Search

Interest in using internet search data, such as that from the Google Health Trends Application Programming Interface (GHT-API), to measure epidemiologically relevant exposures or health outcomes is growing due to their accessibility and timeliness. Researchers enter search term(s), geography, and time period, and the GHT-API returns a scaled probability of that search term, given all searches within the specified geographic-time period. In this study, we detailed a method for using these data to measure a construct of interest in 5 iterative steps: first, identify phrases the target population may use to search for the construct of interest; second, refine candidate search phrases with incognito Google searches to improve sensitivity and specificity; third, craft the GHT-API search term(s) by combining the refined phrases; fourth, test search volume and choose geographic and temporal scales; and fifth, retrieve and average multiple samples to stabilize estimates and address missingness. An optional sixth step involves accounting for changes in total search volume by normalizing. We present a case study examining weekly state-level child abuse searches in the United States during the coronavirus disease 2019 pandemic (January 2018 to August 2020) as an application of this method and describe limitations.

The COVID-19 pandemic has led to unemployment, school closures, movement restrictions, and social isolation, all of which are child abuse risk factors. Our objective was to estimate the effect of COVID-19 shelter in place (SIP) policies on child abuse as captured by Google searches. We applied a differences-in-differences design to estimate the effect of SIP on child abuse search volume. We linked state-level SIP policies to outcome data from the Google Health Trends Application Programming Interface. The outcome was searches for child abuse-related phrases as a scaled proportion of total searches for each state-week between December 31, 2017 and June 14, 2020. Between 914 and 1512 phrases were included for each abuse subdomain (physical, sexual, and emotional). Eight states and DC were excluded because of suppressed outcome data. Of the remaining states, 38 introduced a SIP policy between March 19, 2020 and April 7, 2020 and 4 states did not. The introduction of SIP generally led to no change, except for a slight reduction in child abuse search volume in weeks 8-10 post-SIP introduction, net of changes experienced by states that did not introduce SIP at the same time. We did not find strong evidence for an effect of SIP on child abuse searches. However, an increase in total search volume during the pandemic that may be differential between states with and without SIP policies could have biased these findings. Future work should examine the effect of SIP at the individual and population level using other data sources.

Supplemental Digital Content is available in the text., Background: Fetal growth restriction is commonly defined using small for gestational age (SGA) birth (birthweight < 10th percentile) as a proxy, but this approach is problematic because most SGA infants are small but healthy. In this proof-of-concept study, we sought to develop a new approach for identifying fetal growth restriction at birth that combines information on multiple, imperfect measures of fetal growth restriction in a probabilistic manner. Methods: We combined information on birthweight, placental weight, placental malperfusion lesions, maternal disease, and fetal acidemia using latent profile analysis to classify fetal growth in births at the Royal Victoria Hospital in Montreal, Canada, 2001–2009. We examined the clinical characteristics and health outcomes of infants classified as growth-restricted and nongrowth-restricted by our model, and among the subgroup of growth-restricted infants who had a birthweight ≥10th percentile (i.e., would have been missed by the conventional SGA proxy). Results: Among 26,077 births, 345 (1.3%) were classified as growth-restricted by our latent profile model. Growth-restricted infants were more likely than nongrowth-restricted infants to have an Apgar score

Background US preterm-birth rates are 1.6 times higher for Black mothers than for White mothers. Although traffic-related air pollution (TRAP) may increase the risk of preterm birth, evaluating its effect on preterm birth and disparities has been challenging because TRAP is often measured inaccurately. This study sought to estimate the effect of TRAP exposure, measured at the street level, on the prevalence of preterm birth by race/ethnicity. Methods We linked birth-registry data with TRAP measured at the street level for singleton births in sampled communities during 2013–2015 in Oakland and San Jose, California. Using logistic regression and marginal standardization, we estimated the effects of exposure to black carbon, nitrogen dioxide and ultrafine particles on preterm birth after confounder adjustment and stratification by race/ethnicity. Results There were 8823 singleton births, of which 760 (8.6%) were preterm. Shifting black-carbon exposure from the 10th to the 90th percentile was associated with: 6.8%age point higher risk of preterm birth (95% confidence interval = 0.1 to 13.5) among Black women; 2.1%age point higher risk (95% confidence interval = –1.1 to 5.2) among Latinas; and inconclusive null findings among Asian and White women. For Latinas, there was evidence of a positive association between the other pollutants and risk of preterm birth, although effect sizes were attenuated in models that co-adjusted for other TRAP. Conclusions Exposure to TRAP, especially black carbon, may increase the risk of preterm birth for Latina and Black women but not for Asian and White women.

