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Health Behavior And Health Education Theory Research And Practice

Modeling human decisions in coupled human and natural systems: Review of agent-based models

BACKGROUND
Depression, anxiety and somatization are the most common mental disorders in primary care as well as medical specialty populations; each is present in at least 5-10% of patients and frequently comorbid with one another. An efficient means for measuring and monitoring all three conditions would be desirable.


METHODS
Evidence regarding the psychometric and pragmatic characteristics of the Patient Health Questionnaire (PHQ)-9 depression, generalized anxiety disorder (GAD)-7 anxiety and PHQ-15 somatic symptom scales are synthesized from two sources: (1) four multisite cross-sectional studies (three conducted in primary care and one in obstetric-gynecology practices) comprising 9740 patients, and (2) key studies from the literature that have studied these scales.


RESULTS
The PHQ-9 and its abbreviated eight-item (PHQ-8) and two-item (PHQ-2) versions have good sensitivity and specificity for detecting depressive disorders. Likewise, the GAD-7 and its abbreviated two-item (GAD-2) version have good operating characteristics for detecting generalized anxiety, panic, social anxiety and post-traumatic stress disorder. The optimal cutpoint is > or = 10 on the parent scales (PHQ-9 and GAD-7) and > or = 3 on the ultra-brief versions (PHQ-2 and GAD-2). The PHQ-15 is equal or superior to other brief measures for assessing somatic symptoms and screening for somatoform disorders. Cutpoints of 5, 10 and 15 represent mild, moderate and severe symptom levels on all three scales. Sensitivity to change is well-established for the PHQ-9 and emerging albeit not yet definitive for the GAD-7 and PHQ-15.


CONCLUSIONS
The PHQ-9, GAD-7 and PHQ-15 are brief well-validated measures for detecting and monitoring depression, anxiety and somatization.

Iconography : The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review

Complex systems abound in public health. Complex systems are made up of heterogeneous elements that interact with one another, have emergent properties that are not explained by understanding the individual elements of the system, persist over time, and adapt to changing circumstances. Public health is starting to use results from systems science studies to shape practice and policy, for example in preparing for global pandemics. However, systems science study designs and analytic methods remain underutilized and are not widely featured in public health curricula or training. In this review we present an argument for the utility of systems science methods in public health, introduce three important systems science methods (system dynamics, network analysis, and agent-based modeling), and provide three case studies in which these methods have been used to answer important public health science questions in the areas of infectious disease, tobacco control, and obesity.

Systems science methods in public health: dynamics, networks, and agents.

Influenza virus infections are believed to spread mostly by close contact in the community. Social distancing measures are essential components of the public health response to influenza pandemics. The objective of these mitigation measures is to reduce transmission, thereby delaying the epidemic peak, reducing the size of the epidemic peak, and spreading cases over a longer time to relieve pressure on the healthcare system. We conducted systematic reviews of the evidence base for effectiveness of multiple mitigation measures: isolating ill persons, contact tracing, quarantining exposed persons, school closures, workplace measures/closures, and avoiding crowding. Evidence supporting the effectiveness of these measures was obtained largely from observational studies and simulation studies. Voluntary isolation at home might be a more feasible social distancing measure, and pandemic plans should consider how to facilitate this measure. More drastic social distancing measures might be reserved for severe pandemics.

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Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings-Social Distancing Measures.

Walking distance by trip purpose and population subgroups.

A spatial agent-based model for the simulation of adults' daily walking within a city.

Background: Despite the multiple advantages of active travel to school, only a small percentage of US children and adolescents walk or bicycle to school. Intervention studies are in a relatively early stage and evidence of their effectiveness over long periods is limited. The purpose of this study was to illustrate the utility of agent-based models in exploring how various policies may influence children’s active travel to school. Methods: An agent-based model was developed to simulate children’s school travel behavior within a hypothetical city. The model was used to explore the plausible implications of policies targeting two established barriers to active school travel: long distance to school and traffic safety. The percent of children who walk to school was compared for various scenarios. Results: To maximize the percent of children who walk to school the school locations should be evenly distributed over space and children should be assigned to the closest school. In the case of interventions to improve traffic safety, targeting a smaller area around the school with greater intensity may be more effective than targeting a larger area with less intensity. Conclusions: Despite the challenges they present, agent based models are a useful complement to other analytical strategies in studying the plausible impact of various policies on active travel to school.

/pdf/using-an-agent-based-model-to-simulate-children-s-active-1ejpz7nizx.pdf

Using an agent-based model to simulate children’s active travel to school

Objectives To examine active travel and public transportation use among U.S. older adults and the built environment characteristics associated with them. Methods Total active travel, public transportation trips, travel purpose diversity, total and maximum distance traveled for older adults (>= 65 years) relative to middle age adults (45–64 years) as reported in the 2009 National Household Travel Survey (NHTS) were compared using linear regression models and logistic regression models. Results Overall, adults over 65 years of age had 3.3 trips and total travel distance of 20.6 miles per day. Active travel and daily transport trips declined from younger to older age groups. Among the older adults, the daily transport varied by a number of characteristics. The association of built environment on older adults’ transport differed by the specific environmental characteristics and travel modes. Discussion Existing transportation facilities and policies need to be adjusted to meet the challenge. Our study has a potential to contribute to the design of effective interventions that enhance health and quality of life for the burgeoning aging population in the U.S.

Active travel, public transportation use, and daily transport among older adults: The association of built environment

https://repub.eur.nl/pub/118030/REPUB_118030_SAM.pdf

Yong Yang

Papers

Walking distance by trip purpose and population subgroups.

A spatial agent-based model for the simulation of adults' daily walking within a city.

Using an agent-based model to simulate children’s active travel to school

Active travel, public transportation use, and daily transport among older adults: The association of built environment

Urban-Rural Differences in Older Adult Depression: A Systematic Review and Meta-analysis of Comparative Studies.