Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties.

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. CONCLUSIONS: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

Privacy and contact tracing efficacy.

As the COVID-19 pandemic emerged, public health authorities and software designers considered the possibility that smartphones could be used for contact tracing to control disease spread. Smartphone-based contact tracing was attractive in part because it promised to allow the tracing of contacts that might not be reported using traditional contact tracing methods. Comprehensive contact tracing raises distinctive privacy concerns, however, that have not been previously explored. Contacts outside of an individual's ordinary social network are more likely to be privacy-sensitive, making fear that such contacts will be disclosed a potential disincentive to adoption of smartphone contact tracing. Here, we modify the standard SEIR infectious disease transmission model to incorporate contact tracing and perform a series of simulations aimed at studying the importance of tracing socially distant (and potentially privacy-sensitive) contacts. We find that, for a simple model network, ensuring that distant contacts are traced is surprisingly unimportant as long as contact tracing adoption is sufficiently high. These results suggest that policy-makers designing contact tracing systems should be willing to trade off comprehensiveness for more widespread adoption.

Triple contagion: a two-fears epidemic model.

We present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation. These behavioural dynamics in turn can amplify or suppress disease transmission, which feeds back to affect behaviour. The model reveals several coupled contagion mechanisms for multiple epidemic waves. Methodologically, the paper advances infectious disease modelling by including human behavioural adaptation, drawing on the neuroscience of fear learning, extinction and transmission.

Evidence for localization and urbanization economies in urban scaling.

We study the scaling of (i) numbers of workers and aggregate incomes by occupational categories against city size, and (ii) total incomes against numbers of workers in different occupations, across the functional metropolitan areas of Australia and the USA. The number of workers and aggregate incomes in specific high-income knowledge economy-related occupations and industries show increasing returns to scale by city size, showing that localization economies within particular industries account for superlinear effects. However, when total urban area incomes and/or gross domestic products are regressed using a generalized Cobb-Douglas function against the number of workers in different occupations as labour inputs, constant returns to scale in productivity against city size are observed. This implies that the urbanization economies at the whole city level show linear scaling or constant returns to scale. Furthermore, industrial and occupational organizations, not population size, largely explain the observed productivity variable. The results show that some very specific industries and occupations contribute to the observed overall superlinearity. The findings suggest that it is not just size but also that it is the diversity of specific intra-city organization of economic and social activity and physical infrastructure that should be used to understand urban scaling behaviours.

Cities and regions in Britain through hierarchical percolation.

Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North-South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density.

On the problem of boundaries and scaling for urban street networks.

Urban morphology has presented significant intellectual challenges to mathematicians and physicists ever since the eighteenth century, when Euler first explored the famous Königsberg bridges problem. Many important regularities and scaling laws have been observed in urban studies, including Zipf's law and Gibrat's law, rendering cities attractive systems for analysis within statistical physics. Nevertheless, a broad consensus on how cities and their boundaries are defined is still lacking. Applying an elementary clustering technique to the street intersection space, we show that growth curves for the maximum cluster size of the largest cities in the UK and in California collapse to a single curve, namely the logistic. Subsequently, by introducing the concept of the condensation threshold, we show that natural boundaries of cities can be well defined in a universal way. This allows us to study and discuss systematically some of the regularities that are present in cities. We show that some scaling laws present consistent behaviour in space and time, thus suggesting the presence of common principles at the basis of the evolution of urban systems.

Influence of provider and urgent care density across different socioeconomic strata on outpatient antibiotic prescribing in the USA.

OBJECTIVES: Despite a strong link between antibiotic use and resistance, and highly variable antibiotic consumption rates across the USA, drivers of differences in consumption rates are not fully understood. The objective of this study was to examine how provider density affects antibiotic prescribing rates across socioeconomic groups in the USA. METHODS: We aggregated data on all outpatient antibiotic prescriptions filled in retail pharmacies in the USA in 2000 and 2010 from IMS Health into 3436 geographically distinct hospital service areas and combined this with socioeconomic and structural factors that affect antibiotic prescribing from the US Census. We then used fixed-effect models to estimate the interaction between poverty and the number of physician offices per capita (i.e. physician density) and the presence of urgent care and retail clinics on antibiotic prescribing rates. RESULTS: We found large geographical variation in prescribing, driven in part by the number of physician offices per capita. For an increase of one standard deviation in the number of physician offices per capita there was a 25.9% increase in prescriptions per capita. However, the determinants of the prescription rate were dependent on socioeconomic conditions. In poorer areas, clinics substitute for traditional physician offices, reducing the impact of physician density. In wealthier areas, clinics increase the effect of physician density on the prescribing rate. CONCLUSIONS: In areas with higher poverty rates, access to providers drives the prescribing rate. However, in wealthier areas, where access is less of a problem, a higher density of providers and clinics increases the prescribing rate, potentially due to competition.

