Medicaid Expansion and Racial-Ethnic and Sex Disparities in Cardiovascular Diseases Over 6 Years: A Generalized Synthetic Control Approach.

BACKGROUND: Studies have suggested Medicaid expansion enacted in 2014 has resulted in a reduction in overall cardiovascular disease (CVD) mortality in the United States. However, it is unknown whether Medicaid expansion has a similar effect across race-ethnicity and sex. We investigated the effect of Medicaid expansion on CVD mortality across race-ethnicity and sex. METHODS: Data come from the behavioral risk factor surveillance system and the US Centers for Disease Control's Wide-ranging Online Data for Epidemiologic Research, spanning the period 2000-2019. We used the generalized synthetic control method, a quasi-experimental approach, to estimate effects. RESULTS: Medicaid expansion was associated with -5.36 (mean difference [MD], 95% confidence interval [CI] = -22.63, 11.91) CVD deaths per 100,000 persons per year among Blacks; -4.28 (MD, 95% CI = -30.08, 21.52) among Hispanics; -3.18 (MD, 95% CI = -8.30, 1.94) among Whites; -5.96 (MD, 95% CI = -15.42, 3.50) among men; and -3.34 (MD, 95% CI = -8.05, 1.37) among women. The difference in mean difference (DMD) between the effect of Medicaid expansion in Blacks compared with Whites was -2.18; (DMD, 95% CI = -20.20, 15.83); between that in Hispanics compared with Whites: -1.10; (DMD, 95% CI = -27.40, 25.20) and between that in women compared with men: 2.62; (DMD, 95% CI = -7.95, 13.19). CONCLUSIONS: Medicaid expansion was associated with a reduction in CVD mortality overall and in White, Black, Hispanic, male, and female subpopulations. Also, our study did not find any difference or disparity in the effect of Medicaid on CVD across race-ethnicity and sex-gender subpopulations, likely owing to imprecise estimates.

The role of breastfeeding support in racial/ethnic disparities in breastfeeding practices.

BACKGROUND: Persistent racial/ethnic disparities in breastfeeding practices in the United States are well documented but the underlying causes remain unclear. While racial/ethnic disparities are often intertwined with socioeconomic disparities in breastfeeding, studies suggest that lack of breastfeeding support from family, health care organizations and workplaces may contribute to racial/ethnic disparities in breastfeeding rates. No studies have investigated the extent to which racial/ethnic disparities in breastfeeding practices can be explained by breastfeeding support. METHODS: We used survey data from participants of a federal nutrition assistance program in Los Angeles County, the most populous county in the United States, to examine causal mechanisms underlying racial/ethnic disparities in breastfeeding in five groups: Spanish-speaking Latina, English-speaking Latina, Non-Hispanic White (NHW), Non-Hispanic Black (NHB) and Non-Hispanic Asian (NHA). Applying causal mediation analysis, this study estimated the proportion of racial/ethnic differences in breastfeeding ('any' breastfeeding, i.e., partial or exclusive) rates at 6 months that could be explained by differential access to breastfeeding support from family, birth hospitals and workplaces. RESULTS: NHB and English-speaking Latina mothers were less likely, and Spanish-speaking Latina mothers more likely to breastfeed through 6 months than NHW mothers. Lack of breastfeeding support from family, hospitals and workplaces accounted for approximately 68% of the difference in any breastfeeding rates at 6 months between NHW and NHB mothers and 36% of the difference between NHW and English-speaking Latina mothers. CONCLUSION: These findings highlight the importance of improving support from family, hospitals and workplaces for breastfeeding mothers to reduce racial/ethnic disparities in breastfeeding.

Contributions of neighborhood physical and social environments to racial and ethnic disparities in birth outcomes in California: A mediation analysis.

