Per capita incidence of sexually transmitted infections increases systematically with urban population size: a cross-sectional study.

OBJECTIVES: Rampant urbanisation rates across the globe demand that we improve our understanding of how infectious diseases spread in modern urban landscapes, where larger and more connected host populations enhance the thriving capacity of certain pathogens. METHODS: A data-driven approach is employed to study the ability of sexually transmitted diseases (STDs) to thrive in urban areas. The conduciveness of population size of urban areas and their socioeconomic characteristics are used as predictors of disease incidence, using confirmed-case data on STDs in the USA as a case study. RESULTS: A superlinear relation between STD incidence and urban population size is found, even after controlling for various socioeconomic aspects, suggesting that doubling the population size of a city results in an expected increase in STD incidence larger than twofold, provided that all other socioeconomic aspects remain fixed. Additionally, the percentage of African-Americans, income inequalities, education and per capita income are found to have a significant impact on the incidence of each of the three STDs studied. CONCLUSIONS: STDs disproportionately concentrate in larger cities. Hence, larger urban areas merit extra prevention and treatment efforts, especially in low-income and middle-income countries where urbanisation rates are higher.

Assessing the long-term implications of age 9 initiation of HPV vaccination on series completion by age 13-15 in the US: projections from an age-structured vaccination model.

Introduction: Routine human papillomavirus (HPV) vaccination in the US is recommended at ages 11 or 12 years and can be given at age 9. Vaccination completion rates among adolescents 13-15 years in the US remain below the 80% goal. This study evaluated the long-term effects of increasing proactive HPV vaccination initiation rates at age 9 years in completion rates of adolescents. Methods: An age-structured vaccination model was developed and parametrized based on the National Immunization Survey-Teen (NIS-Teen) survey data. The model projected vaccination coverage (by vaccination status and age group), for 20 years, for a routine initiation scenario (no increase in initiation rates of 9-year-olds) and different proactive initiation (increased age 9 initiation) scenarios. The time to reach a completion rate of 80% for 13-15-year-olds was estimated. The model also generated projections stratified for subgroups of interest. Results: Results indicated that vaccine completion rates of 80% in 13-15-year-olds may not be achieved by 2040 under current trends of routine initiation at ages 11 or 12 years. However, increasing initiation rates in 9-year-olds by 1% and 3% annually could shorten the time to achieve 80% completion by 4 and 8 years, respectively. Stratification analyses showed that increasing initiation rates in 9-year-olds can also reduce disparities across subgroups in the time to achieve vaccination completion targets. Discussion: Increasing HPV vaccination initiation rates in 9-year-olds by as little as 1%-3% annually may be an effective strategy to improve HPV vaccination completion rates in adolescents by age 15 and reach the Healthy People goal of 80% completion much earlier.

Structured Literature Review to Identify Human Papillomavirus's Natural History Parameters for Dynamic Population Models of Vaccine Impacts.

Human papillomavirus (HPV) is a common sexually transmitted virus that can cause cervical cancer and other diseases. Dynamic transmission models (DTMs) have been developed to evaluate the health and economic impacts of HPV vaccination. These models typically include many parameters, such as natural history of the disease, transmission, demographic, behavioral, and screening. To ensure the accuracy of DTM projections, it is important to parameterize them with the best available evidence. This study aimed to identify and synthesize data needed to parametrize DTMs on the natural history of HPV infection and related diseases. Parameters describing data of interest were grouped by their anatomical location (genital warts, recurrent respiratory papillomatosis, and cervical, anal, vaginal, vulvar, head and neck, and penile cancers), and natural history (progression, regression, death, cure, recurrence, detection), and were identified through a systematic literature review (SLR) and complementary targeted literature reviews (TLRs). The extracted data were then synthesized by pooling parameter values across publications, and summarized using the range of values across studies reporting each parameter and the median value from the most relevant study. Data were extracted and synthesized from 223 studies identified in the SLR and TLRs. Parameters frequently reported pertained to cervical cancer outcomes, while data for other anatomical locations were less available. The synthesis of the data provides a large volume of parameter values to inform HPV DTMs, such as annual progression rates from cervical intraepithelial neoplasia (CIN) 1 to CIN 2+ (median of highest quality estimate 0.0836), CIN 2 to CIN 3+ (0.0418), carcinoma in situ (CIS) 2 to local cancer+ (0.0396), and regional to distant cancer (0.0474). Our findings suggest that while there is a large body of evidence on cervical cancer, parameter values featured substantial heterogeneity across studies, and further studies are needed to better parametrize the non-cervical components of HPV DTMs.

