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.

Study Design and Methodology for Project Fresh Air and Eliminating In-Home Smoking project: Overview/Background: This archive submission combines (A) interview and particle measurement data from Project Fresh Air (PFA), a randomized controlled trial––funded by an NIH R01 and conducted from 2011 to 2016––with (B) data collected from a new, Eliminating Indoor Smoking (EIS) project––funded by a 2020 Tobacco-Related Disease Research Program grant and conducted from 2020 to 2024. The new EIS data consist of the values of assays performed by the Centers for Disease Control (CDC) on biomarkers of nicotine and cannabis in lab-freezer-archived urine samples from children who participated in PFA. The PFA trial was designed to determine whether light and sound feedback based on indoor air particle levels plus brief counseling episodes could reduce in-home smoking. During 2012–2015, PFA enrolled 298 participants from predominantly low-income households with an adult smoker and a child aged <14 years old residing in the home. Participants were recruited from multiple sources in San Diego County, mainly Women, Infants and Children Program sites. Monitors to provide immediate feedback on air particle levels were installed in the homes of both intervention and control groups. Assays of cotinine (a biomarker of nicotine) were intended to provide the gold standard main outcome measure of child exposure to tobacco smoke. Due to a calibration problem with the university chemistry laboratory’s mass spectrometer, valid measures of cotinine were not available, so published PFA manuscripts relied on air monitor particle measures as their main outcomes. The EIS project was designed to leverage interview, air particle, and air nicotine measures from PFA, by combining those data with CDC analyses of biomarkers of cannabis (THC and its metabolites) and of a widely-used biomarker of nicotine (cotinine) in freezer-archived urinary samples from children in the PFA trial. Our two current in-progress EIS manuscripts, based on the combined data from the two projects, are targeted for submission to journals before the end of 2024. In the PFA trial, from pretest to posttest, there were significant reductions of in-home air particle concentrations, air nicotine levels, and reported cigarette smoking for the intervention group as compared with controls. Manuscript 1 investigates whether child exposures to tobacco smoke and to cannabis smoke, as assessed by nicotine and cannabis biomarkers in children's urine, also show differential group-by-time reductions favoring the intervention group. Analyses are in progress. In Manuscript 2, we compute the number of daily smoking events, using a validated air particle count algorithm. We then use a residualization procedure to capture the portion of this air particle measure that is uniquely attributed to parent/guardian-reports of cannabis smoking in the home, after removing the variance in daily smoking events contributed by other in-home sources: air nicotine dosimeters, reported in-home tobacco smoking variables, and other reported in-home air particle generating and ventilation activities. This residualization-ascertained number of daily cannabis smoking events can then be tested for its association with child exposure to cannabis smoke, as measured by child urinary cannabinoid biomarkers. Study Methodology: Project Fresh Air (PFA) was a two-group randomized controlled intervention trial aimed at reducing SHS and fine particle levels in homes with children. The sample comprised 298 homes. Study procedures were approved by the San Diego State University IRB. Recruitment began with Women, Infants, and Children Programs in San Diego County in May 2012. The Women, Infants, and Children Program is a federal assistance program providing supplemental food and nutrition education for pregnant, breastfeeding, or non-breastfeeding postpartum low-income women, infants, and children up to age 5 years who are at nutritional risk. Recruitment was expanded to include community tabling events, the U.S. Naval Medical Center San Diego and Branch Clinic Kearny Mesa, local organizations (e.g., 2-1-1 San Diego), advertisements in local papers, schools, and referrals from healthcare professionals. Recruitment ended in December 2015. Adults who submitted a recruitment form (in English or Spanish) reporting children aged <14 years in their household and allowing tobacco smoking inside/outside their home were eligible for a phone screen interview. A phone screen confirmed eligibility before scheduling a consent visit at the home. After informed consent during a home visit, study research assistants installed two air particle monitors in the home, one in the room where the most smoking occurred, and another in the room where the study child slept (as reported by the participant). At the end of a baseline period, research assistants made a second home visit to administer the pretest interview, which included questions about demographics and smoking. From 2012 to 2015, 298 families who met the following criteria were enrolled: adult parent/guardian aged ≥18 years; smoker living in the household; at least one child aged <14 years (the youngest child was selected for participation); planned to stay in San Diego County for the next 3 months; at least three air particle peaks >15,000 counts per 0.01 ft3 (i.e., 53 million counts per m3) for particles with a diameter between 0.5 and 2.5 μm during baseline that were consistent with smoking in the home; and at least one of the following: reported child exposure to SHS in the home, reported smoking in the home, reported partial ban or no ban on smoking in the home, or RA’s observation of tobacco smoking in the home. To balance group sizes, participants were allocated to treatment group (intervention or control) in a 1:1 ratio. The first participant was assigned by the field coordinator based on a computer-generated random number, and the second participant was assigned to the alternate group. This process resulted in 149 enrolled participants per group. Technical Details of Acquired Data: Research staff visited adult participants in their own home, and conducted face-to-face interviews from October 2012 to February 2016, using Questionnaire Development System (QDS) generated Computer Assisted Personal Interview (CAPI) software installed on laptop computers. QDS output the data as Excel files, which the Statistical Package for the Social Sciences (SPSS) statistical package read into an SPSS database. The data file configuration is LONG, which is common when analyzing panel data. "measure" is an index variable, and indicates the time span during which data collection occurred. The 4 measurement intervals are: 1. baseline, 2. pretest, 3. mid, 4. posttest. Data collected during measurement periods 1 and 3 are not included in the current database archive. 298 households (cases) were enrolled and randomized. Due to dropouts and missing data, there are only 288 rows in the SPSS data set for measure 2 (pretest) and even fewer, 267, at measure 4 (posttest). In variable names and labels, the abbreviation “TC” stands for Target Child (the enrolled child) the abbreviation “TP” stands for Target Parent (the enrolled parent). Additional Methodological Information: The citations of our 3 previously published journal articles are included in the metadata of this archive, and provide links to the full text of each article. The Methods sections of these articles provide a more extensive description of the methodology used.

State-level firearm suicide proxy (FSS) for household gun ownership 1949-2020. Unlike most gun prevalence measures that are representative at the national or regional level, this proxy represents household gun ownership trends at the state level and is not reliant on self-reported data that are prone to social desirability bias. This extended proxy represents the longest-ranging dataset of state-level gun ownership rates to date. This dataset also includes historic data on firearm homicide and homicide counts and rates per 100,000 residents.

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...