BACKGROUND: Racially minoritized populations experience higher rates of adverse birth outcomes than White populations in the U.S. We estimated the mediating effect of neighborhood social and physical environments on disparities in adverse birth outcomes in California. METHOD: We used birthing parent's residential address for California live birth records from 2019 to estimate census block group Area Deprivation Index and census tract level measures of ambient fine particulate matter (PM2.5), drinking water contamination, tree canopy coverage, as a measure of greenspace, potential heat vulnerability, and noise. We performed mediation analysis to assess whether neighborhood factors explain racial/ethnic disparities in preterm birth (PTB) and term-birth low birth weight (TLBW) comparing Black, Latinx, and Asian with White births after controlling for individual-level factors. RESULTS: Black, Latinx, and Asian parents had PTB rates that were 67%, 36%, and 11% higher, and TLBW rates that were 150%, 38%, and 81% higher than Whites. Neighborhood deprivation contributed 7% (95% CI: 3%, 11%) to the Black-White and 9% (95% CI: 6%, 12%) to the Latinx-White disparity in PTB, and 8% (95% CI: 3%, 12%) of the Black-White and 9% (95% CI: 5%, 15%) of the Latinx-White disparity in TLBW. Drinking water contamination contributed 2% (95% CI: 1%, 4%) to the Latinx-White disparity in PTB. Lack of greenspace accounted for 7% (95% CI: 2%, 10%) of the Latinx-White PTB disparity and 7% (95% CI: 3%, 12%) of the Asian-White PTB disparity. PM2.5 contributed 11% (95% CI: 5%, 18%), drinking water contamination contributed 3% (95% CI: 1%, 7%), and potential heat vulnerability contributed 2% (95% CI: 1%, 3%) to the Latinx-White TLBW disparity. Lack of green space contributed 3% (95% CI: 1%, 6%) to the Asian-White TLBW disparity. CONCLUSIONS: Our study suggests social environments explain portions of Black/Latinx-White disparities while physical environments explain Latinx/Asian-White disparities in PTB and TLBW.

Medicaid Expansion's Impact on Emergency Department Use by State and Payer.

OBJECTIVES: The Affordable Care Act's Medicaid Expansion Program influences healthcare use by increasing insurance coverage. Of particular interest is how this will affect inefficient and expensive emergency department (ED) visits. We estimated the impact of the Medicaid expansion on ED use by states and payer (Medicaid, private insurance, and uninsured) 5 years after the implementation of the Medicaid expansion and illustrated the use of the generalized synthetic control method. METHODS: In this quasi-experiment study, we implemented the generalized synthetic control method to compare states with Medicaid expansion and states without Medicaid expansion. Data were from the Healthcare Cost Utilization Project Fast Stats, which cover >95% of all ED visits. We included states with complete data from 2010 to 2018. RESULTS: Overall, the Medicaid expansion increased Medicaid share of ED visits (average treatment effect on the treated [ATT] 11.39%; 95% confidence interval [CI] 8.76-14.02) and decreased private share of ED visits (ATT -5.80%; 95% CI -7.40 to -4.12) and uninsured share of ED visits (ATT -6.66%; 95% CI -9.78 to -3.55). CONCLUSIONS: Medicaid Expansion Program shifted ED payer mix to Medicaid ED visits from private insurance and uninsured ED visits for adults at age of 19 to 64 years, whereas its effect on total ED volume is mixed among states. States that experienced the largest increase in Medicaid enrollment seem to experience an increase in ED visits although such results did not reach statistical significance.

Optimal uses of pooled testing for COVID-19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County.

INTRODUCTION: Pooled testing is a potentially efficient alternative strategy for COVID-19 testing in congregate settings. We evaluated the utility and cost-savings of pooled testing based on imperfect test performance and potential dilution effect due to pooling and created a practical calculator for online use. METHODS: We developed a 2-stage pooled testing model accounting for dilution. The model was applied to hypothetical scenarios of 100 specimens collected during a one-week time-horizon cycle for varying levels of COVID-19 prevalence and test sensitivity and specificity, and to 338 skilled nursing facilities (SNFs) in Los Angeles County (Los Angeles) (data collected and analyzed in 2020). RESULTS: Optimal pool sizes ranged from 1 to 12 in instances where there is a least one case in the batch of specimens. 40% of Los Angeles SNFs had more than one case triggering a response-testing strategy. The median number (minimum; maximum) of tests performed per facility were 56 (14; 356) for a pool size of 4, 64 (13; 429) for a pool size of 10, and 52 (11; 352) for an optimal pool size strategy among response-testing facilities. The median costs of tests in response-testing facilities were $8250 ($1100; $46,100), $6000 ($1340; $37,700), $6820 ($1260; $43,540), and $5960 ($1100; $37,380) when adopting individual testing, a pooled testing strategy using pool sizes of 4, 10, and optimal pool size, respectively. CONCLUSIONS: Pooled testing is an efficient strategy for congregate settings with a low prevalence of COVID-19. Dilution as a result of pooling can lead to erroneous false-negative results.

