The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we ...live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal ...perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (
< 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (
< 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (
< 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space-time disaggregation.
Analysis of covariance models were used to compute within-group and between-group change in average symptom scores including treatment arm and geographic region as factors and baseline values as a ...covariate. Results: Of the 1,992 patients (994 LIN, 998 PBO) included in this study, similar demographic and baseline characteristics were identified between groups (Table 1). Baseline demographics, characteristics, and disease severity Placebo N = 1998 Linaclotide 145 μg N = 994 Total N = 1992 Patient Demographics Age, mean (SD) 46.7 (14.2) 47.5 (13.5) 47.1 (13.9) Female, n (%) 853 (85.5) 838 (84.3) 1691 (84.9) Race, n (%) White 724 (72.5) 723 (72.7) 1447 (72.6) Black or African American 237 (23.7) 235 (23.6) 472 (23.7) Asian 24 (2.4) 20 (2) 44 (2.2) American Indian or Alaska Native 1 (0.1) 5 (0.5) 6 (0.3) Native Hawaiian or Other Pacific Islander 1 (0.1) 0 1 (0.1) Multiple 3 (0.3) 4 (0.4) 7 (0.4) Other 8 (0.8) 7 (0.7) 15 (0.8) Hispanic or Latino, n (%) 237 (23.7) 236 (23.7) 473 (23.7) Baseline Characteristics Weight, kg, mean (SD) 78.46 (19.0) 78.11 (18.1) 78.28 (18.6) BMI, kg/m2, mean (SD) 28.74 (6.4) 28.64 (6.1) 28.69 (6.2) Disease Severity at Baseline Abdominal pain score, mean (SD) 2.51 (1.0) 2.48 (1.0) 2.50 (1.0) Abdominal discomfort score, mean (SD) 2.86 (1.0) 2.79 (1.0) 2.82 (1.0) Bloating score, mean (SD) 3.08 (0.9) 3.05 (0.9) 3.07 (0.9) SD, standard deviation.
Density-ratio models are receiving increasing attention, particularly because of their relationship with generalized linear models and their applications in missing-data analyses. The density-ratio ...assumption, however, may not be true in some applications, and an important limitation is that the standard density-ratio model does not accommodate heterogeneity within the underlying population. To address these issues, we propose a new density-ratio model that incorporates a stratification procedure and dispersion parameters. The resulting stratified density-ratio model 1) retains attractive properties of the standard density-ratio model, while allowing the density-ratio assumption to be violated for some covariate, and 2) provides a validation tool, using a Kolmogorov-Smirnov-type statistic, to check the modeling assumption. We estimate the finite-dimensional and infinite-dimensional parameters simultaneously using an efficient nonparametric maximum likelihood approach. The resulting estimators are shown to be consistent and asymptotically normal. The asymptotic covariance matrix of the estimators for the finite-dimensional parameters attains the semiparametric efficiency bound.
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BFBNIB, NMLJ, NUK, PNG, UL, UM, UPUK
Previous research has noted that many factors greatly influence the spread of COVID‐19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the ...daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID‐19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision‐making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county‐level covariates and unobservable state‐level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model‐based metrics—the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID‐19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.
Key Points
A hierarchical linear model was established to associate the infection rate with the collected explicit factors, which were demonstrated to greatly influence the spreading of COVID‐19 in previous studies, and the unobserved heterogeneity was also incorporated to better reflect the hierarchical structure
Two model‐based metrics were proposed for assessing the state performance by adjusting the measurable county‐level covariates and the unobservable state‐specific variation
These metrics can give insight into certain aspects of a state's performance in combating the COVID‐19 pandemic in addition to the widely used crude infection rate
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
INTRODUCTION:
Elagolix+estradiol/norethindrone acetate add-back therapy (ELA+AB) significantly improves heavy menstrual bleeding (HMB) in patients with uterine fibroids (UFs). Data on the effect of ...ELA+AB on nonbleeding symptoms in HMB-UF patients are limited.
METHODS:
Elaris UF-1 and UF-2 (NCT02654054 and NCT02691494) were duplicate, IRB-approved, randomized, double-blind, placebo-controlled, 6-month phase 3 studies. This post hoc analysis evaluated the Patients Global Impression of Change (PGIC) for menstrual bleeding (MB) and nonbleeding symptoms. Patients rated symptom change on a 7-point scale from “very much improved” (1) to “very much worse” (7).
