Milestoning estimators of dissipation in systems observed at a coarse resolution.

Many nonequilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases, the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycles. As a result, the observations appear less irreversible, and it is very challenging to infer the degree of broken time-reversal symmetry. Here we show, contrary to intuition, that by ignoring parts of the already coarse-grained state space we may-via a process called milestoning-improve entropy-production estimates. We present diverse examples where milestoning systematically renders observations "closer to underlying microscopic dynamics" and thereby improves thermodynamic inference from lumped data assuming a given range of memory, and we hypothesize that this effect is quite general. Moreover, whereas the correct general physical definition of time reversal in the presence of memory remains unknown, we here show by means of physically relevant examples that at least for semi-Markov processes of first and second order, waiting-time contributions arising from adopting a naive Markovian definition of time reversal generally must be discarded.

Preslaughter factors affecting mobility, blood parameters, bruising, and muscle pH of finished beef cattle in the United States.

Decades of work have focused on reducing fear, stress, and discomfort in cattle during the preslaughter phase by improving and promoting animal handling, transportation, and management processes. Even still, there is limited information about the effects of preslaughter factors on animal welfare and meat quality outcomes in finished cattle in the United States. This study aimed to track individual animals through the slaughter process to identify preslaughter factors associated with key welfare and quality outcomes. A total of 454 cattle from one commercial slaughter facility were studied. Preslaughter factors assessed included distance traveled, truck waiting time, lairage density, lairage duration, and season. Animal characteristics, i.e., body weight, breed, and sex, were also recorded. One trained observer scored the mobility of all cattle using the North American Meat Institute's 1-4 scale (i.e., normal to extremely reluctant to move). Exsanguination blood was collected and analyzed for cortisol, creatine kinase, and lactate. Carcass bruising was scored using a modified version of the National Beef Quality Audit's bruise-scoring methodology (i.e., no bruise, one bruise ≤ the size of a deck of cards, one bruise > than the size of a deck of cards, and multiple bruises). Ultimate muscle pH was measured 32 to 36 h postmortem. Multi-predictor models were selected for each outcome variable using Akaike Information Criterion. Continuous outcome variables were analyzed using linear mixed-effect models and categorical outcome variables with mixed-effect logistic regression models. Longer truck waiting times were associated with increased cortisol (P = 0.04) and lactate (P = 0.02) concentrations. Similarly, increased lairage duration was associated with increased creatine kinase concentrations (P = 0.05) and the odds of cattle being bruised (P = 0.03). Less space allowance per animal in lairage was associated with increased odds of cattle having impaired mobility (P = 0.01). There was a seasonal effect for many of the measured outcomes; the summer season was associated with greater lactate concentrations (P < 0.0001), increased odds of impaired mobility (P < 0.0001), and increased odds of carcass bruising (P = 0.003). The findings of this study indicate that many of the preslaughter factors assessed influence critical welfare and meat quality outcomes of finished beef cattle, warranting future research and consideration.

Effects of lipids on the rate-limiting steps in the dark-to-light transition of Photosystem II core complex of Thermostichus vulcanus.

In our earlier works, we have shown that the rate-limiting steps, associated with the dark-to-light transition of Photosystem II (PSII), reflecting the photochemical activity and structural dynamics of the reaction center complex, depend largely on the lipidic environment of the protein matrix. Using chlorophyll-a fluorescence transients (ChlF) elicited by single-turnover saturating flashes, it was shown that the half-waiting time (Δτ 1/2) between consecutive excitations, at which 50% of the fluorescence increment was reached, was considerably larger in isolated PSII complexes of Thermostichus (T.) vulcanus than in the native thylakoid membrane (TM). Further, it was shown that the addition of a TM lipid extract shortened Δτ 1/2 of isolated PSII, indicating that at least a fraction of the 'missing' lipid molecules, replaced by detergent molecules, caused the elongation of Δτ 1/2. Here, we performed systematic experiments to obtain information on the nature of TM lipids that are capable of decreasing Δτ 1/2. Our data show that while all lipid species shorten Δτ 1/2, the negatively charged lipid phosphatidylglycerol appears to be the most efficient species - suggesting its prominent role in determining the structural dynamics of PSII reaction center.

