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Drug interactions involving benzodiazepines and related drugs (BZDs) are increasingly recognized as a contributor to increased risk of unintentional traumatic injury. Yet, it remains unknown to what extent drug interaction triads (3DIs) may amplify BZDs’ inherent injury risk. We identified BZD 3DI signals associated with increased injury rates by conducting high-throughput pharmacoepidemiologic screening of 2000–2019 Optum’s health insurance data. Using self-controlled case series design, we included patients aged ≥ 16 years with an injury while using a BZD + co-dispensed medication (i.e., base pair). During base pair-exposed observation time, we identified other co-dispensed medications as candidate interacting precipitants. Within each patient, we compared injury rates during time exposed to the drug triad versus to the base pair only using conditional Poisson regression, adjusting for time-varying covariates. We calculated rate ratios (RRs) with 95% confidence intervals (CIs) and accounted for multiple estimation via semi-Bayes shrinkage. Among the 65,123 BZD triads examined, 79 (0.1%) were associated with increased injury rates and considered 3DI signals. Adjusted RRs for signals ranged from 3.01 (95% CI = 1.53–5.94) for clonazepam + atorvastatin with cefuroxime to 1.42 (95% CI = 1.00–2.02, p = 0.049) for alprazolam + hydrocodone with tizanidine. These signals may help researchers prioritize future etiologic studies to investigate higher-order BZD interactions.
Aim: To identify skeletal muscle relaxant (SMR) drug-drug-drug (3DI) signals associated with increased rates of unintentional traumatic injury. Methods: We conducted automated high-throughput pharmacoepidemiologic screening of 2000-2019 healthcare data for members of United States commercial and Medicare Advantage health plans. We performed a self-controlled case series study for each drug triad consisting of an SMR base-pair (i.e., concomitant use of an SMR with another medication), and a co-dispensed medication (i.e., candidate interacting precipitant) taken during ongoing use of the base-pair. We included patients aged ≥16 years with an injury occurring during base-pair-exposed observation time. We used conditional Poisson regression to calculate adjusted rate ratios (RRs) with 95% confidence intervals (CIs) for injury with each SMR base-pair + candidate interacting precipitant (i.e., triad) versus the SMR-containing base-pair alone. Results: Among 58,478 triads, 29 were significantly positively associated with injury; confounder-adjusted RRs ranged from 1.39 (95% CI=1.01-1.91) for tizanidine+omeprazole with gabapentin to 2.23 (95% CI=1.02-4.87) for tizanidine+diclofenac with alprazolam. Most identified 3DI signals are new and have not been formally investigated. Conclusion: We identified 29 SMR 3DI signals associated with increased rates of injury. Future etiologic studies should confirm or refute these SMR 3DI signals.
Benzodiazepine receptor agonists and related medications, such as Z-drugs and dual orexin receptor antagonists (BZDs), have been associated with unintentional traumatic injury due to their central nervous system (CNS)-depressant effects. Drug-drug interactions (DDIs) may contribute to the known relationship between BZD use and unintentional traumatic injury, yet evidence is still lacking. We conducted high-throughput pharmacoepidemiologic screening using the self-controlled case series design in a large US commercial health insurance database to identify potentially clinically relevant DDI signals among new users of BZDs. We used conditional Poisson regression to estimate rate ratios (RRs) between each co-exposure (vs. not) and unintentional traumatic injury (primary outcome), typical hip fracture (secondary outcome), and motor vehicle crash (secondary outcome). We identified 48 potential DDI signals (1.1%, involving 39 unique co-dispensed drugs), i.e., with statistically significant elevated adjusted RRs for injury. Signals were strongest for DDI pairs involving zolpidem, lorazepam, temazepam, alprazolam, eszopiclone, triazolam, and clonazepam. We also identified four potential DDI signals for typical hip fracture, but none for motor vehicle crash. Many signals have biologically plausible explanations through additive or synergistic pharmacodynamic effects of co-dispensed antidepressants, opioids, or muscle relaxants on CNS depression, impaired psychomotor and cognitive function, and/or somnolence. While other signals that lack an obvious mechanism may represent true associations that place patients at risk of injury, it is also prudent to consider the roles of chance, reverse causation, and/or confounding by indication, which merit further exploration. Given the high-throughput nature of our investigation, findings should be interpreted as hypothesis generating.
