Unmitigated spread in Brazil: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Using daily data from state health offices, Castro et al. analyzed the pattern of spread of COVID-19 cases and deaths in the country from February to October 2020. Clusters of deaths before cases became apparent indicated unmitigated spread. SARS-CoV-2 circulated undetected in Brazil for more than a month as it spread north from Sã o Paulo. In Manaus, transmission reached unprecedented levels after a momentary respite in mid-2020. Faria et al. tracked the evolution of a new, more aggressive lineage called P.1, which has 17 mutations, including three (K417T, E484K, and N501Y) in the spike protein. After a period of accelerated evolution, this variant emerged in Brazil during November 2020. Coupled with the emergence of P.1, disease spread was accelerated by stark local inequalities and political upheaval, which compromised a prompt federal response. Science , abh1558 and abh2644, this issue p. 821 and p. 815

The aim of this study was to assess how coronavirus disease 2019 (COVID-19) clustered across districts in South Korea and to assess whether the pattern and duration of clusters changed following the country's containment strategy. A spatiotemporal analysis of COVID-19 daily confirmed cases by 250 districts in South Korea from January 20 to May 31, 2020, obtained from the Korea Centers for Disease Control and Prevention and each provincial website, was conducted. The global Moran's I statistic was used for spatial autocorrelation analysis, and the retrospective space-time scan statistic was used to analyze spatiotemporal clusters of COVID-19. The geographical distribution showed strong spatial autocorrelation, with a global Moran's I coefficient of 0.784 (p = 0.0001). Twelve statistically significant spatiotemporal clusters were identified by space–time scan statistic using a discrete Poisson model. The spatial pattern of clusters changed and the duration of clusters became shorter over time. The results indicate that South Korea's containment strategy for COVID-19 was highly effective in both early detection and mitigation, with recent clusters being small in size and duration. Lessons from South Korea should spark a discussion on epidemic response.

Background: Older age is a risk factor for tuberculosis (TB) in low incidence settings. Using data from the US National TB Surveillance System and American Community Survey, we estimated trends and racial/ethnic differences in TB incidence among US-born cohorts aged ≥50 years. Methods: In total, 42 000 TB cases among US-born persons ≥50 years were reported during 2001–2019. We used generalized additive regression models to decompose the effects of birth cohort and age on TB incidence rates, stratified by sex and race/ethnicity. Using genotype-based estimates of recent transmission (available 2011–2019), we implemented additional models to decompose incidence trends by estimated recent versus remote infection. Results: Estimated incidence rates declined with age, for the overall cohort and most sex and race/ethnicity strata. Average annual percentage declines flattened for older individuals, from 8.80% (95% confidence interval [CI] 8.34–9.23) in 51-year-olds to 4.51% (95% CI 3.87–5.14) in 90-year-olds. Controlling for age, incidence rates were lower for more recent birth cohorts, dropping 8.79% (95% CI 6.13–11.26) on average between successive cohort years. Incidence rates were substantially higher for racial/ethnic minorities, and these inequalities persisted across all birth cohorts. Rates from recent infection declined at approximately 10% per year as individuals aged. Rates from remote infection declined more slowly with age, and this annual percentage decline approached zero for the oldest individuals. Conclusions: TB rates were highest for racial/ethnic minorities and for the earliest birth cohorts and declined with age. For the oldest individuals, annual percentage declines were low, and most cases were attributed to remote infection.

The societal disruptions resulting from the coronavirus disease 2019 (COVID-19) pandemic may have caused changes in smoking and alcohol consumption. Using data from the Koreans’ Happiness Survey, a nationally representative survey in South Korea, we (1) described population-level smoking and drinking behaviors; (2) assessed changes in smoking and drinking behaviors during the COVID-19 pandemic; and (3) identified employment, economic, and sociodemographic factors associated with these changes using multinomial logistic regression. The overall amount of smoking and drinking decreased during the pandemic, but the changes were heterogeneous across subgroups. Male gender, receipt of the basic living allowance, self-employment, unemployment, and chronic disease status were associated with increased smoking, while higher household income, temporary worker status, living with someone (versus alone), and having fewer offline friends were associated with decreased smoking. Male gender, self-employment, living alone, having more offline friends, and chronic disease status were associated with increased drinking, while younger age, male gender, low and high household income (i.e., a U-shaped relationship), long-term rent with a deposit, temporary worker status, and chronic disease status were associated with decreased drinking. Our findings provide evidence on changes in smoking and drinking during the COVID-19 pandemic in South Korea and differential changes across subgroups.

Background: Even after successful treatment, individuals surviving tuberculosis (TB) disease experience elevated mortality rates. However, there is limited evidence on how these risks vary over time and by individual characteristics. Methods: We conducted a retrospective cohort study of individuals diagnosed with TB in Brazil, using national TB notifications and linked mortality records for 2007–2016. We estimated mortality rate ratios (MRRs) and cumulative mortality by year since TB diagnosis, compared to general population mortality matched on age, sex, year, and state. We identified clinical and sociodemographic factors associated with elevated post-TB mortality, and compared the distribution of causes of death to the general population. Results: The study sample included 834 594 individuals, with 4.1 million person-years of follow-up (average, 4.9 years). The TB cohort had elevated mortality compared to the general population, particularly in the first year postdiagnosis (MRR, 11.28 [95% confidence interval {CI}, 11.18–11.37]). Post-TB MRRs declined from 3.59 (95% CI, 3.53–3.64) in year 2 to 1.46 (95% CI, 1.34–1.59) in year 10. Cumulative excess mortality was 6.12% (95% CI, 6.07%–6.17%) after 1 year and 9.90% (95% CI, 9.58%–10.24%) after 10 years. MRRs were highest for individuals 30–44 years old at diagnosis. Relapse, loss to follow-up, and co-prevalent conditions like human immunodeficiency virus (HIV) and alcohol use disorder were strongly associated with higher MRRs. Over time, major causes of death in the TB cohort shifted from TB and HIV to cardiovascular disease, cancer, and non-TB respiratory diseases. Conclusions: Individuals developing TB disease face elevated mortality up to 10 years after diagnosis. These excess risks vary across demographic and clinical characteristics.