Disentangling Selection into Mode from Mode Effects.

Objectives: We investigate the impact of data collection mode on responses to variables in NSHAP Round 4 and discuss how potential mode differences should (and should not) be addressed in substantive analyses.

Methods: Among the set of respondents who were eligible to be contacted remotely in Round 4, we randomly selected 398 to be contacted instead for an in-person interview. We compare response rates and the distributions of selected key outcomes among those 398 respondents to those among the control group who were initially approached remotely.

Design and Implementation of the Surveys of Women: Protocol for an Address-Based Sampling Multimodal Study.

Background: Studies conducted in the United States such as the National Survey of Family Growth (NSFG) and the Pregnancy Risk Assessment Monitoring System (PRAMS) collect data on pregnancy intentions to aid in improving health education, services, and programs. PRAMS collects data from specific sites, and NSFG is a national household-based survey. Like NSFG, the Surveys of Women was designed to survey participants residing in households using an address-based sample and a multimode data collection approach.

Making Sense of Sensor Data: How Local Environmental Conditions Add Value to Social Science Research.

Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The U.S.

Sample Design and Estimation in the National Social Life, Health, and Aging Project: Round 3 (2015-2016).

Objectives: This article, and corresponding articles for the earlier rounds of the National Social Life, Health, and Aging Project (NSHAP), provide the scientific underpinning for the statistical analysis of NSHAP data. The 2015-2016 round of data collection for NSHAP comprised the third wave of data collection for the original cohort born 1920-1947 (C1) and the first wave of data collection for a second cohort born 1948-1965 (C2). Here we describe (a) our protocol for reinterviewing C1; (b) our approach to the sample design for C2, including the frame construction, stratification, clustering, and within-household selection; and (c) the construction of cross-sectional weights for the entire 2015-2016 sample when analyzed at the individual level or when analyzed as a sample of cohabiting couples.

Image Processing for Public Health Surveillance of Tobacco Point-of-Sale Advertising: Machine Learning-Based Methodology.

Background: With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available.

Objective: The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph.

Sociodemographic Disparities in the Tobacco Retail Environment in Washington, DC: A Spatial Perspective.

Objective: Studies assessing sociodemographic disparities in the tobacco retail environment have relied heavily on non-spatial analytical techniques, resulting in potentially misleading conclusions. We utilized a spatial analytical framework to evaluate neighborhood sociodemographic disparities in the tobacco retail environment in Washington, DC (DC) and the DC metropolitan statistical area (DC MSA).

Methods: Retail tobacco availability for DC (n=177) and DC MSA (n=1,428) census tract was assessed using adaptive-bandwidth kernel density estimation.

Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP.

Objectives: The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible.

Method: There were 2 important innovations. First, the scope of the study was expanded by collecting data from coresident spouses or romantic partners.

The onset of depression during the great recession: foreclosure and older adult mental health.

Objectives: We examined neighborhood-level foreclosure rates and their association with onset of depressive symptoms in older adults.

Methods: We linked data from the National Social Life, Health, and Aging Project (2005-2006 and 2010-2011 waves), a longitudinal, nationally representative survey, to data on zip code-level foreclosure rates, and predicted the onset of depressive symptoms using logit-linked regression.

Results: Multiple stages of the foreclosure process predicted the onset of depressive symptoms, with adjustment for demographic characteristics and changes in household assets, neighborhood poverty, and visible neighborhood disorder.