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A private–academic partnership built the Vaccine Equity Planner (VEP) to help decision-makers improve geographic access to COVID-19 vaccinations across the United States by identifying vaccine deserts and facilities that could fill those deserts. The VEP presented complex, updated data in an intuitive form during a rapidly changing pandemic situation. The persistence of vaccine deserts in every state as COVID-19 booster recommendations develop suggests that vaccine delivery can be improved. Underresourced public health systems benefit from tools providing real-time, accurate, actionable data. (Am J Public Health. 2023;113(4):363–367. https://doi.org/10.2105/AJPH.2022.307198 )

The Janssen COVID-19 vaccine came to market in February 2021 as the first non-mRNA and first single-dose formula approved for use in the US. In April 2021, a temporary pause was recommended for the vaccine after the discovery of rare but serious post-vaccination side-effects. We fielded a large-scale nationally representative survey (n = 401,398) on individual confidence in each of the COVID-19 vaccine formulas available in the US before, during, and after this pause. We find widespread loss of confidence in the Janssen vaccine across gender, age, and other demographics, which persisted over time and after lifting of the halt. Despite this drop, overall reasons for remaining unvaccinated were stable and there was a concurrent minor bump in confidence towards other vaccine formulas. This contrast between the persistent reduction in confidence in the Janssen vaccine and the apparent maintenance of the broader campaign's integrity, highlights the complex dynamics and downstream effects of the pause.

ImportanceAbortion facility closures resulted in a substantial decrease in access to abortion care in the US.ObjectivesTo investigate the changes in travel time to the nearest abortion facility after the Dobbs v Jackson Women’s Health Organization (referred to hereafter as Dobbs) US Supreme Court decision.Design, Setting, and ParticipantsRepeated cross-sectional spatial analysis of travel time from each census tract in the contiguous US (n = 82 993) to the nearest abortion facility (n = 1134) listed in the Advancing New Standards in Reproductive Health database. Census tract boundaries and demographics were defined by the 2020 American Community Survey. The spatial analysis compared access during the pre-Dobbs period (January-December 2021) with the post-Dobbs period (September 2022) for the estimated 63 718 431 females aged 15 to 44 years (reproductive age for this analysis) in the US (excluding Alaska and Hawaii).ExposuresThe Dobbs ruling and subsequent state laws restricting abortion procedures. The pre-Dobbs period measured abortion access to all facilities providing abortions in 2021. Post-Dobbs abortion access was measured by simulating the closure of all facilities in the 15 states with existing total or 6-week abortion bans in effect as of September 30, 2022.Main Outcomes and MeasuresMedian and mean changes in surface travel time (eg, car, public transportation) to an abortion facility in the post-Dobbs period compared with the pre-Dobbs period and the total percentage of females of reproductive age living more than 60 minutes from abortion facilities during the pre- and post-Dobbs periods.ResultsOf 1134 abortion facilities in the US (at least 1 in every state; 8 in Alaska and Hawaii excluded), 749 were considered active during the pre-Dobbs period and 671 were considered active during a simulated post-Dobbs period. Median (IQR) and mean (SD) travel times to pre-Dobbs abortion facilities were estimated to be 10.9 (4.3-32.4) and 27.8 (42.0) minutes. Travel time to abortion facilities in the post-Dobbs period significantly increased (paired sample t test P Dobbs period, an estimated 33.3% (sensitivity interval, 32.3%-34.8%) of females of reproductive age lived in a census tract more than 60 minutes from an abortion facility compared with 14.6.% (sensitivity interval, 13.0%-16.9%) of females of reproductive age in the pre-Dobbs period.Conclusions and RelevanceIn this repeated cross-sectional spatial analysis, estimated travel time to abortion facilities in the US was significantly greater in the post-Dobbs period after accounting for the closure of abortion facilities in states with total or 6-week abortion bans compared with the pre-Dobbs period, during which all facilities providing abortions in 2021 were considered active.

BackgroundCOVID-19 vaccination rates among children have stalled, while new coronavirus strains continue to emerge. To improve child vaccination rates, policymakers must better understand parental preferences and reasons for COVID-19 vaccination among their children.Methods and FindingsCross-sectional surveys were administered online to 30,174 US parents with at least one child of COVID-19 vaccine eligible age (5-17 years) between January 1 and May 9, 2022. Participants self-reported willingness to vaccinate their child and reasons for hesitancy, and answered additional questions about demographics, pandemic related behavior, and vaccination status. Willingness to vaccinate a child for COVID-19 was strongly associated with parental vaccination status (multivariate odds ratio 97.9, 95% confidence interval 86.9-111.0). The majority of fully vaccinated (86%) and unvaccinated (84%) parents reported concordant vaccination preferences for their eligible child. Age and education had differing relationships by vaccination status, with higher age and education positively associated with willingness among vaccinated parents. Among all parents hesitant to vaccinate their children, the two most frequently reported reasons were possible side effects (47%) and that vaccines are too new (44%). Among hesitant parents, parental vaccination status was inversely associated with reported lack of trust in government (pConclusionFactors associated with willingness to vaccinate children and reasons for hesitancy may inform targeted approaches to increase vaccination.