Gastroesophageal reflux disease (GERD) is a common digestive disorder that usually has symptoms including reflux, heartburn, pain when swallowing, etc. Evolving from traditional needle acupuncture and electroacupuncture (EA), transcutaneous electrical acustimulation (TEA) becomes a popular method for treating GERD with its non-invasive intervention feature. Recently, an even more effective method synchronized with respiration in TEA is emerging. However, the current procedure for conducting synchronized TEA (STEA) treatment is mostly based on patients’ manual synchronization, which can generate a big delay or error in the synchronization, significantly compromising the effectiveness of this method. To solve this issue, this research presents a novel STEA device that can automatically detect the user’s respiration wave and synchronize with the breath to conduct TEA. With this automated synchronization device, the patients can inhale and exhale with an uninterrupted and normal respiration pace while receiving the TEA treatment, largely simplifying the treatment procedures and enhancing the effectiveness of the method. The system of the STEA device consists of a chest strip respiration sensing element, a stimulation point identifier, and a stimulation current generator. Experiments were conducted to verify human respiration detection, electrical current generation and synchronization. The results demonstrated the feasibility, effectiveness and reliability of the automated device system.
Background: Use of muscle relaxants is rapidly increasing in the USA. Little is understood about the role of drug interactions in the known association between muscle relaxants and unintentional traumatic injury, a clinically important endpoint causing substantial morbidity, disability, and death. Objective: We examined potential associations between concomitant drugs (i.e., precipitants) taken with muscle relaxants (affected drugs, i.e., objects) and hospital presentation for unintentional traumatic injury. Methods: In a series of self-controlled case series studies, we screened to identify drug interaction signals for muscle relaxant + precipitant pairs and unintentional traumatic injury. We used Optum's de-identified Clinformatics® Data Mart Database, 2000-2019. We included new users of a muscle relaxant, aged 16-90 years, who were dispensed at least one precipitant drug and experienced an unintentional traumatic injury during the observation period. We classified each observation day as precipitant exposed or precipitant unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to estimate rate ratios adjusting for time-varying confounders and then accounted for multiple estimation via semi-Bayes shrinkage. Results: We identified 74,657 people who initiated muscle relaxants and experienced an unintentional traumatic injury, in whom we studied concomitant use of 2543 muscle relaxant + precipitant pairs. After adjusting for time-varying confounders, 16 (0.6%) pairs were statistically significantly and positively associated with injury, and therefore deemed signals of a potential drug interaction. Among signals, semi-Bayes shrunk, confounder-adjusted rate ratios ranged from 1.29 (95% confidence interval 1.04-1.62) for baclofen + sertraline to 2.28 (95% confidence interval 1.14-4.55) for methocarbamol + lamotrigine. Conclusions: Using real-world data, we identified several new signals of potential muscle relaxant drug interactions associated with unintentional traumatic injury. Only one among 16 signals is currently reported in a major drug interaction knowledge base. Future studies should seek to confirm or refute these signals.
