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1. A population-based study of the trend in SARS-CoV-2 diagnostic modalities from the beginning of the pandemic to the Omicron surge in Kyoto City, Kyoto, Japan

   https://doi.org/10.1186/s12889-023-17498-3  

   Kyo, Hiroki; Patel, Shivani A; Yamamoto, Masaki; Matsumura, Yasufumi; Ikeda, Takeshi; Nagao, Miki (December 2023, BMC Public Health)

   Abstract BackgroundThe coronavirus disease 2019 (COVID-19) presents critical diagnostic challenges for managing the pandemic. We investigated the 30-month changes in COVID-19 testing modalities and functional testing sites from the early period of the pandemic to the most recent Omicron surge in 2022 in Kyoto City, Japan. MethodsThis is a retrospective-observational study using a local anonymized population database that included patients' demographic and clinical information, testing methods and facilities from January 2020 to June 2022, a total of 30 months. We computed the distribution of symptomatic presentation, testing methods, and testing facilities among cases. Differences over time were tested using chi-square tests of independence. ResultsDuring the study period, 133,115 confirmed COVID-19 cases were reported, of which 90.9% were symptomatic. Although nucleic acid amplification testing occupied 68.9% of all testing, the ratio of lateral flow devices (LFDs) rapidly increased in 2022. As the pandemic continued, the testing capability was shifted from COVID-19 designated facilities to general practitioners, who became the leading testing providers (57.3% of 99,945 tests in 2022). ConclusionsThere was a dynamic shift in testing modality during the first 30 months of the pandemic in Kyoto City. General practitioners increased their role substantially as the use of LFDs spread dramatically in 2022. By comprehending and documenting the evolution of testing methods and testing locations, it is anticipated that this will contribute to the establishment of an even more efficient testing infrastructure for the next pandemic. 

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2. Comparing the Reliability of Virtual and In-Person Post-Stroke Neuropsychological Assessment with Language Tasks

   https://doi.org/10.1093/arclin/acac100  

   Duricy, Erin; Durisko, Corrine; Dickey, Michael Walsh; Fiez, Julie A (December 2022, Archives of Clinical Neuropsychology)

   Abstract ObjectiveNeuropsychological testing is essential for both clinical and basic stroke research; however, the in-person nature of this testing is a limitation. Virtual testing overcomes the hurdles of geographic location, mobility issues and permits social distancing, yet its validity has received relatively little investigation, particularly in comparison with in-person testing. MethodWe expand on our prior findings of virtual testing feasibility by assessing virtual versus in-person administration of language and communication tasks with 48 left-hemisphere stroke patients (21 F, 27 M; mean age = 63.4 ± 12; mean years of education = 15.3 ± 3.5) in a quasi-test–retest paradigm. Each participant completed two testing sessions: one in their home and one in the research lab. Participants were assigned to one of the eight groups, with the testing condition (fully in-person, partially virtual), order of home session (first, second) and technology (iPad, Windows tablet) varied across groups. ResultsAcross six speech-language tasks that utilized varying response modalities and interfaces, we found no significant difference in performance between virtual and in-person testing. However, our results reveal key considerations for successful virtual administration of neuropsychological tests, including technology complications and disparities in internet access. ConclusionsVirtual administration of neuropsychological assessments demonstrates comparable reliability with in-person data collection involving stroke survivors, though technology issues must be taken into account. 

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3. Towards Testing Monotonicity of Distributions Over General Posets

   Alaikbarpour, A.; Gouleakis, T.; Peebles, J.; Rubinfeld, R.; Yodpinyanee, A (January 2019, Proceedings of the Thirty-Second Conference on Learning Theory (COLT 2019))

   In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is monotone if, for any pair of domain elements x and y such that x ⪯ y, p(x) ≤ p(y). To understand the sample complexity of this problem, we introduce a new property called bigness over a finite domain, where the distribution is T-big if the minimum probability for any domain element is at least T. We establish a lower bound of Ω(n/ log n) for testing bigness of distributions on domains of size n. We then build on these lower bounds to give Ω(n/ log n) lower bounds for testing monotonicity over a matching poset of size n and significantly improved lower bounds over the hypercube poset. We give sublinear sample complexity bounds for testing bigness and for testing monotonicity over the matching poset. We then give a number of tools for analyzing upper bounds on the sample complexity of the monotonicity testing problem. The previous lower bound for testing Monotonicity of 

