Association between universal health coverage and the disease burden of acute illness and injury at the global level.

BACKGROUND: This study examines the relationship between universal health coverage (UHC) and the burden of emergency diseases at a global level. METHODS: Data on Disability-Adjusted Life Years (DALYs) from emergency conditions were extracted from the Institute for Health Metrics and Evaluation (IHME) database for the years 2015 and 2019. Data on UHC, measured using two variables 1) coverage of essential health services and 2) proportion of the population spending more than 10% of household income on out-of-pocket health care expenditure, were extracted from the World Bank Database for years preceding our outcome of interest. A linear regression was used to analyze the association between UHC variables and DALYs for emergency diseases, controlling for other variables. RESULTS: A total of 132 countries were included. The median national coverage of essential health services index was 67.5/100, while the median national prevalence of catastrophic spending in the sample was 6.74% of households. There was a strong significant relationship between health service coverage and the burden of emergency diseases, with an 11.5-point reduction in DALYs of emergency medical diseases (95% CI -9.5, -14.8) for every point increase in the coverage of essential health services index. There was no statistically significant relationship between catastrophic expenditures and the burden of emergency diseases, which may be indicative of inelastic demand in seeking services for health emergencies. CONCLUSION: Increasing the coverage of essential health services, as measured by the essential health services index, is strongly correlated with a reduction in the burden of emergency conditions. In addition, data affirms that financial protection remains inadequate in many parts of the globe, with large numbers of households experiencing significant economic duress related to seeking healthcare. This evidence supports a strategy of strengthening UHC as a means of combating death and disability from health emergencies, as well as extending protection against impoverishment related to healthcare expenses.

Latino Household Transmission of Severe Acute Respiratory Syndrome Coronavirus 2.

We assessed temporal changes in the household secondary attack rate of severe acute respiratory syndrome coronavirus 2 and identified risk factors for transmission in vulnerable Latino households of Baltimore, Maryland. The household secondary attack rate was 45.8%, and it appeared to increase as the alpha variant spread, highlighting the magnified risk of spread in unvaccinated populations.

Coalition of Faith Leaders, Community Organizations, and Governmental Organizations to Implement a COVID-19 Campaign in a Latino Neighborhood, Baltimore, Maryland, 2020-2021.

The disproportionate impact of COVID-19 on low-income Latinos with limited access to health care services prompted the expansion of community-based COVID-19 services. From June 25, 2020, to May 20, 2021, we established a coalition of faith leaders, community organizations, and governmental organizations to implement a Spanish-language hotline and social media campaign that linked people to a COVID-19 testing site at a local church in a high-density Latino neighborhood in Baltimore, Maryland. This retrospective analysis compared the characteristics of Latinos accessing testing in community versus health care facility-based settings. (Am J Public Health. 2022;112(S9):S913-S917. https://doi.org/10.2105/AJPH.2022.307074).

Prospective adverse event risk evaluation in clinical trials.

Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial's risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial's risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6-0.66 (with p-values < 0.001), thereby demonstrating improved identification of risk of adverse outcomes. Whereas related frameworks highlight the prediction benefits of incorporating data that is highly context-specific, our results indicate that Adverse Event (AE) risks can be reliably predicted through a framework of mild data requirements. We propose three potential applications in leading regulatory remits, highlighting opportunities to support regulatory oversight and informed consent decisions.

The Waiting Game - How Cooperation Between Public and Private Hospitals Can Help Reduce Waiting Lists.

Prolonged waiting to access health care is a primary concern for nations aiming for comprehensive effective care, due to its adverse effects on mortality, quality of life, and government approval. Here, we propose two novel bargaining frameworks to reduce waiting lists in two-tier health care systems with local and regional actors. In particular, we assess the impact of 1) trading patients on waiting lists among hospitals, the 2) introduction of the role of private hospitals in capturing unfulfilled demand, and the 3) hospitals' willingness to share capacity on the system performance. We calibrated our models with 2008-2018 Chilean waiting list data. If hospitals trade unattended patients, our game-theoretic models indicate a potential reduction of waiting lists of up to 37%. However, when private hospitals are introduced into the system, we found a possible reduction of waiting lists of up to 60%. Further analyses revealed a trade-off between diagnosing unserved demand and the additional expense of using private hospitals as a back-up system. In summary, our game-theoretic frameworks of waiting list management in two-tier health systems suggest that public-private cooperation can be an effective mechanism to reduce waiting lists. Further empirical and prospective evaluations are needed.