Some asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19. Here, we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate the relationship between asymptomatic proportion and age. Epidemiological data obtained before mass vaccination provide valuable insights into the nature of pathogenicity of SARS-CoV-2. The data were collected by multiple rounds of city-wide PCR testing with contact tracing, where each patient was monitored for symptoms through the whole course of infection. The clinical endpoint (asymptomatic or symptomatic) for each patient was recorded (the pre-symptomatic patients were classified as symptomatic). We find that the proportion of infections that are asymptomatic declines with age (coefficient = −0.006, 95% CI: −0.008 to −0.003,p< 0.01), falling from 42% (95% CI: 6–78%) in age group 0–9 years to 11% (95% CI: 0–25%) in age group greater than 60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern, together with the distribution of cases by age, can explain most of the reported variation in asymptomatic proportions among cities. Our analysis suggests that SARS-CoV-2 surveillance strategies should take account of the variation in asymptomatic proportion with age.

The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n= 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing. The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior (n = 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing.

COVID-19 testing provides information regarding exposure and transmission risks, guides preventative measures (e.g., if and when to start and end isolation and quarantine), identifies opportunities for appropriate treatments, and helps assess disease prevalence (1). At-home rapid COVID-19 antigen tests (at-home tests) are a convenient and accessible alternative to laboratory-based diagnostic nucleic acid amplification tests (NAATs) for SARS-CoV-2, the virus that causes COVID-19 (2-4). With the emergence of the SARS-CoV-2 B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants in 2021, demand for at-home tests increased

The coronavirus disease 2019 (COVID-19) pandemic is straining public health systems worldwide, and major non-pharmaceutical interventions have been implemented to slow its spread1,2,3,4. During the initial phase of the outbreak, dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily determined by human mobility from Wuhan, China5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic transmission is lacking7. In this study, we analyzed highly resolved spatial variables in cities, together with case count data, to investigate the role of climate, urbanization and variation in interventions. We show that the degree to which cases of COVID-19 are compressed into a short period of time (peakedness of the epidemic) is strongly shaped by population aggregation and heterogeneity, such that epidemics in crowded cities are more spread over time, and crowded cities have larger total attack rates than less populated cities. Observed differences in the peakedness of epidemics are consistent with a meta-population model of COVID-19 that explicitly accounts for spatial hierarchies. We paired our estimates with globally comprehensive data on human mobility and predict that crowded cities worldwide could experience more prolonged epidemics.

ObjectiveTo investigate evidence of the potential role of early cortical vulnerability in the development of primary progressive aphasia (PPA).MethodA woman with a diagnosis of PPA and her 9 adult siblings, 7 with developmental language disabilities, underwent neuropsychological testing, structural MRI, and resting-state fMRI. Whole-exome sequencing was conducted for genes associated with dyslexia or with neurodegenerative dementia.ResultsThe siblings demonstrated lower verbal than nonverbal cognitive test scores in a developmental dyslexia pattern. On structural MRI, although the siblings did not differ from controls in total brain volume, the left hemisphere language area volume was significantly smaller than the right. Furthermore, cortical connectivity between the left superior temporal area, previously identified as the region of peak atrophy in the proband early in the course of illness, and adjacent language network components, including the planum temporale, was decreased in the siblings. No distinctive genetic signatures were identified.ConclusionThis report further supports the hypothesis that at least some cases of PPA may be based on a familial language network vulnerability that interferes with the acquisition of language in some members and that makes the language network a locus of least resistance to the effects of an independently late-arising neurodegenerative disease in others. This association offers a conceptual model to explain why identical neurodegenerative diseases may selectively target the language network in some individuals while targeting networks that regulate memory or behavior in others. The genetic basis for this vulnerability remains to be determined.