Comparative effectiveness and safety of ozanimod versus other oral DMTs in relapsing-remitting multiple sclerosis: a synthesis of matching-adjusted indirect comparisons.

Background: Several oral disease-modifying therapies (DMTs) have been approved by the Food and Drug Administration for the treatment of relapsing-remitting multiple sclerosis (RRMS). In the absence of head-to-head randomized data, matching-adjusted indirect comparisons (MAICs) can evaluate the comparative effectiveness and safety of ozanimod versus other oral DMTs in RRMS. Objectives: To synthesize results from the published MAICs of ozanimod and other oral DMTs for 2-year outcomes in RRMS. Methods: Published MAICs involving ozanimod for the treatment of RRMS were identified. Extracted data elements included efficacy [annualized relapse rate (ARR), confirmed disability progression (CDP), and brain volume loss] and safety [adverse events (AEs), serious AEs (SAEs), AEs leading to discontinuation, and infection] outcomes. Results: The four MAIC studies identified compared ozanimod with fingolimod, teriflunomide, dimethyl fumarate (DMF), and ponesimod. All comparisons were adjusted for differences in age, sex, relapses within the previous year, Expanded Disability Status Scale score, and percentage of patients with prior DMTs. Outcomes at 2 years were analyzed based on comparisons that lacked a common comparator arm. Ozanimod was associated with significantly lower ARR versus teriflunomide [ARR ratio (95% CI) 0.73 (0.62, 0.84) and DMF 0.80 (0.67, 0.97)], with no significant difference versus fingolimod or ponesimod. The proportions of patients treated with ozanimod or fingolimod had similar 3- and 6-month CDP. Compared with teriflunomide and DMF, ozanimod was associated with a significantly lower risk of 3-month CDP; 6-month CDP was comparable. Ozanimod was associated with significantly lower rates of any AE and AEs leading to discontinuation compared with the other oral DMTs evaluated. Ozanimod also had significantly lower rates of SAEs versus teriflunomide and DMF and lower rates of reported infection outcomes versus fingolimod and ponesimod. Conclusion: Compared with the other oral DMTs evaluated in MAICs, ozanimod was associated with a favorable safety profile and improved or comparable efficacy outcomes.

An indirect comparison of ozanimod vs other oral treatments in relapsing-remitting multiple sclerosis The many treatment options available for relapsing-remitting multiple sclerosis (RRMS) make treatment decisions difficult. While direct head-to-head treatment comparisons provide useful information, these studies are not available for every pair of treatments. Indirect comparisons of published study results can help fill that evidence gap. A technique called matching-adjusted indirect comparison (MAIC) offers a statistically robust way to compare safety/efficacy outcomes from different studies by accounting for important differences across the studies. We collected data from four MAIC studies that compared 2-year treatment outcomes in patients treated with ozanimod versus those treated with fingolimod, teriflunomide, dimethyl fumarate (DMF), or ponesimod. Each study accounted for differences in age, sex, relapses within the previous year, disability status, and previous therapy use. We found ozanimod was either better than or similar to other treatments based on the outcomes measured. The annual rate of RRMS relapse was lower for patients treated with ozanimod than for patients treated with teriflunomide or DMF and similar for patients treated with ponesimod or fingolimod. Ozanimod-treated patients saw their RRMS progress at rates similar to those treated with fingolimod at 3 and 6 months and teriflunomide and DMF at 6 months; RRMS was more likely to progress at 3 months in patients treated with teriflunomide and DMF versus those treated with ozanimod. Our analyses also found that patients treated with ozanimod had lower rates of side effects, including those serious enough to cause treatment discontinuation, compared with patients receiving other treatments. By comparing findings from existing MAIC studies, we found that patients with RRMS treated with ozanimod had fewer side effects and better or similar efficacy outcomes compared with patients who received other treatments for RRMS. These findings can potentially inform treatment decisions for patients with RRMS.

Comparative efficacy and safety of ozanimod and ponesimod for relapsing multiple sclerosis: A matching-adjusted indirect comparison.