The Role of Point-of-Care Testing to Improve Acute Care and Health Care Services.

Health care is one of the most important services that need to be provided to any community. Many challenges exist in delivering proper and effective health services, including ensuring timely delivery, providing adequate care through effective management and achieving good outcomes. Point-of-care testing (POCT) plays a crucial role in delivering urgent and appropriate health services, especially in peripheral communities, emergency situations, disaster areas and overcrowded areas. We collected and reviewed secondary data about point-of-care testing from PubMed, Scopus and Google Scholar. Our findings emphasize that POCT provides fast care with minimal waiting time, avoids unnecessary investigations, aids in triage, and provides decision-makers with a clear understanding of the patient's condition to make informed decisions. We recommend point-of-care testing as a frontline investigation in emergency departments, intensive care units, peripheral hospitals, primary health care centers, disaster areas and field hospitals. Point-of-care testing can improve the quality of health services and ensure the provision of necessary health care.

An Electronic Health Record-Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study.

BACKGROUND: In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician's schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations. OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled. METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer. RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million. CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.

Effect of waiting time for radiotherapy after last induction chemotherapy on prognosis of locally advanced nasopharyngeal carcinoma.

BACKGROUND: The effect of radiotherapy waiting time after last induction chemotherapy (IC-RT) on prognosis of patients with locally advanced nasopharyngeal carcinoma (LANPC) needs further discussion. METHODS: Three hundred and six patients with LANPC diagnosed pathologically by induction chemotherapy (IC) and radiotherapy (RT) from 2013 to 2018 were selected for this study. RESULTS: The IC-RT was a risk factor for the post-treatment progression of LANPC (OR = 1.017 95%CI: 1.003-1.031), For patients with LANPC, the IC-RT > 40 days significantly reduced 5-year PFS (70% vs. 55%; p = 0.0012), 5-year OS (84% vs. 73%; p = 0.028), 5-year DMFS (80% vs. 66%; p = 0.003), 5-year LRFS (77% vs. 67%; p = 0.012). Indicating that patients with stage IVa who IC-RT > 40 days were found to be a significant predictor of aggravated PFS (HR = 2.69; 95%CI: 1.57-4.6), OS (HR = 2.55; 95%CI: 1.29-5.03), DMFS (HR = 3.07; 95%CI: 1.64-5.76) and LRFS (HR = 2.26; 95%CI: 1.21-4.21). CONCLUSION: The prognosis of patients will be adversely affected if the IC-RT exceeds 40 days, especially for stage IVa patients.

Effects of private health insurance on waiting time in public hospitals.

The Australian government pays $6.7 billion per year in rebates to encourage Australians to purchase private health insurance (PHI) and an additional $6.1 billion to cover services provided in private hospitals. What is the justification for large government subsidies to a private industry when all Australians already have free coverage under Medicare? The government argues that more people buying PHI will relieve the burden on the public system and may reduce waiting times. However, the evidence supporting this is sparse. We use an instrumental variable approach to study the causal effects of higher PHI coverage in the area on waiting times in public hospitals in the same area. The instrument used is area-level average house prices, which correlate with average income and wealth, thus influencing the purchase of PHI due to tax incentives, but not directly affecting waiting times in public hospitals. We use 2014-2018 hospital admission and elective surgery waiting list data linked at the patient level from the Victorian Center for Data Linkage. These data cover all inpatient admissions in all hospitals in Victoria (both public and private hospitals) and those registered on the waiting list for elective surgeries in public hospitals in Victoria. We find that one percentage point increase in PHI coverage leads to about 0.34 days (or 0.5%) reduction in waiting times in public hospitals on average. The effects vary by surgical specialities and age groups. However, the practical significance of this effect is limited, if not negligible, despite its statistical significance. The small effect suggests that raising PHI coverage with the aim to taking the pressure off the public system is not an effective strategy in reducing waiting times in public hospitals. Alternative policies aiming at improving the efficiency of public hospitals and advancing equitable access to care should be a priority for policymakers.

