Trained enumerators interviewed participants face to face for approximately 20–30 minutes and administered a structured checklist covering 25 possible daily health complaints. The total score ranged from zero to 25 and was divided into three domains: sensory function or pain, body-system function, and activity participation. Participants were eligible if they were at least 15 years old, had diagnosed diabetes and reported a previous COVID-19 infection. Vaccination status was verified using electronic records from Indonesia’s SatuSehat platform and physical vaccination cards. Missing data were addressed through K-nearest-neighbours imputation with k = 5, producing an analytic sample of 420 participants. The count outcomes were modelled with negative binomial regression adjusted for sociodemographic and clinical variables.

The participants had a median age of 60 years, and 70.2% were female. A total of 36.2% had received a COVID-19 vaccine before their infection. Joint or muscle pain was the most prevalent complaint, reported by 46.0% of participants, followed by visual impairment at 43.1% and fatigue at 24.3%. The multivariable results also identified associations between demographic or clinical characteristics and particular complaint domains. Female participants had a higher rate of total daily complaints than male participants, with an incidence rate ratio of 1.25 and a 95% confidence interval of 1.02–1.53. Participants with secondary education had a lower rate of body-system dysfunction, with an incidence rate ratio of 0.66, while the absence of other comorbidities was associated with a lower rate of sensory or pain complaints, with an incidence rate ratio of 0.84. The supplied abstract did not provide confidence intervals for these two domain-specific estimates.

Vaccination after the index COVID-19 infection was not significantly associated with a lower rate of complaints related to that infection. The estimated incidence rate ratio was 1.06, with a 95% confidence interval of 0.90–1.26, which includes no difference. This estimate does not show that vaccination after infection reduced complaints, but it also does not establish that vaccination before infection was superior to vaccination after infection. The significant pre-infection estimate and the non-significant post-infection estimate came from separate comparisons, and the reported confidence interval for the post-infection association includes both lower and higher values than one. The clearest finding was therefore the observed association between pre-infection vaccination status and the overall rate of daily complaints, rather than a demonstrated advantage of one vaccination timing over the other.

The study offers a primary-care view of health burdens reported by people with diabetes after COVID-19, a population for which evidence from low- and middle-income settings was described as limited. The authors proposed that vaccination promotion could be integrated into routine chronic-disease management in these settings, while calling for prospective confirmation of the association. Several features constrain that conclusion. The cross-sectional design assessed vaccination and infection histories retrospectively and collected the other data at a single time point, allowing the analysis to estimate rates but not establish temporal or causal relationships. The sample was drawn from one Indonesian province and included only people with self-reported prior infection, with no uninfected control group. Unmeasured differences between vaccinated and never-vaccinated participants could therefore contribute to the observed pattern, and the findings may not generalize to other populations or primary-care systems. Within these limitations, the study linked pre-infection vaccination with a lower overall burden of daily complaints among 420 Indonesian primary-care patients with diabetes after COVID-19.

Subject of Research: Association of pre-infection COVID-19 vaccination with daily health complaints among Indonesian primary-care patients with diabetes.

Article Title: Beyond the pandemic declaration: daily health complaints in diabetic COVID-19 survivors and the association with prior vaccination in Indonesian primary care

Article References: Machmud, P. B., Khatimah, H., Mahkota, R., Veronica, J., & Wijayanto, A. W. (2026). Beyond the pandemic declaration: daily health complaints in diabetic COVID-19 survivors and the association with prior vaccination in Indonesian primary care. BMC Public Health. https://doi.org/10.1186/s12889-026-30043-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-30043-2

Keywords: COVID-19 vaccination, diabetes mellitus, daily health complaints, post-COVID symptoms, cross-sectional study, primary care, Indonesia, long COVID, negative binomial regression, Beyond, pandemic, declaration

When the 1:5 mixture was applied to naturally infested water containers in one village near Shaoxing, larval density fell by 100% at every monitored point under an every-two-weeks regimen and by 87.03% to 96.80% under a less frequent, higher-concentration regimen. Both treatment groups had significantly lower larval densities than untreated controls, although the difference between schedules was not statistically significant. The study addresses two practical limitations in mosquito larval control.

Temephos has been widely used for decades, but sustained use has contributed to resistance in Aedes populations, including populations in Zhejiang Province. Bti provides a biological alternative with target specificity, limited environmental persistence and low toxicity to non-target organisms, but its field efficacy can be lower against older larvae, particularly fourth-instar mosquitoes. Earlier compatibility research had identified temephos as a promising partner for Bti, but the optimal ratio for A. albopictus and the practical performance of the mixture in field containers had not been established.

The laboratory phase used early fourth-instar larvae from a susceptible laboratory colony maintained at the Zhejiang Provincial Center for Disease Control and Prevention and originally sourced from the Chinese Center for Disease Control and Prevention. Bioassays were conducted at 26 ± 1 °C and a relative humidity of 70 ± 5%. Each assay placed 30 larvae in 50 mL of water within a 150 mL test vessel.

Mortality was recorded after 24 hours, every concentration was tested in triplicate, and distilled water served as the control; control mortality was 0%. The researchers used log-probit analysis to estimate median lethal concentrations, or LC50 values, and then calculated a co-toxicity coefficient, or CTC, for each mixture. Under the study’s classification, a CTC above 120 indicated synergy, 80 to 120 indicated additive action and a value below 80 indicated antagonism.

The tested mixtures were formulation A at 1:1, formulation B at 1:3 and formulation C at 1:5 by mass. Temephos alone had an LC50 of 0.017 mg/L, with a 95% confidence interval of 0.010 to 0.022 mg/L, whereas Bti alone had an LC50 of 8.059 mg/L, with a 95% confidence interval of 6.901 to 9.249 mg/L. These values describe potency in this particular laboratory assay and do not establish equivalent operational doses.

Mixed formulation A produced an LC50 of 0.024 mg/L, formulation B an LC50 of 0.048 mg/L and formulation C an LC50 of 0.067 mg/L. Their CTC values were 141.477, 140.299 and 150.009, respectively. The lower limits of all three confidence intervals remained above 120.

Formulation C was selected for the field trial because it had the greatest measured synergistic potency, rather than because it had the lowest LC50 when evaluated as a complete mixture. The field experiment was conducted in Yingjia Village from early July to late September 2024. During the trial period, temperatures ranged from 26 to 31 °C and rainfall ranged from 147.7 to 264.6 mm.

Staff from local disease-control centers selected 150 permanent water containers naturally infested with A. albopictus larvae. These included flowerpots, earthenware jars and cisterns with capacities of 4 to 25 liters. Fifty containers received formulation C every two weeks at twice the laboratory-derived 99% lethal concentration, or 2 × LC99.

Another 50 received the formulation once a month at four times that concentration, or 4 × LC99, and 50 remained untreated. The groups were positioned close to one another because mosquito movement and oviposition patterns could affect comparisons. Water levels were standardized against marked graduation lines before dosing, although evaporation and rainfall later caused some variation in active-ingredient concentrations.