The biomarker-adaptive threshold design (BATD) allows researchers to simultaneously study the efficacy of treatment in the overall group and to investigate the relationship between a hypothesized predictive biomarker and the treatment effect on the primary outcome. It was originally developed for survival outcomes for Phase III clinical trials where the biomarker of interest is measured on a continuous scale. In this paper, generalizations of the BATD to accommodate count biomarkers and outcomes are developed and then studied in the multiple sclerosis (MS) context where the number of relapses is a commonly used outcome. Through simulation studies, we find that the BATD has increased power compared with a traditional fixed procedure under varying scenarios for which there exists a sensitive patient subgroup. As an illustration, we apply the procedure for two hypothesized markers, baseline enhancing lesion count and disease duration at baseline, using data from a previously completed trial. MS duration appears to be a predictive marker relationship for this dataset, and the procedure indicates that the treatment effect is strongest for patients who have had MS for less than 7.8 years. The procedure holds promise of enhanced statistical power when the treatment effect is greatest in a sensitive patient subgroup.

BackgroundIn recent decades, suicide and fatal overdose rates have increased in the US, particularly for working-age adults with no college education. The coincident decline in manufacturing has limited stable employment options for this population. Erosion of the Michigan automobile industry provides a striking case study.MethodsWe used individual-level data from a retrospective cohort study of 26 804 autoworkers in the United Autoworkers-General Motors cohort, using employment records from 1970 to 1994 and mortality follow-up from 1970 to 2015. We estimated HRs for suicide or fatal overdose in relation to leaving work, measured as active or inactive employment status and age at worker exit.ResultsThere were 257 deaths due to either suicide (n=202) or overdose (n=55); all but 21 events occurred after leaving work. The hazard rate for suicide was 16.1 times higher for inactive versus active workers (95% CI 9.8 to 26.5). HRs for suicide were elevated for all younger age groups relative to those leaving work after age 55. Those 30–39 years old at exit had the highest HR for suicide, 1.9 (95% CI 1.2 to 3.0). When overdose was included, the rate increased by twofold for both 19- to 29-year-olds and 30- to 39-year-olds at exit. Risks remained elevated when follow-up was restricted to 5 years after exit.ConclusionsAutoworkers who left work had a higher risk of suicide or overdose than active employees. Those who left before retirement age had higher rates than those who left after, suggesting that leaving work early may increase the risk.

Background The extent to which differences in homicide and suicide rates in black versus white men vary by U.S. state is unknown. Objective To compare the rates of firearm and nonfirearm homicide and suicide in black and white non-Hispanic men by U.S. state and to examine whether these deaths are associated with state prevalence of gun ownership. Design Surveillance study. Setting 50 states and the District of Columbia, 2008 to 2016. Cause-of-death data were abstracted by using the Centers for Disease Control and Prevention's WONDER (Wide-ranging Online Data for Epidemiologic Research) database. Participants Non-Hispanic black and non-Hispanic white males, all ages. Measurements Absolute rates of and rate differences in firearm and nonfirearm homicide and suicide in black and white men. Results During the 9-year study period, 84 113 homicides and 251 772 suicides occurred. Black-white differences in rates of firearm homicide and suicide varied widely across states. Relative to white men, black men had between 9 and 57 additional firearm homicides per 100 000 per year, with black men in Missouri, Michigan, Illinois, Indiana, and Pennsylvania having more than 40 additional firearm homicides per 100 000 per year. White men had between 2 fewer and 16 more firearm suicides per 100 000 per year, with the largest inequalities observed in southern and western states and the smallest in the District of Columbia and densely populated northeastern states. Limitations Some homicides and suicides may have been misclassified as deaths due to unintentional injury. Survey data on state household gun ownership were collected in 2004 and may have shifted during the past decade. Conclusion The large state-to-state variation in firearm homicide and suicide rates, as well as the racial inequalities in these numbers, highlights states where policies may be most beneficial in reducing homicide and suicide deaths and the racial disparities in their rates. Primary funding source McGill University and the National Institutes of Health.

