Still Separate, Still Not Equal: An Ecological Examination of Redlining and Racial Segregation with COVID-19 Vaccination Administration in Washington D.C.

Racial residential segregation has been deemed a fundamental cause of health inequities. It is a result of historical and contemporary policies such as redlining that have created a geographic separation of races and corresponds with an inequitable distribution of health-promoting resources. Redlining and racial residential segregation may have contributed to racial inequities in COVID-19 vaccine administration in the early stages of public accessibility. We use data from the National Archives (historical redlining), Home Mortgage Disclosure Act (contemporary redlining), American Community Survey from 1940 (historical racial residential segregation) and 2015-2019 (contemporary racial residential segregation), and Washington D.C. government (COVID-19 vaccination administration) to assess the relationships between redlining, racial residential segregation, and COVID-19 vaccine administration during the early stages of vaccine distribution when a tiered system was in place due to limited supply. Pearson correlation was used to assess whether redlining and racial segregation, measured both historically and contemporarily, were correlated with each other in Washington D.C. Subsequently, linear regression was used to assess whether each of these measures associate with COVID-19 vaccine administration. In both historical and contemporary analyses, there was a positive correlation between redlining and racial residential segregation. Further, redlining and racial residential segregation were each positively associated with administration of the novel COVID-19 vaccine. This study highlights the ongoing ways in which redlining and segregation contribute to racial health inequities. Eliminating racial health inequities in American society requires addressing the root causes that affect access to health-promoting resources.

Characterization and Association of the Missing Ventral Tubercle(s) from the Sixth Cervical Vertebra and Transpositions on the Ventral Surface of the Seventh Cervical Vertebra in Modern Equus ferus caballus.

In recent years, equine complex vertebral malformation (ECVM) has been of concern in the equine community, with studies identifying numerous associative morphological variations. Here, we examine the morphological association between C6 and C7 for dependency in ECVM cases, where the partially absent ventral process of C6 transposes on the ventral surface of C7. A C6 ventral process presents two tubercles, one cranial (CrVT) and one caudal (CVT). In this study, the C6 osseous specimens (n = 85) demonstrated a partial or completely absent CVT (aCVT) graded 1-4 that often extended cranially creating a partially absent cranial ventral tubercle (aCrVT) graded 1-3. In the 85 C6 osseous specimens examined, the corresponding C7s demonstrated either a complete or incomplete transposition of the ventral process from C6 in 44/85, with 30/44 replicating a transverse foramen. A strong statistical dependency existed between C6 grade 4 aCVTs and grades 1-3 aCrVTs and C7 transpositions with replicated transverse foramen. Sidedness was also demonstrated, where a left sided absent C6 associated with transposition on the left ventral surface of C7. This likewise applied to right sidedness and most bilateral cases. These findings might benefit practitioners when radiographing the extent of the ECVM configuration in patients presenting caudal cervical pain.

Prevalence and burden of difficult-to-treat and severe asthma in Australia: A national population survey.

BACKGROUND AND OBJECTIVE: Most evidence about difficult-to-treat and severe asthma (DTTA) comes from clinical trials and registries. We aimed to identify people with DTTA from a large nationally representative asthma population and describe their characteristics and healthcare utilization compared with people whose asthma was not 'difficult-to-treat'. METHODS: We conducted a cross-sectional survey of Australians aged ≥18 years with current asthma from large web-based survey panels. Enrolment was stratified by gender, age-group and state/territory based on national population data for people with asthma. Difficult-to-treat or severe asthma was defined by poor symptom control, exacerbations and/or oral corticosteroid/biologic use despite medium/high-dose inhaled therapy. Outcomes included exacerbations, healthcare utilization, multimorbidity, quality of life and coronavirus disease of 2019 (COVID-19)-related behaviour. Weighted data were analysed using SAS version 9.4. RESULTS: The survey was conducted in February-March 2021. The weighted sample comprised 6048 adults with current asthma (average age 47.3 ± SD 18.1 years, 59.9% female), with 1313 (21.7%) satisfying ≥1 DTTA criteria. Of these, 50.4% had very poorly controlled symptoms (Asthma Control Test ≤15), 36.2% were current smokers, and 85.4% had ≥1 additional chronic condition, most commonly anxiety/depression. More than twice as many participants with DTTA versus non-DTTA had ≥1 urgent general practitioner (GP) visit (61.4% vs. 27.5%, OR 4.8 [4.2-5.5, p < 0.0001]), or ≥1 emergency room visit (41.9% vs. 17.9%, OR 3.8 [3.3-4.4, p < 0.0001]) in the previous 12 months. CONCLUSION: Our findings emphasize the burden of uncontrolled symptoms, current smoking, multimorbidity and healthcare utilization in people with DTTA in the community, who may be under-represented in registries or clinical trials.

Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.

Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance.

BACKGROUND: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. OBJECTIVE: Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles. DISCUSSION: We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.

Mobile app activity engagement by cancer patients and their caregivers informs remote monitoring.

Mobile phone applications ("apps") are potentially an effective, low-burden method to collect patient-reported outcomes outside the clinical setting. Using such apps consistently and in a timely way is critical for complete and accurate data capture, but no studies of concurrent reporting by cancer patient-caregiver dyads have been published in the peer-reviewed literature. This study assessed app engagement, defined as adherence, timing, and attrition with two smartphone applications, one for adult cancer patients and one for their informal caregivers. This was a single-arm, pilot study in which adult cancer patients undergoing IV chemotherapy or immunotherapy used the DigiBioMarC app, and their caregivers used the TOGETHERCare app, for approximately one month to report weekly on the patients' symptoms and wellbeing. Using app timestamp metadata, we assessed user adherence, overall and by participant characteristics. Fifty patient-caregiver dyads completed the study. Within the one-month study period, both adult cancer patients and their informal caregivers were highly adherent, with app activity completion at 86% for cancer patients and 84% for caregivers. Caregivers completed 86% of symptom reports, while cancer patients completed 89% of symptom reports. Cancer patients and their caregivers completed most activities within 48 h of availability on the app. These results suggest that the DigiBioMarC and TOGETHERCare apps can be used to collect patient- and caregiver-reported outcomes data during intensive treatment. From our research, we conclude that metadata from mobile apps can be used to inform clinical teams about study participants' engagement and wellbeing outside the clinical setting.

Correction: Correlation Between Remote Symptom Reporting by Caregivers and Adverse Clinical Outcomes: Mixed Methods Study.

[This corrects the article DOI: 10.2196/49100.].

Correlation Between Remote Symptom Reporting by Caregivers and Adverse Clinical Outcomes: Mixed Methods Study.

BACKGROUND: Timely collection of patient-reported outcomes (PROs) decreases emergency department visits and hospitalizations and increases survival. However, little is known about the outcome predictivity of unpaid informal caregivers' reporting using similar clinical outcome assessments. OBJECTIVE: The aim of this study is to assess whether caregivers and adults with cancer adhered to a planned schedule for electronically collecting patient-reported outcomes (PROs) and if PROs were associated with future clinical events. METHODS: We developed 2 iPhone apps to collect PROs, one for patients with cancer and another for caregivers. We enrolled 52 patient-caregiver dyads from Kaiser Permanente Northern California in a nonrandomized study. Participants used the apps independently for 4 weeks. Specific clinical events were obtained from the patients' electronic health records up to 6 months following the study. We used logistic and quasi-Poisson regression analyses to test associations between PROs and clinical events. RESULTS: Participants completed 97% (251/260) of the planned Patient-Reported Outcomes Common Terminology Criteria for Adverse Events (PRO-CTCAE) surveys and 98% (254/260) of the Patient-Reported Outcomes Measurement Information System (PROMIS) surveys. PRO-CTCAE surveys completed by caregivers were associated with patients' hospitalizations or emergency department visits, grade 3-4 treatment-related adverse events, dose reductions (P<.05), and hospice referrals (P=.03). PROMIS surveys completed by caregivers were associated with hospice referrals (P=.02). PRO-CTCAE surveys completed by patients were not associated with any clinical events, but their baseline PROMIS surveys were associated with mortality (P=.03), while their antecedent or final PROMIS surveys were associated with all clinical events examined except for total days of treatment breaks. CONCLUSIONS: In this study, caregivers and patients completed PROs using smartphone apps as requested. The association of caregiver PRO-CTCAE surveys with patient clinical events suggests that this is a feasible approach to reducing patient burden in clinical trial data collection and may help provide early information about increasing symptom severity.

