A scalable approach for continuous time Markov models with covariates.

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set.

Single-world intervention graphs for defining, identifying, and communicating estimands in clinical trials.

Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.

How patients with multiple sclerosis acquire disability.

Patients with multiple sclerosis acquire disability either through relapse-associated worsening (RAW) or progression independent of relapse activity (PIRA). This study addresses the relative contribution of relapses to disability worsening over the course of the disease, how early progression begins and the extent to which multiple sclerosis therapies delay disability accumulation. Using the Novartis-Oxford multiple sclerosis (NO.MS) data pool spanning all multiple sclerosis phenotypes and paediatric multiple sclerosis, we evaluated ∼200 000 Expanded Disability Status Scale (EDSS) transitions from >27 000 patients with ≤15 years follow-up. We analysed three datasets: (i) A full analysis dataset containing all observational and randomized controlled clinical trials in which disability and relapses were assessed (n = 27 328); (ii) all phase 3 clinical trials (n = 8346); and (iii) all placebo-controlled phase 3 clinical trials (n = 4970). We determined the relative importance of RAW and PIRA, investigated the role of relapses on all-cause disability worsening using Andersen-Gill models and observed the impact of the mechanism of worsening and disease-modifying therapies on the time to reach milestone disability levels using time continuous Markov models. PIRA started early in the disease process, occurred in all phenotypes and became the principal driver of disability accumulation in the progressive phase of the disease. Relapses significantly increased the hazard of all-cause disability worsening events; following a year in which relapses occurred (versus a year without relapses), the hazard increased by 31-48% (all P < 0.001). Pre-existing disability and older age were the principal risk factors for incomplete relapse recovery. For placebo-treated patients with minimal disability (EDSS 1), it took 8.95 years until increased limitation in walking ability (EDSS 4) and 18.48 years to require walking assistance (EDSS 6). Treating patients with disease-modifying therapies delayed these times significantly by 3.51 years (95% confidence limit: 3.19, 3.96) and 3.09 years (2.60, 3.72), respectively. In patients with relapsing-remitting multiple sclerosis, those who worsened exclusively due to RAW events took a similar length of time to reach milestone EDSS values compared with those with PIRA events; the fastest transitions were observed in patients with PIRA and superimposed relapses. Our data confirm that relapses contribute to the accumulation of disability, primarily early in multiple sclerosis. PIRA begins in relapsing-remitting multiple sclerosis and becomes the dominant driver of disability accumulation as the disease evolves. Pre-existing disability and older age are the principal risk factors for further disability accumulation. The use of disease-modifying therapies delays disability accrual by years, with the potential to gain time being highest in the earliest stages of multiple sclerosis.

How to estimate health service coverage in 58 districts of Benin with no survey data: Using hybrid estimation to fill the gaps.

The global movement to use routine information for managing health systems to achieve the Sustainable Development Goals, relies on administrative data which have inherent biases when used to estimate coverage with health services. Health policies and interventions planned with incorrect information can have detrimental impacts on communities. Statistical inferences using administrative data can be improved when they are combined with random probability survey data. Sometimes, survey data are only available for some districts. We present new methods for extending combined estimation techniques to all districts by combining additional data sources. Our study uses data from a probability survey (n = 1786) conducted during 2015 in 19 of Benin's 77 communes and administrative count data from all of them for a national immunization day (n = 2,792,803). Communes are equivalent to districts. We extend combined-data estimation from 19 to 77 communes by estimating denominators using the survey data and then building a statistical model using population estimates from different sources to estimate denominators in adjacent districts. By dividing administrative numerators by the model-estimated denominators we obtain extrapolated hybrid prevalence estimates. Framing the problem in the Bayesian paradigm guarantees estimated prevalence rates fall within the appropriate ranges and conveniently incorporates a sensitivity analysis. Our new methodology, estimated Benin's polio vaccination rates for 77 communes. We leveraged probability survey data from 19 communes to formulate estimates for the 58 communes with administrative data alone; polio vaccination coverage estimates in the 58 communes decreased to ranges consistent with those from the probability surveys (87%, standard deviation = 0.09) and more credible than the administrative estimates. Combining probability survey and administrative data can be extended beyond the districts in which both are collected to estimate coverage in an entire catchment area. These more accurate results will better inform health policy-making and intervention planning to reduce waste and improve health in communities.

Identifying treatment effects using trimmed means when data are missing not at random.