Larval counts were made before treatment and on days 1, 3, 7, 15 and 30 after each application, with larvae returned to their containers after counting. The trial comprised three consecutive one-month cycles. Baseline mean larval densities did not differ significantly among the control, every-two-weeks treatment and monthly treatment groups: 6.94 larvae per container in the control, 6.29 in the every-two-weeks group and 7.12 in the monthly group, with F = 0.069 and p = 0.933.

Generalized linear mixed models with negative-binomial regression compared larval densities over time, including group, average temperature and rainfall during the preceding week as fixed effects and date as a random effect. Mean larval densities over the three cycles were zero in the every-two-weeks group; 0.28, 0.58 and 0.30 in the monthly group; and 8.52, 4.36 and 9.11 in controls. Both treated groups had significantly lower larval densities than controls at p < 0.001, but there was no significant difference between the two application schedules at p = 0.804.

The every-two-weeks regimen achieved a 100% observed reduction and a control-adjusted standard reduction of 100% in each of the three months. Under the monthly regimen, observed reductions were 96.07%, 91.85% and 95.79%, while standard reductions adjusted for background mosquito fluctuations were 96.80%, 87.03% and 96.79%. Control-container density changed by −22.78%, 37.19% and −31.21% relative to baseline, showing that larval populations continued to fluctuate seasonally even without treatment.

Standardized reductions therefore provided a conservative estimate when control density increased, a contrast that was especially relevant during the first and third months. The measured persistence of the mixture was one of the study’s notable features. In earlier laboratory work, Bti applied at 2 × LC99 produced mortality below 99.8% after one week and below 66.0% after two weeks.

A separate field study reported that 50% of sewers treated with temephos granules were reinfested with larvae after two weeks. In the present trial, the 1:5 mixture remained effective at sampling points extending to 30 days under the monthly regimen. The researchers also calculated that temephos at the LC99 level in formulation C represented 65.97% of the amount used in pure temephos.

This supports the narrower observation that the combination used less of the chemical component in that comparison. It does not by itself demonstrate reduced environmental contamination, lower application costs or a measurable resistance-management benefit. Several limitations prevent the field results from demonstrating eradication or long-term control.

Only three ratios, from 1:1 to 1:5, were tested, so mixtures containing more Bti were not evaluated. Resistance in the local mosquito population was not measured during the experiment, although earlier monitoring had detected a 12-fold temephos resistance ratio in Shaoxing relative to a susceptible strain. The apparent 100% reductions could also reflect temporary suppression of larval development, mortality at another mosquito life stage, oviposition avoidance or insufficient sampling rather than complete elimination of breeding.

The trial covered one village, three months and 150 containers, limiting generalization to other mosquito populations, seasons and environmental settings. The researchers noted that the mixture may retain activity against temephos-resistant populations, but that possibility requires direct testing, as do its long-term effects on insecticide resistance and the environment.

Subject of Research: Laboratory and field evaluation of a temephos–Bti mixture against Aedes albopictus larvae

Article Title: Synergistic larvicidal effects of temephos and Bacillus thuringiensis israelensis against Aedes albopictus: a laboratory and field study

Article References: Wu, Y., Liu, Q., Sun, J., Hong, Y., Fu, Y., Zhou, D., Ling, F., & Jiang, G. (2026). Synergistic larvicidal effects of temephos and Bacillus thuringiensis israelensis against Aedes albopictus: a laboratory and field study. Frontiers in Veterinary Science, 13, Article 1930489. https://doi.org/10.3389/fvets.2026.1930489

Image Credits: AI Generated

DOI: 10.3389/fvets.2026.1930489

Keywords: Aedes albopictus, Bacillus thuringiensis israelensis, temephos, larvicidal synergy, mosquito bioassay, field vector control, insecticide resistance, arbovirus vectors, dengue, negative-binomial regression, Synergistic, larvicidal

The study arrives at a moment when wildfire has become an inescapable feature of Southern California life. Los Angeles sits in a landscape where fire ecology predates human settlement by a millennium; chaparral ecosystems historically burned roughly every thirty years, with extended droughts allowing dead vegetation to accumulate as fuel. Fire, in turn, rejuvenates chaparral seeds that inhibit germination until after a blaze, returning nutrients to the soil and enabling new growth. That natural cycle now collides with millions of residents, and the catastrophic fires of recent years have pushed questions about disaster and social order into public debate. Popular narratives often assume that disasters unleash looting and lawlessness, but decades of criminological research on hurricanes, floods, and tornadoes tell a more complicated story, and wildfires have remained a conspicuous gap in that literature.

To close that gap, the researchers assembled tract-level data for the City of Los Angeles and identified seven major wildfires that occurred inside the municipal boundaries during the 2016 to 2022 window. Their statistical strategy centered on random-effects negative binomial regression models, a choice tailored to the structure of crime data. Crime counts in small geographic units are overdispersed, meaning their variance exceeds their mean, and a negative binomial framework accommodates that distribution far better than ordinary linear regression. The random-effects specification, meanwhile, accounts for unobserved, stable characteristics of each census tract, such as entrenched disadvantage or built environment features, that could otherwise confound the estimated relationship between fire exposure and crime.

The team measured wildfire exposure in two distinct ways, and that distinction turned out to be analytically decisive. The first measure identified census tracts that experienced direct wildfire damage, that is, tracts where the fire itself burned. The second captured proximity, identifying tracts located within a quarter mile of burned areas even if no fire touched them directly. Separating these two forms of exposure allowed the authors to test whether the crime effects of wildfire are confined to physically damaged neighborhoods or spill over into surrounding communities, a question with direct implications for how police and emergency managers allocate resources after a disaster.

The results paint a nuanced picture. Census tracts that experienced direct wildfire damage reported significantly lower levels of property crime. Violent crime was also marginally lower in those directly burned tracts, though the evidence there was weaker. When the researchers accounted for proximity to wildfire damage, however, the direct-damage effect on property crime was no longer statistically significant. What proved most robust was the proximity effect: tracts within a quarter mile of burned areas reported significantly lower levels of both violent and property crime. In other words, the strongest crime-suppressing signal appeared not in the neighborhoods the flames consumed, but in the neighborhoods standing just outside them.

The authors interpret these findings through the lens of two foundational criminological frameworks. Social disorganization theory holds that neighborhood crime is shaped by the capacity of local communities to exercise informal social control, a capacity rooted in stable networks, shared trust, and collective efficacy. Disasters can disrupt or reorganize those networks in unpredictable ways. Research after Hurricane Katrina, for example, documented both crime displacement and, in some domains, strengthened collective resources among repopulating residents. The routine activities approach offers a complementary mechanism: crime requires a motivated offender, a suitable target, and the absence of a capable guardian converging in time and space. Wildfires radically alter that convergence calculus. Evacuations empty neighborhoods of suitable targets, closed roads and fire-perimeter checkpoints supply guardianship, and burned or damaged structures simply cease to be burglarizable.

