Factors Influencing the Medication Adherence in Adults With Attention-Deficit/Hyperactivity Disorder (ADHD) and Its Impact on Healthcare Utilization.

OBJECTIVE: This study aims to investigate the factors influencing medication adherence among adults with Attention-Deficit/Hyperactivity Disorder (ADHD) and impact of central nervous system stimulants (CNS) adherence on healthcare utilization (HCU). Methods: This was a cross-sectional study using Medical Expenditure Panel Survey 2013 to 2019, with participants (≥18 years of age) with ADHD and had at least one CNS prescription. Multivariate logistic and linear regression were utilized to evaluate the medication adherence and its impact on HCU, respectively. RESULT: Total 798 (10,718,005 weighted) ADHD patients, were mostly White (81%), aged between 18 and 25 (35%), and non-adherent to CNS (65%). The use of extended-release medications (OR = 1.51 [1.03, 2.23]) and new users (OR = 3.46 [2.12, 5.63], p ≤ .05) were positively associated with medication adherence. The adherent group utilized more outpatient visits (0.04 vs. 0.46) and prescription refills (18.38 vs. 31.25) compared to non-adherent. CONCLUSION: Our findings can be applied to improve the medication adherence, patient education, and optimize intervention for adults with ADHD.

The association between polypharmacy and health-related quality of life among non-dialysis chronic kidney disease patients.

BACKGROUND AND OBJECTIVE: The United States government spends over $85 billion annually on treating non-dialysis chronic kidney disease (CKD). Patients with CKD are prescribed a multitude of medications to manage numerous comorbidities associated with CKD. Thus, this study aims to investigate the association between polypharmacy and health-related quality of life (HRQoL) in non-dialysis CKD patients. METHODS: This cross-sectional study utilized data from the Medical Expenditure Panel Survey (MEPS) from 2010 through 2019. We classified polypharmacy into three groups based on the number of medication classes: ≤ 4 (minor polypharmacy), 5 through 9 (major polypharmacy), and ≥ 10 (hyperpolypharmacy). To measure HRQoL, a Physical Component Summary (PCS) and a Mental Component Summary (MCS) were obtained from the 12-item Short-Form Health Survey version 2 and Veteran's Rand 12 item. We applied multivariable ordinary least squares regression to assess the association between polypharmacy and HRQoL in non-dialysis CKD patients. RESULTS: A total of 649 CKD patients (weighted n = 667,989) were included. Patients with minor polypharmacy, major polypharmacy, and hyperpolypharmacy were 22.27%, 48.24%, and 29.48%, respectively. Major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower PCS scores when compared with minor polypharmacy [Beta = -3.12 (95% CI: -3.62, -2.62), p-value<0.001; Beta = -4.13 (95CI: -4.74, -3.52), p-value<0.001]. Similarly, major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower MCS scores when compared to minor polypharmacy [Beta = -0.38 (95% CI: -0.55, -0.20), p-value<0.001; Beta = -1.70 (95% CI: -2.01, -1.40), p-value<0.001]. The top 5 classes of medications used by CKD patients were antihyperlipidemic (56.31%), beta-adrenergic blockers (49.71%), antidiabetics (42.14%), analgesics (42.17%), and diuretics (39.65%). CONCLUSION: Our study found that both major polypharmacy and hyperpolypharmacy were associated with lower HRQoL among non-dialysis CKD patients. This study highlights the need for further evaluation of the combination of medications taken by non-dialysis CKD patients to minimize unnecessary and inappropriate medication use.

Thiamine utilization and the lack of prescribing standardization: A critical examination.