Impact of time from diagnosis to chemotherapy on prognosis in advanced pancreatic cancer.

BACKGROUND: Due to the aggressive nature and poor prognosis of advanced pancreatic cancer, prompt initiation of treatment is critical. We investigated the effect of the interval between cancer diagnosis and initiation of chemotherapy on survival in patients with advanced pancreatic cancer. METHODS: In this retrospective, single-centre study, consecutive patients with advanced pancreatic cancer between April 2013 and March 2022 were analyzed. Data were extracted from the electronic medical records of patients who received chemotherapy for metastatic, locally advanced or resectable pancreatic cancer or who received chemotherapy due to either being intolerant of or declining surgery. We compared overall survival between two groups: the early waiting time group (waiting time ≤30 days from diagnosis to chemotherapy initiation) and the elective waiting time group (waiting time ≥31 days). Prognostic factors, including biliary drainage, were considered. The impact of waiting time on survival was assessed by univariate and multivariate analyses with Cox proportional hazard models. A 1:1 propensity score matching approach was used to balance bias, accounting for significant poor prognosis factors, age and sex. RESULTS: The study involved 137 patients. Overall survival exhibited no statistically significant difference between the early and elective waiting time groups (207 and 261 days, P = 0.2518). Univariate and multivariate analyses identified poor performance status and metastasis presence as predictors of worse prognosis. This finding persisted post propensity score matching (275 and 222 days, P = 0.8223). CONCLUSIONS: Our study revealed that initiating chemotherapy ˃30 days later does not significantly affect treatment efficacy compared to within 30 days of diagnosis.

The Impact of Waiting Time-to-Surgery on Survival in Endometrial Cancer Patients.

BACKGROUND: Endometrial cancer (EC) is the most common gynecological cancer in developed countries and a standard treatment of surgery should be performed as expediently as possible. Delay time to surgery and survival was debated. The aim of this investigation was to evaluate the effect of time-interval between diagnosis and surgery (TDS) in EC patients with regards to prognosis and mortality rates. METHODS: This retrospective study was conducted between January 2009 and May 2021 at Bhumibol Adulyadej Hospital, Thailand. Subjects were EC cases who underwent primary surgery during the study period. Cases with partial treatment were disqualified from the study. Subjects who underwent surgery before and after 6 weeks were classified as early and delayed surgery groups. Baseline and clinical characteristics were collected and analyzed. RESULTS: During the study period, 419 EC cases were recruited. The mean age of participants was 56.8 years. Two-thirds (338/491) of subjects were menopausal. Endometrioid histology (406/491) was the most common histology subtype. Five years disease free survival (DFS) of early and delayed surgery groups were comparable at a percentage of 82.5 and 83.0, respectively. Among advanced stage and non-endometrioid EC cases, the delayed surgery group had significantly shorter DFS than the early group. Advanced stage, high grade and positive lympho-vascular space invasion (LVSI) were independent factors for poor DFS. Predictive factors for mortality were advanced stage and tumor recurrence. CONCLUSION: The TDS was not a prognostic factor for disease recurrence or overall mortality. Time to surgery equal to or more than 6 weeks gave worse prognosis for DFS among advanced stage or non-endometrioid histology EC.

Improving Waiting Time for Chemotherapy with Ahead-of-Time Drug Preparation.

