Book edited by Sarah B. Macfarlane and Carla AbouZahr: “This handbook compiles methods for gathering, organizing and disseminating data to inform policy and manage health systems worldwide. Contributing authors describe national and international structures for generating data and explain the relevance of ethics, policy, epidemiology, health economics, demography, statistics, geography and qualitative methods to describing population health. The reader, whether a student of global health, public health practitioner, programme manager, data analyst or policymaker, will appreciate the methods, context
Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand
Hasan S. Merali, Li-Yi Lin, Qingfeng Li, and Kavi Bhalla in Injury Prevention: “The majority of Thailand’s road traffic deaths occur on
Using Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use
After processing, 462 unique motorcycle drivers were
This novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce
Balancing information governance obligations when accessing social care data for collaborative research
Paper by Malkiat Thiarai, Sarunkorn Chotvijit
The paper documents the process involved in identifying, accessing and using data held in Birmingham City Council’s social care system for collaborative research with a partner
The findings will include approaches to understanding the data, its structure and accessibility tasks involved in addressing ethical and legal obligations and requirements of the ethical and governance processes….(More)”.
Are Requirements to Deposit Data in Research Repositories Compatible With the European Union’s General Data Protection Regulation?
Paper by Deborah Mascalzoni et al: “To reproduce study findings and facilitate new discoveries, many funding bodies, publishers, and professional communities are encouraging—and increasingly requiring—investigators to deposit their data, including individual-level health information, in research repositories. For example, in some
The GDPR establishes legally binding rules for processing personal data in the EU, as well as outside the EU in some cases. Researchers in the EU, and often their global collaborators, must comply with the regulation. Health and genetic data are considered special categories of personal data and are subject to relatively stringent rules for processing….(More)”.
A Parent-To-Parent Campaign To Get Vaccine Rates Up
Alex Olgin at NPR: “In 2017, Kim Nelson had just moved her family back to her hometown in South Carolina. Boxes were still scattered around the apartment, and while her two young daughters played, Nelson scrolled through a newspaper article on her phone. It said religious exemptions for vaccines had jumped nearly 70 percent in recent years in the Greenville area — the part of the state she had just moved to.
She remembers yelling to her husband in the other room, “David, you have to get in here! I can’t believe this.”
Up until that point, Nelson hadn’t run into mom friends who didn’t vaccinate
Nelson started her own group, South Carolina Parents for Vaccines. She began posting scientific articles online. She started responding to private messages from concerned parents with specific questions. She also found that positive reinforcement was important and would roam around the mom groups, sprinkling affirmations.
“If someone posts, ‘My child got their two-months shots today,’ ” Nelson says, she’d quickly post a follow-up comment: “Great job, mom!”
Nelson was inspired by peer-focused groups around the country doing similar work. Groups with national reach like Voices for Vaccines and regional groups like Vax Northwest in Washington state take a similar approach, encouraging parents to get educated and share facts about vaccines with other parents….
Public health specialists are raising concerns about the need to improve vaccination rates. But efforts to reach vaccine-hesitant parents often fail. When presented with facts about vaccine safety, parents often remained entrenched in a decision not to vaccinate.
Pediatricians could play a role — and many do — but they’re not compensated to have lengthy discussions with parents, and some of them find it a frustrating task. That has left an opening for alternative approaches, like Nelson’s.
Nelson thought it would be best to zero in on moms who were still on the fence about vaccines.
“It’s easier to pull a hesitant parent over than it is somebody who is firmly anti-vax,” Nelson says. She explains that parents who oppose vaccination often feel so strongly about it that they won’t engage in a discussion. “They feel validated by that choice — it’s part of
Governance of artificial intelligence and personal health information
Jenifer Sunrise Winter in Digital Policy, Regulation
This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.
Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security….(More).
The Role of Big Data Analytics in Predicting Suicide
Chapter by Ronald C. Kessler et al: “…reviews the long history of using electronic medical records and other types of big data to predict suicide. Although a number of the most recent of these studies used machine learning (ML) methods, these studies were all suboptimal both in the features used as predictors and in the analytic approaches used to develop the prediction models. We review these limitations and describe opportunities for making improvements in future applications.
We also review the controversy among clinical experts about using structured suicide risk assessment tools (be they based on ML or older prediction methods) versus in-depth clinical evaluations of needs for treatment planning. Rather than seeing them as competitors, we propose integrating these different approaches to capitalize on their complementary strengths. We also emphasize the distinction between two types of ML analyses: those aimed at predicting which patients are at highest suicide risk, and those aimed at predicting the treatment options that will be best for individual patients. We explain why both are needed to optimize the value of big data ML methods in addressing the suicide problem….(More)”.
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
Our model demonstrates high diagnostic accuracy across multiple organ systems and is comparable to experienced pediatricians in diagnosing common childhood diseases. Our study provides a proof of concept for implementing an AI-based system as a means to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity. Although this impact may be most evident in areas where healthcare providers are in relative shortage, the benefits of such an AI system are likely to be universal….(More)”.
Impact of a nudging intervention and factors associated with vegetable dish choice among European adolescents
A cross-sectional quasi-experimental study was implemented in restaurants in four European countries: Denmark, France, Italy and United Kingdom. In total, 360 individuals aged 12-19 years were allocated into control or intervention groups, and asked to select from meat-based, fish-based, or vegetable-based meals. All three dishes were identically presented in appearance (balls with similar size and weight) and with the same sauce (tomato sauce) and side dishes (pasta and salad). In the intervention condition, the vegetable-based option was presented as the “dish of the day” and numbers of dishes chosen by each group were compared using the Pearson chi-square test. Multivariate logistic regression analysis was run to assess associations between choice of vegetable-based dish and its potential associated factors (adherence to Mediterranean diet, food neophobia, attitudes towards nudging for vegetables, food choice questionnaire, human values scale, social norms and self-estimated health, country, gender and belonging to control or intervention groups). All analyses were run in SPSS 22.0.
The nudging strategy (dish of the day) did not show a difference on the choice of the vegetable-based option among adolescents tested (p = 0.80 for Denmark and France and p = 0.69 and p = 0.53 for Italy and UK, respectively). However,
The “dish of the day” strategy did not work under the study conditions. Choice of the vegetable-based dish was predicted by natural dimension, social norms, gender and attitudes towards vegetable nudging. An understanding of factors related to choosing
Using Personal Informatics Data in Collaboration among People with Different Expertise
Dissertation by Chia-Fang Chung: “Many people collect and analyze data about themselves to improve their health and wellbeing. With the prevalence of smartphones and wearable sensors, people are able to collect detailed and complex data about their everyday behaviors, such as diet, exercise, and sleep. This everyday behavioral data can support individual health goals, help manage health conditions, and complement traditional medical examinations conducted in clinical visits. However, people often need support to interpret this self-tracked data. For example, many people share their data with health experts, hoping to use this data to support more personalized diagnosis and recommendations as well as to receive emotional support. However, when attempting to use this data in collaborations, people and their health experts often struggle to make sense of the data. My dissertation examines how to support collaborations between individuals and health experts using personal informatics data.
My research builds an empirical understanding of individual and collaboration goals around using personal informatics data, current practices of using this data to support collaboration, and challenges and expectations for integrating the use of this data into clinical workflows. These understandings help designers and researchers advance the design of personal informatics systems as well as the theoretical understandings of patient-provider collaboration.
Based on my formative work, I propose