States have fared differently in their progress towards eliminating the black-white life expectancy gap. Our objective is to describe the pattern of contributions of each of six major causes of deaths to the sex-specific black-white life expectancy gap across states over the last half-century, and identify divergent states.Using vital statistics and census data, we extracted the number of deaths and population sizes for the years 1969 to 2013, by state, gender, race, 19 age groups, and six major causes of death.Although mortality from cardiovascular disease has decreased dramatically, its contribution to the life expectancy gap increased over time for men (from 0.9 to 1.2 years), but decreased for women (from 2.4 to 1 years). The contribution of non-communicable diseases to the gap was stable over time for men (approximately 0.4 years) but decreased for women (from 0.7 to 0.2 years), while cancers exhibited an inverted-U trend for men (peaking at 1.1 years in 1988) and a stable contribution for women (approximately 0.5 years). Both genders exhibited a decreased contribution from injuries (men: 2.2 to 0.4 years), that became negative for women (women: 0.5 to -0.1 years). Several states diverged from these general trends.Life expectancy for both races has improved substantially in the US. For men, much of this improvement was due to narrowing differences in injury-related mortality, but these contributions were rivaled by an increasing gap in CVD-related mortality. In women, a crossover in injury-related mortality led to a narrower gap, realized partially by increasing mortality among whites.

Background Gestational weight gain is often characterized by the total amount of weight gained during pregnancy, however, the pattern of gain may be an important determinant of health outcomes. The SITAR (Super Imposition by Translation And Rotation) model has been used to describe childhood growth trajectories and has appeal because of the biological interpretability of its parameters. The objective of this study was to determine the feasibility of applying this model to gestational weight gain trajectories. Methods The study cohort included 3470 normal-weight, overweight, and obese women delivering at Magee-Womens Hospital in Pittsburgh, Pennsylvania, 1998 to 2010. We applied the SITAR model, a non-linear mixed effects model, to serial prenatal weight gain measurements in each pre-pregnancy body mass index (BMI) category. We fit models of varying complexity, and chose the best-fitting model to describe the pattern of weight gain (by its absolute amount, timing, and acceleration) for each BMI group. Results The most complex SITAR models failed to converge, but reduced models could successfully be fit by specifying fewer random effects and simplifying the modelling of gestational age. Best-fitting models for each BMI group explained between 95% and 97% of the variation in weight gain trajectories. Peak rates of weight gain were reached between the 20th and 22nd weeks, and were higher for normal and overweight women (0.59 kg/week and 0.57 kg/week, respectively) than obese women (0.46 kg/week). Conclusions Following some modifications, the SITAR model can be used to characterize pregnancy weight gain patterns.

Introduction Studies on the association of a polymorphism in codon 72 of the p53 tumour suppressor gene (rs1042522) with cervical neoplasia have inconsistent results. While several methods for genotyping p53 exist, they vary in accuracy and are often discrepant. Methods We used latent class models (LCM) to examine the accuracy of six methods for p53 determination, all conducted by the same laboratory. We also examined the association of p53 with cytological cervical abnormalities, recognising potential test inaccuracy. Results Pairwise disagreement between laboratory methods occurred approximately 10% of the time. Given the estimated true p53 status of each woman, we found that each laboratory method is most likely to classify a woman to her correct status. Arg/Arg women had the highest risk of squamous intraepithelial lesions (SIL). Test accuracy was independent of cytology. There was no strong evidence for correlations of test errors. Discussion Empirical analyses ignore possible laboratory errors, and so are inherently biased, but test accuracy estimated by the LCM approach is unbiased when model assumptions are met. LCM analysis avoids ambiguities arising from empirical test discrepancies, obviating the need to regard any of the methods as a “gold” standard measurement. The methods we presented here to analyse the p53 data can be applied in many other situations where multiple tests exist, but where none of them is a gold standard.