Constructing cities, deconstructing scaling laws.

Cities can be characterized and modelled through different urban measures. Consistency within these observables is crucial in order to advance towards a science of cities. Bettencourt et al. have proposed that many of these urban measures can be predicted through universal scaling laws. We develop a framework to consistently define cities, using commuting to work and population density thresholds, and construct thousands of realizations of systems of cities with different boundaries for England and Wales. These serve as a laboratory for the scaling analysis of a large set of urban indicators. The analysis shows that population size alone does not provide us enough information to describe or predict the state of a city as previously proposed, indicating that the expected scaling laws are not corroborated. We found that most urban indicators scale linearly with city size, regardless of the definition of the urban boundaries. However, when nonlinear correlations are present, the exponent fluctuates considerably.

Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: a study for land use in Europe.

Land change model outcomes are vulnerable to multiple types of uncertainty, including uncertainty in input data, structural uncertainties in the model and uncertainties in model parameters. In coupled model systems the uncertainties propagate between the models. This paper assesses uncertainty of changes in future spatial allocation of agricultural land in Europe as they arise from a general equilibrium model coupled to a spatial land use allocation model. Two contrasting scenarios are used to capture some of the uncertainty in the development of typical combinations of economic, demographic and policy variables. The scenario storylines include different measurable assumptions concerning scenario specific drivers (variables) and parameters. Many of these assumptions are estimations and thus include a certain level of uncertainty regarding their true values. This leads to uncertainty within the scenario outcomes. In this study we have explored how uncertainty in national-level assumptions within the contrasting scenario assumptions translates into uncertainty in the location of changes in agricultural land use in Europe. The results indicate that uncertainty in coarse-scale assumptions does not translate into a homogeneous spread of the uncertainty within Europe. Some regions are more certain than others in facing specific land change trajectories irrespective of the uncertainty in the macro-level assumptions. The spatial spread of certain and more uncertain locations of land change is dependent on location conditions as well as on the overall scenario conditions. Translating macro-level uncertainties to uncertainties in spatial patterns of land change makes it possible to better understand and visualize the land change consequences of uncertainties in model input variables.

Defining localities of inadequate treatment for childhood asthma: a GIS approach.

BACKGROUND: The use of Geographic Information Systems (GIS) has great potential for the management of chronic disease and the analysis of clinical and administrative health care data. Asthma is a chronic disease associated with substantial morbidity, mortality, and health care use. Epidemiologic data from all over the world show an increasing prevalence of asthma morbidity and mortality despite the availability of effective treatment. These facts led to the emergence of strategies developed to improve the quality of asthma care. THE OBJECTIVE: To develop an efficient tool for quality assurance and chronic disease management using a Geographic Information System (GIS). GEOGRAPHIC LOCATION: The southern region of Israel. January 1998 - October 2000. DATABASES: Administrative claims data of the largest HMO in Israel: drug dispensing registry, demographic data, Emergency Room visits, and hospitalization data bases. METHODS: We created a list of six markers for inadequate pharmaceutical treatment of childhood asthma from the Israeli clinical guidelines. We used this list to search the drug dispensing registry to identify asthmatic children who received inadequate treatment and to assess their health care utilization and bad outcomes: emergency room visits and hospitalizations. Using GIS we created thematic maps on which we located the clinics with a high percentage of children for whom the treatment provided was not in adherence with the clinical guidelines. RESULTS: 81% of the children were found to have at least one marker for inadequate treatment; 17.5% were found to have more than one marker. Children with markers were found to have statistically significant higher rates of Emergency Room visits, hospitalizations and longer length of stay in hospital compared with children without markers. The maps show in a robust way which clinics provided treatment not in accord with the clinical guidelines. Those clinics have high rates of Emergency Room visits, hospitalizations and length of stay. CONCLUSION: Integration of clinical guidelines, administrative data and GIS can create an efficient interface between administrative and clinical information. This tool can be used for allocating sites for quality assurance interventions.