The proximity finding fits neatly into this routine-activities logic. A quarter-mile buffer around a burned area is precisely the zone where emergency access controls, evacuation orders, utility shutoffs, and heavy first-responder presence are most intense, even when the homes themselves remain intact. Fewer people on the street, restricted vehicular movement, and a saturated official guardianship presence can suppress both opportunistic property offenses and interpersonal violence. At the same time, the social disorganization perspective suggests that shared threat experiences can galvanize neighborly cohesion, with residents monitoring one another’s properties and coordinating informally during and after evacuations. The study cannot adjudicate between these mechanisms, but the spatial pattern it documents, with effects strongest near rather than within fire perimeters, is consistent with guardianship and mobility explanations operating at the edge of disaster zones.

The findings also connect to a broader and rapidly growing research program on environment, green space, and crime. Recent work has linked urban greenspace to lower crime risk across hundreds of major U.S. cities, examined park size and community safety, and used quasi-experimental designs to test whether urban greenways such as the Atlanta Beltline reduce offending. Related studies have explored how drought conditions relate to violent crime trends in California counties and how natural hazards reshape spatiotemporal crime patterns nationally. The Los Angeles wildfire analysis adds a distinctive dimension to this literature: rather than a gradual environmental amenity, wildfire is an acute, spatially bounded shock, and its crime effects appear to radiate outward from the burn scar in measurable ways.

For practitioners, the implications are concrete. If crime genuinely declines in and around burned neighborhoods, then post-fire policing strategies premised on looting panics may be misdirected, diverting officers from communities where they are actually needed and potentially exposing evacuated areas to over-policing. Emergency response planning, similarly, could incorporate the finding that a quarter-mile buffer around fire perimeters functions as a distinct criminological zone, one where the priorities may be public safety and recovery support rather than crime suppression. For neighborhood recovery, the results hint that the social fabric of fire-adjacent communities may be more resilient than disaster folklore assumes, though the authors caution that their analysis captures associations over a defined study period rather than the full arc of long-term recovery.

The authors are explicit about the limits of their design and the directions it opens. Their data cover seven wildfires within one city over seven years, and census tracts, while useful units of neighborhood analysis, aggregate individual behavior into coarse spatial bins. The proximity threshold of a quarter mile is one plausible buffer among many, and future work could test whether the effect decays smoothly with distance or exhibits spatial spillovers of the kind modeled in wildfire risk mitigation research. Longer follow-up periods could reveal whether suppressed crime rebounds as neighborhoods rebuild, whether reconstruction and gentrification dynamics, of the kind documented after the 1994 Northridge earthquake in Los Angeles, reshape local crime trajectories, and whether the patterns hold in other fire-prone regions. As climate change intensifies fire regimes across the American West, understanding how wildfires reshape not only landscapes but also the social order of the neighborhoods that survive them is becoming an urgent scientific question, and this study provides one of the clearest spatial maps yet of where that reshaping begins.

Subject of Research: The association between neighborhood wildfire exposure and violent and property crime patterns in Los Angeles

Article Title: We Didn’t Start the Fire: How Neighborhood Crime Patterns in the City of Los Angeles Were Associated with Wildfires

Article References: Barton, M. S., Valasik, M., LaPointe, R., & Lopez, S. (2026). We Didn’t Start the Fire: How Neighborhood Crime Patterns in the City of Los Angeles Were Associated with Wildfires. American Journal of Criminal Justice. https://doi.org/10.1007/s12103-026-09955-7

Image Credits: AI Generated

DOI: 10.1007/s12103-026-09955-7

Keywords: wildfires, crime, Los Angeles, violent crime, property crime, social disorganization theory, routine activities approach, disasters, criminology, neighborhoods, negative binomial regression, emergency response

The research team, led by Afra Tanjim Shammi, Prosun Kumar Ghosh, and Md Rony Golder, surveyed 561 adult park users between April 2021 and March 2022. Respondents were recruited on-site across three seasons using a standardized intercept procedure, with only one adult interviewed per visiting group to reduce selection bias. The visitor profile was distinctly urban and educated: 96.3 percent came from urban areas, 95.4 percent were local residents, and more than 86 percent held a bachelor’s degree or higher. Nearly 60 percent of respondents were aged 26 to 35, and just over half were employed. Household incomes clustered in the 30,001 to 50,000 Taka range, with a mean of about 45,684 Taka per month. This demographic picture suggests the park primarily serves a socio-economically stable, middle-aged urban population seeking respite from city life, a pattern consistent with other Bangladeshi urban parks.

Why do people come? The answer, overwhelmingly, is fresh air and relaxation, cited by 57.9 percent of respondents, followed by children’s play at 28.9 percent. Facility ratings were generally positive but pointed to room for improvement. Sports facilities were rated moderate by nearly 90 percent of visitors, walking paths were rated moderate or excellent by all but a handful, and solid waste management and security each earned moderate-to-excellent marks from virtually everyone surveyed. These perceptions matter because they capture the cultural ecosystem services that urban green spaces provide, benefits that are notoriously difficult to quantify but that underpin public support for maintaining parks in rapidly urbanizing cities.

When asked about the park’s environmental services, visitors showed a clear hierarchy of awareness. Nearly all respondents agreed that the park improves air quality, regulates the local climate, provides habitat for plants and birds, offers aesthetic and scenic value, and contributes oxygen-related benefits. These are the visible, directly experienced services that people encounter every time they walk beneath the trees. In contrast, recognition dropped sharply for less tangible functions: perceived contributions to water purification, pest regulation, and spiritual or religious value were low, with many respondents expressing uncertainty or no opinion. The authors interpret this gap not as evidence that such services are absent, but as a sign that the public underestimates indirect ecosystem processes, a finding that argues for stronger environmental education alongside biophysical monitoring.

The study also ventured into the park’s two ponds, collecting 90 surface-water samples across summer, monsoon, and winter to assess whether the water bodies meet Bangladesh’s recreational water-quality standards. Most parameters told a reassuring story. Total dissolved solids stayed well below the 1,000 milligrams per liter guideline, salinity remained low except for seasonal winter peaks, and pH values largely fell within the acceptable range of 6 to 9. Dissolved oxygen generally met the minimum threshold of 5 milligrams per liter, though one monsoon reading dipped slightly below it, and a single summer biochemical oxygen demand measurement in Pond 1 marginally exceeded the 3 milligrams per liter limit. These were localized, seasonal excursions rather than chronic failures.

The standout problem was chemical oxygen demand, or COD, a measure of the total oxidizable organic and inorganic matter in water. Bangladesh’s Environmental Conservation Rules set a guideline of no more than 10 milligrams per liter for recreational waters, but the park’s ponds recorded values ranging from roughly 100 to 590 milligrams per liter, with the most extreme readings in Pond 1 during the monsoon. The authors attribute this surge to rainfall-driven runoff carrying domestic wastewater, organic debris, and urban pollutants into the ponds, compounded by the resuspension of organically enriched bottom sediments under hydraulic disturbance. Because the sampling design involved only two ponds with spatial subsamples rather than independent replicates, the results are descriptive and site-specific, but the seasonal pattern is a clear warning sign for park managers.