Investigating the Role of Childhood Adiposity in the Development of Adult Type 2 Diabetes in a 64-year Follow-up Cohort: An Application of the Parametric G-formula Within an Agent-based Simulation Study.

BACKGROUND: The contribution of childhood obesity to adult type 2 diabetes (T2DM), not through adult adiposity, as well as the causal pathways through which childhood obesity increases adult T2DM risk are not well understood. This study investigated the contribution of childhood obesity to incident T2DM including pathways not through adult adiposity, and explored whether race modified this contribution. METHODS: We used data from the Virtual Los Angeles Cohort, an agent-based longitudinal birth cohort composed of 98,230 simulated individuals born in 2009 and followed until age 65 years. We applied the parametric mediational g-formula to the causal mediation analysis investigating the impact of childhood obesity on the development of adult T2DM. RESULTS: The marginal adjusted odds ratio (aOR) for the total effect of childhood obesity on adult T2DM was 1.37 (95% CI = 1.32, 1.46). Nearly all the effect of childhood obesity on adult T2DM was mostly attributable to pathways other than through adult obesity; the aOR for the pure direct effect was 1.36 (95% CI = 1.31, 1.41). In all racial subpopulations, a similar 3% of the total effect of childhood obesity on adult T2DM was attributable to its effect on adult obesity. CONCLUSIONS: Childhood obesity remains a risk factor for adult T2DM separate from its effects on adult obesity. This study emphasizes the potential benefits of early interventions and illustrates that agent-based simulation models could serve as virtual laboratories for exploring mechanisms in obesity research.

Economic evaluation of California prenatal participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) to prevent preterm birth.

There is growing evidence that prenatal participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) reduces the risk of adverse birth outcomes. With recent changes in health care, rising health care costs, and increasing rates of prematurity in the U.S., there is urgency to estimate the potential cost savings associated with prenatal WIC participation. A cost-benefit analysis from a societal perspective with a time horizon over the newborn's life course for a hypothetical cohort of 500,000 Californian pregnant women was conducted in 2017. A universal coverage, a status quo ('business as usual') and a reference scenario (absence of WIC) were compared. Total societal costs, incremental cost savings, return on investment, number of preterm births prevented, and incremental net monetary benefits were reported. WIC resulted in cost-savings of about $349 million and the prevention of 7575 preterm births and would save more if it were universal. Spending $1 on prenatal WIC resulted in mean savings of $2.48 (range: $1.24 to $6.83). Decreasing prenatal WIC enrollment by 10% would incur additional costs (i.e. loss) of about $45.3 million to treat the resulting 981 preterm babies. In contrast, a 10% increase in prenatal WIC enrollment would prevent 141 preterm births and achieve additional cost-savings of $6.5 million. The findings confirm evaluations from the early 1990s that prenatal WIC participation is cost-saving and cost-effective. Further savings could be achieved if all eligible women were enrolled in WIC. Substantial preterm birth-related costs would result from reductions in WIC participation.

G-computation of average treatment effects on the treated and the untreated.

BACKGROUND: Average treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation. METHODS: To obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention. RESULTS: The estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was -0.019 (95% CI: -0.040, -0.007) and that among those who have less than a high school education in India (ATU) was -0.012 (95% CI: -0.036, 0.010). CONCLUSIONS: The g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.

Substance use and sexual risk behaviors among Peruvian MSM social media users.

