False-negative rate of ultrasound-guided fine-needle aspiration cytology for identifying axillary lymph node metastasis in breast cancer patients.

Axillary nodal status remains an important determinant of prognosis and of the therapeutic strategy in patients with a newly diagnosed breast cancer. The aim of this study was to assess the false-negative rate of ultrasound (US)-guided fine-needle aspiration cytology (FNAC) in axillary node staging at breast cancer diagnosis. All patients with a newly diagnosed breast cancer who had an indeterminate or suspicious axillary node sampled with an FNAC between 2007 and 2014 were included in the study. FNAC results were compared to the final histopathological results of surgically removed axillary lymph nodes. Patient demographics, tumor, and nodal characteristics were analyzed. Diagnostic accuracy tests were performed using IBM SPSS, version 22. A total of 3515 patients with breast cancer were identified, 675 of whom had ultrasound-guided FNAC of ipsilateral axillary lymph nodes (mean age: 55 years; Range: 26-84). A benign (C2) result was observed in 52% (n = 351) and a malignant (C5) result in 35% (n = 238). C1 was obtained in 11% (n = 76), C3 in 0.6% (n = 4), and C4 in 0.9% (n = 6). Of the 238 patients with a malignant (C5) FNAC, 99.6% had confirmed axillary lymph node metastatic disease on histopathology. Of the 351 patients with benign FNAC (C2), 31% (n = 108) of patients had a positive lymph node on histology. The false-negative rate of preoperative FNAC remains too high (31%) to omit definitive surgical staging of the axilla. The high diagnostic accuracy when a positive FNAC is obtained allows appropriate tailored decisions regarding definitive therapy.

Daily Temperatures and Child Hospital Admissions in Aotearoa New Zealand: Case Time Series Analysis.

The influence of global climate change on temperature-related health outcomes among vulnerable populations, particularly young children, is underexplored. Using a case time series design, we analysed 647,000 hospital admissions of children aged under five years old in New Zealand, born between 2000 and 2019. We explored the relationship between daily maximum temperatures and hospital admissions across 2139 statistical areas. We used quasi-Poisson distributed lag non-linear models to account for the delayed effects of temperature over a 0-21-day window. We identified broad ICD code categories associated with heat before combining these for the main analyses. We conducted stratified analyses by ethnicity, sex, and residency, and tested for interactions with long-term temperature, socioeconomic position, and housing tenure. We found J-shaped temperature-response curves with increased risks of hospital admission above 24.1 °C, with greater sensitivity among Maori, Pacific, and Asian compared to European children. Spatial-temporal analysis from 2013-2019 showed rising attributable fractions (AFs) of admissions associated with increasing temperatures, especially in eastern coastal and densely populated areas. Interactive maps were created to allow policymakers to prioritise interventions. Findings emphasize the need for child-specific and location-specific climate change adaptation policies, particularly for socioeconomically disadvantaged groups.

Quantifying uncertainty in aggregated climate change risk assessments.

High-level assessments of climate change impacts aggregate multiple perils into a common framework. This requires incorporating multiple dimensions of uncertainty. Here we propose a methodology to transparently assess these uncertainties within the 'Reasons for Concern' framework, using extreme heat as a case study. We quantitatively discriminate multiple dimensions of uncertainty, including future vulnerability and exposure to changing climate hazards. High risks from extreme heat materialise after 1.5-2 °C and very high risks between 2-3.5 °C of warming. Risks emerge earlier if global assessments were based on national risk thresholds, underscoring the need for stringent mitigation to limit future extreme heat risks.

Using Detection And Attribution To Quantify How Climate Change Is Affecting Health.

The question of whether, how, and to what extent climate change is affecting health is central to many climate and health studies. We describe a set of formal methods, termed detection and attribution, used by climatologists to determine whether a climate trend or extreme event has changed and to estimate the extent to which climate change influenced that change. We discuss events where changing weather patterns were attributed to climate change and extend these analyses to include health impacts from heat waves in 2018 and 2019 in Europe and Japan, and we show how such impact attribution could be applied to melting ice roads in the Arctic. Documenting the causal chain from emissions of greenhouse gases to observed human health outcomes is important input into risk assessments that prioritize health system preparedness and response interventions and into financial investments and communication about potential risk to policy makers and to the public.

Ocean and land forcing of the record-breaking Dust Bowl heatwaves across central United States.

The severe drought of the 1930s Dust Bowl decade coincided with record-breaking summer heatwaves that contributed to the socio-economic and ecological disaster over North America's Great Plains. It remains unresolved to what extent these exceptional heatwaves, hotter than in historically forced coupled climate model simulations, were forced by sea surface temperatures (SSTs) and exacerbated through human-induced deterioration of land cover. Here we show, using an atmospheric-only model, that anomalously warm North Atlantic SSTs enhance heatwave activity through an association with drier spring conditions resulting from weaker moisture transport. Model devegetation simulations, that represent the wide-spread exposure of bare soil in the 1930s, suggest human activity fueled stronger and more frequent heatwaves through greater evaporative drying in the warmer months. This study highlights the potential for the amplification of naturally occurring extreme events like droughts by vegetation feedbacks to create more extreme heatwaves in a warmer world.

Extreme heat-related mortality avoided under Paris Agreement goals.

In key European cities, stabilizing climate warming at 1.5 °C would decrease extreme heat-related mortality by 15-22% per summer compared with stabilization at 2 °C.