To translate visitation into monetary value, the researchers turned to the individual travel cost method, a revealed-preference technique that treats the money and effort visitors spend reaching a site as a proxy for what the site’s recreational benefits are worth to them. Because the number of annual visits is a non-negative count, ordinary linear regression was inappropriate. A Poisson model was tried first but suffered severe overdispersion, with a dispersion ratio of nearly 55, meaning the variance in visitation far exceeded the mean. The team therefore adopted a negative-binomial regression, which explicitly models that extra variability. Model comparison statistics strongly favored this choice: the negative-binomial specification improved the log likelihood from about minus 12,905 to minus 2,396 and slashed the Akaike information criterion from over 25,800 to roughly 4,807.

The econometric results followed economic theory closely. Travel cost carried a significant negative coefficient, meaning that as the expense of reaching the park rose, visitation fell. Household income, by contrast, was positively and significantly associated with visit frequency, while expenditure, age, gender, and education showed no independent effects once other variables were controlled. From the travel-cost coefficient of minus 0.006436, the researchers calculated a consumer surplus of 155.38 Taka per visit, with a 95 percent confidence interval of 118.06 to 192.70 Taka. Multiplying this by the estimated 10,689 annual visits, derived from 15 days of entrance-gate counts per season extrapolated across the year, yielded the headline annual recreational value of approximately 1.66 million Taka. A sensitivity analysis spanning plausible visitation ranges and the confidence interval for consumer surplus bounded the aggregate value between 1.16 and 2.22 million Taka per year.

The authors are candid about their limitations. The exact day-by-day visitor count series was not retained in the archived dataset, so the annual visitation estimate rests on seasonal summaries rather than a fully reconstructable record, and the aggregate value is presented as a deterministic sensitivity range rather than a formal confidence interval. Travel time was not monetized separately because most visitors were local, and the questionnaire, while pilot-tested and carefully administered, did not undergo formal psychometric validation. An exact response rate could not be calculated because refusals were not systematically logged. These caveats do not undermine the central findings, but they do mean the figures should be read as a well-grounded baseline for one site rather than a universal benchmark.

What elevates the study beyond a single park’s accounting is its proposed path forward. The authors sketch the concept of an Entry Charge Index, a composite tool that would combine normalized water-quality indicators, facility and service ratings, and an affordability component derived from visitor socio-economics to help park authorities set transparent, evidence-based fee tiers. Applied across multiple parks, such a framework could transform scattered site assessments into a comparable national picture of what urban green spaces are worth. For a country where unplanned urbanization is eroding green cover at an alarming pace, demonstrating that even a modest neighborhood park delivers measurable ecological, social, and economic returns is a powerful argument for protecting what remains. Jashore Pouro Park, it turns out, is worth far more than the sum of its ponds and pathways, and now there are numbers to prove it.

Subject of Research: Economic valuation of ecosystem services and recreational water quality in an urban park in Jashore, Bangladesh

Article Title: Assessing environmental services and recreational value of Jashore Pouro Park in Jashore district of Bangladesh

Article References: Shammi, A. T., Ghosh, P. K., & Golder, M. R. (2026). Assessing environmental services and recreational value of Jashore Pouro Park in Jashore district of Bangladesh. Heliyon, 12(15), Article e45497. https://doi.org/10.1016/j.heliyon.2026.e45497

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45497

Keywords: urban parks, ecosystem services, travel cost method, recreational value, water quality, Bangladesh, Jashore, consumer surplus, negative binomial regression, monsoon pollution, urban green space, cultural ecosystem services

The central conceptual move is a sharp separation between what is observed and what is inferred. For each unit i, the method defines a parameter-conditional tail functional, the probability that a replicated count drawn from the fitted model would exceed a threshold, given that unit’s covariates. Averaging this functional over the posterior distribution of the model parameters yields the posterior predictive exceedance probability, the quantity used for ranking and calibration. A large realized count, the paper stresses, is informative but is not itself the inferential target: a person with moderate observed utilization may still carry high replicated-count risk once health status, insurance characteristics, and model uncertainty are accounted for. Conversely, an isolated spike in the data may reflect contamination rather than genuine tail risk.

The working model is a negative binomial regression, a standard choice for overdispersed counts because it relaxes the restrictive variance assumption of the Poisson model while retaining an interpretable conditional mean. The robustness, however, comes from an additional layer. Each observation receives a positive latent scale that locally inflates or deflates its mean, drawn from a two-component mixture: a point mass at one, which preserves the ordinary negative binomial baseline, and a Beta-prime slab with substantial mass near zero and a polynomially decaying right tail. This construction, adapted from recent robust Bayesian count modeling, allows excess zeros and isolated large counts to be absorbed by the local scale rather than forcing them to distort the regression coefficients or the fitted tail probabilities. In effect, unusual observations are quarantined instead of being allowed to hijack the analysis.

The theory is deliberately framed for the realistic case where the model is wrong. Rather than assuming the data follow the negative binomial family exactly, the contraction results are stated around the Kullback–Leibler projection of the true law onto the working family — a pseudo-true parameter that represents the best available approximation. Following the classical misspecification perspective of Berk and of Kleijn and van der Vaart, the posterior is shown to concentrate near this projection, and a Lipschitz transfer argument shows that concentration in parameters carries over to uniform concentration of the exceedance functionals themselves. Pinsker’s inequality then bounds the error between true tail probabilities and the working-model targets by the Kullback–Leibler discrepancy, giving a quantitative account of how much misspecification can distort screening decisions.

Discovery, as opposed to ranking, is handled by a separate layer built on e-values. Posterior thresholding of the evidence probability — the posterior probability that a unit’s exceedance probability exceeds a chosen level gamma — is a natural local Bayes rule under a loss function penalizing false positives and false negatives, but it does not by itself control the false discovery rate across many simultaneous tests. The paper therefore converts posterior odds into Bayes factors and submits them to the e-BH procedure of Wang and Ramdas, which controls the false discovery rate under arbitrary dependence among the test statistics. The validity of this step is stated with unusual care: under the Bayes-marginal null induced by the same hierarchical model, posterior odds are Bayes factors and hence valid e-values with null expectation at most one. Under a mere working-model interpretation, the same quantities are evidence scores unless calibrated, and the author includes an approximate-validity result in which the FDR bound is inflated by a calibration error term. This honesty avoids the common pitfall of treating a parametric Bayesian fit as automatically producing model-free frequentist guarantees.

Computation is provided at three levels of fidelity. A reference Markov chain Monte Carlo sampler uses Pólya–Gamma augmentation to update the regression coefficients in a single Gaussian block, with slice sampling and adaptive random-walk Metropolis steps for the latent scales, dispersion parameter, and slab hyperparameters; convergence is monitored with rank-normalized split R-hat statistics and effective sample size thresholds. For large datasets and repeated simulations, a bounded-influence iteratively reweighted least squares Laplace approximation downweights extreme Pearson residuals and high-leverage observations, producing a Gaussian approximate posterior from which the exceedance summaries can be drawn directly. A mean-field variational option offers fast screening. A theorem bounds the error of any such approximation: the discrepancy between exact and approximate posterior exceedance probabilities is controlled by the total variation distance between the two posteriors, which in turn is bounded by a square root of the Kullback–Leibler divergence between them.