High Uptake and Series Completion of COVID-19 Vaccine at Community-Based Vaccination for Latinos With Limited English Proficiency.

BACKGROUND: Despite the disproportionate impact of COVID-19 on Latinos, there were disparities in vaccination, especially during the early phase of COVID-19 immunization rollout. METHODS: Leveraging a community-academic partnership established to expand access to SARS-CoV2 testing, we implemented community vaccination clinics with multifaceted outreach strategies and flexible appointments for limited English proficiency Latinos. RESULTS: Between February 26 and May 7 2021, 2250 individuals received the first dose of COVID-19 vaccination during 18 free community events. Among them, 92.4% (95% confidence interval [CI], 91.2%-93.4%) self-identified as Hispanic, 88.7% (95% CI, 87.2%-89.9%) were limited English proficiency Spanish speakers, 23.1% (95% CI, 20.9%-25.2%) reported prior COVID-19 infection, 19.4% (95% CI, 16.9%-22.25%) had a body mass index of more than 35, 35.0% (95% CI, 32.2%-37.8%) had cardiovascular disease, and 21.6% (95% CI, 19.2%-24.0%) had diabetes. The timely second-dose completion rate was high (98.7%; 95% CI, 97.6%-99.2%) and did not vary by outreach method. CONCLUSION: A free community-based vaccination initiative expanded access for Latinos with limited English proficiency at high risk for COVID-19 during the early phase of the immunization program in the US.

A patient-centered framework for health systems engineering in gastroenterology: improving inpatient colonoscopy bowel preparation.

BACKGROUND: Inpatient colonoscopy bowel preparation (ICBP) is frequently inadequate and can lead to adverse events, delayed or repeated procedures, and negative patient outcomes. Guidelines to overcome the complex factors in this setting are not well established. Our aims were to use health systems engineering principles to comprehensively evaluate the ICBP process, create an ICBP protocol, increase adequate ICBP, and decrease length of stay. Our goal was to provide adaptable tools for other institutions and procedural specialties. METHODS: Patients admitted to our tertiary care academic hospital that underwent inpatient colonoscopy between July 3, 2017 to June 8, 2018 were included. Our multi-disciplinary team created a protocol employing health systems engineering techniques (i.e., process mapping, cause-effect diagrams, and plan-do-study-act cycles). We collected demographic and colonoscopy data. Our outcome measures were adequate preparation and length of stay. We compared pre-intervention (120 ICBP) vs. post-intervention (129 ICBP) outcomes using generalized linear regression models. Our new ICBP protocol included: split-dose 6-L polyethylene glycol-electrolyte solution, a gastroenterology electronic note template, and an education plan for patients, nurses, and physicians. RESULTS: The percent of adequate ICBPs significantly increased with the intervention from 61% pre-intervention to 74% post-intervention (adjusted odds ratio of 1.87, p value = 0.023). The median length of stay decreased by approximately 25%, from 4 days pre-intervention to 3 days post-intervention (p value = 0.11). CONCLUSIONS: By addressing issues at patient, provider, and system levels with health systems engineering principles, we addressed patient safety and quality of care provided by improving rates of adequate ICBP.

Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.

STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidney disease, and death. Early acute kidney injury treatment can improve outcomes. However, current decision support is not able to detect patients at the highest risk of developing acute kidney injury. We analyzed routinely collected emergency department (ED) data and developed prediction models with capacity for early identification of ED patients at high risk for acute kidney injury. METHODS: A multisite, retrospective, cross-sectional study was performed at 3 EDs between January 2014 and July 2017. All adult ED visits in which patients were hospitalized and serum creatinine level was measured both on arrival and again with 72 hours were included. We built machine-learning-based classifiers that rely on vital signs, chief complaints, medical history and active medical visits, and laboratory results to predict the development of acute kidney injury stage 1 and 2 in the next 24 to 72 hours, according to creatinine-based international consensus criteria. Predictive performance was evaluated out of sample by Monte Carlo cross validation. RESULTS: The final cohort included 91,258 visits by 59,792 unique patients. Seventy-two-hour incidence of acute kidney injury was 7.9% for stages greater than or equal to 1 and 1.0% for stages greater than or equal to 2. The area under the receiver operating characteristic curve for acute kidney injury prediction ranged from 0.81 (95% confidence interval 0.80 to 0.82) to 0.74 (95% confidence interval 0.74 to 0.75), with a median time from ED arrival to prediction of 1.7 hours (interquartile range 1.3 to 2.5 hours). CONCLUSION: Machine learning applied to routinely collected ED data identified ED patients at high risk for acute kidney injury up to 72 hours before they met diagnostic criteria. Further prospective evaluation is necessary.