Environments that make it easier for people to incorporate physical activity into their daily life may help to reduce high rates of cardiometabolic conditions. Local zoning codes are a policy and planning tool to create more walkable and bikeable environments. This study evaluated relationships between active living-oriented zoning code environments and cardiometabolic conditions (body mass index, hyperlipidemia, hypertension). The study used county identifiers to link electronic health record and other administrative data for a sample of patients utilizing primary care services between 2012 and 2016 with county-aggregated zoning code data and built environment data. The analytic sample included 7,441,991 patients living in 292 counties in 44 states. Latent class analysis was used to summarize municipal- and unincorporated county-level data on seven zoning provisions (e.g., sidewalks, trails, street connectivity, mixed land use), resulting in classes that differed in strength of the zoning provisions. Based on the probability of class membership, counties were categorized as one of four classes. Linear and logistic regression models estimated cross-sectional associations with each cardiometabolic condition. Models were fit separately for youth (aged 5–19), adults (aged 20–59), and older adults (aged 60+). Little evidence was found that body mass index in youth, adults, or older adults or the odds of hyperlipidemia or hypertension in adults or older adults differed according to the strength of active living-oriented zoning. More research is needed to identify the health impacts of zoning codes and whether alterations to these codes would improve population health over the long term.
Post-sedation escort policies are not evidence-based but traditional consensus recommendations made by professional societies. As people travel further for abortion care, escort policies are increasingly difficult to navigate and force people to delay care, compromise privacy, or undergo procedures without sedation. At worst, clinics may turn away people who present without an escort. Recent research shows that patients can be discharged safely after sedation using rideshare or transport services without a known escort. Updating escort policies lowers barriers to abortion and preserves autonomy, comfort, and choice.
Research Objective The quality of chronic disease management is highly variable and low on average. As chronic care is increasingly delivered by teams instead of solo providers, an important yet unresolved question concerns the structure of teams that tend to perform best. Optimal team structure likely depends on the mechanisms by which team-based care may improve outcomes. Mechanisms that may favor larger, more diverse teams include more frequent patient touchpoints, complementarities between team member skill, or a higher chance of developing a strong patient-provider relationship. Conversely, coordination costs or diffusion of responsibility may cap the optimal team size. We examine the relationship between team structure and chronic disease outcomes. Study Design Analysis of patient outcomes by team structure (i.e., number and types of providers seen) using rich, de-identified EHR data. We classify teams by constructing a measure analogous to the Herfindahl–Hirschman Index (HHI) to assess provider concentration across a patient's primary care visits. Our chronic disease outcomes include biomarkers that often determine clinical decision-making, including hemoglobin A1c, blood pressure, and LDL cholesterol, as well as process measures. First, we compare outcomes for patients who receive care from solo providers (HHI 10,000), concentrated teams (5000 < HHI < 10,000), balanced teams (HHI 5000), and diffuse teams (HHI < 5000), adjusting for organization fixed effects and baseline disease biomarkers. Second, we identify the effect of team-based care by studying practices that switch from solo to team-based care, using a difference-in-differences design. Population Studied >10 million primary care visits of >1.2 million patients cared for by ~2000 providers at >250 practices part of the athenahealth network from 2013–2018. Principal Findings Taking diabetes as an example, we find that 75.7% of patients with new-onset disease receive care from a solo provider, 12.3% from a concentrated team, 8.5% from a balanced team, and 3.5% from a diffuse team. We find that process measures differ by team structure, with solo providers ordering fewer A1c tests and prescribing fewer anti-diabetics, compared to concentrated and diffuse teams, but not compared to balanced teams. Finally, we find that solo providers are less likely to bring their patients' diabetes under control compared to diffuse teams, but not compared to balanced or concentrated teams. Conclusions Performance differed by team concentration, suggesting that optimal team structure may trade off team size and frequency of visits per team member. Our study is the first, to our knowledge, to investigate the effect of different team structures on individual biomarker-level outcomes. Implications for Policy or Practice Our results may provide insights about the most effective team structures for increasingly consolidated primary care practices (e.g., PCMHs and ACOs). For practices seeking to adopt a team-based care model, team structure is a potentially important factor to consider when deciding on staffing models. For payers, our results suggest that novel care coordination incentives, such as the CMS Chronic Care Management Services CPT code introduced in 2015, can be designed and further improved upon to encourage team structures that optimize the value of team-based care.