The simulation evidence demonstrates a clear and quantified robustness–efficiency tradeoff. Under clean data, the ordinary negative binomial fit is slightly more efficient, with marginally higher true positive rates and smaller calibration error. Under contamination — engineered as a mixture of near-zero structural zeros and heavy upper-tail multiplicative contamination at rates of ten and twenty percent — the picture reverses dramatically. At ten percent contamination, the robust procedure cuts the false discovery proportion from 0.029 to 0.002 and halves the calibration error metrics. At twenty percent contamination and a sample size of one thousand, the naive method’s false discovery proportion climbs to roughly 0.099 while the robust method holds near 0.004, with expected calibration error of about 0.090 versus 0.040. A more aggressive variant using Tukey’s biweight weights proves most conservative of all, illustrating that the degree of robustness tunes a power–conservatism dial rather than dominating uniformly. Reliability plots confirm that the robust fit tracks the clean oracle tail probabilities across the range, while the naive fit inflates the upper bins under contamination.

The real-data application turns to the RAND Health Insurance Experiment, a classic dataset of 20,190 observations on outpatient physician visits, adjusted for insurance plan, income, physical limitation, chronic disease burden, and self-rated health. Screening for individuals whose replicated visit count exceeds eight visits, with a discovery level of 0.3 and e-BH applied at a false discovery rate of 0.1, both fits select subgroups whose observed exceedance rates — 0.339 for the naive fit and 0.315 for the robust fit — tower over the full-sample rate of 0.071. The robust fit selects a somewhat larger set while shrinking tail probabilities more aggressively over most of the sample, yet still assigns high risk to individuals with severe health profiles. A training–holdout split with 2,000 posterior predictive replicates showed the observed zero rate falling at the edge of its predictive interval, while the mean count and exceedance rate exceeded theirs — a candid indication that the working model remains conservative in the upper tail and not fully distributionally adequate.

Perhaps the most practically important feature of the paper is its insistence on reporting three distinct outputs separately: calibrated tail-risk rankings, posterior evidence scores, and multiplicity-controlled discovery sets. Some individuals selected by the procedure did not actually exceed eight visits in the observed record, which the author presents as expected behavior rather than a failure — the method targets replicated-count risk conditional on covariates and model uncertainty, not the realized indicator alone. The selected individuals are characterized by physical limitation and high chronic-disease burden, exactly the covariate profiles one would associate with latent high utilization. The paper also warns against a subtle computational trap: e-value caps that are too small can mechanically prevent the e-BH procedure from making any rejections, so decision steps must use uncapped Bayes factors or caps compatible with the rejection thresholds.

Taken together, the study offers a transparent template for combining Bayesian tail modeling, robust count regression, and e-value-based multiple testing in settings where the data are messy, overdispersed, and possibly contaminated. The author suggests that future work may extend the framework to richer dependence structures, more flexible count families, and sequential e-value methods for online monitoring — a natural next step for applications ranging from epidemiological surveillance to industrial quality control, where the question is not merely how many events occur, but who is genuinely at risk of crossing the line.

Subject of Research: Robust Bayesian exceedance screening and false discovery rate control for overdispersed count data

Article Title: Robust Bayesian exceedance screening for count data

Article References: Sadeghkhani, A. (2026). Robust Bayesian exceedance screening for count data. International Journal of Data Science and Analytics, 22(1), Article 323. https://doi.org/10.1007/s41060-026-01312-5

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01312-5

Keywords: Bayesian inference, exceedance probability, e-values, negative binomial regression, robust statistics, false discovery rate, count data, Kullback–Leibler projection, RAND Health Insurance Experiment, multiple testing, posterior contraction, health utilization

The research team, led by Cristian Candia of Universidad del Desarrollo and Northwestern University, together with Mohammad Atari of the University of Massachusetts Amherst and Nour Kteily and Brian Uzzi of Northwestern University, examined 1,621,147 observations spanning 13 socio-political topics. The corpus included 530,104 Twitter posts, 1,048,653 Reddit submissions and 42,390 posts from 8chan, giving the analysis an unusually broad cross-platform scope. All content was drawn from publicly accessible archives, and all results were reported in aggregate at the platform-by-topic level, with no user identifiers retained.

Central to the study was a methodological innovation in how moral language is quantified. Rather than simply counting moral words, the researchers used Distributed Dictionary Representations, a technique that scores word embeddings against an expert-validated moral dictionary. This approach captures the semantic proximity of any word to moral concepts, allowing the team to detect moral content even when it is expressed indirectly. Crucially, the method distinguishes two separate properties of a post: moral loading, the overall degree of moral relevance, and moral density, the concentration of moral content across the words used.

That distinction proved decisive. Using negative-binomial regression models appropriate for count-based engagement data, the team found that moral loading was positively associated with engagement across all platforms and topics, with effect sizes ranging from 1.12 to 9.07, all statistically significant. In other words, posts that were more morally relevant overall did attract more attention, confirming the well-established moral contagion effect. But conditional on that level of moral loading, moral density showed the opposite relationship: the more concentrated the moral vocabulary within a post, the lower the engagement, with coefficients ranging from −4.71 to −0.40.

The relationship between density and engagement was not simply linear. Instead, the models revealed a clear peak: engagement was maximized at a moral density of approximately 0.30. Below that threshold, engagement dropped by a factor of 2.28; above it, engagement fell even more steeply, by a factor of 2.78. The pattern held consistently across the three platforms, which differ markedly in culture, moderation norms and user demographics, suggesting the effect reflects a general property of how audiences process moralized messages rather than an artifact of any single online community.

The authors interpret the penalty through the lens of cognitive processing. Highly moral-dense posts may impose a heavier reading burden, triggering the kind of processing friction that previous work on fluency has linked to reduced persuasion and sharing. There is also the possibility of habituation: just as repeated exposure to extreme stimuli dulls emotional response, a wall of moral rhetoric may exhaust rather than mobilize readers. A mediation analysis using cognitive-processing language measures from LIWC was conducted to probe this mechanism, though the researchers are careful to frame it as exploratory rather than definitive proof of causation.

The findings arrive amid an ongoing scholarly debate about moral contagion. Earlier influential studies reported that each additional moral-emotional word in a tweet increased its diffusion rate, but a 2021 reanalysis in Nature Human Behaviour questioned the robustness of that effect, and a 2025 pre-registered replication and meta-analysis sought to pin down the true effect size. The new study offers a potential resolution: moral language may indeed promote engagement on average, but only within a bounded range. Studies that pool posts across the full density spectrum could obscure the penalty that kicks in at high concentrations, helping explain why different teams have reached different conclusions.

Methodologically, the study demonstrates the value of embedding-based measurement over simple dictionary counts. The team validated their Distributed Dictionary Representation approach against alternatives, including transformer-based sentence embeddings and the publicly released MoralBERT models, and the core results survived these comparisons. The replication package, built in R and Python with documented package versions, is publicly available on the Open Science Framework, though it deliberately excludes verbatim post text and user identifiers to prevent re-identification of individuals. The researchers note that the study involved retrospective analysis of public content only, with no intervention in human participants’ behaviour.