Waiting time for chemotherapy infusion is a fundamental factor to measure quality of care. It has been shown that a prolonged waiting time is related to a higher incidence of anticipatory nausea and poor patient adherence to scheduled appointments and recommended oncology treatment programs. Some chemotherapy regimens can be prepared hours ahead-of-time, due to long stability. We aimed to study the effect of an informatic-led workflow redesign intervention, facilitating workflow changes in the Oncology Pharmacy, on patient waiting time. This intervention included changes on EHR processes and the chemotherapy CPOE. Their main effect was allowing ahead-of-time preparation of selected chemotherapy regimes. We conducted a cross sectional study, comparing waiting times pre and post intervention periods. A total of 4600 programmed chemotherapy episodes were included. We found a 26.5 % decrease in the mean wait time in the post intervention period (p > 0.02). We were able to show a decrease in waiting time and a measurable impact of the intervention. This evaluation produced valuable and actionable data for Oncology units and adds a valuable, Latin American experience to the literature.

Impact of changes in skeletal muscle mass and quality during the waiting time on outcomes of lung transplantation.

INTRODUCTION: The association of changes in skeletal muscle mass and quality during the waiting time with outcomes of lung transplantation (LT) remains unclear. We aimed to examine the association of changes in skeletal muscle mass and quality during the waiting time, as well as preoperative skeletal muscle mass and quality, with outcomes of LT. METHODS: This study included individuals who underwent LT from brain-dead donors. Skeletal muscle mass (cm2 /m2 ) and quality (mean Hounsfield units [HU]) of the erector spinae muscle at the 12th thoracic level were evaluated using computed tomography. Preoperative skeletal muscle mass and quality, and their changes during the waiting time were calculated. We evaluated the associations among mechanical ventilation (MV) duration, intensive care unit (ICU) length of stay (LOS), hospital LOS, 6-minute walk distance at discharge, and 5-year survival after LT. RESULTS: This study included 98 patients. The median waiting time was 594.5 days (interquartile range [IQR], 355.0-913.0). The median changes in skeletal muscle mass and quality were -4.4% (IQR, -13.3-3.1) and -2.9% (IQR, -16.0-4.1), respectively. Severe low skeletal muscle mass at LT was associated with prolonged ICU LOS (B = 8.46, 95% confidence interval [CI]: .51-16.42) and hospital LOS (B = 36.00, 95% CI: 3.23-68.78). Pronounced decrease in skeletal muscle mass during the waiting time was associated with prolonged MV duration (B = 7.85, 95% CI: .89-14.81) and ICU LOS (B = 7.97, 95% CI: .83-15.10). CONCLUSION: Maintaining or increasing skeletal muscle mass during the waiting time would be beneficial to improve the short-term outcomes of LT.

The impact of the COVID-19 pandemic in the clinical assistance to breast cancer patients.

PURPOSE: We aimed to disclose the impact of the pandemic on breast cancer patients in a specialized breast cancer center (BCC). METHODS: A total of 501 breast cancer patients with a first appointment in the BCC from April 1st, 2019 to March 31st, 2021 were divided into four consecutive periods of 6 months. Data from the homologous semesters was compared. Patients with an appointment in the BCC during the study period were eligible for the secondary aim of our study (BCC workload). RESULTS: After the pandemic declaration (period 3), we found a decrease in the referral by screening programs (p = 0.002) and a reduction in the waiting time between the primary care referral and the first BCC appointment (p < 0.001). There were higher rates of palpable axillary nodes (p = 0.001), an increase in N stage 2 and 3 (p = 0.050), and a trend for primary endocrine therapy as the first treatment (p = 0.021) associated with higher rates of complete axillary node dissection (p = 0.030). In period 4, there were more outward diagnoses (p = 0.003) and a higher rate of surgery as the first treatment (p = 0.013). CONCLUSION: COVID-19 pandemic implied a more advanced nodal stage, which may be related to the delay in breast cancer screening.

Process reengineering using DMAIC framework for reduction of waiting time in daycare infusion therapy for better patient experience.