OBJECTIVES: Thiamine is often prescribed for thiamine deficiency during hospitalization despite the lack of US-based clinical guidelines. This study aims to evaluate thiamine prescribing patterns and key characteristics associated with the deficiency to address gaps in care. METHODS: Data were obtained from electronic health records of hospitalized patients between September 1, 2021, and March 30, 2022. Alcohol use disorder (AUD) was defined by a positive Clinical Institute Withdrawal Assessment score or a positive serum alcohol level upon admission. Geriatric patients were defined as age ≥65. Cohort 1 was defined as: AUD, albumin <4 g/L, INR >1.5, and total bilirubin >3 mg/dL. Cohort 2 was defined as: age >65, albumin <4 g/L, hemoglobin <15 g/dL, and folate <4 ng/mL. A multivariable LASSO regression model was used to identify characteristics associated with higher thiamine dosing (>100 mg/day). RESULTS: Among 780 patients, 520 (66.7%) were identified as AUD, of which 265 (50.1%) were between the ages of 45-64 years. The AUD cohort was significantly different (p < 0.05) in the mean serum albumin 4.16 g/L (IQR: 3.8-4.5), AST 73.55 U/L (23.75-82.00), ALT 52.57 U/L (17.00-57.00), total bilirubin 0.98 (0.3-1.0), and INR 1.1 (0.99-1.12), compared to non-AUD patients with a mean serum albumin 3.75 g/L (3.3-4.2), AST 35.07 U/L (11.00-42.00), ALT 32.77 U/L (5.00-34.00), total bilirubin 0.89 (0.2-0.9), and INR 1.21 (1.0-1.22). In the geriatric cohort, 136 patients (17%) had a mean serum albumin 3.77 g/L (3.4-4.2), AST 38.66 U/L (14.0-41.0), ALT 29.36 U/L (9.0-37.0), total bilirubin 0.62 mg/dL (0.30-0.90), and direct bilirubin 0.12 mg/dL (0.00-0.20), compared to the non-geriatric cohort with a mean serum albumin 4.10 g/L (3.8-4.40), AST 66.44 U/L (21.0-75.0), ALT 50.03 U/L (16.00-53.75), total bilirubin 1.02 mg/dL (0.30-1.00), and direct bilirubin 0.31 mg/dL (0.00-0.20). In cohort 1, 40.6% patients were between 51 and 64 years old, (66.5%) male, and had a BMI <25 (36.4%). In cohort 2, 52.6% were between 65 and 70 years old, (57.9%) male, and had a BMI <25 (57.9%). Cohort 1 were prescribed a dose of 100 mg (47.7 %), oral (63.5%), intramuscular (18.2%), daily (58.9%), one-day duration (49.4%) most frequently. Cohort 2 were prescribed a dose of 100 mg (56.0%), oral (77.2%), daily (77.2%), one-day duration (29.8%) most frequently. The AUD was significantly associated with having a higher dosage (e.g., >100 mg) of thiamine prescribed per day OR 1.62 (1.11-2.37) (p < 0.01). CONCLUSIONS: This study confirms that thiamine prescribing patterns vary during hospitalization and suggest specific laboratory findings may aid in identifying cohorts associated with the deficiency.

Opioid-related polysubstance use and its effect on mortality and health resource utilization among trauma patients.

BACKGROUND: Pre-injury opioid use is common, but the effects of opioid-related polysubstance use on mortality and health resources utilization (HRU) have not been investigated yet. The objective of this study was to investigate the effects of opioid-related polysubstance use on mortality and HRU among patients in trauma centres in the US. METHODS: We conducted a retrospective cross-sectional study using the US National Trauma Databank from the year 2017 to 2019. Patients (≥ 18 years of age) who tested positive for opioids were included. Patients were analysed based on the number of substances used (i.e., opioids only, two substances (opioids + 1 substance), and three or more than three substances (opioids + ≥ 2 substances)), and polysubstance by type (i.e., opioids only, opioids and alcohol, opioids and stimulants, opioids and benzodiazepine, and other combinations). Multivariate logistic regression was used to determine the association between polysubstance use, mortality and HRU (i.e., need for hospital admission, ICU, and mechanical ventilation). RESULTS: Both polysubstance by number and type analyses showed that opioid-related polysubstance use was not significantly associated with mortality compared to opioids only. The odds of hospital admission were higher among the opioids and benzodiazepines group (OR 1.15, 95% CI 1.06-1.24, p < 0.01). The need for ICU was magnified using benzodiazepines and stimulants with opioids (OR 1.44, 95% CI 1.27-1.63, p < 0.01) when compared to the opioids only group. CONCLUSION: Opioid-related pre-injury polysubstance use was associated with higher HRU in trauma patients. The evidence can be used by policymakers and practitioners to improve patient outcomes in trauma centers.

Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.

BACKGROUND: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient's condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as predictors of clinical outcomes remains unexplored. METHODS: A retrospective cohort study was conducted using the medical records of 322 intensive care unit (ICU) patients. The predictors of interest included the medication regimen complexity index (MRCI) at admission, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Sequential Organ Failure Assessment (SOFA) score, or a combination thereof. Outcomes included mortality, length of stay, and the need for mechanical ventilation. Machine learning algorithms were used for outcome classification after correcting for class imbalances in the general population and across the racial continuum. RESULTS: The home medication model could predict all clinical outcomes accurately 70% of the time. Among Whites, it improved to 80%, whereas among non-Whites it remained at 70%. The addition of SOFA and APACHE II yielded the best models among non-Whites and Whites, respectively. SHapley Additive exPlanations (SHAP) values showed that low MRCI scores were associated with reduced mortality and LOS, yet an increased need for mechanical ventilation. CONCLUSION: Home medication histories represent a viable addition to traditional predictors of health outcomes.

Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill.

Background: Medication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The objective of this study was to examine the relationship between the scores of different MRC tools and the clinical outcomes. Methods: We conducted a retrospective cohort study at Roger William Medical Center, Providence, Rhode Island, which included 317 adult patients admitted to the intensive care unit (ICU) between 1 February 2020 and 30 August 2020. MRC was assessed using the MRC Index (MRCI) and MRC for the Intensive Care Unit (MRC-ICU). A multivariable logistic regression model was used to identify associations among MRC scores, clinical outcomes, and a logistic classifier to predict clinical outcomes. Results: Higher MRC scores were associated with increased mortality, a longer ICU length of stay (LOS), and the need for mechanical ventilation (MV). MRC-ICU scores at 24 h were significantly (p < 0.001) associated with increased ICU mortality, LOS, and MV, with ORs of 1.12 (95% CI: 1.06−1.19), 1.17 (1.1−1.24), and 1.21 (1.14−1.29), respectively. Mortality prediction was similar using both scoring tools (AUC: 0.88 [0.75−0.97] vs. 0.88 [0.76−0.97]. The model with 15 medication classes outperformed others in predicting the ICU LOS and the need for MV with AUCs of 0.82 (0.71−0.93) and 0.87 (0.77−0.96), respectively. Conclusion: Our results demonstrated that both MRC scores were associated with poorer clinical outcomes. The incorporation of MRC scores in real-time therapeutic decision making can aid clinicians to prescribe safer alternatives.

Survival and recovery modeling of acute kidney injury in critically ill adults.

Objectives: Acute kidney injury is common among the critically ill. However, the incidence, medication use, and outcomes of acute kidney injury have been variably described. We conducted a single-center, retrospective cohort study to examine the risk factors and correlates associated with acute kidney injury in critically ill adults with a particular focus on medication class usage. Methods: We reviewed the electronic medical records of all adult patients admitted to an intensive care unit between 1 February and 30 August 2020. Acute kidney injury was defined by the 2012 Kidney Disease: Improving Global Outcomes guidelines. Data included were demographics, comorbidities, symptoms, laboratory parameters, interventions, and outcomes. The primary outcome was acute kidney injury incidence. A Least Absolute Shrinkage and Selection Operator regression model was used to determine risk factors associated with acute kidney injury. Secondary outcomes including acute kidney injury recovery and intensive care unit mortality were analyzed using a Cox regression model. Results: Among 226 admitted patients, 108 (47.8%) experienced acute kidney injury. 37 (34.3%), 39 (36.1%), and 32 patients (29.6%) were classified as acute kidney injury stages I-III, respectively. Among the recovery and mortality cohorts, analgesics/sedatives, anti-infectives, and intravenous fluids were significant (p-value < 0.05). The medication classes IV-fluid electrolytes nutrition (96.7%), gastrointestinal (90.2%), and anti-infectives (81.5%) were associated with an increased odds of developing acute kidney injury, odd ratios: 1.27, 1.71, and 1.70, respectively. Cox regression analyses revealed a significantly increased time-varying mortality risk for acute kidney injury-stage III, hazard ratio: 4.72 (95% confidence interval: 1-22.33). In the recovery cohort, time to acute kidney injury recovery was significantly faster in stage I, hazard ratio: 9.14 (95% confidence interval: 2.14-39.06) cohort when compared to the stage III cohort. Conclusion: Evaluation of vital signs, laboratory, and medication use data may be useful to determine acute kidney injury risk stratification. The influence of particular medication classes further impacts the risk of developing acute kidney injury, necessitating the importance of examining pharmacotherapeutic regimens for early recognition of renal impairment and prevention.

The influence of demographic and meteorological factors on temporal patterns of rotavirus infection in Dhaka, Bangladesh.