ObjectiveTo determine if Alzheimer disease (AD) is associated with aphasic rather than amnestic dementias in certain circumstances related in part to perturbations in different networks.MethodsThree groups were investigated: 14 participants suspected of having the neuropathology of AD based on clinically diagnosed amnestic dementia of the Alzheimer type (DAT), 26 individuals with primary progressive aphasia (PPA) with either a positive 18F-florbetapir amyloid PET scan or confirmed AD at autopsy, and 26 neurologically intact controls. The groups were compared using rs-fMRI. Seeds included the left hemisphere inferior frontal gyrus (IFG) for the language network, the left hippocampus for the episodic memory network, and the left posterior cingulate for the default mode network (DMN).ResultsGreater connectivity perturbations were found from the hippocampus for the DAT group and from the IFG for the PPA group. Furthermore, connectivity alterations in the PPA group were more asymmetric and favored the language-dominant left hemisphere. Loss of connectivity from the DMN seed was of a similar magnitude in the PPA and DAT groups.ConclusionsDespite the presumptive common underlying neuropathology of amyloid plaques and neurofibrillary tangles, the 2 groups displayed 2 different patterns of network perturbation, each concordant with the clinical presentation and the anatomy of neurodegeneration.

Using spatial modeling techniques, we highlight disparities in access to monoclonal antibodies (mAbs) used to treat COVID-19 patients. Fifteen million individuals in the United States must travel over 30 min to access mAbs. Areas with lower COVID-19 vaccination rates have worse access to essential COVID-19 therapeutics.

ObjectiveTo compare the proportion of APOE ε4 genotype carriers in aphasic vs amnestic variants of Alzheimer disease (AD).MethodThe proportion of APOE ε4 carriers was compared among the following 3 groups: (1) 42 patients with primary progressive aphasia (PPA) and AD pathology (PPA/AD) enrolled in the Northwestern Alzheimer Disease Center Clinical Core; (2) 1,418 patients with autopsy-confirmed AD and amnestic dementia of the Alzheimer type (DAT/AD); and (3) 2,608 cognitively normal controls (NC). The latter 2 groups were compiled from the National Alzheimer Coordinating Center database. Logistic regression models analyzed the relationship between groups and APOE ε4 carrier status, adjusting for age at onset and sex as needed.ResultsUsing NC as the reference and adjusting for sex and age, the DAT/AD group was 3.97 times more likely to be APOE ε4 carriers. Adjusting for sex and age at symptom onset, the DAT/AD group was 2.46 times as likely to be carriers compared to PPA/AD. There was no significant difference in the proportion of APOE ε4 carriers for PPA/AD compared to NC. PPA subtypes included 24 logopenic, 10 agrammatic nonfluent, and 8 either mixed (n = 5) or too severe (n = 3) to subtype. The proportion of carriers and noncarriers was similar for logopenic and agrammatic subtypes, both having fewer carriers.ConclusionThe proportion of APOE ε4 carriers was elevated in amnestic but not aphasic manifestations of AD. These results suggest that APOE ε4 is an anatomically selective risk factor that preferentially increases the vulnerability to AD pathology of memory-related medial temporal areas rather than language-related neocortices.

COVID-19 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We sought to measure if the disparities seen in the geographic distribution of other COVID-19 healthcare resources were also present during the initial rollout of the COVID-19 vaccine.Using a comprehensive COVID-19 vaccine database (VaccineFinder), we built an empirically parameterized spatial model of access to essential resources that incorporated vaccine supply, time-willing-to-travel for vaccination, and previous vaccination across the US. We then identified vaccine deserts-US Census tracts with localized, geographic barriers to vaccine-associated herd immunity. We link our model results with Census data and two high-resolution surveys to understand the distribution and determinates of spatially accessibility to the COVID-19 vaccine.We find that in early 2021, vaccine deserts were home to over 30 million people,10% of the US population. Vaccine deserts were concentrated in rural locations and communities with a higher percentage of medically vulnerable populations. We also find that in locations of similar urbanicity, early vaccination distribution disadvantaged neighborhoods with more people of color and older aged residents.Given sufficient vaccine supply, data-driven vaccine distribution to vaccine deserts may improve immunization rates and help control COVID-19.COVID-19 has affected the elderly, people of color, and individuals with chronic illnesses more than the general population. Large barriers to accessing the COVID-19 vaccine could make this problem worse. We used a website called VaccineFinder, which has information on the location of most COVID-19 vaccine doses in the US, to measure vaccine accessibility in early 2021. We then identified vaccine deserts, defined as small US regions with poor access to the COVID-19 vaccine. We found that over 10% of the US lived in a vaccine desert. Overall, we found that vaccines were less available to people in rural areas, people of color, and individuals with chronic illnesses. It will be important to reverse this pattern and ensure enough vaccines are sent to these communities to help reduce the spread of COVID-19.

