Phenological responses to climate change (e.g., earlier leaf-out or egg hatch date) are now well documented and clearly linked to rising temperatures in recent decades. Such shifts in the phenologies of interacting species may lead to shifts in their synchrony, with cascading community and ecosystem consequences. To date, single-system studies have provided no clear picture, either finding synchrony shifts may be extremely prevalent [Mayor SJ, et al. (2017) 7:1902] or relatively uncommon [Iler AM, et al. (2013) 19:2348-2359], suggesting that shifts toward asynchrony may be infrequent. A meta-analytic approach would provide insights into global trends and how they are linked to climate change. We compared phenological shifts among pairwise species interactions (e.g., predator-prey) using published long-term time-series data of phenological events from aquatic and terrestrial ecosystems across four continents since 1951 to determine whether recent climate change has led to overall shifts in synchrony. We show that the relative timing of key life cycle events of interacting species has changed significantly over the past 35 years. Further, by comparing the period before major climate change (pre-1980s) and after, we show that estimated changes in phenology and synchrony are greater in recent decades. However, there has been no consistent trend in the direction of these changes. Our findings show that there have been shifts in the timing of interacting species in recent decades; the next challenges are to improve our ability to predict the direction of change and understand the full consequences for communities and ecosystems.

Background Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated crosssectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0•40% (95% credible interval 0•29-0•54) to 0•06% (0•04-0•07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patientfacing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time.Interpretation Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards.

Objective: To estimate the percentage of individuals infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over time in the community in England and to quantify risk factors. Design: Repeated cross-sectional surveys of population-representative households with longitudinal follow-up if consent given. Setting: England. Participants: 34,992 Individuals aged 2 years and over from 16,722 private residential households. Data were collected in a pilot phase of the survey between 26 April and 28 June 2020. Main outcome measures: Percentage of individuals in the community testing positive for SARS-CoV-2 RNA using throat and nose swabs. Individuals were asked about any symptoms and potential risk factors. Results: The percentage of people in private-residential households testing positive for SARS-CoV-2 reduced from 0.32% (95% credible interval (CrI) 0.19% to 0.52%) on 26 April to 0.08% (95% CrI 0.05% to 0.12%) on 28 June, although the prevalence stabilised near the end of the pilot. Factors associated with an increased risk of testing positive included having a job with direct patient contact (relative exposure (RE) 4.06, 95% CrI 2.42 to 6.77)), working outside the home (RE 2.49, 95% CrI 1.39 to 4.45), and having had contact with a hospital (RE 2.20, 95% CrI 1.09 to 4.16 for having been to a hospital individually and RE 1.95, 95% CrI 0.81 to 4.09 for a household member having been to a hospital). In 133 visits where individuals tested positive, 82 (61%, 95% CrI 53% to 69%) reported no symptoms, stably over time. Conclusion: The percentage of SARS-CoV-2 positive individuals declined between 26 April and 28 June 2020. Positive tests commonly occurred without symptoms being reported. Working outside your home was an important risk factor, indicating that continued monitoring for SARS-CoV-2 in the community will be essential for early detection of increases in infections following return to work and other relaxations of control measures.

Daily patterns of pedestrian activity in young children have important health implications, primarily because of the risk of road traffic injury, but also because they may reflect the commencement of exercise habits with long-term consequences. A crosssectional survey in two Australian cities, Melbourne and Perth, aimed to collect, by parent self-administered questionnaire, population-based data on modes of travel, numbers of street crossings (both accompanied and unaccompanied by an adult), and sociodemographic factors for six-and nine-year-old children. Results indicate that 35 per cent (95 per cent confidence interval (CI) 31 to 39 per cent) and 31 per cent (CI 28 to 34 per cent) walk to school in Melbourne and Perth respectively, while over 60 per cent are driven to school by car, with very small proportions riding bicycles or taking public transport. A higher level of walking was associated with lower levels of several indicators of socioeconomic status. Logistic regression analysis showed that the strongest predictor of walking activity was school type (government versus independent), and after adjusting for this, lesser car ownership, non-English-speaking background and lower occupational category were associated with walking to school, while a different set of predictors-age, sex and maternal education-was associated with the unaccompanied crossing of streets. There was little difference in overall walking levels between boys and girls, but boys were significantly more likely to cross streets unaccompanied (adjusted odds ratio 1.41, CI 1.14 to 1.72), providing a partial explanation of documented sex differences in injury rates. ( A w t N Z

In PNAS, Case and Deaton (1) show a figure illustrating the "marked increase in the all-cause mortality of middle-aged white non-Hispanic men and women in the United States between 1999 and 2013." The authors state that their numbers "are not age-adjusted within the 10-y 45-54 age group" (1).We suspected an aggregation bias and examined whether the increase in aggregate mortality rates could be due to the changing composition of this age group. Adjusting for age confirmed this suspicion. Contrary to Case and Deaton's figure (1), we find there is not a steady increase in mortality rates for this age group. Instead there is an increasing trend from 1999 to 2005 and a constant trend thereafter. Moreover, stratifying age-adjusted mortality rates by sex shows a marked increase only for women and not men, contrary to the article's headline.Age-adjustment is not merely an academic exercise. Fig. 1A shows the unadjusted mortality rates over the 1999-2013 time period. During this period, however, the average age in this group increased as the baby boom generation passed through (Fig. 1B).We calculated the change in the group mortality rate due solely to the change in the underlying age of the population. We took the 2013 mortality rates for each age and computed a weighted average rate each year using the number of individuals in each age group. Fig, 1C shows that the changing composition in age alone explains about half the change in the mortality rate of this group since 1999 and all of the change since 2005.We then age-adjusted the mortality rates published in the Case and Deaton paper (1). Fig. 2A shows adjustment to a uniform age distribution over ages 45-54, where the mortality rate is calculated each year by dividing the number of deaths for each age between 45 and 54 by the population of that age and then taking the average. Consistent with Fig. 1C, the adjusted mortality rate increased from 1999 to 2005 and then stopped.We find that age-adjustment is not sensitive to the age distribution used to normalize the mortality rates. Fig. 2B shows three adjustments: first, under the aforementioned uniform age distribution; second, using the distribution of ages that existed in 1999, which is skewed toward the younger end of the 45-54 group; and third, using the 2013 age distribution, which is skewed older. The general pattern does not change.Calculating the age-adjusted rates separately for each sex reveals a crucial result (Fig. 2C). The mortality rate among women increased markedly, but the corresponding group of men nearly reversed its 1999-2005 increase over the 2005-2013 period.We stress that this does not change a key finding of the Case and Deaton paper (1): the comparison of nonHispanic United States middle-aged whites to other populations. It affects claims concerning the absolute increase in mortality among United States middleaged white non-Hispanics. We believe it is vital that future researchers understand the aggregation bias as they read Case and Deaton's article and consider how to investigate these note...