Peru is experiencing a concentrated HIV epidemic among men who have sex with men (MSM). Substance use (alcohol and drug use) has been found to be associated with HIV-related sexual risk behaviors. A recent surge in the number of social media users in Peru has enabled these technologies to be potential tools for reaching HIV at-risk individuals. This study sought to assess the relationship between substance use and sexual risk behaviors among Peruvian MSM who use social media. A total of 556 Peruvian MSM Facebook users (ages 18-59) were recruited to complete a 92-item survey on demographics, sexual risk behaviors, and substance use. We performed a logistic regression of various sexual risk behaviors (e.g., unprotected sex, casual sex) on substance abuse, including alcohol, adjusting for potential covariates. Drinking more than five alcoholic drinks a day in the past three months was associated with an increased odds of having unprotected sex (vaginal and anal) (aOR: 1.52; 95% CL: 1.01, 2.28), casual sex (1.75; 1.17, 2.62), and sex with unknown persons (1.82; 1.23, 2.71). Drug use was not significantly associated with sexual risk behaviors. Among Peruvian MSM social media users, findings suggest that alcohol use was associated with increased HIV-related sexual risk behaviors.

Associations between self-perception of weight, food choice intentions, and consumer response to calorie information: a retrospective investigation of public health center clients in Los Angeles County before the implementation of menu-labeling regulation.

BACKGROUND: Although obesity continues to rise and remains a great public health concern in the U.S., a number of important levers such as self-perception of weight and calorie postings at point-of-purchase in restaurants are still not well-characterized in the literature, especially for low-income and minority groups in Los Angeles County (LAC). To study this gap, we examined the associations of self-perception of weight (as measured by body weight discrepancy) with food choice intentions and consumer response to calorie information among low-income adults residing in LAC during the pre-menu labeling regulation era. METHODS: Descriptive and multivariable logistic regression analyses were performed to examine the aforementioned associations utilizing data from the 2007-2008 Calorie and Nutrition Information Survey (CNIS). The CNIS was a local health department study of 639 low-income adults recruited from five large, multi-purpose public health centers in LAC. RESULTS: Survey participants who reported that their desired weight was less than their current weight (versus desired weight the same as current weight) had (i) higher odds of intending to select lower-calorie foods under the scenario that calorie information was available at point-of-purchase (aOR = 2.0; 95 % CI: 1.0-3.9); and (ii) had higher odds of reporting that it is "very important" to have these calorie postings on food items in grocery stores (aOR = 3.1; 95 % CI: 0.90-10.7) and in fast-food restaurants (aOR = 3.4; 95 % CI: 1.0-11.4). CONCLUSIONS: Self-perception of weight was found to be associated with the intention to select lower-calorie foods under the scenario that calorie information was available at point-of-purchase. Future public health efforts to support menu labeling implementation should consider these and other findings to inform consumer education and communications strategies that can be tailored to assist restaurant patrons with this forthcoming federal law.

Agent-based modeling of noncommunicable diseases: a systematic review.

We reviewed the use of agent-based modeling (ABM), a systems science method, in understanding noncommunicable diseases (NCDs) and their public health risk factors. We systematically reviewed studies in PubMed, ScienceDirect, and Web of Sciences published from January 2003 to July 2014. We retrieved 22 relevant articles; each had an observational or interventional design. Physical activity and diet were the most-studied outcomes. Often, single agent types were modeled, and the environment was usually irrelevant to the studied outcome. Predictive validation and sensitivity analyses were most used to validate models. Although increasingly used to study NCDs, ABM remains underutilized and, where used, is suboptimally reported in public health studies. Its use in studying NCDs will benefit from clarified best practices and improved rigor to establish its usefulness and facilitate replication, interpretation, and application.

Evaluating the impact of the Medicaid expansion program on diabetes hospitalization.

Diabetes is the most expensive chronic disease in the United States, and hospital inpatient care accounts for 30% of the total medical expenditures. Medical costs for people with limited resources are covered by Medicaid, a joint federal and state program, and its expansion that extent the coverage to those with incomes up to 138% of the federal poverty level. We investigated the impact of Medicaid expansion on diabetes hospitalizations by states and payer, among adults aged 19 to 64 years old, 5 years after the expansion. We found that Medicaid expansion decreased total diabetes hospitalization in most states and a diabetes hospitalization payer mix shifted from private insurance and uninsured to Medicaid. The percentage of diabetes hospitalizations paid by Medicaid increased by 11% (95% CI 7%, 16%), while the percentage paid by private insurance decreased by 6% (95% CI - 8%, - 3%) and the percentage of uninsured diabetes hospitalization decreased by 13% (95% CI - 18%, - 9%).

