The impact of abortion bans on short-term housing needs.

OBJECTIVES: State-level abortion bans in the United States have created a complex legal landscape that forces many prospective patients to travel long distances to access abortion care. The financial strain and logistical difficulties associated with travelling out of state for abortion care may present an insurmountable barrier to some individuals, especially to those with limited resources. Tracking the impact of these abortion bans on travel and housing is crucial for understanding abortion access and economic changes following the Dobbs U.S. Supreme Court decision. STUDY DESIGN: This study used occupancy data from an average of 2,349,635 (standard deviation = 111,578) U.S. Airbnb listings each month from October 1st, 2020, through April 30th, 2023, to measure the impact of abortion bans on travel for abortion care and the resulting economic effects on regional economies. METHODS: The study used a synthetic difference-in-differences design to compare monthly-level occupancy rate data from 1-bedroom entire-place Airbnb rentals within a 30-min driving distance of abortion clinics in states with and without abortion bans. RESULTS: The study found a 1.4 percentage point decrease in occupancy rates of Airbnbs around abortion clinics in states where abortion bans were in effect, demonstrating reductions in Airbnb use in states with bans. In the 6-month period post Dobbs, this decrease translates to 16,548 fewer renters and a $1.87 million loss in revenue for 1-bedroom entire-place Airbnbs within a 30-min catchment area of abortion facilities in states with abortion restrictions. CONCLUSION: This novel use of Airbnb data provides a unique perspective on measuring demand for abortion and healthcare services and demonstrates the value of this data stream as a tool for understanding economic impacts of health policies.

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.

Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.

BACKGROUND: Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. METHOD: A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS: Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS: Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.

Effect of environmental factors on the spatio-temporal patterns of influenza spread.

Although spatio-temporal patterns of influenza spread often suggest that environmental factors play a role, their effect on the geographical variation in the timing of annual epidemics has not been assessed. We examined the effect of solar radiation, dew point, temperature and geographical position on the city-specific timing of epidemics in the USA. Using paediatric in-patient data from hospitals in 35 cities for each influenza season in the study period 2000-2005, we determined 'epidemic timing' by identifying the week of peak influenza activity. For each city we calculated averages of daily climate measurements for 1 October to 31 December. Bayesian hierarchical models were used to assess the strength of association between each variable and epidemic timing. Of the climate variables only solar radiation was significantly related to epidemic timing (95% CI -0.027 to -0.0032). Future studies may elucidate biological mechanisms intrinsically linked to solar radiation that contribute to epidemic timing in temperate regions.

Extracting knowledge from dynamics in gene expression.

Most investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in genetic expression level. In doing so, we build upon methods developed in clinical informatics to detect temporal trends of laboratory and other clinical data. We construct relevance networks from Saccharomyces cerevisiae gene-expression dynamics data and find genes with related functional annotations grouped together. While some of these associations are also found using a standard expression level analysis, many are identified exclusively through the dynamic analysis. These results strongly suggest that the analysis of gene expression dynamics is a necessary and important tool for studying regulatory and other functional relationships among genes. The source code developed for this investigation is freely available to all non-commercial investigators by contacting the authors.

Comparing the similarity of time-series gene expression using signal processing metrics.

Many algorithms have been used to cluster genes measured by microarray across a time series. Instead of clustering, our goal was to compare all pairs of genes to determine whether there was evidence of a phase shift between them. We describe a technique where gene expression is treated as a discrete time-invariant signal, allowing the use of digital signal-processing tools, including power spectral density, coherence, and transfer gain and phase shift. We used these on a public RNA expression set of 2467 genes measured every 7 min for 119 min and found 18 putative associations. Two of these were known in the biomedical literature and may have been missed using correlation coefficients. Digital signal processing tools can be embedded and enhance existing clustering algorithms.

The autapse: a simple illustration of short-term analog memory storage by tuned synaptic feedback.

According to a popular hypothesis, short-term memories are stored as persistent neural activity maintained by synaptic feedback loops. This hypothesis has been formulated mathematically in a number of recurrent network models. Here we study an abstraction of these models, a single neuron with a synapse onto itself, or autapse. This abstraction cannot simulate the way in which persistent activity patterns are distributed over neural populations in the brain. However, with proper tuning of parameters, it does reproduce the continuously graded, or analog, nature of many examples of persistent activity. The conditions for tuning are derived for the dynamics of a conductance-based model neuron with a slow excitatory autapse. The derivation uses the method of averaging to approximate the spiking model with a nonspiking, reduced model. Short-term analog memory storage is possible if the reduced model is approximately linear and if its feedforward bias and autapse strength are precisely tuned.

Stability of the memory of eye position in a recurrent network of conductance-based model neurons.

Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic manner: below a threshold position the neuron is silent, and above it the firing rate is linearly related to position. Both the threshold and linear slope vary from neuron to neuron. We have reproduced this behavior in a biophysically plausible network model. Persistence depends on precise tuning of the strength of synaptic feedback, and a relatively long synaptic time constant improves the robustness to mistuning.