Racial and ethnic differences in telemedicine use among community-dwelling older adults with dementia.

BACKGROUND: Routine ambulatory care is essential for older adults with Alzheimer's disease and related dementias (ADRD) to manage their health conditions. The federal government expanded telemedicine coverage to mitigate the impact of the COVID-19 pandemic on ambulatory services, which may provide an opportunity to improve access to care. This study aims to examine differences in telemedicine use for ambulatory services by race, ethnicity, and community-level socioeconomic status among community-dwelling older adults with ADRD. METHODS: This retrospective cohort study used Medicare claims data between April 01, 2020 and December 31, 2021. We included community-dwelling Medicare fee-for-service beneficiaries aged 65 years and older with ADRD. The outcome variable is individual's use (yes/no) of telemedicine evaluation and management (tele-EM) visits in each quarter. The key independent variables are race, ethnicity, and community-level socioeconomic status. RESULTS: The analytical sample size of the study was 2,068,937, including 9.9% Black, 82.7% White, and 7.4% Hispanic individuals. In general, we observed a decreasing trend of tele-EM use, and the average rate of quarterly tele-EM use was 23.0%. Tele-EM utilization varied by individual race, ethnicity, and community-level socioeconomic status. On average, White and Black individuals in deprived communities were 3.5 and 2.4 percentage-points less likely to use tele-EM compared with their counterparts in less-deprived communities (p < 0.001). However, Hispanic individuals in deprived communities were 2.4 percentage-points more likely to utilize tele-EM compared with those in less-deprived communities (p < 0.001). Additionally, we observed various racial and ethnic differences in telemedicine use in deprived communities versus less-deprived communities. CONCLUSIONS: We observed various racial and ethnic differences in telemedicine use, both within and between communities by socioeconomic status. Telemedicine is a viable healthcare delivery option that may influence healthcare access for racial and ethnic minorities and for individuals in socioeconomically deprived communities. Further policies or interventions may be needed to ensure all individuals have equal access to newly available care delivery models.

Telemedicine and Disparities in Mental Health Service Use Among Community-Dwelling Older Adults With Alzheimer Disease and Related Dementias.

OBJECTIVE: To examine disparities in mental health (MH) service utilization, via in-person and telemedicine (ie, tele-MH), by individuals' race, ethnicity, and community socioeconomic status, among community-dwelling older adults with Alzheimer disease and related dementias (ADRD) before and after the expansion of the Centers for Medicare and Medicaid Services' (CMS's) telemedicine policy. DESIGN: Observational study. SETTING AND PARTICIPANTS: A total of 3,003,571 community-dwelling Medicare beneficiaries with ADRD between 2019 and 2021 were included in the study. METHODS: Multiple national data were linked. The unit of analysis was individual-quarter. Three outcomes were defined: any MH visits (in-person or tele-MH), in-person MH visits, and tele-MH visits per quarter. Key independent variables included individual race and ethnicity, the socioeconomic status of the community, and an indicator for the implementation of the telemedicine policy. Regression analyses with individual random effects were used. RESULTS: In general, Black and Hispanic older adults with ADRD and those in socioeconomically deprived communities were less likely to have MH visits than white adults and those from less-deprived communities. In-person and tele-MH visits varied throughout the pandemic and across subpopulations. For instance, at the beginning of the pandemic, white, Black, and Hispanic older adults experienced 5.05, 3.03, and 2.87 percentage point reductions in in-person MH visits, and 3.53, 1.26, and 0.32 percentage point increases in tele-MH visits (with P < .01 for racial/ethnic differences), respectively. During the pandemic, the increasing trend in in-person MH visits and the decreasing trend in tele-MH visits varied across different subgroups. Overall, racial and ethnic differences in any MH visits were reduced, but the gap in any MH visits between deprived and less-deprived communities doubled during the pandemic (P < .01). CONCLUSIONS AND IMPLICATIONS: Telemedicine may have provided an opportunity to improve access to MH services among underserved populations. However, although some disparities in MH care were reduced, others widened, underscoring the importance of equitable health care access strategies to address the unique needs of different populations.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

Building a communication and support network among quality improvement teams in nursing homes: a longitudinal study of the SCOPE trial.

