Abstract Autoimmune diseases (AID) are a collection of many complex disorders of unknown etiology resulting in immune responses to self-antigens and are thought to result from interactions between ...genetic and environmental factors. Here we review the epidemiologic evidence for the role of environmental factors in the development of human AID, the conclusions that can be drawn from the existing data, critical knowledge gaps, and research needed to fill these gaps and to resolve uncertainties. We specifically summarize the state of knowledge and our levels of confidence in the role of specific agents in the development of autoimmune diseases, and we define the areas of greatest impact for future investigations. Among our consensus findings we are confident that: 1) crystalline silica exposure can contribute to the development of several AID; 2) solvent exposure can contribute to the development of systemic sclerosis; 3) smoking can contribute to the development of seropositive rheumatoid arthritis; and 4) an inverse association exists between ultraviolet radiation exposure and the risk of development of multiple sclerosis. We suggest that more studies of phenotypes, genotypes, and multiple exposures are needed. Additional knowledge gaps needing investigation include: defining important windows in the timing of exposures and latencies relating to age, developmental state, and hormonal changes; understanding dose–response relationships; and elucidating mechanisms for disease development. Addressing these essential issues will require more resources to support research, particularly of rare AID, but knowledge of the risks conferred by environmental factors in specific genetic contexts could pave the way for prevention of AID in the future.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
OBJECTIVETo generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets.
METHODSA ...validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017.
RESULTSThe estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval CI 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017.
CONCLUSIONThe estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions.
To determine whether the prevalence of multiple sclerosis (MS) varies by race and ethnicity.
We conducted a retrospective cohort study of more than 2.6 million adults from the multiethnic, ...community-dwelling members of Kaiser Permanente Southern California. The complete electronic health record of individuals with at least one ICD9 code for MS between January 1, 2008 to December 31, 2010 were reviewed. MS prevalence and 95% confidence intervals (CI) stratified by age-, sex- and race and ethnicity among 2010 members were estimated using binomial regression. Age- and sex-standardized prevalence was estimated according to the 2010 US Census population.
We identified 3863 patients with MS. The average age of prevalent MS patients was 51.7 years (standard deviation, 13.1) and 76.8% were women. The female preponderance was more pronounced among Black (81.2%) and Asian (83.6%) than White (76.3%) or Hispanic (74.5%) individuals with MS. Age- and sex-standardized MS prevalence per 100,000 was similarly high among Black (225.8, 95%CI=207.1-244.5) and White (237.7, 95%CI=228.2-247.2) and significantly lower among Hispanic (69.9, 95%CI=64.4-75.5) and Asian persons (22.6, 95%CI=17.1-28.1). MS prevalence was highest between the ages of 35 and 64 years and declined after 65, across all racial and ethnic groups. Among adults ages 18-24, crude MS prevalence was low, but was highest among Black and Hispanic young adults, lower in White and lowest in Asian/PI individuals (48.5, 25.0, 18.0 and 7.1/100,000, respectively).
MS prevalence varies by race and ethnicity, being similarly high in White and Black and significantly lower in Hispanic and Asian persons in Southern California. Taken together with previous studies, these findings indicate that the burden of MS in the US Black community has long been under-recognized. More studies are needed to determine whether MS is an emerging disease among US Hispanic adults and whether MS susceptibility and prevalence vary among Hispanic or Asian individuals from different cultures and/or ancestral backgrounds.
BACKGROUND:Women who undergo intrapartum caesarean delivery (CD) are at increased risk of postpartum hemorrhage (PPH) compared with those undergoing prelabor CD. To determine whether the presence and ...strength of the associations between individual risk factors and severe PPH vary among women undergoing prelabor CD or intrapartum CD, stratified analyses are needed according to CD subtype.
METHODS:To identify risk factors for severe PPH within 2 distinct CD populations, prelabor CD and intrapartum CD, we performed 2 case-control studies. Women in each study cohort delivered at a tertiary obstetric center in the United States between 2002 and 2012. For each study, cases were women who had a blood loss ≥1500 mL or who received an intraoperative or postoperative transfusion up to 48 hours after delivery. Risk factors for severe PPH among women undergoing prelabor CD or intrapartum CD were examined in separate logistic regression models.