Patients often discontinue from a clinical trial because their health condition is not improving or they cannot tolerate the assigned treatment. Consequently, the observed clinical outcomes in the trial are likely better on average than if every patient had completed the trial. If these differences between trial completers and non-completers cannot be explained by the observed data, then the study outcomes are missing not at random (MNAR). One way to overcome this problem-the trimmed means approach for missing data due to study discontinuation-sets missing values as the worst observed outcome and then trims away a fraction of the distribution from each treatment arm before calculating differences in treatment efficacy (Permutt T, Li F. Trimmed means for symptom trials with dropouts. Pharm Stat. 2017;16(1):20-28). In this paper, we derive sufficient and necessary conditions for when this approach can identify the average population treatment effect. Simulation studies show the trimmed means approach's ability to effectively estimate treatment efficacy when data are MNAR and missingness due to study discontinuation is strongly associated with an unfavorable outcome, but trimmed means fail when data are missing at random. If the reasons for study discontinuation in a clinical trial are known, analysts can improve estimates with a combination of multiple imputation and the trimmed means approach when the assumptions of each hold. We compare the methodology to existing approaches using data from a clinical trial for chronic pain. An R package trim implements the method. When the assumptions are justifiable, using trimmed means can help identify treatment effects notwithstanding MNAR data.

Predictors of Health Insurance, Life Insurance, and Retirement Savings Among NYC's Immigrant Taxi and For-Hire Vehicle Drivers.

Taxi and for-hire vehicle (FHV) drivers are a predominantly immigrant population facing a range of occupational stressors, including lack of workplace benefits and increasing financial strain from tumultuous industry changes and now COVID-19's devastating impact. Bilingual research staff surveyed 422 New York City taxi/FHV drivers using a stratified sampling approach in driver-frequented locations to examine drivers' health and financial planning behaviors for the first time. Drivers lacked health insurance at double the NYC rate (20% vs. 10%). Life insurance and retirement savings rates were lower than U.S. averages (20% vs. 60%, 25% vs. 58%, respectively). Vehicle ownership was a significant predictor of health insurance, life insurance, and retirement savings. Compared to South Asian drivers, Sub-Saharan African drivers were significantly less likely to have health insurance and North African, and Middle Eastern drivers were significantly less likely to have retirement savings. Although most drivers indicated the importance of insurance and benefits, < 50% understood how to use them. Drivers felt primary care coverage to be most important followed by other health-related coverage, retirement benefits, and life insurance. Results reveal compelling addressable gaps in insurance and benefits coverage and the need to implement accessible financial literacy with navigation and advising services and programs.

Use of Traditional Chinese Herbal Medicine Concurrently with Conventional Cancer Treatment Among Chinese Cancer Patients.

In the U.S. and Canada, Traditional Chinese Medicine (TCM) use has become increasingly common; Chinese immigrants have particularly high rates of TCM use. In this study, we used a cross sectional survey study design to assess the specific types of Traditional Chinese Herbal Medicine (TCHM) used, the concurrent use of TCHM and conventional cancer treatment, and communication with providers about TCHM use, among Chinese immigrant cancer patients in New York City (NYC). We surveyed 114 patients from several community and clinical settings in NYC. The mean age was 63, 59% were female, and 83% originated from mainland China. Breast (18%) and lung (21%) cancer were the most common cancer diagnoses, and 60% were receiving conventional cancer treatment at the time of the survey. 75% reported ever using TCHM since their most recent primary cancer diagnosis. 68% of those who used herbs reported concurrent use of TCHM with conventional cancer treatment. Only 13% of those who used herbs reported sharing TCHM use with a provider, and only 19% reported that a provider had ever discussed TCHM use with them. Our findings demonstrated an alarmingly high rate of concurrent use of TCHM and conventional cancer treatment and low rate of communication with providers about TCHM use. A wide variety of herbs were used, including those with potentially negative interactions with conventional treatment. This study highlights the urgent need for the development of interventions to assist providers and patients in improving communication around this important topic.

The Nutrition Benefits Participation Gap: Barriers to Uptake of SNAP and WIC Among Latinx American Immigrant Families.

To examine nutrition benefit under-enrollment in Latinx American immigrant families, we administered a survey to 100 adults attending a NY Latinx American community serving organization. We used a logistic regression approach to analyze misinformation impact on enrollment, and examined non-enrollment explanations, among participants in whose families a child or pregnant or breastfeeding woman appeared SNAP- or WIC-eligible. Among households (N = 51) with ≥ 1 SNAP-eligible child, 49% had no child enrolled. Reasons included repercussion fears (e.g. payback obligation, military conscription, college aid ineligibility, child removal, non-citizen family member penalties), and logistical barriers. In multivariable regression models, having heard the rumor that SNAP/WIC participation makes unauthorized status family members vulnerable to being reported to the government was associated with an 85% lower enrollment rate (OR 0.15, CI 0.03, 0.94). Misinformation impedes nutrition benefit participation. A multi-level intervention is necessary to inform potential applicants and providers regarding eligibility criteria and erroneous rumors, along with an informed discussion of the risks versus benefits of using resources, especially as public charge criteria change.