Exploring the role of blood pressure in the black-white disparity in cardiovascular disease mortality: a causal mediation analysis.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading cause of death in the USA, and high blood pressure is a major risk factor for CVD. Despite the overall declining rates of CVD mortality in the USA in recent years, marked disparities between racial and ethnic groups persist, with black adults having a higher mortality rate than white adults. We investigated the extent to which blood pressure mediated the black-white disparity in CVD mortality. METHODS: Data came from the Multi-Ethnic Study of Atherosclerosis, a diverse longitudinal cohort. We included 1325 black and 2256 white community-based adults aged 45-80 years free of clinical CVD at baseline and followed for 14 years. We used causal mediation analysis to estimate the effect of race on CVD mortality that was mediated through blood pressure. RESULTS: Black participants had a higher hazard of dying from CVD compared with white participants (adjusted hazard ratio (HR): 1.28 (95% CI 0.88, 1.88)), though estimates were imprecise. Systolic blood pressure mediated 27% (HR: 1.02, 95% CI 1.00, 1.06) and diastolic blood pressure mediated 55% (HR: 1.07, 95% CI 1.01, 1.10) of the racial disparities in CVD mortality between white and black participants. Mediation effects were present in men but not in women. CONCLUSIONS: We found that black-white differences in blood pressure partially explain the observed black-white disparity in CVD mortality, particularly among men. Our findings suggest that public health interventions targeting high blood pressure prevention and management could be important strategies for reducing racial disparities in CVD mortality.

Forecasting the 20-Year Incidence of Dementia by Socioeconomic Status, Race/Ethnicity, and Region Based on Mid-Life Risk Factors in a U.S. Nationally Representative Sample.

Background: Alzheimer's disease and related dementias (ADRD) incidence varies based on demographics, but mid-life risk factor contribution to this variability requires more research. Objective: The purpose of this study is to forecast the 20-year incidence of dementia in the U.S. overall and stratified by race/ethnicity, socioeconomic status (SES), and U.S. geographic region given prior mid-life risk factor prevalence and to examine the extent to which risk factor differences 20 years ago may explain current SES, race/ethnicity, or regional disparities in dementia incidence. Methods: We applied the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) prediction model to the 2006 wave of the Health and Retirement Study (HRS) in participants aged 45 to 64 to estimate the 20-year risk of incident ADRD. Results: The 20-year risk of dementia among middle-aged Americans was 3.3% (95% CI: 3.2%, 3.4%). Dementia incidence was forecast to be 1.51 (95% CI: 1.32, 1.71) and 1.27 (95% CI: 1.14, 1.44) times that in Hispanic and Non-Hispanic Black individuals respectively compared statistically to Non-Hispanic White individuals given mid-life risk factors. There was a progressive increase in dementia risk from the lowest versus highest SES quintile. For geographic region, dementia incidence was forecast to be 1.17 (95% CI: 1.06, 1.30) and 1.27 (95% CI: 1.14, 1.43) times that in Midwestern and Southern individuals respectively compared statistically to Western individuals. Conclusions: Some disparities in dementia incidence could be explained by differences in mid-life risk factors and may point toward policy interventions designed to lessen the ADRD disease burden through early prevention.

Cost-effectiveness analysis of insecticide ban aimed at preventing Parkinson's disease in Central California.