A framework for understanding label leakage in machine learning for health care.

INTRODUCTION: The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." FRAMEWORK: We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. RECOMMENDATIONS: Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.

Characterization of the Caudal Ventral Tubercle in the Sixth Cervical Vertebra in Modern Equus ferus caballus.

This study examined the anomalous variations of the ventral process of C6 in modern E. ferus caballus. The aim was to provide an incremental grading protocol measuring the absence of the caudal ventral tubercle (CVT) in this ventral process. The findings revealed the most prevalent absent CVT (aCVT) was left unilateral (n = 35), with bilateral (n = 29) and right unilateral (n = 12). Grading was determined in equal increments of absence 1/4, 2/4, 3/4, with 4/4 representing a complete aCVT in 56/76, with a significance of p = 0.0013. This also applied to bilateral specimens. In those C6 osseous specimens displaying a 4/4 grade aCVT, 41/56 had a partial absence of the caudal aspect of the cranial ventral tubercle (CrVT). Here, grading absent CrVTs (aCrVT) followed similarly to aCVTs, though 4/4 was not observed. The significance between 4/4 grade aCVTs and the presentation of an aCrVT was left p = 0.00001 and right p = 0.00018. In bilateral specimens, C6 morphologically resembled C5, implying a homeotic transformation that limited the attachment sites for the cranial and thoracal longus colli muscle. This potentially diminishes function and caudal cervical stability. Therefore, it is recommended that further studies examine the morphological extent of this equine complex vertebral malformation (ECVM) as well as its interrelationships and genetic code/blueprint.

Usability evaluation of mobile phone technologies for capturing cancer patient-reported outcomes and physical functions.

Background: By eliminating the requirement for participants to make frequent visits to research sites, mobile phone applications ("apps") may help to decentralize clinical trials. Apps may also be an effective mechanism for capturing patient-reported outcomes and other endpoints, helping to optimize patient care during and outside of clinical trials. Objectives: We report on the usability of Digital BioMarkers for Clinical Impact (DigiBioMarC™ (DBM)), a novel smartphone-based app used by cancer patients in conjunction with a wearable device (Apple Watch®). DBM is designed to collect patient-reported outcomes and record physical functions. Methods: In a fully decentralized "bring-your-own-device" smartphone study, we enrolled 54 cancer patient and caregiver dyads from Kaiser Permanente Northern California (KPNC) from October 2020 through March 2021. Patients used the app for at least 28 days, completed weekly questionnaires about their symptoms, physical functions, and mood, and performed timed physical tasks. Usability was determined through a subset of the Mobile App Rating Scale (MARS), the full System Usability Scale (SUS), the Net Promoter Score (NPS), and semi-structured interviews. Results: We obtained usability survey data from 50 of 54 patients. Median responses to the selected MARS questions and the mean SUS scores indicated above average usability. The NPS from the semi-structured interviews at the end of the study was 24, indicating a favorable score. Conclusions: Cancer patients reported above average usability for the DBM app. Qualitative analyses indicated that the app was easy to use and helpful. Future work will emphasize implementing further patient recommendations and evaluating the app's clinical efficacy in multiple settings.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.

INTRODUCTION: Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance. METHODS: To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices. RESULTS: We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations. CONCLUSION: Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Morphology of the Ventral Process of the Sixth Cervical Vertebra in Extinct and Extant Equus: Functional Implications.