BACKGROUND: We applied a longitudinal network analysis approach to assess the formation of knowledge sharing and collaboration networks among care aide-led quality improvement (QI) teams in Canadian nursing homes participating in the Safer Care for Older Persons (in residential) Environments (SCOPE) trial which aimed to support unregulated front-line staff to lead unit-based quality improvement (QI) teams in nursing homes. We hypothesized that SCOPE's communicative and participatory nature would provide opportunities for peer support, knowledge sharing, and collaboration building among teams. METHODS: Fourteen QI teams in Alberta (AB) and seventeen QI teams in British Columbia (BC) participated in the study. Communications across nursing homes occurred through a series of 4 collaborative Learning Congresses (training sessions) over a 1-year period. The senior leaders of QI teams participated in two online network surveys about the communication/collaboration between teams in their province, 1 month after the first, and 6 months later, after the fourth Learning Congress. We developed communication and collaboration network maps pertaining to three time points: before SCOPE, at 2 months, and at 9 months. RESULTS: Over time, teams made significantly more new connections and strengthened existing ones, within and across regions. Geographic proximity and co-membership in organizational chains were important predictors of connectivity before and during SCOPE. Teams whose members were well connected at baseline disproportionately improved connectivity over time. On the other hand, teams that did not have prior opportunities to connect appeared to use SCOPE to build new ties. CONCLUSIONS: Our findings suggest the importance of network-altering activities to the formation of collaboration networks among QI teams across nursing homes. Active strategies could be used to better connect less connected teams and facilitate collaboration among geographically proximate teams. These findings may inform the development of interventions to leverage existing networks and provide new networking opportunities to develop and sustain organizational improvements.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Racial/Ethnic Differences in Risk Factors Associated With Severe COVID-19 Among Older Adults With ADRD.

OBJECTIVE: To examine racial/ethnic differences in risk factors, and their associations with COVID-19-related outcomes among older adults with Alzheimer's disease and related dementias (ADRD). DESIGN: Observational study. SETTING AND PARTICIPANTS: National Medicare claims data and the Minimum Data Set 3.0 from April 1, 2020, to December 31, 2020, were linked in this study. We included community-dwelling fee-for-service Medicare beneficiaries with ADRD, diagnosed with COVID-19 between April 1, 2020, and December 1, 2020 (N = 138,533). METHODS: Two outcome variables were defined: hospitalization within 14 days and death within 30 days of COVID-19 diagnosis. We obtained information on individual sociodemographic characteristics, chronic conditions, and prior health care utilization based on the Medicare claims and the Minimum Dataset. Machine learning methods, including lasso regression and discriminative pattern mining, were used to identify risk factors in racial/ethnic subgroups (ie, White, Black, and Hispanic individuals). The associations between identified risk factors and outcomes were evaluated using logistic regression and compared across racial/ethnic subgroups using the coefficient comparison approach. RESULTS: We found higher risks of COVID-19-related outcomes among Black and Hispanic individuals. The areas under the curve of the models with identified risk factors were 0.65 to 0.68 for mortality and 0.61 to 0.62 for hospitalization across racial/ethnic subgroups. Although some identified risk factors (eg, age, gender) for COVID-19-related outcomes were common among all racial/ethnic subgroups, other risk factors (eg, hypertension, obesity) varied by racial/ethnic subgroups. Furthermore, the associations between some common risk factors and COVID-19-related outcomes also varied by race/ethnicity. Being male was related to 138.2% (95% CI: 1.996-2.841), 64.7% (95% CI: 1.546-1.755), and 37.1% (95% CI: 1.192-1.578) increased odds of death among Hispanic, White, and Black individuals, respectively. In addition, the racial/ethnic disparity in COVID-19-related outcomes could not be completely explained by the identified risk factors. CONCLUSIONS AND IMPLICATIONS: Racial/ethnic differences were detected in the likelihood of having COVID-19-related outcomes, specific risk factors, and relationships between specific risk factors and COVID-19-related outcomes. Future research is needed to elucidate the reasons for these differences.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results: We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion: HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election.

PURPOSE: The purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level. METHODS: Tweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents. RESULTS: Of 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week. CONCLUSION: Surveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.

The relationship between Jim Crow laws and social capital from 1997-2014: A 3-level multilevel hierarchical analysis across time, county and state.