New infections from the omicron variant of SARS-CoV-2 have been increasing dramatically in South Africa since first identification in November 2021. Despite increasing uptake of COVID-19 vaccine, there are concerns vaccine protection against omicron may be reduced compared to other variants. We sought to characterize a surrogate measure of vaccine efficacy in Gauteng, South Africa by leveraging real-time syndromic surveillance data. The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS) is an online, cross-sectional survey conducted among users sampled from the Facebook active user base. We derived three COVID-like illness (CLI) definitions (stringent, classic, and broad) using combinations of self-reported symptoms (present or not in the prior 24 hours) that broadly tracked with reported COVID-19 cases during June 18, 2021 - December 14, 2021 (inclusive of the delta wave and up-trend of the omicron wave). We used syndromic-surveillance-based CLI prevalence measures among the vaccinated (PV) and unvaccinated (PU) respondents to estimate V ECLIP = 1 - (PV /PU), a proxy for vaccine efficacy, during the delta (June 18-July 18, N= 9,387 surveys) and omicron (December 4-14, N= 2,389 surveys) wave periods. We assume no waning immunity, CLI prevalence approximates incident infection with each variant, and vaccinated and unvaccinated survey respondents in the two variant wave periods are exchangeable. The vaccine appears to have consistently lower V ECLIP against omicron, compared to delta, regardless of the CLI definition used. Stringent CLI (i.e. anosmia plus fever, cough and/or myalgias) yielded a delta V ECLIP = 0.85 [0.54, 0.95] higher than omicron V ECLIP = 0.62 [0.46, 0.72]. Classic CLI (cough plus anosmia, fever, and/or myalgias) gave lower estimates (delta V ECLIP = 0.76 [0.54, 0.87], omicron V ECLIP = 0.51 [0.42, 0.59]), but omicron was still lower than delta. We acknowledge the potential for measurement, confounding, and selection bias, as well as limitations for generalizability for these self-reported, syndromic surveillance-based V ECLIP measures. Thus V ECLIP as estimates of true, population-level vaccine efficacy should therefore be taken with caution. Nevertheless, these preliminary findings demonstrating declining V ECLIP raise concern for a true decline in vaccine efficacy versus waning immunity as a potential contributor to the omicron variant taking hold in Gauteng and elsewhere.

Significance The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats., Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19, but it is not yet clear how the proportion of asymptomatic infections varies by age and geographic location. Here we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate this question. Data were collected by multiple rounds of city-wide PCR test with detailed contact tracing, where each patient was monitored for symptoms through the whole course of infection. We find that the proportion of asymptomatic infections declines with age (coefficient =-0.006, P60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern together with the age distribution of overall cases can explain most of the geographic differences in reported asymptomatic proportions. Combined with demography and contact matrices from other countries worldwide, we estimate that a maximum of 22%–55% of SARS-CoV-2 infections would come from asymptomatic cases in an uncontrolled epidemic based on asymptomatic proportions in China. Our analysis suggests that flare-ups of COVID-19 are likely if only adults are vaccinated and that surveillance and possibly control measures among children will be still needed in the future to contain epidemic resurgence.

Simultaneously tracking the global COVID-19 impact across multiple populations is challenging due to regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide reliable and standardized data streams to support monitoring and decision-making world-wide, in real time, and with limited local resources. The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, invites daily cross-sectional samples from the social media platform’s active users to participate in the survey since launch April 23, 2020. COVID-19 indicators through December 20, 2020, from N=31,142,582 responses representing N=114 countries, weighted for nonresponse and adjusted to basic demographics, were benchmarked with government data. COVID-19-related signals showed similar concordance with reported benchmark case and test positivity. Bonferroni significance and minimal Spearman correlation strength thresholds were met in the majority. Light Gradient Boost machine learning trained on national and pooled global data verified known symptom indicators, and predicted COVID-19 trends similar to other signals. Risk mitigation behavior trends are correlated with, but sometimes lag, risk perception trends. In regions with strained health infrastructure, but active social media users, we show it is possible to define suitable COVID-19 impact trajectories. This syndromic surveillance public health tool is the largest global health survey to date, and, with brief participant engagement, can provide meaningful, timely insights into the COVID-19 pandemic and response in regions under-represented in epidemiological analyses.Significance StatementThe University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 23, 2020, is the largest remote global health monitoring system. This study includes about 30 million UMD-CTIS responses over 34 weeks (through December 2020) from N=114 countries with survey-weights to adjust for nonresponse and demographics. Using limited self-reported data, sampled daily from an international cohort of Facebook users, we demonstrate validity and utility for COVID-19 impacts trends, even in regions with scant or delayed government data. We predict COVID-19 cases in the absence of testing, and characterize perceived COVID-19 risk versus risk-lowering measures. The UMD-CTIS has the potential to support existing monitoring systems for the COVID-19 pandemic, as well as other new, as-yet-undefined global health threats.