RESULTS:For prelabor CD, we identified 269 cases and 550 controls. Clinical factors with the highest adjusted odds for severe PPH during prelabor CD were general anesthesia (adjusted odds ratio aOR = 22.3; 95% confidence interval CI, 4.9–99.9; reference group = spinal anesthesia), multiple pregnancies (aOR = 8.0; 95% CI, 4.2–15.0; reference group = singleton pregnancy), and placenta previa (aOR = 6.3; 95% CI, 3.4–11.8). For intrapartum CD, we identified 278 cases and 572 controls. Clinical factors with the highest adjusted odds for severe PPH during intrapartum CD were general anesthesia (aOR = 5.4; 95% CI, 1.7–17.1), multiple pregnancies (aOR = 3.2; 95% CI, 1.7–6.3), and a predelivery hemoglobin ≤ 9.9 g/dL (aOR = 3.0; 95% CI, 1.3–6.9; reference group = predelivery hemoglobin ≥ 11 g/dL).
CONCLUSIONS:Women who undergo prelabor CD and intrapartum CD have several shared risk factors for severe PPH (general anesthesia and multiple pregnancies). However, the risk factor profiles for severe PPH differed between these CD cohorts. Recognizing these differences may be important when planning resources and interventions for high-risk patients undergoing either prelabor or intrapartum CD.
Objective
To develop and internally validate a multivariable predictive model for days with new‐onset migraine headaches based on patient self‐prediction and exposure to common trigger factors.
...Background
Accurate real‐time forecasting of one’s daily risk of migraine attack could help episodic migraine patients to target preventive medications for susceptible time periods and help decrease the burden of disease. Little is known about the predictive utility of common migraine trigger factors.
Methods
We recruited adults with episodic migraine through online forums to participate in a 90‐day prospective daily‐diary cohort study conducted through a custom research application for iPhone. Every evening, participants answered questions about migraine occurrence and potential predictors including stress, sleep, caffeine and alcohol consumption, menstruation, and self‐prediction. We developed and estimated multivariable multilevel logistic regression models for the risk of a new‐onset migraine day vs a healthy day and internally validated the models using repeated cross‐validation.
Results
We had 178 participants complete the study and qualify for the primary analysis which included 1870 migraine events. We found that a decrease in caffeine consumption, higher self‐predicted probability of headache, a higher level of stress, and times within 2 days of the onset of menstruation were positively associated with next‐day migraine risk. The multivariable model predicted migraine risk only slightly better than chance (within‐person C‐statistic: 0.56, 95% CI: 0.54, 0.58).
Conclusions
In this study, episodic migraine attacks were not predictable based on self‐prediction or on self‐reported exposure to common trigger factors. Improvements in accuracy and breadth of data collection are needed to build clinically useful migraine prediction models.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The U.S. has experienced an unprecedented number of orders to shelter in place throughout the ongoing COVID-19 pandemic. We aimed to ascertain whether social distancing; difficulty with daily ...activities; and levels of concern regarding COVID-19 changed after the March 16, 2020 announcement of the nation's first shelter-in-place orders (SIPO) among individuals living in the seven affected counties in the San Francisco Bay Area.
We conducted an online, cross-sectional social media survey from March 14 -April 1, 2020. We measured changes in social distancing behavior; experienced difficulties with daily activities (i.e., access to healthcare, childcare, obtaining essential food and medications); and level of concern regarding COVID-19 after the March 16 shelter-in-place announcement in the San Francisco Bay Area versus elsewhere in the U.S.
In this non-representative sample, the percentage of respondents social distancing all of the time increased following the shelter-in-place announcement in the Bay Area (9.2%, 95% CI: 6.6, 11.9) and elsewhere in the U.S. (3.4%, 95% CI: 2.0, 5.0). Respondents also reported increased difficulty obtaining hand sanitizer, medications, and in particular respondents reported increased difficulty obtaining food in the Bay Area (13.3%, 95% CI: 10.4, 16.3) and elsewhere (8.2%, 95% CI: 6.6, 9.7). We found limited evidence that level of concern regarding the COVID-19 crisis changed following the announcement.