Antidepressants are very widely used and associated with traumatic injury, yet little is known about their potential for harmful drug interactions. We aimed to identify potential drug interaction signals by assessing concomitant medications (precipitant drugs) taken with individual antidepressants (object drugs) that were associated with unintentional traumatic injury. We conducted pharmacoepidemiologic screening of 2000-2015 Optum Clinformatics data, identifying drug interaction signals by performing self-controlled case series studies for antidepressant + precipitant pairs and injury. We included persons aged 16-90 years co-dispensed an antidepressant and ≥1 precipitant drug(s), with an injury during antidepressant therapy. We classified antidepressant person-days as either precipitant-exposed or precipitant-unexposed. The outcome was an emergency department or inpatient discharge diagnosis for unintentional traumatic injury. We used conditional Poisson regression to calculate confounder adjusted rate ratios (RRs) and accounted for multiple estimation via semi-Bayes shrinkage. We identified 330,884 new users of antidepressants who experienced an injury. Among such persons, we studied concomitant use of 7,953 antidepressant + precipitant pairs. Two hundred fifty-six (3.2%) pairs were positively associated with injury and deemed potential drug interaction signals; twenty-two of these signals had adjusted RRs > 2.00. Adjusted RRs ranged from 1.06 (95% confidence interval: 1.00-1.12, p=0.04) for citalopram + gabapentin to 3.06 (1.42-6.60) for nefazodone + levonorgestrel. Sixty-five (25.4%) signals are currently reported in a seminal drug interaction knowledgebase. We identified numerous new population-based signals of antidepressant drug interactions associated with unintentional traumatic injury. Future studies, intended to test hypotheses, should confirm or refute these potential interactions.
Background: Physicians' time with patients is a critical input to care, but is typically measured retrospectively through survey instruments. Data collected through the use of electronic health records (EHRs) offer an alternative way to measure visit length. Objective: To measure how much time primary care physicians spend with their patients, during each visit. Research design: We used a national source of EHR data for primary care practices, from a large health information technology company. We calculated exam length and schedule deviations based on timestamps recorded by the EHR, after implementing sequential data refinements to account for non-real-time EHR use and clinical multitasking. Observational analyses calculated and plotted the mean, median, and interquartile range of exam length and exam length relative to scheduled visit length. Subjects: A total of 21,010,780 primary care visits in 2017. Measures: We identified primary care visits based on physician specialty. For these visits, we extracted timestamps for EHR activity during the exam. We also extracted scheduled visit length from the EHR's practice management functionality. Results: After data refinements, the average primary care exam was 18.0 minutes long (SD=13.5 min). On average, exams ran later than their scheduled duration by 1.2 minutes (SD=13.5 min). Visits scheduled for 10 or 15 minutes were more likely to exceed their allotted time than visits scheduled for 20 or 30 minutes. Conclusions: Time-stamped EHR data offer researchers and health systems an opportunity to measure exam length and other objects of interest related to time.
Background: Primary care practices increasingly include nurse practitioners (NPs), in addition to physicians. Little is known about how the patient mix and clinical activities of colocated physicians and NPs compare. Objectives: To describe the clinical activities of NPs, compared with physicians. Research design: We used claims and electronic health record data from athenahealth Inc., on primary care practices in 2017 and a cross-sectional analysis with practice fixed effects. Subjects: Patients receiving treatment from physicians and NPs within primary care practices. Measures: First, we measured patient characteristics (payer, age, sex, race, chronic condition count) and visit characteristics (new patient, scheduled duration, same-day visit, after-hours visit). Second, we measured procedures performed and diagnoses recorded during each visit. Finally, we measured daily quantity (visit volume, minutes scheduled for patient care, total work relative value units billed) of care. Results: Relative to physicians, NPs treated younger and healthier patients. NPs also had a larger share of patients who were female, non-White, and covered by Medicaid, commercial insurance, or no insurance. NPs scheduled longer appointments and treated more patients on a same-day or after-hours basis. On average, "overlapping" services-those performed by NPs and physicians within the same practice-represented 92% of all service volume. The small share of services performed exclusively by physicians reflected greater clinical intensity. On a daily basis, NPs provided fewer and less intense visits than physicians within the same practice. Conclusions: Our findings suggest considerable overlap between the clinical activities of colocated NPs and physicians, with some differentiation based on intensity of services provided.