Triage Performance in Emergency Medicine: A Systematic Review.

STUDY OBJECTIVE: Rapid growth in emergency department (ED) triage literature has been accompanied by diversity in study design, methodology, and outcome assessment. We aim to synthesize existing ED triage literature by using a framework that enables performance comparisons and benchmarking across triage systems, with respect to clinical outcomes and reliability. METHODS: PubMed, EMBASE, Scopus, and Web of Science were systematically searched for studies of adult ED triage systems through 2016. Studies evaluating triage systems with evidence of widespread adoption (Australian Triage Scale, Canadian Triage and Acuity Scale, Emergency Severity Index, Manchester Triage Scale, and South African Triage Scale) were cataloged and compared for performance in identifying patients at risk for mortality, critical illness and hospitalization, and interrater reliability. This study was performed and reported in adherence to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: A total of 6,160 publications were identified, with 182 meeting eligibility criteria and 50 with sufficient data for inclusion in comparative analysis. The Canadian Triage and Acuity Scale (32 studies), Emergency Severity Index (43), and Manchester Triage Scale (38) were the most frequently studied triage scales, and all demonstrated similar performance. Most studies (6 of 8) reported high sensitivity (>90%) of triage scales for identifying patients with ED mortality as high acuity at triage. However, sensitivity was low (<80%) for identification of patients who had critical illness outcomes and those who died within days of the ED visit or during the index hospitalization. Sensitivity varied by critical illness and was lower for severe sepsis (36% to 74%), pulmonary embolism (54%), and non-ST-segment elevation myocardial infarction (44% to 85%) compared with ST-segment elevation myocardial infarction (56% to 92%) and general outcomes of ICU admission (58% to 100%) and lifesaving intervention (77% to 98%). Some proportion of hospitalized patients (3% to 45%) were triaged to low acuity (level 4 to 5) in all studies. Reliability measures (κ) were variable across evaluations, with only a minority (11 of 42) reporting κ above 0.8. CONCLUSION: We found that a substantial proportion of ED patients who die postencounter or are critically ill are not designated as high acuity at triage. Opportunity to improve interrater reliability and triage performance in identifying patients at risk of adverse outcome exists.

Prolonged wait time is associated with increased mortality for Chilean waiting list patients with non-prioritized conditions.

BACKGROUND: Most data on mortality and prognostic factors of universal healthcare waiting lists come from North America, Australasia, and Europe, with little information from South America. We aimed to determine the relationship between medical center-specific waiting time and waiting list mortality in Chile. METHOD: Using data from all new patients listed in medical specialist waitlists for non-prioritized health problems from 2008 to 2015 in three geographically distant regions of Chile, we constructed hierarchical multivariate survival models to predict mortality risk at two years after registration for each medical center. Kendall rank correlation analysis was used to measure the association between medical center-specific mortality hazard ratio and waiting times. RESULT: There were 987,497 patients waiting for care at 77 medical centers, including 33,546 (3.40%) who died within two years after registration. Male gender (hazard ratio [HR] = 1.17, 95% confidence interval [CI] 1.1-1.24), older age (HR = 2.88, 95% CI 2.72-3.05), urban residence (HR = 1.19, 95% CI 1.09-1.31), tertiary care (HR = 2.2, 95% CI 2.14-2.26), oncology (HR = 3.57, 95% CI 3.4-3.76), and hematology (HR = 1.6, 95% CI 1.49-1.73) were associated with higher risk of mortality at each medical center with large region-to-region variations. There was a statistically significant association between waiting time variability and death (Z = 2.16, P = 0.0308). CONCLUSION: Patient wait time for non-prioritized health conditions was associated with increased mortality in Chilean hospitals.

Genetic and phenotypic intra-species variation in Candida albicans.