This study characterizes early changes in attitudes, behaviors, and difficulties. As states and localities implement, rollback, and reinstate shelter-in-place orders, ongoing efforts to more fully examine the social, economic, and health impacts of COVID-19, especially among vulnerable populations, are urgently needed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background:
The timed 25-foot walk (T25FW) is a key clinical outcome measure in multiple sclerosis patient management and clinical research.
Objectives:
To evaluate T25FW performance and factors ...associated with its change in the Multiple Sclerosis Outcome Assessments Consortium (MSOAC) Placebo Database (n = 2465).
Methods:
We created confirmed disability progression (CDP) variables for T25FW and Expanded Disability Status Scale (EDSS) outcomes. We used intraclass correlation coefficients (ICCs) and Bland Altman plots to evaluate reliability. We evaluated T25FW changes and predictive validity using a mixed-effects model, survival analysis, and nested case–control analysis.
Results:
The mean baseline score for the T25FW in this study population was 9.2 seconds, median = 6.1 (standard deviation = 11.0, interquartile range (IQR) = 4.8, 9.0). The T25FW measure demonstrated excellent test–retest reliability (ICC = 0.98). Walk times increased with age, disability, disease type, and disease duration; relapses were not associated with an increase. Patients with T25FW progression had a faster time to EDSS-CDP compared to those without (hazards ratio (HR): 2.6; confidence interval (CI): 2.2, 3.1). Changes in the T25FW were more likely to precede changes in EDSS.
Conclusion:
This research confirms the association of the T25FW with disability and provides some evidence of predictive validity. Our findings support the continued use of the T25FW in clinical practice and clinical trials.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Socioeconomically marginalized communities have been disproportionately affected by the COVID-19 pandemic. Income inequality may be a risk factor for SARS-CoV-2 infection and death from COVID-19.
To ...evaluate the association between county-level income inequality and COVID-19 cases and deaths from March 2020 through February 2021 in bimonthly time epochs.
This ecological cohort study used longitudinal data on county-level COVID-19 cases and deaths from March 1, 2020, through February 28, 2021, in 3220 counties from all 50 states, Puerto Rico, and the District of Columbia.
County-level daily COVID-19 case and death data from March 1, 2020, through February 28, 2021, were extracted from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University in Baltimore, Maryland.
The Gini coefficient, a measure of unequal income distribution (presented as a value between 0 and 1, where 0 represents a perfectly equal geographical region where all income is equally shared and 1 represents a perfectly unequal society where all income is earned by 1 individual), and other county-level data were obtained primarily from the 2014 to 2018 American Community Survey 5-year estimates. Covariates included median proportions of poverty, age, race/ethnicity, crowding given by occupancy per room, urbanicity and rurality, educational level, number of physicians per 100 000 individuals, state, and mask use at the county level.
As of February 28, 2021, on average, each county recorded a median of 8891 cases of COVID-19 per 100 000 individuals (interquartile range, 6935-10 666 cases per 100 000 individuals) and 156 deaths per 100 000 individuals (interquartile range, 94-228 deaths per 100 000 individuals). The median county-level Gini coefficient was 0.44 (interquartile range, 0.42-0.47). There was a positive correlation between Gini coefficients and county-level COVID-19 cases (Spearman ρ = 0.052; P < .001) and deaths (Spearman ρ = 0.134; P < .001) during the study period. This association varied over time; each 0.05-unit increase in Gini coefficient was associated with an adjusted relative risk of COVID-19 deaths: 1.25 (95% CI, 1.17-1.33) in March and April 2020, 1.20 (95% CI, 1.13-1.28) in May and June 2020, 1.46 (95% CI, 1.37-1.55) in July and August 2020, 1.04 (95% CI, 0.98-1.10) in September and October 2020, 0.76 (95% CI, 0.72-0.81) in November and December 2020, and 1.02 (95% CI, 0.96-1.07) in January and February 2021 (P < .001 for interaction). The adjusted association of the Gini coefficient with COVID-19 cases also reached a peak in July and August 2020 (relative risk, 1.28 95% CI, 1.22-1.33).
This study suggests that income inequality within US counties was associated with more cases and deaths due to COVID-19 in the summer months of 2020. The COVID-19 pandemic has highlighted the vast disparities that exist in health outcomes owing to income inequality in the US. Targeted interventions should be focused on areas of income inequality to both flatten the curve and lessen the burden of inequality.
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