Mask-wearing has been a controversial measure to control the COVID-19 pandemic. While masks are known to substantially reduce disease transmission in healthcare settings [1–3], studies in community settings report inconsistent results [4–6].Investigating the inconsistency within epidemiological studies, we find that a commonly used proxy, government mask mandates, does not correlate with large increases in mask-wearing in our window of analysis. We thus analyse the effect of mask-wearing on transmission instead, drawing on several datasets covering 92 regions on 6 continents, including the largest survey of individual-level wearing behaviour (n=20 million) [7]. Using a hierarchical Bayesian model, we estimate the effect of both mask-wearing and mask-mandates on transmission by linking wearing levels (or mandates) to reported cases in each region, adjusting for mobility and non-pharmaceutical interventions.We assess the robustness of our results in 123 experiments spanning 22 sensitivity analyses. Across these analyses, we find that an entire population wearing masks in public leads to a median reduction in the reproduction number R of 25.8%, with 95% of the medians between 22.2% and 30.9%. In our window of analysis, the median reduction in R associated with the wearing level observed in each region was 20.4% [2.0%, 23.3%]1. We do not find evidence that mandating mask-wearing reduces transmission. Our results suggest that mask-wearing is strongly affected by factors other than mandates.We establish the effectiveness of mass mask-wearing, and highlight that wearing data, not mandate data, are necessary to infer this effect.

SARS-CoV-2 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We identify vaccine deserts - US Census tracts with localized, geographic barriers to vaccine-associated herd immunity - using a comprehensive supply database (VaccineFinder) and an empirically parameterized model of spatial access to essential resources. Incorporating high-resolution COVID-19 burden and time-willing-to-travel for vaccination, we show that early (February – March 2021) vaccine allocation disadvantaged rural and medically vulnerable populations. Data-driven vaccine distribution to vaccine deserts may improve immunization in the hesitant and control SARS-CoV-2.

Background: until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide. Methods: in this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high–middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression. Findings: the reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=–0·47, pInterpretation: although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level—eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality. Funding: Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.

Tests of grammar, repetition and semantics were administered to 62 prospectively enrolled right-handed participants with primary progressive aphasia (PPA). Structural brain images were obtained at the time of testing. Regression analyses uncovered 3 clearly delineated non-overlapping left hemisphere clusters where cortical thinning (atrophy) was significantly correlated with impaired performance. A morphosyntactic cluster associated with the grammaticality of sentence construction was located predominantly within the middle and inferior frontal gyri; a phonolexical cluster associated with language repetition was located in the temporoparietal junction; a lexicosemantic cluster associated with object naming and single word comprehension was located within the middle and anterior parts of the temporal lobe and extended into insular, orbitofrontal, and mediotemporal cortices. Commonality analyses were undertaken to explore whether these three clusters were as modular as indicated by the regression analyses or whether some underlying functional granularity could be uncovered. Modularity was defined as the exclusive association of an anatomical cluster with a single type of language task whereas granularity was defined as the association of a single anatomical cluster with more than one type of language task. The commonality analyses revealed a predominantly modular organization with quantitatively minor instances of inter-cluster granularity. The results also reconfirmed previous work on PPA which had shown that Wernicke's area is not essential for word comprehension, that naming impairments can be based either on deficits of lexical retrieval or word comprehension, and that the essential substrates of word comprehension encompass much wider areas of the temporal lobe than the temporal pole. The anatomy of the language network has traditionally been explored through patients with focal cerebrovascular accidents and experiments based on functional activation. Investigations on PPA are showing that focal neurodegenerations can add new perspectives to existing models of the language network.