In this study, we examined the ventral process of C6 in extinct and extant Equus (sister taxa to Equus ferus caballus only) with the purpose of describing normal morphology and identifying anomalous variations relevant to recent studies describing a congenital malformation in E. ferus caballus. Overall, 83 specimens from 9 museums and 3 research/educational facilities were examined, totalling 71 extinct specimens from 12 species and 12 extant specimens from 5 species. The lateral view revealed that a large convexity exists in the ventral process between the cranial ventral tubercle (CrVT) and the caudal ventral tubercle (CVT) in the earliest ancestor, Hyracotherium grangeri, from 55 mya, which receded throughout the millennia to become a smaller convexity in E. ferus caballus and the sister taxa. The CrVT is visibly shorter and narrower than the CVT, with a constricted section directly ventral to the transverse process, essentially demarcating the CrVT and CVT. No congenital malformations were evident. As the ventral process of C6 is an integral component for muscle attachment in supporting the head/neck during posture and locomotion, this would indicate that the caudal module in the cervical column might be compromised when a partial or complete absence of the CVT is detected via radiographs in modern E. ferus caballus.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

A novel smartphone application for the informal caregivers of cancer patients: Usability study.

Informal caregivers are a critical source of support for cancer patients. However, their perspectives are not routinely collected, despite health impacts related to the burden of caregiving. We created the TOGETHERCare smartphone application (app) to collect observer-reported outcomes regarding the cancer patient's health and caregiver's perceptions of their own mental and physical health, and to provide tips and resources for self-care and patient care. We enrolled 54 caregivers between October 2020 and March 2021 from Kaiser Permanente Northern California (KPNC), an integrated healthcare system. Fifty caregivers used the app for approximately 28 days. Usability and acceptability were assessed using questions from the Mobile App Rating Scale (MARS), the System Usability Scale (SUS), the Net Promoter Score (NPS), and semi-structured interviews. The caregivers' mean age was 54.4 years, 38% were female and 36% were non-White. The SUS total mean score was 83.4 (SD = 14.2), for a percentile rank of 90-95 ("excellent"). Median MARS responses to the functionality questions were also high. The NPS score of 30 at the end of the study indicated that most caregivers would recommend the app. Themes from semi-structured interviews were consistent across the study period and indicated that the app was easy to use and helpful. Caregivers indicated a need for feedback from the app, suggested some changes to the wording of questions, the app's visuals, and timing of notifications. This study demonstrated that caregivers are willing to complete frequent surveys about themselves and their patients. The app is unique because it provides a remote method to collect caregivers' observations about the patient that may be useful for clinical care. To our knowledge, TOGETHERCare is the first mobile app developed specifically to capture adult cancer patient symptoms from the informal caregiver's perspective. Future research will examine whether use of this app can help improve patient outcomes.

Team-Based Coaching Intervention to Improve Contrast-Associated Acute Kidney Injury: A Cluster-Randomized Trial.

BACKGROUND: Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance). METHODS: The IMPROVE AKI trial was a 2×2 factorial cluster-randomized trial across 20 Veterans Affairs medical centers (VAMCs). Participating VAMCs received Assistance, Assistance with Surveillance, Collaborative, or Collaborative with Surveillance for 18 months to implement AKI prevention strategies. The Assistance and Collaborative approaches promoted hydration and limited NPO and contrast dye dosing. We fit logistic regression models for AKI with site-level random effects accounting for the clustering of patients within medical centers with a prespecified interest in exploring differences across the four intervention arms. RESULTS: Among VAMCs' 4517 patients, 510 experienced AKI (235 AKI events among 1314 patients with preexisting CKD). AKI events in each intervention cluster were 110 (13%) in Assistance, 122 (11%) in Assistance with Surveillance, 190 (13%) in Collaborative, and 88 (8%) in Collaborative with Surveillance. Compared with sites receiving Assistance alone, case-mix-adjusted differences in AKI event proportions were -3% (95% confidence interval [CI], -4 to -3) for Assistance with Surveillance, -3% (95% CI, -3 to -2) for Collaborative, and -5% (95% CI, -6 to -5) for Collaborative with Surveillance. The Collaborative with Surveillance intervention cluster had a substantial 46% reduction in AKI compared with Assistance alone (adjusted odds ratio=0.54; 0.40-0.74). CONCLUSIONS: This implementation trial estimates that the combination of Collaborative with Surveillance reduced the odds of AKI by 46% at VAMCs and is suggestive of a reduction among patients with CKD. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: IMPROVE AKI Cluster-Randomized Trial (IMPROVE-AKI), NCT03556293.