Discrimination as a social determinant of stress and health among New York City taxi drivers.

Discrimination is associated with poorer mental and physical health outcomes. Taxi drivers have a higher risk of exposure to discrimination and higher rates of chronic conditions. A cross-sectional needs assessment was conducted with a multilingual group of 535 male taxi drivers in New York City. Drivers reporting higher discrimination were more likely to have higher perceived stress and were more likely to have anxiety/depression and chronic pain, adjusting for confounders. Workplace-based interventions designed to help drivers cope with discrimination, stress, and chronic health conditions, interventions to educate the taxi-riding public, and greater attention to these issues from administrative agencies are warranted.

The Use of a Mobile Application to Increase Access to Interpreters for Cancer Patients With Limited English Proficiency: A Pilot Study.

BACKGROUND: Language barriers can influence the quality of health care and health outcomes of limited English proficient patients with cancer. The use of medical interpretation services can be a valuable asset for improving communications in emergency care settings. OBJECTIVE: To evaluate whether a mobile translation application increased call frequency to interpreter services among providers in an Urgent Care Center at a comprehensive cancer center and to assess provider satisfaction of the mobile application. RESEARCH DESIGN: Prospective pre-post nonrandomized intervention of a mobile translation application with access to an over the phone interpreter (OPI) service at the push of a button and poststudy satisfaction survey. SUBJECTS: Sixty-five clinicians working at the Urgent Care Center in a cancer center in New York City. MEASURES: Mean call frequency to OPI services, tested by the nonparametric Wilcoxon Mann Whitney test, and self-reported provider satisfaction descriptives. RESULTS: The mobile application contributed to increasing the frequency of phone calls to OPI services during the intervention period (mean=12.8; P=0.001) as compared with the preintervention period (mean=4.3), and showed continued use during the postintervention period (mean=5.7). Most clinicians were satisfied with the use of the mobile application and access to the OPI services. CONCLUSIONS: The results suggest that mobile application tools contribute to increasing the use and ease of access to language services. This has the potential to improve the quality of communication between medical providers and limited English proficient patients in the delivery of cancer care in urgent care settings.

Discrimination and Health Among Taxi Drivers in New York and Toronto.

Immigrant taxi drivers in metropolitan cities are exposed to experiences of discrimination and occupation-based health risks. Given the structural differences in health care systems in the United States and Canada, we investigated the differences in reports of discrimination, health conditions and concern about health conditions between taxi drivers in New York City and Toronto, Ontario. Participants were recruited for a taxi driver Needs Assessment Survey as part of a Taxi Network needs assessment project using a street side convenience sampling technique in New York City and Toronto. The matched sample contained 33 drivers from Toronto and 33 drivers from NYC. All Toronto drivers in our sample reported having health insurance while over a quarter of NYC drivers did not have health insurance. Toronto drivers reported greater everyday and workplace discrimination. Drivers in both cities experienced higher rates than average, and reported concern about, major health conditions. We also found preliminary evidence suggesting a relationship between experiencing discrimination and reporting chronic pain. Our findings suggest the need for future research to more closely examine the associations between discrimination and health among the taxi driver population.

Examining transdiagnostic vulnerabilities among HIV positive smokers seen at three inner city community based organizations.

Investigators have proposed a "transdiagnostic vulnerability framework" that examines the relationship between smoking and broader emotional factors, including anhedonia, anxiety sensitivity, and distress tolerance. Because smoking and depression are more common in persons living with HIV and AIDS (PLWHA) than in the general population, understanding the relationship between smoking and mental health is critical. The following study aims to characterize levels of clinically significant depressive symptoms and these broader emotional factors as well as the relationship between these factors and smoking-related variables in a sample of PLWHA. This cross-sectional study employed convenience sampling to survey adult clients who attended one of three AIDS service organizations in New York City. The questionnaires assessed sociodemographic and HIV health care variables, tobacco use, and anxiety - and depression-related constructs. 150 PLWHA completed surveys. Among the 118 smokers, the prevalence of clinically depressive symptoms was 53%. Participants with clinically significant depressive symptoms had significantly higher mean anhedonia scores and anxiety sensitivity scores and lower mean distress tolerance total scores compared to participants without clinically significant depressive symptoms (p < 0.001). Smoking cessation treatment for persons with co-morbid psychiatric disorders has been suboptimal and treatment for co-morbid mental health conditions tends to align with disorder-specific treatment. Given that PLWHA are a priority population, further research should address how to best tailor interventions to a group with multiple obstacles to successful tobacco cessation.

Using search queries for malaria surveillance, Thailand.

BACKGROUND: Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region. METHODS: Official Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models' accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC). RESULTS: Each model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand. CONCLUSIONS: Models built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005-2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.