ObjectiveTo explore atrophy–deficit correlations of word comprehension and repetition in temporoparietal cortices encompassing the Wernicke area, based on patients with primary progressive aphasia (PPA).MethodsCortical thickness in regions within and outside the classical Wernicke area, measured by FreeSurfer, was correlated with repetition and single word comprehension scores in 73 right-handed patients at mild to moderate stages of PPA.ResultsAtrophy in the Wernicke area was correlated with repetition (r = 0.42, p = 0.001) but not single word comprehension (r = −0.072, p = 0.553). Correlations with word comprehension were confined to more anterior parts of the temporal lobe, especially its anterior third (r = 0.60, p < 0.001). A single case with postmortem autopsy illustrated preservation of word comprehension but not repetition 6 months prior to death despite nearly 50% loss of cortical volume and severe neurofibrillary degeneration in core components of the Wernicke area.ConclusionsTemporoparietal cortices containing the Wernicke area are critical for language repetition. Contrary to the formulations of classic aphasiology, their role in word and sentence comprehension is ancillary rather than critical. Thus, the Wernicke area is not sufficient to sustain word comprehension if the anterior temporal lobe is damaged. Traditional models of the role of the Wernicke area in comprehension are based almost entirely on patients with cerebrovascular lesions. Such lesions also cause deep white matter destruction and acute network diaschisis, whereas progressive neurodegenerative diseases associated with PPA do not. Conceptualizations of the Wernicke area that appear to conflict, therefore, can be reconciled by considering the hodologic and physiologic differences of the underlying lesions.

BackgroundSymptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing.MethodsWe analysed a cohort of untested symptomatic app users (N=1,237), nested in the Zoe COVID Symptom Study (Zoe, N= 4,394,948); and symptomatic survey respondents who wanted, but did not have a test (N=1,956), drawn from the University of Maryland-Facebook Covid-19 Symptom Survey (UMD-Facebook, N=775,746).FindingsThe proportion tested among individuals with incident test-qualifying symptoms rose from ∼20% to ∼75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (73.0% vs 85.0%), or short vs long symptom duration (72.6% vs 87.8%). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR=0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-Facebook respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR=1.207 [1.129-1.292]) and for every 4-years fewer in education (OR=0.685 [0.599-0.783]).InterpretationDespite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ∼25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages.FundingZoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer’s Society, Facebook Sponsored Research Agreement.Research in contextEvidence before this studyTo assess current evidence on test uptake in symptomatic testing programmes, and the reasons for not testing, we searched PubMed from database inception for research using the keywords (COVID-19) AND (testing) AND ((access) OR (uptake)). We did not find any work reporting on levels of test uptake amongst symptomatic individuals. We found three papers investigating geographic barriers to testing. We found one US based survey reporting on knowledge barriers to testing, and one UK based survey reporting on barriers in the period March - August 2020. Neither of these studies were able to combine testing behaviour with prospectively collected symptom reports from the users surveyed.Added value of this studyThrough prospective collection of symptom and test reports, we were able to estimate testing uptake amongst individuals with test-qualifying symptoms in the UK. Our results indicate that whilst testing has improved since the start of the pandemic, there remains a considerable testing gap. Investigating this gap we find that individuals with just one test-qualifying symptom or short symptom duration are less likely to get tested. We also find knowledge barriers to testing: a substantial proportion of individuals do not know which symptoms qualify them for a COVID-19 test, and do not know where to seek testing. We find a larger knowledge gap in individuals with older age and fewer years of education.Implications of all the available evidenceDespite the UK having a simple set of symptom-based testing criteria, with tests made freely available through nationalised healthcare, a quarter of individuals with qualifying symptoms do not get tested. Our findings suggest testing uptake may be limited by individuals not acting on mild or transient symptoms, not recognising the testing criteria, and not knowing where to get tested. Improved messaging may help address this testing gap, with opportunities to target individuals of older age or fewer years of education. Messaging may prove even more valuable in countries with more fragmented testing infrastructure or more nuanced testing criteria, where knowledge barriers are likely to be greater.

BackgroundLimitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing COVID-19 pandemic globally. Syndromic surveillance of COVID-19 is an important public health tool that can help detect outbreaks, mobilize a rapid response, and thereby reduce morbidity and mortality. The primary objective of this study was to determine whether syndromic surveillance through self-reported COVID-19 symptoms could be a timely proxy for laboratory-confirmed case trends in the Canadian province of Ontario.MethodsWe retrospectively analyzed self-reported symptoms data collected using an online tool – Outbreaks Near Me (ONM) – from April 20th to Oct 11th, 2020 in Ontario, Canada. We estimated the correlation coefficient between the weekly proportion of respondents reporting a COVID-like illness (CLI) to both the weekly number of PCR-confirmed COVID-19 cases and the percent positivity in the same period for the same week and with a one-week lag.ResultsThere were 314,686 responses from 188,783 unique respondents to the ONM symptom survey. Respondents were more likely to be female and be in the 40-59 age demographic compared to the Ontario general population. There was a strong positive correlation between the weekly number of reported cases in Ontario and the percent of respondents reporting CLI each week (r = 0.89, p InterpretationWe demonstrate a strong positive and significant correlation (r = 0.89, p