To quantify the potential impact of rotavirus vaccines and identify strategies to improve vaccine performance in Bangladesh, a better understanding of the drivers of pre-vaccination rotavirus patterns is required. We developed and fitted mathematical models to 23 years (1990-2012) of weekly rotavirus surveillance data from Dhaka with and without incorporating long-term and seasonal variation in the birth rate and meteorological factors. We performed external model validation using data between 2013 and 2019 from the regions of Dhaka and Matlab. The models showed good agreement with the observed age distribution of rotavirus cases and captured the observed shift in seasonal patterns of rotavirus hospitalizations from biannual to annual peaks. The declining long-term trend in the birth rate in Bangladesh was the key driver of the observed shift from biannual to annual winter rotavirus patterns. Meteorological indices were also important: a 1°C, 1% and 1 mm increase in diurnal temperature range, surface water presence and degree of wetness were associated with a 19%, 3.9% and 0.6% increase in the transmission rate, respectively. The model demonstrated reasonable predictions for both Dhaka and Matlab, and can be used to evaluate the impact of rotavirus vaccination in Bangladesh against changing patterns of disease incidence.

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes. METHODS: This study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model's prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality. RESULTS: The results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables. CONCLUSION AND RELEVANCE: Inclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.

Corrigendum: Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining.

[This corrects the article DOI: 10.3389/fmicb.2019.00687.].

Modeling of rotavirus transmission dynamics and impact of vaccination in Ghana.

BACKGROUND: Rotavirus incidence remains relatively high in low-income countries (LICs) compared to high-income countries (HICs) after vaccine introduction. Ghana introduced monovalent rotavirus vaccine in April 2012 and despite the high coverage, vaccine performance has been modest compared to developed countries. The predictors of low vaccine effectiveness in LICs are poorly understood, and the drivers of subnational heterogeneity in rotavirus vaccine impact are unknown. METHODS: We used mathematical models to investigate variations in rotavirus incidence in children <5 years old in Ghana. We fit models to surveillance and case-control data from three different hospitals: Korle-Bu Teaching Hospital in Accra, Komfo Anokye Teaching Hospital in Kumasi, and War Memorial Hospital in Navrongo. The models were fitted to both pre- and post-vaccine data to estimate parameters describing the transmission rate, waning of maternal immunity, and vaccine response rate. RESULTS: The seasonal pattern and age distribution of rotavirus cases varied among the three study sites in Ghana. Our model was able to capture the spatio-temporal variations in rotavirus incidence across the three sites and showed good agreement with the age distribution of observed cases. The rotavirus transmission rate was highest in Accra and lowest in Navrongo, while the estimated duration of maternal immunity was longer (~5 months) in Accra and Kumasi and shorter (~3 months) in Navrongo. The proportion of infants who responded to the vaccine was estimated to be high in Accra and Kumasi and low in Navrongo. CONCLUSIONS: Rotavirus vaccine impact varies within Ghana. A low vaccine response rate was estimated for Navrongo, where rotavirus is highly seasonal and incidence limited to a few months of the year. Our findings highlight the need to further explore the relationship between rotavirus seasonality, maternal immunity, and vaccine response rate to determine how they influence vaccine effectiveness and to develop strategies to improve vaccine impact.

Correlates of Nonrandom Patterns of Serotype Switching in Pneumococcus.

BACKGROUND: Pneumococcus is a diverse pathogen, with >90 serotypes, each of which has a distinct polysaccharide capsule. Pneumococci can switch capsules, evading vaccine pressure. Certain serotype pairs are more likely to occur on the same genetic background as a results of serotype switching, but the drivers of these patterns are not well understood. METHODS: We used the PubMLST and Global Pneumococcal Sequencing Project databases to quantify the number of genetic lineages on which different serotype pairs occur together. We also quantified the genetic diversity of each serotype. Regression model were used to evaluate the relationship between shared polysaccharide components and the frequency of serotype co-occurrence and diversity. RESULTS: A number of serotype pairs occurred together on the same genetic lineage more commonly than expected. Co-occurrence of between-serogroup pairs was more common when both serotypes had glucose as a component of the capsule (and, potentially, glucuronic acid, any-N-acetylated sugar, or ribitol). Diversity also varied markedly by serotype and was associated with the presence of specific sugars in the capsule. CONCLUSIONS: Certain pairs of serotypes are more likely to co-occur on the same genetic background. These patterns were correlated with shared polysaccharide components. This might reflect adaptation of strains to produce capsules with specific characteristics.

Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining.

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 21,243 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a <1% false-discovery rate. MDR rules were more stable across consecutive years than between slaughter and retail. Rules were decomposed into networks with antimicrobials as nodes and rules as edges. A strong subnetwork of beta-lactam resistance existed in each year and the beta-lactam resistances also had strong associations with sulfisoxazole, gentamicin, streptomycin and tetracycline resistances. The association rules concur with previously identified E. coli resistance patterns but provide significant flexibility for studying MDR in large datasets.