The growing ranks of nurse practitioners (NPs) in rural areas of the United States have the potential to help alleviate existing primary care shortages. This study uses a nationwide source of claims- and EHR-data from 2017 to construct measures of NP clinical autonomy and complexity of care. Comparisons between rural and urban primary care practices reveal greater clinical autonomy for rural NPs, who were more likely to have an independent patient panel, to practice with less physician supervision, and to prescribe Schedule II controlled substances. In contrast, rural and urban NPs provided care of similar complexity. These findings provide the first claims- and EHR-based evidence for the commonly held perception that NPs practice more autonomously in rural areas than in urban areas.
Importance Prior studies have identified an association between obesity and prescription opioid use in the US. However, the pain conditions that are factors in this association remain unestablished. Objective To investigate the association between obesity and pain diagnoses recorded by primary care clinicians as reasons for prescription of opioids. Design, Setting, and Participants A cross-sectional study including 565 930 patients aged 35 to 64 years with a body mass index (BMI) measurement recorded in 2016 was conducted. Electronic health records of patients seen by primary care clinicians in the US in the multipayer athenahealth network from January 1, 2015, to December 31, 2017, were reviewed, and data were analyzed from March 1 to September 15, 2019. Main Outcomes and Measures Any prescription of opioids in the 365 days before or after the first BMI measurement in 2016 were identified. All International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, claims within 7 days before each opioid prescription were captured and classified using a pain diagnosis typologic system. Weight was categorized as underweight (BMI, 18.5-19.9), normal weight (BMI, 20.0-24.9), overweight (BMI, 25.0-29.9), obese I (BMI, 30-34.9), obese II (BMI, 35.0-39.9), obese III (BMI, 40.0-49.9), and obese IV (BMI, 50.0-80.0). Results Among 565 930 patients, 329 083 (58.1%) were women. A total of 125 093 patients (22.1%) were aged 35 to 44 years, 199 384 patients (35.2%) were 45 to 54 years, and 241 453 patients (42.7%) were 55 to 64 years. A total of 177 631 patients (31.4%) were overweight and 273 135 patients (48.2%) were obese at baseline. Over 2 years, 93 954 patients (16.6%) were prescribed opioids. The risk of receiving prescription opioids increased progressively with BMI (adjusted relative risk for overweight: 1.08; 95% CI, 1.06-1.10; obese I: 1.24; 95% CI, 1.22-1.26; obese II: 1.33; 95% CI, 1.30-1.36; obese III: 1.48; 95% CI, 1.45-1.51; and obese IV, 1.71; 95% CI, 1.65-1.77). The percentage of patients with opioid prescriptions attributable to an overweight or obese BMI was 16.2% (95% CI, 15.0%-17.4%). Prescription opioids for management of osteoarthritis (relative risk for obese vs normal weight, 1.90; 95% CI, 1.77-2.05) and other joint disorders (relative risk, 1.63; 95% CI, 1.55-1.72) both had stronger associations with obesity than the mean for any pain diagnosis (relative risk, 1.33; 95% CI, 1.31-1.36). Osteoarthritis, other joint disorders, and other back disorders comprised a combined 53.4% of the absolute difference in prescription of opioids by obesity. Conclusions and Relevance Joint and back disorders appear to be the most important diagnoses in explaining the increased receipt of opioid prescriptions among patients with obesity. Addressing the opioid crisis will require attention to underlying sources of demand for prescription opioids, including obesity, through its associations with pain.
athenahealth’s Technology-Enabled Services—Service Outcomes team is responsible for the optimization and scaling of healthcare administration transactions—one in which we complete millions of transactions each day on our customers’ behalf. athenahealth was looking to create technology tooling to gain a better understanding of total process workflows across these service lines—both legacy and new services included. With the legacy service lines, the processes were more set in place (well-known happy paths, built out homegrown tooling, known pain points and exceptions, etc.) and with the newer service, developments were shifting frequently. From all of this, athenahealth turned to Process Mining as the tool to gain clean process insights, to help improve our customer’s experience, and bring more value to their practice.
