Looking backwards: A personal look at community mental health

This paper is a personal review of the forces affecting community mental health over the past 50 years from a psychiatrist who played a significant role in the movement.     

Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.Well-designed transportation systems are a key element in the economic welfare of major cities. Design and planning of these systems requires a quantitative understanding of traffic patterns and relies on the ability to predict the effects of disruptions to such patterns, both planned and unplanned (1).There is a long history of analytic and modeling approaches to the study of traffic patterns (2), for example using simulated scenarios in simple transportation systems (3), and analysis of real traffic data in complex systems, either focusing on a small samples (4) or using more aggregate data (5, 6). Here we take this approach to the next level by making use of smart-card data and incident logs to (i) predict traffic patterns and (ii) estimate the effect of unplanned disruptions on these patterns. We analyzed 70 d of smart-card transactions from the London transportation network, composed of ∼10 million unique IDs and 6 million transactions per day on average, resulting in one of the largest statistical analyses of transportation systems to date.A related literature deals with various aspects of dynamics in complex networks and complex systems in general (7–9), using a variety of data sources, from emails (10) to the circulation of bank notes (11) to online experiments on Amazon Turk (12). More recently, a number of analyses have leveraged mobile phone data as proxies for mobility (4, 13–15).However, smart-card technology allows us to obtain large samples of passenger location and movements without requiring noisy and potentially unreliable proxies such as mobile Global Positioning System traces (16), while also leveraging a more structured environment that imposes hard constraints on patterns of urban mobility (17). In particular, these constraints of the system allow us to identify a global model of passenger behavior under local line and station closures.     

Trichomonas vaginalis antimicrobial drug resistance in 6 US cities, STD Surveillance Network, 2009-2010

Nitroimidazoles (metronidazole and tinidazole) are the only recommended drugs for treating Trichomonas vaginalis infection, and previous samples that assessed resistance of such isolates have been limited in geographic scope. We assessed the prevalence of in vitro aerobic metronidazole and tinidazole resistance among T. vaginalis isolates from multiple geographic sites in the United States. Swab specimens were obtained from women who underwent routine pelvic examinations at sexually transmitted disease clinics in 6 US cities. Cultured T. vaginalis isolates were tested for nitroimidazole resistance (aerobic minimum lethal concentration [MLC] >50 μg/mL). Of 538 T. vaginalis isolates, 23 (4.3%) exhibited low-level in vitro metronidazole resistance (minimum lethal concentrations 50-100 μg/mL). No isolates exhibited moderate- to high-level metronidazole resistance or tinidazole resistance. Results highlight the possibility that reliance on a single class of antimicrobial drugs for treating T. vaginalis infections may heighten vulnerability to emergence of resistance. Thus, novel treatment options are needed.     

Healthy research in cities: a case study on the translation of health research into action in The Netherlands

Health research plays a prominent role in Healthy City projectsin urban neighbourhoods. This article explores the problemsthat arise when research is used as a tool to promote healthaction. A special focus is on strategies to assess health needs.Additionally, a theoretical framework of ‘Cultures ofHealth’ is introduced. In a case study, a health promotionproject in an urban neighbourhood is examined in which researchplays a major role. The research, designed as action research,originates positive outcomes and demonstrates an adequate rolefor research on healthy cities.     

张謇的城市与区域规划思想及其现实意义

张謇在南通及其地区的规划与建设卓有成效。区域是城市发展的基础。张謇城市建设的思想实基于他对区域的关心和一系列的行动。张謇较之他同时代人甚至后来人高明之处，在于科技与人文并重，汇理想与笃行于一身。张謇的创新与开拓，不仅为南通建设之所需，且对于处在城市化加速时期的当今中国或许能有新的启示与鼓舞。     

Evidence and theory for lower rates of depression in larger US urban areas

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual’s accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

