American Journal of Preventive Medicine

The DiCOM is a probabilistic state-transition cohort model that projects the population effect of nutrition policies on cancer outcomes. The model consists of 1) six health states: healthy without cancer, initial treatment with colorectal cancer, continuous care with colorectal cancer, initial treatment with stomach cancer, continuous care with stomach cancer, and dead (from cancer or other causes); 2) the annual likelihood of changes in health; and 3) the lifetime consequences of such changes on health outcomes and economic costs.14 Our model estimated health benefits (life years, quality-adjusted life-years (QALYs,) cancer incidence, and years living with cancer) and economic impact (e.g., policy implementation costs, healthcare costs, and productivity benefits).

Introduction: Various built environment factors might influence certain health behaviors and outcomes. Reliable and resource-efficient methods that are feasible for assessing built environment characteristics across large geographies are needed for larger and more robust studies. We report the prevalence and reliability of a new virtual neighborhood audit technique, drop-and-spin auditing, developed specifically for assessment of walkability and physical disorder characteristics across large geographic areas. Methods: Drop-and-spin auditing, a method where a GSV scene was rated by spinning 360o around the location to be rated, was developed using a modified version of the extant virtual audit tool CANVAS. Approximately 8,000 locations within Essex County, New Jersey were assessed by eleven trained auditors. Thirty-two built environment items per a location within Google Street View (GSV) were audited using a standardized protocol. Test-retest and inter-rater Kappa statistics were from a 5% subsample of locations. Data were collected 2017-2018 and were analyzed in 2018. Results: Roughly 70% of GSV scenes had sidewalks. Among those, two thirds were in good condition. At least 5 obvious items of garbage or litter were present in 41% of GSV scenes. Maximum test-retest reliability indicates substantial agreement (κ ≥ 0.61) for all items. Inter-rater reliability of each item, generally, was lower than test-retest reliability. The median time to rate each item was 7.3 seconds. Conclusions: Drop-and-spin virtual neighborhood auditing might be a reliable, resource-efficient method for assessing built environment characteristics across large geographies.

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting the adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing it to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

The PPI measures public health government responses to COVID-19 at all levels of government throughout the world. The PPI measure considers the extent of COVID-19 policy responses in the following categories: state of emergencies, border closures, school closures, social gathering, and social distancing limitations, home-bound policies, medical isolation policies, closure/restriction of businesses and services, and the mandates to wear face masks. The coding for public health policies is based on government websites and reputable news sources reporting the adoption of these policies. Total, National, and Subnational Indices are calculated based on the standing public health policies adopted at various levels of government for each unit (state, province, etc.) for each day, by adding together the highest values across levels of government in each category on that day and normalizing it to range between 0 and 1. National and subnational PPIs were constructed with the values in each category from just national- or just subnational-level policies. The current version of the data set contains public health protective policies on the national and sub-national levels, while we plan to expand to the municipal level in the future. The unit of analysis is unit-day. Regular updates of this dataset are posted in the dedicated GitHub repository (https://github.com/COVID-policy-response-lab/PPI-data). An ArcGIS dashboard with the project data can be found here: https://elcamaleon.binghamton.edu/portal/apps/opsdashboard/index.html#/cc61a3652eb74b8ea8864928e8026...

Data include individual-level health data, including results from cardiovascular tests and medical history. This is linked to air quality data at participants' residence.