The practical implications extend to anyone who crafts online messages, from activists and journalists to political campaigns and platform designers. The data suggest a counterintuitive strategy: moral framing works best when it is present but restrained. A post that signals moral stakes with moderate density can ride the engagement advantage of moral relevance, while one that saturates every sentence with moral condemnation risks triggering the penalty. For platforms whose recommendation algorithms amplify morally charged content, the results hint that not all moralization is equal, and that saturation may be a signal of diminishing, or negative, returns.

Caveats remain. The analysis is observational, so it identifies associations between language patterns and engagement rather than experimentally established causes. Engagement metrics themselves are shaped by platform algorithms, and the topics studied were socio-political, leaving open questions about whether the same density peak applies to non-political moral discourse. Still, by separating how much moral content a post contains from how densely that content is packed, the study reframes a decade of moral contagion research around a single elegant idea: when it comes to moral language online, there can be too much of a good thing.

Subject of Research: The relationship between moral language saturation and user engagement on social media platforms

Article Title: Saturation of moral language predicts lower content engagement on social media

Article References: Candia, C., Atari, M., Kteily, N., & Uzzi, B. (2026). Saturation of moral language predicts lower content engagement on social media. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02560-y

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02560-y

Keywords: moral language, social media, engagement, moral contagion, overmoralization, word embeddings, Twitter, Reddit, 8chan, negative-binomial regression, computational social science, Nature Human Behaviour

The research, led by Ryota Ideno and colleagues at Tokyo University of Science, addresses a pressing problem for developed nations: while much of the world urbanizes rapidly, many mature cities are shrinking. Population decline and de-densification raise fears of economic deterioration, declining safety as vacant land and abandoned houses accumulate, and the fraying of local communities. Against this backdrop, green spaces have attracted attention not merely as recreational amenities but as strategic urban assets. They promote physical activity, reduce stress, provide venues for social interaction, and can stabilize surrounding land values while restoring degraded landscapes. The question the researchers posed was deceptively simple: does more green cover around a station actually translate into more people staying there?

Answering it required measuring both sides of the equation objectively. On the greenery side, the team turned to the European Space Agency’s Sentinel-2 satellite, whose multispectral sensor captures light from the visible to the shortwave infrared. Vegetation betrays itself spectrally because healthy leaves reflect near-infrared light strongly while absorbing red light. The normalized difference vegetation index, or NDVI, exploits this contrast by computing the difference between near-infrared and red reflectance divided by their sum, yielding a value that rises with vegetation density. The researchers calculated NDVI for every pixel within an 800-meter radius of each station, a distance corresponding to roughly a ten-minute walk and the standard unit of Japan’s transit-oriented planning policy.

Converting NDVI values into a clean vegetation map required choosing a threshold, a decision with real consequences. Set the cutoff too low, and bare soil, paved surfaces, or even water bodies get misclassified as green. Set it too high, and sparse urban vegetation such as lawns and scattered street trees slips through undetected. The team tested five thresholds, from 0.20 to 0.40, against ground-truth maps created by visually interpreting high-resolution aerial imagery for 20 randomly selected station areas spanning dense downtown cores, suburbs, and waterfront districts. They evaluated each cutoff using precision, recall, and the F-measure, the harmonic mean of the two. Precision climbed as the threshold rose while recall fell, tracing the classic trade-off between false positives and false negatives. The F-measure peaked at 0.721 with a threshold of 0.30, which the researchers adopted for all subsequent analysis, a value consistent with vegetation classification studies elsewhere.

With vegetation pixels identified, the team computed the green coverage ratio, the proportion of each catchment area classified as green. Across the 440 station areas, this ratio ranged from a mere 0.2 percent to a lush 42.1 percent, averaging 10.1 percent, a spread that captures Osaka’s full spectrum from concrete-dominated commercial hubs to leafy residential districts. Because the goal was to measure urban greenery specifically, the analysis focused on densely built-up areas identified through national land use mesh data, allowing the satellite approach to capture fine-scale vegetation, such as street trees and small plantings, that conventional administrative green space datasets routinely miss.

Measuring human presence required a different kind of data entirely. The researchers drew on the KDDI Location Analyzer, a platform that anonymizes and statistically processes mobile phone base station logs from one of Japan’s largest carriers. Rather than counting people simply passing through, the study defined the staying population as the average daily number of non-resident visitors who remained within a station catchment area for at least 15 minutes during a one-week window in early October 2018. The 15-minute minimum filters out transient pass-through movements such as simple transfers, isolating purposeful activity like shopping, dining, and socializing that constitutes genuine urban vibrancy. The week was deliberately chosen to avoid national holidays and major events, capturing a representative baseline of station-area life.

To link greenery to footfall without being fooled by confounding factors, the team built negative binomial regression models, a statistical framework suited to count data whose variance exceeds its mean, as visitor counts notoriously do. The models controlled for an array of urban characteristics, including resident population, commercial zoning, the density of commercial facilities, and the number of bus stops, with multicollinearity checked through variance inflation factors, all of which fell safely below the conventional threshold of concern. The result was striking: the green coverage ratio carried a significant positive coefficient, indicating that greenery attracts visitors independently of commercial function and accessibility. Quantitatively, a one percentage-point increase in green coverage was associated with roughly a 1.34 percent increase in the staying population, equivalent to adding about 20,000 square meters of urban green space to lift visiting numbers by approximately 1.35 percent.

The subgroup analyses added a layer of nuance with implications for aging societies. Splitting the data by gender and by six age cohorts, from people in their twenties to those in their seventies and older, the researchers found that the effect of greenery remained remarkably stable across generations, with coefficients only slightly larger for younger groups. Commercial zones, by contrast, told a different story: they exerted strong, highly significant attraction on younger demographics, but their influence faded noticeably among older visitors. Although statistical significance for the green coverage variable weakened in the subdivided models, a likely artifact of reduced statistical power in smaller samples, the consistent direction and magnitude of the estimates suggest that the benefits of greenery are experienced broadly across age groups, while commercial vibrancy caters disproportionately to the young.

The authors are candid about the limitations of their approach. The green coverage ratio is a top-down, area-based measure that says nothing about whether the vegetation is publicly accessible, well maintained, or pleasant to sit beneath; perceived greenery at the eye level of a pedestrian may matter as much as the raw quantity of leaves visible from orbit. The cross-sectional design also cannot establish causality, and a single week of autumn mobility data leaves open questions about seasonal variation and long-term trends. Future work, the researchers suggest, could fuse satellite data with street-level imagery and deep learning to quantify perceived greenness, apply spatial econometric models to capture spillover effects between neighboring stations, and exploit before-and-after comparisons around new park developments to test causal claims.

Even with those caveats, the study’s central message lands with force. As railway stations evolve from bare transit nodes into destinations designed to encourage people to stay, green infrastructure emerges as a complementary lever to commercial development, one whose appeal spans generations. For cities contending with shrinking populations, the finding reframes planting trees and preserving vegetation not as cosmetic expenditure but as measurable infrastructure: every additional hectare of green around a station is statistically associated with more people choosing to spend time there. In an era when cities compete for residents, visitors, and vitality, the view from 800 kilometers above suggests that the path to livelier streets may run through the leaves.

