SoundRocket (United States)

The National Dementia Workforce Study was designed to improve our understanding of the individuals and systems who care for people with dementia, but designing and implementing such a study is challenging due to the large number of patient care organizations, clinical and direct care roles, and locations in which care is provided. Specifically, developing a probability sample of organizations and staff caring for people with dementia is a complex and difficult process. While there are national sampling frames available for federally certified nursing homes (i.e., via data from the Center for Medicare and Medicaid Services), there are no national sampling frames for assisted living communities or home care agencies. The latter frames must be developed through querying state-level regulatory agencies and through other, supplemental strategies such as working with professional organizations, large employers, and organizations that provide services (e.g., payroll services) to this sector. Further, since there are no national sampling frames that allow for direct sampling of staff working in any of these types of organizations, we opted for a two-stage design. In the first stage, organizations are identified, sampled, recruited to participate in an organizational-level survey, and asked to provide a roster of eligible staff. In the second stage, individual staff members are recruited for a staff-level survey. We describe the plan for sampling and recruitment procedures to be used in each stage and discuss limitations, including implications for coverage of the target population. Data collected through these surveys will be available to the research community.

Sedentary lifestyles, poor nutritional choices, inadequate sleep, risky substance use, limited social connections, and high stress contribute to the growing prevalence of chronic diseases. Lifestyle medicine, emphasizing therapeutic lifestyle changes for prevention and treatment, has demonstrated effectiveness but remains underutilized in clinical settings. The Complete Lifestyle Medicine Intervention Program-Ontario (CLIP-ON) was developed to educate the rural population of Northern Ontario in lifestyle medicine to improve health outcomes and engagement. This study evaluates the implementation and effectiveness of the CLIP-ON program for patients with chronic diseases in the Parry Sound area, focusing on lifestyle behaviors, health outcomes, enrollment, retention rates, and interdisciplinary team engagement. This observational cohort study guided by the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) includes pre- and postintervention assessments from participants and health care providers. A hybrid type II mixed methods design evaluates the intervention's effectiveness and implementation process in real-world settings through quantitative and qualitative data collection. CLIP-ON is tailored to the residents of the Parry Sound catchment area in Northern Ontario. Participants (≥18 years old) with chronic conditions such as prediabetes, type II diabetes, systemic hypertension, cardiovascular vascular disease, dyslipidemia, or high BMI (≥25) will be recruited through self-referral or provider referral. Approximately 10 participants per cohort will be enrolled in the CLIP-ON program, consisting of 22 weeks of weekly group sessions and monthly individual consultations with physicians, health coaches, kinesiologists, and registered dieticians either in person or through a web-based platform. CLIP-ON will cover the 6 pillars of lifestyle medicine through 14 group sessions followed by an 8-week supervised exercise program. Anthropometric and cardiometabolic variables will be measured before and after the program. Participants will be surveyed on lifestyle habits, wellness, perceived barriers, and program satisfaction at 3 and 6 months. Focus groups and dropout interviews with participants (n=10 per cohort) and providers (n=6 per cohort) will guide program adaptations. Quantitative and qualitative data collected at baseline and follow-up will assess the program's implementation and identify barriers and opportunities for improvement. This study was approved by the Laurentian University Research Ethics Board (6021397) on July 6, 2023. The first cohort was enrolled in late 2023 and is still under evaluation. The second cohort began in mid-2024, and data collection is currently underway. A mixed methods analysis will be used at enrollment, program completion (22 weeks), and follow-up (6 months after program completion). Focus groups assessing the program's effectiveness and implementation will take place after the 22-week intervention. Data will be analyzed in early 2025. This protocol provides insights into the implementation of this lifestyle medicine program and its impact on participants' health. The findings will guide future advancements and establish a scalable model for other communities. ClinicalTrials.gov NCT06192251; https://clinicaltrials.gov/study/NCT06192251. DERR1-10.2196/59179.

Background: Life course socioeconomic and demographic factors including educational attainment, race and ethnicity, and childhood (SES) are very powerful predictors of large inequalities in aging, morbidity, and mortality. Immune aging, including accumulation of late-differentiated, senescent-like lymphocytes and lower level of naive lymphocytes, may play a role in the development of the age-related health inequalities. However, there has been little research investigating association between socioeconomic status and immune aging, particularly age-related changes in lymphocyte percentages. Methods: This study used nationally representative data from more than 9000 US adults from the Health and Retirement Study to investigate associations between educational attainment, race and ethnicity, and childhood SES and lymphocyte percentages. Results: Respondents with lower educational attainment, Hispanic adults, and those who had a parent with less than high school education had lymphocyte percentages consistent with more highly accelerated immune aging compared to those with greater educational attainment, non-Hispanic White adults, and respondents who had parents with a high school education, respectively. Associations between education, Hispanic ethnicity, and parents' education and late differentiated senescent-like T lymphocytes (TemRA) and B cells were largely driven by cytomegalovirus (CMV) seropositivity. Conclusions: Results suggest that CMV is a major driver of observed SES inequalities in immunosenescence and may therefore be an important target for interventions. Naive T lymphocytes may be particularly affected by socioeconomic position and may therefore be of particular interest to research interested in inequalities in health and aging.

Responsive survey design is a technique aimed at improving the efficiency or quality of surveys by using incoming data from the field to make design changes. The technique was pioneered on large national surveys, but the tools can also be applied on the smaller-scale surveys most commonly used by sociologists. We demonstrate responsive survey design in a small-scale, list-based sample survey of students on the topic of sexual misconduct. We investigate the impact of individual incentive levels and a two-phase responsive design with changes to mode of contact as approaches for limiting the potential of nonresponse bias in data from such surveys. Our analyses demonstrate that a two-phase design introducing telephone and face-to-face reminders to complete the survey can produce stronger change in response rates and characteristics of those who respond than higher incentive levels. These findings offer tools for sociologists designing smaller-scale surveys of special populations or sensitive topics.

Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to cluster size, and then units are randomly sampled inside selected clusters. Challenges arise when the sizes of the nonsampled cluster are unknown. We propose nonparametric and parametric Bayesian approaches for predicting the unknown cluster sizes, with this inference performed simultaneously with the model for survey outcome, with computation performed in the open-source Bayesian inference engine Stan. Simulation studies show that the integrated Bayesian approach outperforms classical methods with efficiency gains, especially under informative cluster sampling design with small number of selected clusters. We apply the method to the Fragile Families and Child Wellbeing study as an illustration of inference for complex health surveys.

Prior research has found bidirectional associations between psychotic experiences (PEs) and selected substance use disorders. We aimed to extend this research by examining the bidirectional association between PEs and various types of substance use (SU) and substance use disorders (SUDs), and the influence of antecedent mental disorders on these associations. We used data from the World Health Organization World Mental Health surveys. A total of 30 902 adult respondents across 18 countries were assessed for (a) six types of life-time PEs, (b) a range of types of SU and DSM-IV SUDs and (c) mental disorders using the Composite International Diagnostic Interview. Discrete-time survival analyses based on retrospective age-at-onset reports examined the bidirectional associations between PEs and SU/SUDs controlling for antecedent mental disorders. After adjusting for demographics, comorbid SU/SUDs and antecedent mental disorders, those with prior alcohol use disorders [odds ratio (OR)=1.6, 95% confidence interval (CI)=1.2-2.0], extra-medical prescription drug use (OR=1.5, 95% CI=1.1-1.9), alcohol use (OR=1.4, 95% CI=1.1-1.7) and tobacco use (OR=1.3, 95% CI=1.0-1.8) had increased odds of subsequent first onset of PEs. In contrast, those with temporally prior PEs had increased odds of subsequent onset of tobacco use (OR=1.5, 95% CI=1.2-1.9), alcohol use (OR=1.3, 95% CI=1.1-1.6) or cannabis use (OR=1.3, 95% CI=1.0-1.5) as well as of all substance use disorders (ORs ranged between 1.4 and 1.5). There was a dose response relationship between both count and frequency of PEs and increased subsequent odds of selected SU/SUDs. Associations between psychotic experiences (PEs) and substance use/substance use disorders (SU/SUDs) are often bidirectional, but not all types of SU/SUDs are associated with PEs. These findings suggest that it is important to be aware of the presence of PEs within those with SUDs or at risk of SUDs, given the plausibility that they may each impact upon the other.

Surveys are a critical resource for social, economic, and health research. The ability to efficiently collect these data and develop accurate post-survey adjustments depends upon reliable data about effort required to recruit sampled units. Level-of-effort paradata are data generated by interviewers during the process of collecting data in surveys. These data are often used as predictors in nonresponse adjustment models or to guide data collection efforts. However, recent research has found that these data may include measurement errors, which would lead to inaccurate decisions in the field or reduced effectiveness for adjustment purposes (Biemer, et al., 2013; West and Little, 2013). In order to assess whether errors occur in level-of-effort paradata, we introduce a new source of validation data for call records -- Global Positioning System (GPS) data generated by smartphones carried by interviewers. We examine the quality of the GPS data and then use the GPS data to characterize interviewer travel within sampled area segments. We also link the GPS data with the interviewer-reported call records in order to identify potential errors in the call records. Given the lack of a gold standard, we perform a sensitivity analysis under various assumptions to see how this would change our conclusions.

Abstract. In recent years, space-borne observations of atmospheric carbon dioxide (CO2) have been increasingly used in global carbon-cycle studies. In order to obtain added value from space-borne measurements, they have to suffice stringent accuracy and precision requirements, with the latter being less crucial as it can be reduced by just enhanced sample size. Validation of CO2 column-averaged dry air mole fractions (XCO2) heavily relies on measurements of the Total Carbon Column Observing Network (TCCON). Owing to the sparseness of the network and the requirements imposed on space-based measurements, independent additional validation is highly valuable. Here, we use observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) flights from 01/2009 through 09/2011 to validate CO2 measurements from satellites (Greenhouse Gases Observing Satellite – GOSAT, Thermal Emission Sounder – TES, Atmospheric Infrared Sounder – AIRS) and atmospheric inversion models (CarbonTracker CT2013B, Monitoring Atmospheric Composition and Climate (MACC) v13r1). We find that the atmospheric models capture the XCO2 variability observed in HIPPO flights very well, with correlation coefficients (r2) of 0.93 and 0.95 for CT2013B and MACC, respectively. Some larger discrepancies can be observed in profile comparisons at higher latitudes, in particular at 300 hPa during the peaks of either carbon uptake or release. These deviations can be up to 4 ppm and hint at misrepresentation of vertical transport. Comparisons with the GOSAT satellite are of comparable quality, with an r2 of 0.85, a mean bias μ of −0.06 ppm, and a standard deviation σ of 0.45 ppm. TES exhibits an r2 of 0.75, μ of 0.34 ppm, and σ of 1.13 ppm. For AIRS, we find an r2 of 0.37, μ of 1.11 ppm, and σ of 1.46 ppm, with latitude-dependent biases. For these comparisons at least 6, 20, and 50 atmospheric soundings have been averaged for GOSAT, TES, and AIRS, respectively. Overall, we find that GOSAT soundings over the remote Pacific Ocean mostly meet the stringent accuracy requirements of about 0.5 ppm for space-based CO2 observations.