Candida albicans is a commensal fungus of the human gastrointestinal tract and a prevalent opportunistic pathogen. To examine diversity within this species, extensive genomic and phenotypic analyses were performed on 21 clinical C. albicans isolates. Genomic variation was evident in the form of polymorphisms, copy number variations, chromosomal inversions, subtelomeric hypervariation, loss of heterozygosity (LOH), and whole or partial chromosome aneuploidies. All 21 strains were diploid, although karyotypic changes were present in eight of the 21 isolates, with multiple strains being trisomic for Chromosome 4 or Chromosome 7. Aneuploid strains exhibited a general fitness defect relative to euploid strains when grown under replete conditions. All strains were also heterozygous, yet multiple, distinct LOH tracts were present in each isolate. Higher overall levels of genome heterozygosity correlated with faster growth rates, consistent with increased overall fitness. Genes with the highest rates of amino acid substitutions included many cell wall proteins, implicating fast evolving changes in cell adhesion and host interactions. One clinical isolate, P94015, presented several striking properties including a novel cellular phenotype, an inability to filament, drug resistance, and decreased virulence. Several of these properties were shown to be due to a homozygous nonsense mutation in the EFG1 gene. Furthermore, loss of EFG1 function resulted in increased fitness of P94015 in a commensal model of infection. Our analysis therefore reveals intra-species genetic and phenotypic differences in C. albicans and delineates a natural mutation that alters the balance between commensalism and pathogenicity.

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model.

Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacity-related diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.

A user needs assessment to inform health information exchange design and implementation.

BACKGROUND: Important barriers for widespread use of health information exchange (HIE) are usability and interface issues. However, most HIEs are implemented without performing a needs assessment with the end users, healthcare providers. We performed a user needs assessment for the process of obtaining clinical information from other health care organizations about a hospitalized patient and identified the types of information most valued for medical decision-making. METHODS: Quantitative and qualitative analysis were used to evaluate the process to obtain and use outside clinical information (OI) using semi-structured interviews (16 internists), direct observation (750 h), and operational data from the electronic medical records (30,461 hospitalizations) of an internal medicine department in a public, teaching hospital in Tampa, Florida. RESULTS: 13.7 % of hospitalizations generate at least one request for OI. On average, the process comprised 13 steps, 6 decisions points, and 4 different participants. Physicians estimate that the average time to receive OI is 18 h. Physicians perceived that OI received is not useful 33-66 % of the time because information received is irrelevant or not timely. Technical barriers to OI use included poor accessibility and ineffective information visualization. Common problems with the process were receiving extraneous notes and the need to re-request the information. Drivers for OI use were to trend lab or imaging abnormalities, understand medical history of critically ill or hospital-to-hospital transferred patients, and assess previous echocardiograms and bacterial cultures. About 85 % of the physicians believe HIE would have a positive effect on improving healthcare delivery. CONCLUSIONS: Although hospitalists are challenged by a complex process to obtain OI, they recognize the value of specific information for enhancing medical decision-making. HIE systems are likely to have increased utilization and effectiveness if specific patient-level clinical information is delivered at the right time to the right users.

Racial and Ethnic Disparities in Glycemic Control Among Patients With SARS-CoV-2 in the Baltimore-Washington, District of Columbia Region.

Introduction: Diabetes is a leading risk factor for COVID-19, disproportionally impacting marginalized populations. We analyzed racial/ethnic differences in glycemic control among patients who tested positive for SARS-CoV-2 in the Baltimore-Washington, District of Columbia region. Methods: Glycemic control measured by HbA1c was compared by race and ethnicity among patients with a positive SARS-CoV-2 test at the Johns Hopkins Health System between March 1, 2020, and March 31, 2022. Risk factors associated with poor glycemic control (HbA1c≥8) were identified using logistic regression. Results: Black, Latino, and Asian patients had a higher rate of prediabetes (HbA1c=5.7%-6.49%) and diabetes (HbA1c≥6.5%) than non-Hispanic White patients. Among patients with diabetes, poor glycemic control (HbA1c≥8%) was significantly higher among young adults (aged ≤44 years), Latino patients (AOR=1.5; 95% CI=1.1, 1.9), Black patients (AOR=1.2; 95% CI=1.0, 1.5), uninsured patients (AOR=1.5; 95% CI=1.2, 1.9), and those with limited English proficiency (AOR=1.3; 95% CI=1.0, 1.6) or without a primary care physician (AOR=1.6; 95% CI=1.3, 2.1). Conclusions: Disparities in glycemic control among patients who tested positive for SARS-CoV-2 were associated with underlying structural factors such as access to care, health insurance, and language proficiency. There is a need to implement accessible, culturally and language-appropriate preventive and primary care programs to engage socioeconomically disadvantaged populations in diabetic screening and care.