Subject of Research: The effect of satellite-measured urban green space on visiting populations around railway stations in Osaka, Japan

Article Title: Effects of urban green spaces on visiting population of railway station areas

Article References: Ideno, R., Terabe, S., Yaginuma, H., Ajito, M., & Inagaki, K. (2026). Effects of urban green spaces on visiting population of railway station areas. Discover Cities, 3(1), Article 186. https://doi.org/10.1007/s44327-026-00354-5

Image Credits: AI Generated

DOI: 10.1007/s44327-026-00354-5

Keywords: urban green space, railway stations, Sentinel-2, NDVI, green coverage ratio, mobile phone location data, urban vibrancy, staying population, negative binomial regression, Osaka, transit-oriented development, remote sensing

The study, led by Philipp Yorck Herzberg of Helmut-Schmidt-University in Hamburg together with colleagues at the European Distance University Hamburg and the University of Bern, set out to answer a question posed nearly thirty years earlier by Dean and Malamuth. Those researchers had found in 1997 that the interaction between hostile masculinity and impersonal sexuality robustly predicted sexual aggression only among men high in dominance and low in nurturance, a pattern they interpreted as possibly driven by empathy. Nurturant individuals, after all, tend to be warm, gentle and sensitive to others’ needs, traits closely linked to empathic capacity. But the question remained whether empathy itself was the critical protective ingredient, or merely one facet of the broader dominance-nurturance dimension.

To disentangle the two, the researchers recruited 300 heterosexual men aged 18 to 65 through online advertisements on social networks and internet forums, without disclosing that the survey concerned sexual aggression. Participants completed the German version of the Sexual Experience Survey, a ten-item count of sexually coercive behaviors ranging from verbal manipulation to physical force or incapacitation through alcohol or drugs. Hostile masculinity was measured with the Hostile Sexism Scale of the Ambivalent Sexism Inventory, impersonal sexuality with the revised Sociosexual Orientation Inventory, and dominance and nurturance with the revised Bem Sex-Role Inventory. Crucially, empathy was assessed with the German adaptation of Davis’s Interpersonal Reactivity Index, which captures perspective taking, fantasy, empathic concern and personal distress.

Statistically, the team faced a technical challenge. Sexual aggression scores are count variables, skewed toward zero, with roughly sixty percent of participants reporting no aggressive acts at all. Ordinary linear regression would violate the distributional assumptions of such data. The researchers therefore employed latent negative binomial regression models within a structural equation modeling framework, comparing three nested models: one in which empathy’s effect was constrained to zero, one in which dominance/nurturance’s effect was constrained to zero, and one in which both were freely estimated. Latent modeling also sidestepped the notorious unreliability of difference scores, which the dominance-nurturance variable required.

The results were unambiguous. Across all three models, hostile masculinity and impersonal sexuality emerged as significant positive predictors of sexual aggression, confirming their status as the model’s core risk factors. Empathy, by contrast, acted as a significant negative predictor: men higher in empathy reported lower rates of sexually aggressive behavior. In the model combining all predictors, each unit increase in empathy reduced the expected rate of sexual aggression by roughly a third, while each unit increase in hostile masculinity multiplied the expected rate by about 1.46 and impersonal sexuality by 1.25. The dominance/nurturance difference score, however, contributed nothing, neither directly nor in interaction with the two risk factors.

Model fit statistics told the same story. The model containing empathy but excluding dominance/nurturance achieved the best fit according to the Akaike information criterion, the Bayesian information criterion and sample-size-adjusted variants. Because dominance/nurturance showed no effect to begin with, there was no effect for empathy to replace or explain away, despite a substantial negative correlation of minus 0.52 between empathy and the dominance/nurturance score. The authors conclude that whatever predictive value the dominance-nurturance dimension once appeared to hold was likely attributable to its overlap with empathy, and that future confluence model research should replace it with direct empathy measures.

One finding added an unexpected layer of nuance. Although the hostile masculinity by impersonal sexuality interaction failed to reach significance in the count regression models, complementary response surface analysis confirmed a synergistic effect. Using a shifted and rotated squared difference model, the analysis revealed that risk climbs steeply and nonlinearly when hostile masculinity is high, even when impersonal sexuality is only moderate. The interaction, in other words, is asymmetric and driven primarily by hostile masculinity. The authors caution that previous studies reporting interaction effects with ordinary linear regression may have overestimated them, and that appropriate count regression techniques may simply make interactions harder to detect, making the response surface approach a valuable complement.

Why does empathy matter so much? The authors suggest that when a man can take the perspective of a potential victim and imagine how coercive behavior would feel from her side, the behavior becomes less likely to be carried out, independently of his hostility toward women or his preference for uncommitted sex. The prevalence figures underline how rare protective combinations are. The high-risk profile of high hostile masculinity, high impersonal sexuality and low empathy appeared in only 5.7 percent of the sample, whereas the opposite profile combining all three risk traits with high empathy occurred in fewer than one percent of participants, a scarcity that may also explain why the three-way interaction just failed to reach statistical significance.

The practical implications reach into prevention. Interventions that merely attack hostile attitudes have often proven ineffective over time because the underlying traits are stable, and some programs may even provoke reactance and boomerang effects in high-risk men. Earlier work by Schewe and O’Donohue showed that programs targeting victim empathy can partially succeed in increasing empathic concern and reducing risk factors such as acceptance of interpersonal violence. The present findings strengthen the case for empathy-enhancing components in prevention programs aimed at high-risk men. At the same time, the open question of whether empathy operates as a moderator or as an independent protective factor has direct practical weight: if both pathways exist, different groups of aggressive men may require different interventions.

The study carries limitations that its authors freely acknowledge. The cross-sectional design rules out causal claims, and longitudinal work is needed to establish temporal ordering among the constructs. The sample, though spanning a broad adult age range, was restricted to heterosexual men and relatively small, and hostile masculinity was operationalized solely as hostility toward women rather than the full heterogeneous construct including sexual dominance. Replication in more diverse populations and with broader measures is essential. Nevertheless, the message is clear and consequential. Within the confluence model, hostile masculinity and impersonal sexuality remain the drivers of risk, dominance/nurturance should step aside, and empathy emerges as a measurable, modifiable protective factor that may be the most promising lever for reducing sexual aggression.

Subject of Research: The roles of empathy and dominance/nurturance in predicting male sexual aggression within the confluence model

Article Title: The Critical Role of Empathy Over Dominance/Nurturance in Predicting Sexual Aggression in the Confluence Model

Article References: Herzberg, P. Y., Brunner, H., Wildfang, S., & Troche, S. J. (2026). The Critical Role of Empathy Over Dominance/Nurturance in Predicting Sexual Aggression in the Confluence Model. Archives of Sexual Behavior, 55(6), 2627-2641. https://doi.org/10.1007/s10508-026-03510-5

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03510-5

Keywords: confluence model, sexual aggression, empathy, hostile masculinity, impersonal sexuality, dominance/nurturance, Bem Sex-Role Inventory, negative binomial regression, response surface analysis, prevention programs, Critical, Role

Researchers at the Intensive Care Unit of Hospital Metropolitano Doutor Célio de Castro retrospectively analyzed 178 adult patients who completed brain death protocols between December 2017 and December 2023. Of these, 141 patients underwent transcranial Doppler ultrasonography, or TCD, as the complementary exam, while 37 were evaluated with electroencephalography or cerebral angiography. The findings, published in the journal Neurocritical Care, show that TCD use was independently associated with a 53.9 percent reduction in the median time needed to complete the brain death protocol, a difference so large that it reshapes the logistics of end-of-life care and organ procurement.