BACKGROUND: Ozanimod and ponesimod are sphingosine 1-phosphate receptor modulators approved by the U.S. Food and Drug Administration for treatment of relapsing forms of multiple sclerosis (MS). Given that no head-to-head trials have assessed these two treatments, we performed a matching-adjusted indirect comparison (MAIC) to compare efficacy and safety outcomes between ozanimod and ponesimod for MS. METHODS: A MAIC compared efficacy and safety of ozanimod and ponesimod at 2 years. Outcomes included annualized relapse rate (ARR) and percentage change from baseline in brain volume loss (BVL) as well as rates of any treatment-emergent adverse events (TEAEs), serious adverse events (AEs), AEs leading to discontinuation, and other safety outcomes. Individual patient-level data were obtained for ozanimod from the RADIANCE-B trial, while aggregate-level patient data were obtained for ponesimod from the OPTIMUM trial. The MAIC was not anchored owing to lack of a common comparator across the two trials. The following characteristics were matched between the trials' populations: age, sex, time since MS symptom onset, relapses in prior year, Expanded Disability Status Scale score, disease-modifying therapies received in the prior 2 years, absence of gadolinium-enhancing T1 lesions, and percentage of patients from Eastern Europe. RESULTS: After matching, key baseline characteristics were balanced between patients receiving ozanimod and ponesimod. Compared with ponesimod, ozanimod had a numerically lower ARR (rate ratio: 0.80 [95% CI: 0.57, 1.10]) and was associated with a significant reduction in BVL (% change difference: 0.20 [95% CI: 0.05, 0.36]). Additionally, ozanimod was associated with a significantly lower risk of TEAEs (risk difference: -11.9% [95% CI: -16.8%, -7.0%]), AEs leading to discontinuation (-6.1% [95% CI: -8.9%, -3.4%]), and lymphocyte count <0.2 K/µL (-2.3% [95% CI: -4.2%, -0.5%]). There were no statistically significant differences in the other safety outcomes. CONCLUSION: The MAIC results suggest that, compared with ponesimod, ozanimod is more effective in preserving brain volume, is comparable in terms of reducing relapse rates, and has a favorable safety profile.

Modeling the health and economic implications of adopting a 1-dose 9-valent human papillomavirus vaccination regimen in a high-income country setting: An analysis in the United Kingdom.

Although no human papillomavirus (HPV) vaccine is indicated for single-dose administration, some observational evidence suggests that a 1-dose regimen might reduce HPV infection risk to that achieved with 2 doses. This study estimated the potential health and economic outcomes associated with switching from a 2-dose HPV vaccination program for girls and boys aged 13-14 years to an off-label 9-valent (9vHPV), 1-dose regimen, accounting for the uncertainty of the effectiveness and durability of a single dose. A dynamic HPV transmission infection and disease model was adapted to the United Kingdom and included a probabilistic sensitivity analysis using estimated distributions for duration of protection of 1-dose and degree of protection of 1 relative to 2 doses. One-way sensitivity analyses of key inputs were performed. Outcomes included additional cancer and disease cases and the difference in net monetary benefit (NMB). The 1-dose program was predicted to result in 81,738 additional HPV-related cancer cases in males and females over 100 years compared to the 2-dose program, ranging from 36,673 to 134,347 additional cases (2.5% and 97.5% quantiles, respectively), and had a 7.8% probability of being cost-effective at the £20,000/quality-adjusted life years willingness-to-pay (WTP) threshold. In one-way sensitivity analyses, the number of additional cancer cases was sensitive to the median of the duration of protection distribution and coverage rates. The differences in NMBs were sensitive to the median of the duration of protection distribution, dose price and discount rate, but not coverage variations. Across sensitivity analyses, the probability of 1 dose being cost-effective vs 2 doses was < 50% at the standard WTP threshold. Adoption of a 1-dose 9vHPV vaccination program resulted in more vaccine-preventable HPV-related cancer and disease cases in males and females, introduced substantial uncertainty in health and economic outcomes, and had a low probability of being cost-effective compared to the 2-dose program.

Estimating the drivers of urban economic complexity and their connection to economic performance.

Estimating the capabilities, or inputs of production, that drive and constrain the economic development of urban areas has remained a challenging goal. We posit that capabilities are instantiated in the complexity and sophistication of urban activities, the know-how of individual workers, and the city-wide collective know-how. We derive a model that indicates how the value of these three quantities can be inferred from the probability that an individual in a city is employed in a given urban activity. We illustrate how to estimate empirically these variables using data on employment across industries and metropolitan statistical areas in the USA. We then show how the functional form of the probability function derived from our theory is statistically superior when compared with competing alternative models, and that it explains well-known results in the urban scaling and economic complexity literature. Finally, we show how the quantities are associated with metrics of economic performance, suggesting our theory can provide testable implications for why some cities are more prosperous than others.