RESULTS:
Among responders (6-month MBL <80 mL and ≥50% MBL reduction from baseline), mean (SD) PGIC-MB and PGIC-abdominal bloating were better for ELA+AB versus placebo as early as 1 month (2.1 1.3, n=199 versus 2.8 1.5, n=22; and 3.1 1.2, n=201 versus 3.7 1.0, n=21, respectively) through 6 months (1.3 0.9, n=221 versus 2.9 1.3, n=13; and 2.3 1.3, n=221 versus 3.7 0.9, n=13, respectively). Patients treated with ELA+AB had improvement in symptoms at 3 months compared to placebo and at 6 months reached scores in the domains abdominal/pelvic pain (1.8 1.1 versus 3.2 1.0), abdominal/pelvic pressure (1.9 1.2 versus 3.4 0.9), abdominal/pelvic cramping (1.8 1.0 versus 2.9 1.2), and back pain (2.3 1.3 versus 3.2 1.0). Similar results were observed for the total patient population.
CONCLUSION:
ELA+AB provides rapid bleeding and nonbleeding symptom improvement for patients with UF-associated HMB. Improvement for ELA+AB–treated patients was observed as early as 1 month, with nonbleeding PGIC scores consistently approaching “much and very much improved” in all domains by 6 months.
INTRODUCTION:
Patients with heavy menstrual bleeding (HMB) associated with uterine fibroids (UFs) have significantly improved menstrual blood loss (MBL) when taking elagolix+estradiol/norethindrone ...acetate add-back therapy (ELA+AB) versus placebo. However, data on the effect of ELA+AB on nonbleeding symptoms are limited.
METHODS:
This post hoc analysis from the duplicate, IRB-approved, randomized, double-blind, placebo-controlled, 6-month, phase 3 Elaris UF-1 and UF-2 studies (NCT02654054 and NCT02691494) evaluated the Patients Global Impression of Change (PGIC). Patients rated symptom change for menstrual bleeding (MB) and nonbleeding symptoms on a 7-point scale from “very much improved” (1) to “very much worse” (7).
RESULTS:
By 6 months, scores for the PGIC-MB and the domains of “abdominal or pelvic pain,” “abdominal or pelvic pressure,” “abdominal or pelvic cramping,” “back pain,” and “abdominal bloating” were significantly better (
P
<.001 for all; no adjustment for multiple comparisons were made) in the ELA+AB versus placebo groups, regardless of patient age (<40 years, 40 to <45 years, ≥45 years), baseline MBL (less than median 187.0 mL, greater than or equal to median), International Federation of Gynecology and Obstetrics (FIGO) classification (0–3, 4, 5–8), or baseline uterine volume (less than median 356.5 cm
3
, greater than or equal to median). Patients receiving ELA+AB reported PGIC domain scores that consistently exceeded “minimally improved” (≤3) and often reached or exceeded “much improved” (≤2) by 6 months.
CONCLUSION:
ELA+AB provides better bleeding and nonbleeding symptom improvement versus placebo for patients with HMB associated with UFs, regardless of subpopulation investigated. In all populations, PGIC-MB and domain scores consistently reached or exceeded “much improved” by 6 months with ELA+AB.
We consider a two‐sample problem where data come from symmetric distributions. Usual two‐sample data with only magnitudes recorded, arising from case‐control studies or logistic discriminant ...analyses, may constitute a symmetric two‐sample problem. We propose a semiparametric model such that, in addition to symmetry, the log ratio of two unknown density functions is modeled in a known parametric form. The new semiparametric model, tailor‐made for symmetric two‐sample data, can also be viewed as a biased sampling model subject to symmetric constraint. A maximum empirical likelihood estimation approach is adopted to estimate the unknown model parameters, and the corresponding profile empirical likelihood ratio test is utilized to perform hypothesis testing regarding the two population distributions. Symmetry, however, comes with irregularity. It is shown that, under the null hypothesis of equal symmetric distributions, the maximum empirical likelihood estimator has degenerate Fisher information, and the test statistic has a mixture of χ2‐type asymptotic distribution. Extensive simulation studies have been conducted to demonstrate promising statistical powers under correct and misspecified models. We apply the proposed methods to two real examples.
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BFBNIB, DOBA, FSPLJ, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
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