Living in cities changes the way we behave and think (1–3). Over a century ago, the social changes associated with massive urbanization in Europe and in the United States focused social scientists on the nexus between cities and mental life (2). Along with the urban public health crises of the time, a central question became whether cities are good or bad for mental health.Subsequently, social psychologists (1) started to document and measure the systematic behavioral adaptations among people living in cities. These adaptations included a more intense use of time [e.g., faster walking (4)], a greater tolerance for diversity (5), and strategies to curb unwanted social interactions—such that people in larger cities act in colder and more callous ways (1). These studies attributed the influences of urban environments on mental health to the intensity of social life in larger cities, mediated by densely built spaces and associated dynamic and diverse socioeconomic interaction networks. They did not, however, ultimately clarify whether urban environments promote better or worse mental health. Consequently, concerns persisted that cities are mentally taxing (6–9) and can induce “stimulus overload,” including stress, mental fatigue (10), and low levels of subjective well-being (SWB) (11).More recent studies have focused less on urban environments as a whole and more on contextual and environmental factors associated with depression. For example, a study of the entire population in Sweden (9) uncovered a positive association between neighborhood population density and depression-related hospitalizations. In addition, individual factors of gender, age, socioeconomic status, and race, which vary at neighborhood levels within cities, have been found to be statistically associated with depression (12–14). Other studies using various measures of mental health and broader definitions of urban environments have found evidence for an association between poorer mental health in cities vs. rural areas (7, 8). However, this evidence and that linking SWB and cities (15–18) have remained mixed and often explicitly inconsistent (19, 20) due to differences in 1) reporting (e.g., surveys vs. medical records); 2) types of measurement (e.g., surveys vs. interviews); 3) definitions of what constitutes urban; and 4) the mental disorders studied (e.g., schizophrenia vs. depression).For these reasons, it is desirable to create a systematic framework that organizes this diverse body of research and interrogates how varying levels of urbanization influence mental health across different sets of indicators. Here, we begin to build this framework for depression in US cities. We show that, surprisingly, the per capita prevalence of depression decreases systematically with city size.Like earlier classic approaches, our strategy frames the effects of city size on mental health through the lens of the individual experience of urban physical and socioeconomic environments. Crucial to our purposes, many characteristics of cities have been recently found to vary predictably with city population size. These systematic variations in urban indicators are explained by denser built environments and their associated increases in the intensity of human interactions and resulting adaptive behaviors (21).More specifically, people in larger cities have, on average, more socioeconomic connections mediating a greater variety of functions. This effect is understood theoretically by the statistical likelihood to interact with more people over space per unit time, leading to potential mental “overload” but also, to greater stimulation and choice along more dimensions of life. This expansion of socioeconomic networks is supported structurally by economies of scale (e.g., road length) in urban built environments and by occupational specialization and associated increases in economic productivity and exchange (3).This effect leads to a number of quantitative predictions about the nature of urban spaces and socioeconomic variables, the most central of which is the variation of the average number of socioeconomic interactions, k (network degree), with city size, N, as k(N)=k0Nδeξ. Here, k0 is a prefactor independent of city size, and ξ is a residual measuring the distance from the population average. The exponent 0<δ≃1/6<1 measures the percentage increase in the number of connections with each percentage increase in city population, which is an elasticity in the language of economics. Because the ξ reflects city size–independent statistical fluctuations, these errors average out across cities, and k obeys a scaling relationship on average over cities, such that k(N)∼Nδ. This expectation that k follows a scaling law with city population is directly observed in cell phone networks (22) and indirectly via the faster spread of infectious diseases such as COVID-19 (23), and by higher per capita economic productivity and rates of innovation (4, 21).This result is important to mental health because depression is associated, at the individual level, with fewer social contacts (24, 25). To translate the general scaling of social interactions with city size into a model for the incidence of depression in urban areas, we will now need to pay particular attention not only to the average number of social connections in a city of size N, k(N), but also, to its variance across individuals in that city and how they influence depression.     

社区外来工孕产妇保健模式的调查研究

目的:调查研究深圳外来工孕产妇保健管理模式、保障外来人口的母婴健康。方法:政府增加投入,引进高级人才设备,以社区为基础,社康中心为核心,建立1790名外来工孕产妇个人健康档案,实行社康与医院双向转诊模式,孕产妇保健、产前检查、高危妊娠筛查、健康教育由社康中心完成;产前检查、高危孕妇管理、住院分娩、社区医务培训等转由公立综合医院服务,产妇档案转社区,社康中心负责产妇访视、全面管理上报。结果:1790例外来工孕产妇建卡率由77.87%增加到99.10%,产检率由66.67%增长到95.59%,住院分娩率由84.95%增长到100%,产妇死亡率由132.74/10万降至18.0/10万,围产儿死亡率147.49/10万降至35.97/10万,初步达到同期866例常住孕产妇保健指标。结论:本方法适合深圳外来工孕产妇保健模式,外来孕产妇母婴健康可得到保障。     

DRGs付费制度实施难点问题探讨

结合工作实际,根据《国家医疗保障DRG分组与付费技术规范》和《国家医疗保障DRG(CHS-DRG)分组方案》,探讨了DRGs在实施过程中遇到的难点,并针对性地提出相关建议,以供试点城市和医疗机构参考。     

异地患者门诊服务策略

异地患者到综合性医院门诊就医,具有指向明确、就诊时间紧、对服务流程要求高等特点。针对这些方面,提出了就医咨询、扩大专家号源、相对优先、提供针对性便民服务和建立网络医疗服务等对策,在实践中取得较好效果。