Impact of the COVID-19 pandemic on adolescent vaccinations: projected time to reverse deficits in routine adolescent vaccination in the United States.

OBJECTIVE: The COVID-19 pandemic has led to significant reductions in the administration of routinely recommended vaccines among adolescents in the US including tetanus, diphtheria, and acellular pertussis (Tdap); meningococcal (ACWY); and human papillomavirus (HPV) vaccines. The extent to which these deficits could persist in 2021 and beyond is unclear. To address this knowledge gap, this study estimated the cumulative deficits of routine vaccine doses among US adolescents during the COVID-19 pandemic and estimated the time and effort needed to recover from those deficits. METHODS: Monthly reductions in Tdap, meningococcal, and HPV doses administered to US adolescents during the COVID-19 pandemic were quantified using MarketScan Commercial Claims and Encounters data. The time and effort required to reverse the vaccination deficit under various catch-up scenarios were estimated. RESULTS: Annual doses administered of Tdap, meningococcus, and HPV vaccines decreased by 21.2%, 20.8%, and 24.0%, respectively, in 2020 compared to 2019. For 2021, the reduction in doses administered is projected to be 6%-21% compared to 2019 under different scenarios. The projected deficit of missed doses is expected to be cleared between winter 2023 and fall 2031. CONCLUSIONS: Administration rates of routine vaccines decreased significantly among US adolescents during COVID-19. Reversing these deficits to mitigate long-term health and economic consequences will require a sustained increase in vaccination rates over multiple years.

Machine-learned patterns suggest that diversification drives economic development.

We combine a sequence of machine-learning techniques, together called Principal Smooth-Dynamics Analysis (PriSDA), to identify patterns in the dynamics of complex systems. Here, we deploy this method on the task of automating the development of new theory of economic growth. Traditionally, economic growth is modelled with a few aggregate quantities derived from simplified theoretical models. PriSDA, by contrast, identifies important quantities. Applied to 55 years of data on countries' exports, PriSDA finds that what most distinguishes countries' export baskets is their diversity, with extra weight assigned to more sophisticated products. The weights are consistent with previous measures of product complexity. The second dimension of variation is proficiency in machinery relative to agriculture. PriSDA then infers the dynamics of these two quantities and of per capita income. The inferred model predicts that diversification drives growth in income, that diversified middle-income countries will grow the fastest, and that countries will converge onto intermediate levels of income and specialization. PriSDA is generalizable and may illuminate dynamics of elusive quantities such as diversity and complexity in other natural and social systems.

Contagion in Mass Killings and School Shootings.

BACKGROUND: Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts. METHODS: Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event. CONCLUSIONS: We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.

Urban economies and occupation space: can they get "there" from "here"?

Much of the socioeconomic life in the United States occurs in its urban areas. While an urban economy is defined to a large extent by its network of occupational specializations, an examination of this important network is absent from the considerable body of work on the determinants of urban economic performance. Here we develop a structure-based analysis addressing how the network of interdependencies among occupational specializations affects the ease with which urban economies can transform themselves. While most occupational specializations exhibit positive relationships between one another, many exhibit negative ones, and the balance between the two partially explains the productivity of an urban economy. The current set of occupational specializations of an urban economy and its location in the occupation space constrain its future development paths. Important tradeoffs exist between different alternatives for altering an occupational specialization pattern, both at a single occupation and an entire occupational portfolio levels.

The statistics of urban scaling and their connection to Zipf's law.

Urban scaling relations characterizing how diverse properties of cities vary on average with their population size have recently been shown to be a general quantitative property of many urban systems around the world. However, in previous studies the statistics of urban indicators were not analyzed in detail, raising important questions about the full characterization of urban properties and how scaling relations may emerge in these larger contexts. Here, we build a self-consistent statistical framework that characterizes the joint probability distributions of urban indicators and city population sizes across an urban system. To develop this framework empirically we use one of the most granular and stochastic urban indicators available, specifically measuring homicides in cities of Brazil, Colombia and Mexico, three nations with high and fast changing rates of violent crime. We use these data to derive the conditional probability of the number of homicides per year given the population size of a city. To do this we use Bayes' rule together with the estimated conditional probability of city size given their number of homicides and the distribution of total homicides. We then show that scaling laws emerge as expectation values of these conditional statistics. Knowledge of these distributions implies, in turn, a relationship between scaling and population size distribution exponents that can be used to predict Zipf's exponent from urban indicator statistics. Our results also suggest how a general statistical theory of urban indicators may be constructed from the stochastic dynamics of social interaction processes in cities.