parents, spousal dyads), and designs (e.g., cross-sectional, longitudinal assessment, randomized controlled trial), are represented. This issue highlights psychosocial factors associated with nonadherence, promising interventions to promote adherence, and state-of-the art methods for the study of illness self-management. We hope these articles engender even more high quality, methodologically rigorous research in this important subfield of behavioral medicine.