INTRODUCTION: Jim Crow laws in the United States promoted racial prejudice, which may have reduced social capital. Our study tests the relationship between Jim Crow laws and social capital. METHODS: We conducted 3-level multilevel hierarchical modeling to study differences in the stock of social capital for 1997, 2005, 2009 in Jim Crow states compared to states without Jim Crow laws. We examined the moderation effects of county level median income, percent Black and percent with high school education and Jim Crow laws on social capital. RESULTS: Jim Crow laws significantly reduced stock of social capital across 1997, 2005, 2009. The model was robust to the inclusion of random county, states, time and fixed county and state level covariates for median income, percent Black and percent with high school education. The largest percent of between state variations explained for fixed variables was from the addition of Jim Crow laws with 2.86%. These results demonstrate that although Jim Crow laws were abolished in 1965, the effects of racial segregation appear to persist through lower social connectiveness, community and trust. A positive moderation effect was seen for median income and percent Black with Jim Crow laws on social capital. DISCUSSION: Our study supports a negative association between Jim Crow laws and reduction in the stock of social capital. This may be attributed to the fracturing of trust, reciprocity and collective action produced by legal racial segregation. Findings from this study offer insight on the potential impacts of historical policies on the social structure of a community. Future research is necessary to further identify the mechanistic pathways and develop interventions to improve social capital.

The effects of integrating behavioral health into primary care for low-income children.

OBJECTIVE: To evaluate the impact of TEAM UP-an initiative that fully integrates behavioral health services into pediatric primary care in three Boston-area Community Health Centers (CHCs)-on health care utilization and costs. DATA SOURCES: 2014-2017 claims data on continuously enrolled children from a Massachusetts Medicaid managed care plan. STUDY DESIGN: We used a difference-in-difference approach with inverse probability of treatment weights to compare outcomes in children receiving primary care at TEAM UP CHCs versus comparison site CHCs, in the pre (2014-2016q2)- versus post (2016q3-2017)-intervention periods. Utilization outcomes included emergency department visits, inpatient admissions, primary care visits, and outpatient/professional visits (all cause and those with mental health (MH) diagnoses). Cost outcomes included total cost of care (inpatient, outpatient, professional, pharmacy). We further assessed differential effects by baseline MH diagnosis. PRINCIPAL FINDINGS: After 1.5 years, TEAM UP was associated with a relative increase in the rate of primary care visits (IRR = 1.15, 95% CI 1.04-1.27, or 115 additional visits/1000 patients/quarter), driven by children with a MH diagnosis at baseline. There was no significant change in avoidable health care utilization or cost. CONCLUSIONS: Expanding the TEAM UP behavioral health integration model to other sites has the potential to improve primary care engagement in low-income children with MH needs.

Quantifying the nativeness of antibody sequences using long short-term memory networks.

Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.

Feasibility of using social media to monitor outdoor air pollution in London, England.

Air pollution is a serious public health concern. Innovative and scalable methods for detecting harmful air pollutants such as PM2.5 are necessary. This study assessed the feasibility of using social media to monitor outdoor air pollution in an urban area by comparing data from Twitter and validating it against established air monitoring stations. Data were collected from London, England from July 29, 2016 to March 17, 2017. Daily mean PM2.5 data was downloaded from the LondonAir platform consisting of 26 air pollution monitoring sites throughout Greater London. Publicly available tweets geo-located to Greater London containing air pollution terms were captured from the Twitter platform. Tweets with media URL links were excluded to minimize influence of news stories. Sentiment of the tweets was examined as negative, positive, or neutral. Cross-correlation analyses were used to compare the relationship between trends of tweets about air pollution and levels of PM2.5 over time. There were 16,448 tweets without a media URL link, with a mean of 498.42 (SD = 491.08) tweets per week. A significant cross-correlation coefficient of 0.803 was observed between PM2.5 data and the non-media air pollution tweets (p < 0.001). The cross-correlation coefficient was highest between PM2.5 data and air pollution tweets with negative sentiment at 0.816 (p < 0.001). Discussions about air pollution on Twitter reflect particle PM2.5 pollution levels in Greater London. This study highlights that social media may offer a supplemental source to support the detection and monitoring of air pollution in a densely populated urban area.

Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status.

INTRODUCTION: Subtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer's disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD. METHODS: Longitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory-immediate and delayed recall, visual reproduction-immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD. RESULTS: Our model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. DISCUSSION: Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high- and low-risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.