The numbers are striking. In the TCD group, the median protocol duration was nine hours, with an interquartile range of five to twenty-three hours. In the non-TCD group, the median stretched to thirty-five hours, with an interquartile range of twenty to sixty-six hours. After adjusting for age, sex, comorbidity burden measured by the Charlson Comorbidity Index, illness severity measured by the Simplified Acute Physiology Score III, calendar year, and whether the protocol was opened during business hours or after hours, TCD use carried an incidence rate ratio of 0.46, meaning the protocol took less than half as long. The only other independent predictor of protocol duration was whether the process began during business hours, underscoring how staffing availability shapes this delicate timeline.

Transcranial Doppler ultrasonography is a bedside technique that uses low-frequency ultrasound pulses through the temporal bone and other acoustic windows to measure blood flow velocity in the major arteries of the circle of Willis, including the middle cerebral, anterior cerebral, and basilar arteries. The technique was first described in 1982, when researchers demonstrated that flow velocity in the basal cerebral arteries could be recorded noninvasively. In the context of brain death, rising intracranial pressure progressively exceeds the perfusion pressure, producing a characteristic sequence of Doppler waveforms: first a blunting of diastolic flow, then oscillating or reverberating flow in which blood moves back and forth with each heartbeat without net forward perfusion, then tiny systolic contraction spikes, and finally the disappearance of detectable flow altogether. Demonstration of these arrest patterns in both anterior and posterior circulations, on two examinations separated by an interval defined by national guidelines, provides evidence of cerebral circulatory arrest.

The diagnostic credentials of the technique are well established. A meta-analysis published in Intensive Care Medicine in 2006 reported high specificity for confirming brain death, and a subsequent systematic review and meta-analysis in the American Journal of Neuroradiology reached similar conclusions, with reported sensitivity around 88 percent and specificity approaching 98 percent in pooled analyses. Brazilian guidelines issued in 2012 formally endorsed transcranial ultrasound as a confirmatory test for brain death, and the country’s Federal Council of Medicine incorporated it into the regulatory framework through Resolution 2,173 of 2017. Unlike angiography, TCD requires no transport to a radiology suite, no iodinated contrast, and no exposure of potentially donor organs to nephrotoxic agents. Unlike electroencephalography, it does not require prolonged recording sessions or interpretation delays, and it sidesteps the technical confounders of electrical artifacts in the intensive care environment.

The efficiency gains extended beyond the protocol itself. Length of stay in the intensive care unit after protocol initiation was significantly shorter in the TCD group, with a median of one day compared with two days in the non-TCD group, and an adjusted incidence rate ratio of 0.51. In a health system where intensive care beds are chronically scarce and expensive, each day of occupancy by a patient who has already died by neurologic criteria carries an opportunity cost measured in the patients waiting for that bed. The researchers also examined whether the choice of confirmatory exam influenced organ donation, and found that it did not: donation rates were statistically indistinguishable between the groups, at 35 percent in the TCD group and 38 percent in the non-TCD group. Faster protocols, in other words, did not come at the expense of donation outcomes, though they also did not visibly increase them in this cohort.

The study’s statistical approach was deliberately conservative. Because protocol duration and length of stay are skewed, count-like variables, the team used negative binomial regression rather than ordinary linear models, adjusting for the full panel of demographic, clinical, and logistical covariates. The groups were comparable at baseline in age, comorbidity, severity scores, and sex, with all baseline comparisons showing no significant differences. Supplementary correlation analyses reinforced the main findings: TCD use and business-hours protocol opening showed the strongest negative correlations with protocol duration, while illness severity, age, and comorbidity played smaller or negligible roles. Notably, the underlying diagnoses differed between groups, with subarachnoid hemorrhage more frequent among patients who did not receive TCD, at 49 percent versus 26 percent, and ischemic stroke more frequent in the TCD group, at 28 percent versus 11 percent. This pattern likely reflects a practical reality of neurosonology: adequate acoustic windows are harder to obtain in some patients, and certain clinical scenarios push teams toward angiography or electroencephalography instead.

The authors are careful about the limits of their conclusions. This was a single-center, retrospective study at a Brazilian tertiary hospital where confirmatory testing is mandatory, and they caution that generalizability to settings without such requirements is limited. In countries where confirmatory tests are optional, the operational calculus differs, since the protocol can sometimes be completed with repeated clinical examinations alone. The findings also hinge on the availability of trained practitioners; TCD interpretation in the setting of suspected brain death demands specific expertise in recognizing the arrest waveforms and in distinguishing them from technical limitations such as poor insonation windows. The authors argue that their results support broader implementation of TCD in countries with mandatory confirmatory testing, contingent on that trained practitioner availability being secured.

The implications ripple outward to two of the most pressing issues in critical care. The first is organ donation, where every hour of delay increases the risk of hemodynamic instability, cardiac arrest, and loss of viable organs, and where families awaiting a definitive answer endure prolonged uncertainty at the bedside. The second is intensive care capacity, where protocols that conclude in nine hours rather than thirty-five return beds to the queue of critically ill patients faster. A bedside ultrasound exam that costs a fraction of a digital subtraction angiography study and requires no patient transport may thus be one of the highest-leverage tools available in the delicate interval between devastating brain injury and the formal pronouncement of death. As brain death determination frameworks worldwide undergo revision, including the recent consensus guideline from American neurologic and critical care societies, this Brazilian dataset offers a reminder that the operational details of how death is confirmed can matter almost as much as the criteria themselves.

Subject of Research: Use of transcranial Doppler ultrasonography as a confirmatory exam in brain death determination protocols

Article Title: Transcranial Doppler Ultrasonography as a Complementary Exam in Brain Death Determination: Impact on Protocol Duration and ICU Length of Stay—A Single-Center Study in Brazil

Article References: de Freitas, M. R., de Lima, A. F., & Coutinho, C. F. A. (2026). Transcranial Doppler Ultrasonography as a Complementary Exam in Brain Death Determination: Impact on Protocol Duration and ICU Length of Stay—A Single-Center Study in Brazil. Neurocritical Care. https://doi.org/10.1007/s12028-026-02641-w

Image Credits: AI Generated

DOI: 10.1007/s12028-026-02641-w

Keywords: transcranial Doppler, brain death, brain death protocol, organ donation, intensive care unit, cerebral circulatory arrest, neurocritical care, TCD ultrasonography, ICU length of stay, Brazil, confirmatory testing, negative binomial regression