Daycare infusion therapy is an integral aspect of oncology, but increased waiting time raises concerns for patients. Patient-reported experience measures prompted the need to evaluate reasons for prolonged appointment delays. This study seeks to analyze and address patients' concerns, to streamline the process flow and reduce waiting time for daycare infusion therapy thereby enhancing patient experience. The define, measure, analyze, improve, and control methodology was implemented, and its impact on reducing waiting times was evaluated. The objective is to ensure that >85% of patients enter the daycare infusion unit within an hour of their appointment time in 6 months. The baseline data for patient waiting times was measured for a period of 2 months, and the average waiting time was determined. Potential causes contributing to prolonged waiting times were identified through time-motion analysis, with a fishbone diagram categorizing potential causes and a Pareto chart prioritizing them. Plan, do, study, and act cycles were conducted for implementing the changes, and a new process flow mapped. Baseline data showed 32% average adherence to the defined turnaround time of 1 hour, with an average waiting time of 108 minutes. Forty causes were identified for increased waiting time, of which eight were key. Adherence to waiting time turnaround time improved from 32% to 89% and the average waiting time decreased by 59 minutes from 108 minutes, increasing patient satisfaction index by 7.5%. The balancing measures include an increase in operational efficiency and throughput of the unit and the inventory levels of oncology medicine were decreased, leading to a 50% reduction in inventory value, while medication error declined by 0.62%, improving patient safety. The project gained tangible and intangible benefits impacting staff, patients, and relatives while improving operational efficiency. This study, with its scientific and systematic approach, enhanced patient satisfaction, patient safety, and better utilization of resources.

Adherence to Quality Indicators for Breast Cancer Management in a Multidisciplinary Training Program.

Background: The management of early breast cancer (BC) needs supervision and skill maintenance, and should be performed by specialists working as a team in multidisciplinary breast units. This approach aims to improve the long-term survival and quality of life of patients with BC. Methods: This was a prospective observational study including patients newly diagnosed with operable BC. The study encompassed the pre-surgical phase, throughout the diagnostic and surgical workout, and included post-therapeutic master multidisciplinary team meetings (MTMs) sessions, between 2019 and 2022. Results: We enrolled 280 patients with BC from eight breast units. The Senonetwork indicators regarding diagnosis, waiting time, loco-regional treatment, and adjuvant therapy were collected for each patient discussed. Conclusions: Overall, the majority of quality indicators were respected among breast units. The most critical issue referred to timing indicators: more than 30 days from MTM to surgery, more than 42 days from diagnosis to surgery, and more than 60 days from the first screening mammogram to surgery for many patients. Some aspects of the histopathological diagnosis of intraductal BC also need to be improved. Furthermore, other critical issues in our study regarded some aesthetical indicators, demonstrating low interest in these essential quality indicators.

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.

Background: Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission. Methods: First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. Results: The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Conclusions: Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.

BACKGROUND: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS: This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Utilization of Proximate Healthcare Facilities and Children's Wait Times in Senegal: An IV-Tobit Analysis.

Universal health coverage (UHC) defines individuals' timely access to healthcare services without suffering any health-related financial constraints. The Senegalese government has shown commitments towards achievement of UHC as a way of improving access by the population to quality healthcare services. This is very pertinent for promoting some indicators of under-five health in Senegal. Therefore, this study analyzed the factors influencing sick children's utilization of the nearest healthcare facilities and their wait times in Senegal. The data were from the Service Provision Assessment (SPA) survey, which was conducted in 2018. The instrumental Tobit regression model was used for data analysis. The results showed that 63.50% and 86.01% of the children utilized health posts and publicly owned facilities, respectively. Also, 98.46% of the children utilized urban facilities. The nearest facilities were utilized by 74.55%, and 78.19% spent less than an hour in the facilities. The likelihood of using the nearest healthcare facilities significantly reduced (p < 0.05) with caregivers' primary education, higher education, residence in some regions (Fatick, Kaokack, Saint Louis, Sediou, and Tambacounda), and use of private/NGO not-for-profit facilities, but increased with not having visited any other providers, residence in the Kaffrie region, vomiting symptoms, use of health centers, and use of health posts. Moreover, treatment wait times significantly increased (p < 0.05) with the use of nearest facilities, residence in some regions (Diourbel, Kaokack, Matam and Saint Louis), use of private for-profit facilities, use of private not-for-profit facilities, and urban residence, but decreased with secondary education, use of health centers, use of health posts, vomiting symptoms, and showing other symptoms. It was concluded that reduction in wait times and utilization of the nearest healthcare facilities are fundamental to achieving UHC in Senegal. Therefore, more efforts should be integrated at promoting regional and sectoral equities through facilitated public and private healthcare investment.