A data-driven individual-based model of infectious disease in livestock operation: A validation study for paratuberculosis.

Chronic livestock diseases cause large financial loss and affect animal health and welfare. Controlling these diseases mostly requires precise information on both individual animal and population dynamics to inform the farmer's decisions, but even successful control programmes do by no means assure elimination. Mathematical models provide opportunities to test different control and elimination options rather than implementing them in real herds, but these models require robust parameter estimation and validation. Fitting these models to data is a difficult task due to heterogeneities in livestock processes. In this paper, we develop an infectious disease modeling framework for a livestock disease (paratuberculosis) that is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Infection with MAP leads to reduced milk production, pregnancy rates, and slaughter value and increased culling rates in cattle and causes significant economic losses to the dairy industry. These economic effects are particularly important motivations in the control and elimination of MAP. In this framework, an individual-based model (IBM) of a dairy herd was built and MAP infection dynamics was integrated. Once the model produced realistic dynamics of MAP infection, we implemented an evaluation method by fitting it to data from three dairy herds from the Northeast region of the US. The model fitting exercises used least-squares and parameter space searching methods to obtain the best-fitted values of selected parameters. The best set of parameters were used to model the effect of interventions. The results show that the presented model can complement real herd statistics where the intervention strategies suggest a reduction in MAP prevalence without elimination. Overall, this research not only provides a complete model for MAP infection dynamics in a dairy herd but also offers a method for estimating parameters by fitting IBM models.

An agent-based model evaluation of economic control strategies for paratuberculosis in a dairy herd.

This paper uses an agent-based simulation model to estimate the costs associated with Mycobacterium avium ssp. paratuberculosis (MAP), or Johne's disease, in a milking herd, and to determine the net benefits of implementing various control strategies. The net present value (NPV) of a 1,000-cow milking herd is calculated over 20 yr, parametrized to a representative US commercial herd. The revenues of the herd are generated from sales of milk and culled animals. The costs include all variable and fixed costs necessary to operate a representative 1,000-cow milking herd. We estimate the NPV of the herd with no MAP infection, under an expected endemic infection distribution with no controls, and under an expected endemic infection distribution with various controls. The initial number of cows in a herd with an endemic MAP infection is distributed as 75% susceptible, 13% latent, 9% low MAP shedding, and 3% high MAP shedding. Control strategies include testing using ELISA and fecal culture tests and culling of cows that test positive, and culling based on observable milk production decrease. Results show that culling cows based on test results does not increase the herd's NPV and in most cases decreases NPV due to test costs as well as false positives and negatives with their associated costs (e.g., culling healthy cows and keeping infected cows). Culling consistently low producing cows when MAP is believed to be present in the herd produces higher NPV over the strategy of testing and culling MAP infected animals, and over the case of no MAP control.

Modeling of Mycobacterium avium subsp. paratuberculosis dynamics in a dairy herd: An individual based approach.

In the dairy industry, Johne's disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP) is one of the major investigated diseases. To date, researchers have suggested some control strategies for JD, such as test-and-cull based herd management, isolated calf rearing management, and vaccinations. Due to the slow progressing nature of MAP, tests with low diagnostic test sensitivity and specificity, and economic limitations, implementing these strategies has not resulted in elimination of MAP from farms. To date, no study has integrated detailed dairy herd dynamics with different MAP transmission routes. We have developed an individual-based dairy herd model by incorporating basic herd dynamics in a closed herd environment where no new animals have been bought from outside. The model considered three age groups of animals: calves, heifers and adults. It includes sequential life events of a dairy animal and such key dynamic processes of the dairy herd as lactation cycle, calving, voluntary waiting period, insemination, pregnancy, dry-off period and calf and heifer rearing. After initially validating that the model reproduced typical herd dynamics, it was extended by incorporating MAP infection dynamics, where each individual adult animal belonged to one of four infection compartments: susceptible, latent, low shedding and high shedding. The model includes two disease transmission routes: horizontal transmission (i.e., fecal-oral) and vertical transmission (i.e., in utero infection). The results confirm that this model can simulate a realistic dairy herd and that inclusion of the above-mentioned dynamic processes provides useful information about individual infected animals to farmers. Access to the individual animal information offers more validity to assessment of appropriate control strategies for an endemically MAP infected herd. This model can serve as an accurate and novel tool not only to better understand MAP dynamics, but is also valuable as an individual based system of a typical dairy herd that can be applied to other research questions.