Contribution of obesity to breathlessness in a large nationally representative sample of Australian adults.

BACKGROUND AND OBJECTIVE: Breathlessness is prevalent and associated with medical consequences. Obesity is related to breathlessness. However, the magnitude of its contribution has not been clearly documented. This investigation aimed to determine the contribution of obesity to breathlessness by estimating the population attributable fraction (PAF) in a representative sample of Australian adults. METHODS: A cross-sectional, nationally representative survey of Australian residents aged ≥18 years was conducted in October 2019. Breathlessness was defined as modified Medical Research Council (mMRC) dyspnoea scale grade ≥2. BMI was calculated from self-reported height and weight. Adjusted relative risks (aRRs) were estimated using a generalized linear model with Poisson distribution, adjusted for age group and/or participant-reported diagnosed illnesses. Adjusted PAFs were estimated using aRR and obesity prevalence in Australian adults. RESULTS: Among those who completed the National Breathlessness Survey, 9769 participants (51.4% female) were included in the analysis; 28.1% of participants were obese. The prevalence of breathlessness was 9.54%. The aRR of obesity for breathlessness was 2.04, adjusted for age. Adjusting for various co-morbid conditions, the aRR was slightly attenuated to around 1.85-1.98. The PAF, adjusted only for age, was 24.6% (95% CI 20.1-29.1) and after further adjustment for co-morbid conditions, the PAF ranged from 21.1% to 23.6%. Obesity accounted for a higher proportion of breathlessness in women than in men. CONCLUSION: Our results demonstrate that obesity accounts for around a quarter of breathlessness symptoms in Australian adults. This has important implications for health policy in light of the global trend in increasing obesity.

Integrating Clinical and Air Quality Data to Improve Prediction of COPD Exacerbations.

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research exploring whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistic regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve the clinical utility of COPD prediction. Findings could be used to provide population level health warnings as well as individual-patient risk assessments.

Investigating Changes in Ozone Formation Chemistry during Summertime Pollution Events over the Northeastern United States.

Understanding the local-scale spatial and temporal variability of ozone formation is crucial for effective mitigation. We combine tropospheric vertical column densities (VCDTrop) of formaldehyde (HCHO) and nitrogen dioxide (NO2), referred to as HCHO-VCDTrop and NO2-VCDTrop, retrieved from airborne remote sensing and the TROPOspheric Monitoring Instrument (TROPOMI) with ground-based measurements to investigate changes in ozone precursors and the inferred chemical production regime on high-ozone days in May-August 2018 over two Northeast urban domains. Over New York City (NYC) and Baltimore/Washington D.C. (BAL/DC), HCHO-VCDTrop increases across the domain, but higher NO2-VCDTrop occurs mainly in urban centers on ozone exceedance days (when maximum daily 8 h average (MDA8) ozone exceeds 70 ppb at any monitor in the region). The ratio of HCHO-VCDTrop to NO2-VCDTrop, proposed as an indicator of the sensitivity of local surface ozone production rates to its precursors, generally increases on ozone exceedance days, implying a transition toward a more NOx-sensitive ozone production regime that should lead to higher efficacy of NOx controls on the highest ozone days in NYC and BAL/DC. Warmer temperatures and enhanced influence from emissions in the local boundary layer on the high-ozone days are accompanied by slower wind speeds in BAL/DC but stronger, southwesterly winds in NYC.

How to step down asthma preventer treatment in patients with well-controlled asthma - more is not always better.

Most of the benefit of asthma preventer inhalers is seen with low doses. However, many Australian patients are prescribed doses of inhaled corticosteroids that are higher than necessary to control their asthma. Prescribing unnecessarily high preventer doses increases the patient's risk of adverse effects. They may also increase the patient's out-of-pocket costs. Asthma guidelines recommend considering a step-down in preventer treatment after asthma has been well controlled for two to three months in adults and for six months in children. The step-down process should be individualised for each patient. Preventive therapy should not be stopped completely.