Background Face masks have become commonplace across the USA because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. Although evidence suggests that masks help to curb the spread of the disease, there is little empirical research at the population level. We investigate the association between self-reported mask-wearing, physical distancing, and SARS-CoV-2 transmission in the USA, along with the effect of statewide mandates on mask uptake. Methods Serial cross-sectional surveys were administered via a web platform to randomly surveyed US individuals aged 13 years and older, to query self-reports of face mask-wearing. Survey responses were combined with instantaneous reproductive number (Rt) estimates from two publicly available sources, the outcome of interest. Measures of physical distancing, community demographics, and other potential sources of confounding (from publicly available sources) were also assessed. We fitted multivariate logistic regression models to estimate the association between mask-wearing and community transmission control (Rt

Background Syndromic surveillance systems for COVID-19 are being increasingly used to track and predict outbreaks of confirmed cases. Seasonal circulating respiratory viruses share syndromic overlap with COVID-19, and it is unknown how they will impact the performance of syndromic surveillance tools. Here we investigated the role of non-SARS-CoV-2 respiratory virus test positivity on COVID-19 two independent syndromic surveillance systems in Ontario, Canada. Methods We compared the weekly number of reported COVID-19 cases reported in the province of Ontario against two syndromic surveillance metrics: 1) the proportion of respondents with a self-reported COVID-like illness (CLI) from COVID Near You (CNY) and 2) the proportion of emergency department visits for upper respiratory conditions from the Acute Care Enhanced Surveillance (ACES) system. Separately, we plotted the percent positivity for other seasonal respiratory viruses over the same time period and reported Pearson’s correlation coefficients before and after the uncoupling of syndromic tools to COVID-19 cases. Results There were strong positive correlations of both CLI and ED visits for upper respiratory causes with COVID-19 cases up to and including a rise in entero/rhinovirus (r = 0.86 and 0.87, respectively). There was a strong negative correlation of both CLI and ED visits for upper respiratory causes with COVID-19 cases (r = -0.85 and -0.91, respectively) during a fall in entero/rhinovirus. Interpretation Two methods of syndromic surveillance showed strong positive correlations with COVID-19 confirmed case counts before and during a rise in circulating entero/rhinovirus. However, as positivity for enterovirus/rhinovirus fell in late September 2020, syndromic signals became uncoupled from COVID-19 cases and instead tracked the fall in enter/rhinovirus. This finding provides proof-of-principle that regional transmission of seasonal respiratory viruses may complicate the interpretation of COVID-19 surveillance data. It is imperative that surveillance systems incorporate other respiratory virus testing data in order to more accurately track and forecast COVID-19 disease activity. Emerging evidence is showing that syndromic surveillance systems for COVID-19 can be used to predict outbreaks of confirmed cases with high spatial and temporal resolution.1–3 These methods can be used as an early warning system to guide regional public health policy decisions. Tools include passive methods (e.g. tracking healthcare encounters), as well as more active participatory surveillance, whereby individuals self-report symptoms using phone or internet.2,3 Currently, it is unknown how seasonal circulating respiratory viruses impact the performance of COVID-19 surveillance tools, although their symptomatic overlap makes this a theoretical concern.4 We investigated the role of non-SARS-CoV-2 respiratory virus test positivity on two independent COVID-19 syndromic surveillance systems in Ontario, Canada.

IntroductionCloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, research is limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake.MethodsSerial cross-sectional surveys were administered June 3 through July 27, 2020 via web platform. Surveys queried individuals’ likelihood to wear a face mask to the grocery store or with family and friends. Responses (N=378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates.ResultsWe find an upward trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt ConclusionWidespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.

Background: The United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave. However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/Summary: New York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases. Findings: Areas with the lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare workers had more subway use during the pandemic. The positive associations between subway use and median income, and between subway use and percent non-white and/or Hispanic/Latino do not remain when adjusted for the percent of essential workers. This suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR=1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR=1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. Interpretation: Our results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.