Towards a CdTe Solar Cell Efficiency Promotion: The Role of ZnO:Al and CuSCN Nanolayers.

A numerical simulation is a valuable tool since it allows the optimization of both time and the cost of experimental processes for time optimization and the cost of experimental processes. In addition, it will enable the interpretation of developed measurements in complex structures, the design and optimization of solar cells, and the prediction of the optimal parameters that contribute to manufacturing a device with the best performance. In this sense, a detailed simulation study was carried out in this work by the Solar Cell Capacitance Simulator (SCAPS). In particular, we evaluate the influence of absorber and buffer thickness, absorber defect density, work function in back contact, Rs, Rsh, and carrier concentration on a CdTe/CdS cell to maximize its performance. Furthermore, the incorporation effect of ZnO:Al (TCO) and CuSCN (HTL) nanolayers was studied for the first time. As a result, the efficiency of the solar cell was maximized from 16.04% to 17.74% by increasing the Jsc and Voc. This work will play an essential role in enhancing the performance of CdTe-based devices with the best performance.

The Relationship between Performance, Body Composition, and Processing Yield in Broilers: A Systematic Review and Meta-Regression.

This study aims to model the relationship among performance, whole body composition, and processing yield through meta-regression. Scientific papers found in Scopus and Google Scholar were included if they reported results and variability values of an actual experiment in the three mentioned groups of variables using a single broiler genetic line. Weighted mean effect sizes were determined with a random model, the risk of bias was determined, and heterogeneity was considered an indicator of usefulness. Meta-regressions considered the effect sizes of the response variable and the percent change in one or more variables as predictors. A 78-row database was built from 14 papers, including nine factors tested on 22,256 broilers. No influencing bias was found, and the data was determined useful. Meta-regressions showed that the changes in body weight gain (BWG) are inversely related to the effects in feed conversion ratio (FCR) (p < 0.001) and that the changes in FCR and effects in protein-to-fat gain (PFG) are directly related (p < 0.001). The changes in PFG and the effects on carcass conformation or the market value of birds are directly related (p < 0.001). In conclusion, body composition predicts carcass conformation and its market value, supporting its use to predict the economic value of broilers.

A neurochemical biogeography of the broiler chicken intestinal tract.

The study of neurochemical-based interkingdom signaling and its impact on host-microbe interaction is called microbial endocrinology. Neurochemicals play a recognized role in determining bacterial colonization and interaction with the gut epithelium. While much attention has been devoted to the determination of neurochemical concentrations in the mammalian gut to better understand tissue and region-specific microbial endocrinology-based mechanisms of host-microbe interaction, little is known regarding the biogeography of neurochemicals in the avian gut. Greater resolution of avian gut neurochemical concentrations is needed especially as recent microbial endocrinology-based investigations into bacterial foodborne pathogen colonization of the chicken gut have demonstrated neurochemicals to affect Campylobacter jejuni and Salmonella spp. in vivo and in vitro. The aim of the present study was to determine the concentrations of stress-related neurochemicals in the tissue and luminal content of the duodenum, jejunum, ileum, cecum, and colon of the broiler intestinal tract, and to investigate if this biogeography changes with age of the bird. While all neurochemicals measured were detected in the intestinal tract, many displayed differences in regional concentrations. Whereas the catecholamine norepinephrine was detected in each region of the intestinal tract, epinephrine was present only in the cecum and colon. Likewise, dopamine, and its metabolite 3,4-dihydroxyphenylacetic acid were found in the greatest quantities in the cecum and colon. Serotonin and histamine were identified in each gut region. Region-specific age-related changes were observed (P < 0.05) for serotonin, its metabolite 5-hydroxyindole acetic acid as well as for histamine. Several neurochemicals, including norepinephrine, were found in the contents of each gut region. Epinephrine was not detected in the gut content of any region. Salsolinol, a microbial-produced neuroactive compound was detected in the gut content but not in tissue. Together, our data establish a neurochemical biogeography of the broiler chicken intestinal tract. By providing researchers with a region-by-region map of in vivo gut neurochemical concentrations of a modern broiler chicken breed, this neurochemical map is expected to inform future investigations that seek to utilize avian enteric neurochemistry.