Purpose. Patient portals of electronic health record systems currently present patients with tables of laboratory test results, but visual displays can increase patient understanding and sensitivity to result variations. We sought to assess physician preferences and concerns about visual display designs as potential motivators or barriers to their implementation. Methods. In an online survey, 327 primary care physicians (>50% patient care time) recruited through the online e-community/survey research firm SERMO compared hemoglobin A1c (HbA1c) test results presented in table format to various visual displays (number line formats) previously tested in public samples. Half of participants also compared additional visual formats displaying target goal ranges. Outcome measures included preferred display format and whether any displays were unacceptable, would change physician workload, or would induce liability concerns. Results. Most (85%–89%) respondents preferred visual displays over tables for result communications both to patients tested for diagnosis purposes and to diagnosed patients, with a design with color-coded categories most preferred. However, for each format (including tables), 11% to 23% rated them as unacceptable. Most respondents also preferred adding goal range information (in addition to standard ranges) for diagnosed patients. While most physicians anticipated no workload changes, 19% to 32% anticipated increased physician workload while 9% to 28% anticipated decreased workload. Between 32% and 40% had at least some liability concerns. Conclusions. Most primary care physicians prefer visual displays of HbA1c test results over table formats when communicating results to patients. However, workload and liability concerns from a minority of physicians represent a barrier for adoption of such designs in clinical settings.
The unremitting growth of older inmates in prison populations is one of the most pressing concerns in federal corrections today; however, empirical research on the topic says little about the causes of these changes. This article addresses this gap by applying an established methodology to analyze and quantify the contributions of key factors driving the growth of aging federal prison populations. Specifically, we use data from the Federal Justice Statistics Program (FJSP) to determine how changes to prisoner age at entry, rate of entry, and rate of exit have shaped the prison population over recent decades. Overall, we find that from 1994 to 2004, rapid increases in the rate of prisoner admission explain the majority of growth in the elderly population, but that since 2004, age at admission has been much more important, with longer time served and rate of admission also playing a role. These influences appear to be quite different from those shaping state prison populations. Our results suggest optimal policy responses to aging populations will need to be tailored to their jurisdiction.
Background: Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems. Methods: We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012-16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution. Results: In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity. Conclusions: With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.
Background: The purpose of this study was to examine ventilation patterns, including tidal volume (TV) and positive end-expiratory pressure (PEEP) selection in morbidly obese patients undergoing laparoscopic Roux-en-Y gastric bypass or laparoscopic sleeve gastrectomy. Methods: Intraoperative ventilation data, including TV and PEEP, were abstracted from the electronic anesthesia record (Metavision) at Brigham and Women's Hospital. Ideal body weight (IBW) was calculated using the Devine formula, and TV per kg IBW was calculated for each patient. Results: The mean TV delivered per kg IBW was 7.35 ± 1.07 mL/kg, and 24% (281/1186) of patients received TVs of >8 mL/kg IBW. The median PEEP applied was 5.5 ± 0.6 cmH2O, and 87% (1035/1186) of patients received PEEP >0 cmH2O. There was significant variation in both TV and PEEP selection. Conclusions: The significant variation in TV per kg IBW as well as in PEEP values at our institution may reflect the lack of well-established guidelines for intraoperative ventilation. Many patients in this study received inappropriately large TVs (>8 mL/kg IBW), which may be due to calculation of TVs based on total body weight rather than IBW. Patients of shorter stature and higher body mass index appear to be at higher risk for ventilation with inappropriately large TVs.
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Robert Reimann
  • athenaCollector Experience Design
Sophie Chung
  • Medical Information
Josiah Eikelboom
  • Customer Care
Watertown, United States