BackgroundSyndromic surveillance through web or phone-based polling has been used to track the course of infectious diseases worldwide. Our study objective was to describe the characteristics, symptoms, and self-reported testing rates of respondents in three different COVID-19 symptom surveys in Canada.MethodsData sources consisted of two distinct Canada-wide web-based surveys, and phone polling in Ontario. All three sources contained self-reported information on COVID-19 symptoms and testing. In addition to describing respondent characteristics, we examined symptom frequency and the testing rate among the symptomatic, as well as rates of symptoms and testing across respondent groups.ResultsWe found that 1.6% of respondents experienced a symptom on the day of their survey, 15% of Ontario households had a symptom in the previous week, and 44% of Canada-wide respondents had a symptom in the previous month over March-April 2020. Across the three surveys, SARS-CoV-2-testing was reported in 2-9% of symptomatic responses. Women, younger and middle-aged adults (versus older adults) and Indigenous/First nations/Inuit/Métis were more likely to report at least one symptom, and visible minorities were more likely to report the combination of fever with cough or shortness of breath.InterpretationThe low rate of testing among those reporting symptoms suggests significant opportunity to expand testing among community-dwelling residents of Canada. Syndromic surveillance data can supplement public health reports and provide much-needed context to gauge the adequacy of current SARS-CoV-2 testing rates.

ImportanceAccess to testing is key to a successful response to the COVID-19 pandemic.ObjectiveTo determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time.DesignCross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time.SettingUnited States COVID-19 pandemic.ParticipantsThe United States, including the 48 contiguous states and the District of Columbia.ExposuresPopulation density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data.Main OutcomeSARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites.Results6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time (β = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2×10−12), adjusting for population density.Conclusions and RelevanceGeographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data.Key PointsSARS-CoV-2 testing sites are distributed unevenly in the US geography and population.Median county-level travel time to SARS-CoV-2 testing sites is longer in less densely populated areas, and in areas with a higher percentage of minority or uninsured populations.Improved geographic accessibility to testing sites is imperative to manage the COVID-19 pandemic in the United States.

The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak the spread was primarily determined by human mobility5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking7. We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.

Introduction Primary progressive aphasia (PPA) is a neurological syndrome, associated with both frontotemporal dementia and Alzheimer's disease, in which progressive language impairment emerges as the most salient clinical feature during the initial stages of disease. Methods We screened the main genes associated with Alzheimer's disease and frontotemporal dementia for pathogenic and risk variants in a cohort of 403 PPA cases. Results In this case series study, 14 (3.5%) cases carried (likely) pathogenic variants: four C9orf72 expansions, nine GRN , and one TARDBP mutation. Rare risk variants, TREM2 R47H and MAPT A152T, were associated with a three- to seven-fold increase in risk for PPA. Discussion Our results show that while pathogenic variants within the most common dementia genes were rarely associated with PPA, these were found almost exclusively in GRN and C9orf72 , suggesting that PPA is more TDP43- than tau-related in our series. This is consistent with the finding that PPA frequency in dominantly inherited dementias is the highest in kindreds with GRN variants.

Four patients with primary progressive aphasia displayed a greater deficit in understanding words they heard than words they read, and a further deficiency in naming objects orally rather than in writing. All four had frontotemporal lobar degeneration-transactive response DNA binding protein Type A neuropathology, three determined postmortem and one surmised on the basis of granulin gene (GRN) mutation. These features of language impairment are not characteristic of any currently recognized primary progressive aphasia variant. They can be operationalized as manifestations of dysfunction centered on a putative auditory word-form area located in the superior temporal gyrus of the left hemisphere. The small size of our sample makes the conclusions related to underlying pathology and auditory word-form area dysfunction tentative. Nonetheless, a deeper assessment of such patients may clarify the nature of pathways that link modality-specific word-form information to the associations that mediate their recognition as concepts. From a practical point of view, the identification of these features in patients with primary progressive aphasia should help in the design of therapeutic interventions where written communication modalities are promoted to circumvent some of the oral communication deficits.

Introduction Primary progressive aphasia (PPA) displays variable progression trajectories that require further elucidation. Methods Longitudinal quantitation of atrophy and language over 12 months was completed for PPA patients with and without positive amyloid PET (PPA Aβ+ and PPA Aβ− ), an imaging biomarker of underlying Alzheimer's disease. Results Over 12 months, both PPA groups showed significantly greater cortical atrophy rates in the left versus right hemisphere, with a more widespread pattern in PPA Aβ+ . The PPA Aβ+ group also showed greater decline in performance on most language tasks. There was no obligatory relationship between the logopenic PPA variant and amyloid status. Effect sizes from quantitative MRI data were more robust than neuropsychological metrics. Discussion Preferential language network neurodegeneration is present in PPA irrespective of amyloid status. Clinical and anatomical progression appears to differ for PPA due to Alzheimer's disease versus non–Alzheimer's disease neuropathology, a distinction that may help to inform prognosis and the design of intervention trials.