The current disconnection between access to increasing amounts of data about urbanization, health, and other global changes and the conflicting meanings and values of that data has created uncertainty and reduced the ability of people to act upon available information which they do not necessarily understand. We see a disconnection between increasing data availability and data processing capability and capacity. In response to this disconnection, modeling has been attributed an important role in international and national research programs in order to predict the future based on past and recent trends. Predictive models are often data heavy and founded on assumptions which are difficult to verify, especially regarding urban health issues in specific contexts. Producing large volumes of data warrants debate about what data are prerequisites for better understanding human health in changing urban environments. Another concern is how data and information can be used to apply knowledge. Making sense of empirical knowledge requires a new transdisciplinary knowledge domain created by a commitment to convergence between researchers in multiple academic disciplines and other actors and institutions in cities. Disciplinary-based researchers are no longer the sole producers of empirical knowledge. Today, diverse kinds of knowledge are becoming an emergent product of multiple societal stakeholders acting collectively to address challenges that impact on their habitat, their livelihood, and their health. Insights from complexity science also require a fundamental rethinking of the role and responsibility of human agency while admitting rather than denying complexity and radical uncertainty.
Facing competing demands with limited resources following release from prison, people who inject drugs (PWID) may neglect health needs, with grave implications including relapse, overdose, and non-continuous care. We examined the relative importance of health-related tasks after release compared to tasks of everyday life among a total sample of 577 drug users incarcerated in Ukraine, Azerbaijan, and Kyrgyzstan. A proxy measure of whether participants identified a task as applicable (easy or hard) versus not applicable was used to determine the importance of each task. Correlates of the importance of health-related reentry tasks were analyzed using logistic regression, with a parsimonious model being derived using Bayesian lasso method. Despite all participants having substance use disorders and high prevalence of comorbidities, participants in all three countries prioritized finding a source of income, reconnecting with family, and staying out of prison over receiving treatment for substance use disorders, general health conditions, and initiating methadone treatment. Participants with poorer general health were more likely to prioritize treatment for substance use disorders. While prior drug injection and opioid agonist treatment (OAT) correlated with any interest in methadone in all countries, only in Ukraine did a small number of participants prioritize getting methadone as the most important post-release task. While community-based OAT is available in all three countries and prison-based OAT only in Kyrgyzstan, Kyrgyz prisoners were less likely to choose help staying off drugs and getting methadone. Overall, prisoners consider methadone treatment inapplicable to their pre-release planning. Future studies that involve patient decision-making and scale-up of OAT within prison settings are needed to better improve individual and public health.
Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data-ideally to be made free and publicly available-and offer lay descriptions of some of the difficulties in generating such data products.