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4. Prevalence and factors associated with self-reported HIV testing among adolescent girls and young women in Rwanda: evidence from 2019/20 Rwanda Demographic and Health Survey

   https://doi.org/10.1186/s12889-022-13679-8  

   Musekiwa, Alfred; Silinda, Patricia; Bamogo, Assanatou; Twabi, Halima S; Mohammed, Mohanad; Batidzirai, Jesca Mercy; Matsena_Zingoni, Zvifadzo; Singini, Geoffrey Chiyuzga; Moyo, Maureen; Mchunu, Nobuhle Nokubonga; et al (December 2022, BMC Public Health)

   Abstract BackgroundHIV/AIDS remains a major public health problem globally. The majority of people living with HIV are from Sub-Saharan Africa, particularly adolescent girls and young women (AGYW) aged 15-24 years. HIV testing is crucial as it is the gateway to HIV prevention, treatment, and care; therefore this study determined the prevalence and factors associated with self-reported HIV testing among AGYW in Rwanda. MethodsWe conducted secondary data analysis on the AGYW using data extracted from the nationally representative population-based 2019/2020 cross-sectional Rwanda Demographic and Health Survey (DHS). We described the characteristics of study participants and determined the prevalence of HIV testing and associated factors using the multivariable logistic regression model. We adjusted all our analyses for unequal sampling probabilities using survey weights. ResultsThere were a total of 5,732 AGYW, with the majority (57%) aged 15-19 years, 83% were not living with a man, 80% were from rural areas, 29% were from the East region, and 20% had a history of pregnancy. Self-reported HIV testing prevalence was 55.4% (95%CI: 53.7 to 57.0%). The odds of ever having an HIV test were significantly higher for those aged 20-24 years (aOR 2.87, 95%CI: 2.44 to 3.37); with higher education (aOR 2.41, 95%CI:1.48 to 3.93); who were rich (aOR 2.06, 95%CI:1.57 to 2.70); with access to at least one media (aOR 1.64, 95%CI: 1.14 to 2.37); who had ever been pregnant (aOR 16.12, 95%CI: 9.60 to 27.07); who ever had sex (aOR 2.40, 95%CI: 1.96 to 2.95); and those who had comprehensive HIV knowledge (aOR 1.34, 95%CI: 1.17 to 1.54). ConclusionsWe report an unmet need for HIV testing among AGYW in Rwanda. We recommend a combination of strategies to optimize access to HIV testing services, especially among the 15-19 years adolescent girls, including facility-based testing, school and community outreach, awareness campaigns on HIV testing, and home-based testing through HIV self-testing. 

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5. Which Distribution Distances are Sublinearly Testable?

   Daskalakis, Constantinos; Kamath, Gautam; Wright, John (January 2018, Proceedings of the annual ACM-SIAM Symposium on Discrete Algorithms)

   Given samples from an unknown distribution p and a description of a distribution q, are p and q close or far? This question of “identity testing” has received significant attention in the case of testing whether p and q are equal or far in total variation distance. However, in recent work [VV11a, ADK15, DP17], the following questions have been been critical to solving problems at the frontiers of distribution testing: Alternative Distances: Can we test whether p and q are far in other distances, say Hellinger? Tolerance: Can we test when p and q are close, rather than equal? And if so, close in which distances? Motivated by these questions, we characterize the complexity of distribution testing under a variety of distances, including total variation, ℓ2, Hellinger, Kullback-Leibler, and χ2. For each pair of distances d1 and d2, we study the complexity of testing if p and q are close in d1 versus far in d2, with a focus on identifying which problems allow strongly sublinear testers (i.e., those with complexity O(n1–γ) for some γ > 0 where n is the size of the support of the distributions p and q). We provide matching upper and lower bounds for each case. We also study these questions in the case where we only have samples from q (equivalence testing), showing qualitative differences from identity testing in terms of when tolerance can be achieved. Our algorithms fall into the classical paradigm of χ2-statistics, but require crucial changes to handle the challenges introduced by each distance we consider. Finally, we survey other recent results in an attempt to serve as a reference for the complexity of various distribution testing problems. 

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