Tropical forests are thermally buffered despite intensive selective logging

Tropical rainforests are subject to extensive degradation by commercial selective logging. Despite pervasive changes to forest structure, selectively logged forests represent vital refugia for global biodiversity. The ability of these forests to buffer temperature-sensitive species from climate warming will be an important determinant of their future conservation value, although this topic remains largely unexplored. Thermal buffering potential is broadly determined by: (i) the difference between the "macroclimate" (climate at a local scale, m to ha) and the "microclimate" (climate at a fine-scale, mm to m, that is distinct from the macroclimate); (ii) thermal stability of microclimates (e.g. variation in daily temperatures); and (iii) the availability of microclimates to organisms. We compared these metrics in undisturbed primary forest and intensively logged forest on Borneo, using thermal images to capture cool microclimates on the surface of the forest floor, and information from dataloggers placed inside deadwood, tree holes and leaf litter. Although major differences in forest structure remained 9-12 years after repeated selective logging, we found that logging activity had very little effect on thermal buffering, in terms of macroclimate and microclimate temperatures, and the overall availability of microclimates. For 1°C warming in the macroclimate, temperature inside deadwood, tree holes and leaf litter warmed slightly more in primary forest than in logged forest, but the effect amounted to <0.1°C difference between forest types. We therefore conclude that selectively logged forests are similar to primary forests in their potential for thermal buffering, and subsequent ability to retain temperature-sensitive species under climate change. Selectively logged forests can play a crucial role in the long-term maintenance of global biodiversity

Measurement Error and Environmental Epidemiology: a Policy Perspective

PURPOSE OF REVIEW: Measurement error threatens public health by producing bias in estimates of the population impact of environmental exposures. Quantitative methods to account for measurement bias can improve public health decision making.RECENT FINDINGS: We summarize traditional and emerging methods to improve inference under a standard perspective, in which the investigator estimates an exposure-response function, and a policy perspective, in which the investigator directly estimates population impact of a proposed intervention. Under a policy perspective, the analyst must be sensitive to errors in measurement of factors that modify the effect of exposure on outcome, must consider whether policies operate on the true or measured exposures, and may increasingly need to account for potentially dependent measurement error of two or more exposures affected by the same policy or intervention. Incorporating approaches to account for measurement error into such a policy perspective will increase the impact of environmental epidemiology

Nonparametric Bounds for the Risk Function

Nonparametric bounds for the risk difference are straightforward to calculate and make no untestable assumptions about unmeasured confounding or selection bias due to missing data (e.g., dropout). These bounds are often wide and communicate uncertainty due to possible systemic errors. An illustrative example is provided

What Now? Epidemiology in the Wake of a Pandemic

The coronavirus disease 2019 (COVID-19) pandemic and the coming transition to a postpandemic world where COVID-19 will likely remain as an endemic disease present a host of challenges and opportunities in epidemiologic research. The scale and universality of this disruption to life and health provide unique opportunities to study phenomena and health challenges in all branches of epidemiology, from the obvious infectious disease and social consequences to less clear impacts on chronic disease and cancer. If we are to both take advantage of the largest natural experiment of our lifetimes and provide evidence to inform the numerous public health and clinical decisions being made every day, we must act quickly to ask critical questions and develop new methods for answering them. In doing so, we should build on each of our strengths and expertise and try to provide new insights rather than become yet another voice commenting on the same set of questions with limited evidence

You are smarter than you think: (super) machine learning in context

We discuss an article on super learning by Naimi and Balzer in the current issue of this journal in the context of machine learning. We give a brief example that emphasizes the need for human intelligence in the rapidly evolving field of machine learning

Trends in outpatient malaria cases, following Mass Long Lasting Insecticidal Nets (LLIN) distribution in epidemic prone and endemic areas of Kenya

Background: There were over 6 million case of malaria reported in Kenya in 2015 and it remains a major public health priority despite significant investments in interventions to control and prevent infections in high risk areas.Objectives: To analyse trends from 2011-2015, and report i) outpatient department (OPD) malaria case prevalence, ii) the proportion of confirmed malaria cases of all OPD cases stratified by age category, and iii) the proportion of the population potentially protected by long-lasting insecticidal nets (LLINs), following mass distribution of LLINs in malaria epidemic prone and endemic areas.Design: A retrospective study.Setting: Kenya’s Coast endemic, Lake endemic and Highland epidemic zones.Subjects: All outpatient malaria cases reported in the District Health Information System.Results: The proportion of people who received mass LLINs ranged from 80-95% in epidemic prone and endemic areas of Kenya. The coastal endemic zone had the lowest number of reported malaria cases at almost 840,000 in 2011, compared with the lake endemic zone which reported 4.3 million total cases. Confirmed malaria cases of all the OPD morbidity increased by 1%, 20% and 4% in the Highland epidemic prone, the Lake and Coast endemic region in 2011 to 2015, respectively. There was a trend towards fewer cases across all three high risk regions from 2012-2013, but this reversed with increasing cases being reported in 2014-2015.Conclusion: Despite a high LLIN coverage malaria cases increased over time. There is need for patient-level studies to assess if LLINs are being used appropriately and to look towards other complimentary malaria prevention strategies

Editorial: Structured operational research and training in the public health sector: the Kenyan experience

No Abstrac

Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time

The potential for immortal time bias is pervasive in epidemiologic studies with left truncation or time-varying exposures. Unlike other biases in epidemiologic research (e.g., measurement bias, confounding due to unmeasured factors, and selection based on unmeasured predictors of the outcome), immortal time bias can and should be avoided by the correct assignment of person-time during follow up. However, even when handing person-time correctly, allowing late entry into a study or into an exposure group can open the door to more insidious sources of bias, some of which we explore here. Clear articulation of the study question, including the treatment plans of interest, can provide navigation around these sources of bias and elucidate the assumptions needed for inference given the available data. Here, we use simulated data to illustrate the assumptions required under various approaches to estimate the effect of a time-varying treatment and describe how these assumptions relate to the assumptions necessary to estimate single sample rates and risks in settings with censoring and truncation

A Fundamental Equivalence between Randomized Experiments and Observational Studies

A fundamental probabilistic equivalence between randomized experiments and observational studies is presented. Given a detailed scenario, the reader is asked to consider which of two possible study designs provides more information regarding the expected difference in an outcome due to a time-fixed treatment. A general solution is described, and a particular worked example is also provided. A mathematical proof is given in the appendix. The demonstrated equivalence helps to clarify common ground between randomized experiments and observational studies, and to provide a foundation for considering both the design and interpretation of studies

Surprise!

Measures of information and surprise, such as the Shannon information value (S value), quantify the signal present in a stream of noisy data. We illustrate the use of such information measures in the context of interpreting P values as compatibility indices. S values help communicate the limited information supplied by conventional statistics and cast a critical light on cutoffs used to judge and construct those statistics. Misinterpretations of statistics may be reduced by interpreting P values and interval estimates using compatibility concepts and S values instead of "significance"and "confidence.