The effect of screening on the health burden of chlamydia: An evaluation of compartmental models based on person-days of infection.

Sexually transmitted diseases (STDs) are detrimental to the health and economic well-being of society. Consequently, predicting outbreaks and identifying effective disease interventions through epidemiological tools, such as compartmental models, is of the utmost importance. Unfortunately, the ordinary differential equation compartmental models attributed to the work of Kermack and McKendrick require a duration of infection that follows the exponential or Erlang distribution, despite the biological invalidity of such assumptions. As these assumptions negatively impact the quality of predictions, alternative approaches are required that capture how the variability in the duration of infection affects the trajectory of disease and the evaluation of disease interventions. So, we apply a new family of ordinary differential equation compartmental models based on the quantity person-days of infection to predict the trajectory of disease. Importantly, this new family of models features non-exponential and non-Erlang duration of infection distributions without requiring more complex integral and integrodifferential equation compartmental model formulations. As proof of concept, we calibrate our model to recent trends of chlamydia incidence in the U.S. and utilize a novel duration of infection distribution that features periodic hazard rates. We then evaluate how increasing STD screening rates alter predictions of incidence and disability adjusted life-years over a five-year horizon. Our findings illustrate that our family of compartmental models provides a better fit to chlamydia incidence trends than traditional compartmental models, based on Akaike information criterion. They also show new asymptomatic and symptomatic infections of chlamydia peak over drastically different time frames and that increasing the annual STD screening rates from 35% to 40%-70% would annually avert 6.1-40.3 incidence while saving 1.68-11.14 disability adjusted life-years per 1000 people. This suggests increasing the STD screening rate in the U.S. would greatly aid in ongoing public health efforts to curtail the rising trends in preventable STDs.

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Background: Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. Methods: We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Results: Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Conclusions: Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

Impact of Waiting Times on Mortality in Advanced Stage Non-Small Cell Lung Cancer: A 10-Year Retrospective Cohort Study in Thailand.

OBJECTIVE: This study investigated the relationship between mortality and waiting times from diagnosis to first treatment while also considering other important risk factors associated with mortality. METHODS: This is a cohort study including 497 patients diagnosed with advanced stage non-small cell lung cancer (NSCLC) between 1st January 2012 and 31st December 2021. The risk factors and waiting periods were analysed to determine their association with mortality. The waiting periods were recorded based on the timeline of patient visits, including the time between the 1st visit and imaging, the time between the 1st visit and tissue diagnosis, the time between the procedure and tissue diagnosis, the time between tissue diagnosis and treatment and the time from the 1st visit until treatment. The data were assessed using Cox regression with time-varying covariates. RESULTS: Waiting time for tissue diagnosis had a modest effect on mortality, a waiting time of more than four weeks indicated poor prognosis both in univariate and multivariate analyses [HR 1.48 (95%CI 1.18-1.87), p = < 0.01), adjusted HR 1.007 (95%CI 1.002-1.010), p = 0.02]. Waiting time for other services was not shown to be associated with mortality. The mortality rate was 3 times higher in patients with poor ECOG performance status than good ECOG performance [adjusted HR 3.17(2.04-4.91)]. Patients with EGFR sensitizing mutation who were treated with EGFR TKI therapy had a lower risk of lung cancer death compared to those being treated with chemotherapy [adjusted HR 0.49 (0.33-0.72)]. CONCLUSION: Molecular testing for EGFR sensitizing mutation and the TKI treatment were fundamental changes that assisted in improving survival rates for patients diagnosed with advanced stage lung cancer over the 10-year period. However, poor ECOG performance status remained a strong risk factor for lung cancer death. Longer waiting time for tissue diagnosis might indicate a poor prognosis.