BACKGROUND: Our study assessed whether banning specific insecticides to reduce the PD burden in three Central California (CA) counties is cost-effective. METHOD: We applied a cost-effectiveness analysis using a cohort-based Markov model to estimate the impact and costs of banning seven insecticides that were previously associated with PD in these counties as well as mixture exposures to some of these pesticides. We relied for our estimations on the cohort of 65- and 66-year-olds living in these counties who were unaffected by PD at baseline in 2020 and projected their incidence, costs, and reduction in quality-adjusted-life-years (QALY) loss due to developing PD over a 20-year period. We included a shiny app for modeling different scenarios (https://sherlockli.shinyapps.io/pesticide_pd_economics_part_2/). RESULTS: According to our scenarios, banning insecticides to reduce the occurrence of PD in three Central CA counties was cost-effective relative to not banning insecticides. In the worst-case scenario of exposure to a single pesticide, methomyl, versus none would result in an estimated 205 (95 % CI: 75, 348) additional PD cases or 12 % (95 % CI: 4 %, 20 %) increase in PD cases over a 20-year period based on residential proximity to pesticide applications. The increase in PD cases due to methomyl would increase health-related costs by $72.0 million (95 % CI: $5.5 million, $187.4 million). Each additional PD patient due to methomyl exposure would incur $109,327 (95 % CI, $5554, $347,757) in costs per QALY loss due to PD. Exposure to methomyl based on workplace proximity to pesticide applications generated similar estimates. The highest PD burden and associated costs would be incurred from exposure to multiple pesticides simultaneously. CONCLUSION: Our study provides an assessment of the cost-effectiveness of banning specific insecticides to reduce PD burden in terms of health-related QALYs and related costs. This information may help policymakers and stakeholders to make decisions concerning the regulation of pesticides.

The Role of Inflammation and Psycho-Behavioral Factors as Mediators Between Acculturation and Time to Incident Dementia Among Older Mexican Americans.

Background: The mechanisms through which acculturation influences the onset of cognitive impairment and dementia are not well understood, especially among older Hispanics. Objective: To investigate whether inflammation and psycho-behavioral factors mediate the relationship between acculturation and incident dementia among older Mexican Americans. Methods: We analyzed the Sacramento Area Latino Study on Aging (1998-2007, SALSA), a longitudinal study (N = 1,194) with 10 years of follow-up, and used g-computation for mediation analysis with pooled logistic regression to evaluate whether acculturation (assessed by the Revised Acculturation Rating Scale for Mexican Americans [ARSMA-II]) affected dementia or cognitive impairment but not dementia (CIND) through inflammation (i.e., interleukin 6 [IL-6], tumor necrosis factor-α (TNF-α), high-sensitivity C-reactive protein [hs-CRP]), smoking, alcohol consumption, and depressive symptoms. The potential mediators were assessed at baseline. Results: The 10-year average adjusted risk ratio (aRR) for the effect of high U.S. acculturation and dementia/CIND was 0.66, 95% CI (0.36, 1.30). The indirect effects were: IL-6 (aRR = 0.98, 95% CI (0.88, 1.05)); TNF-α (aRR:0.99, 95% CI (0.93, 1.05)); hs-CRP: (aRR = 1.21, 95% CI (0.84, 1.95)); current smoking: aRR = 0.97, 95% CI (0.84, 1.16); daily/weekly alcohol consumption (aRR = 1.00, 95% CI (0.96, 1.05)); and depressive symptom score (aRR = 1.03, 95% CI (0.95, 1.26)). Hs-CRP yielded a proportion mediated of -26%, suggesting that hs-CRP could suppress the potential effect of high U.S. acculturation. The other factors explored resulted in little to no mediation. Conclusions: The effect of acculturation on time to incident dementia/CIND varied over time. Our study suggests that inflammation could suppress the effect between high U.S. acculturation and dementia risk.

Association Between Gestational Diabetes Mellitus and Hypertension: A Systematic Review and Meta-Analysis of Cohort Studies With a Quantitative Bias Analysis of Uncontrolled Confounding.

BACKGROUND: Whether individuals with gestational diabetes mellitus (GDM) had an increased risk of hypertension remains unclear. We conducted a systematic literature review and meta-analysis to examine the association between GDM and hypertension and performed a quantitative bias analysis to quantify the impact of uncontrolled confounding due to antenatal psychological stress. METHODS: We searched databases (PUBMED, EMBASE, and Web of Science) through 2022/11. Eligible studies were cohort studies that reported the association of GDM with hypertension. We assessed the risk of bias using the Newcastle-Ottawa Scale for cohort studies. We pooled adjusted risk ratios with 95% CIs using a random effects model. We performed the quantitative bias analysis using the bias formula. RESULTS: We included 15 cohort studies, with a total of 3 959 520 (GDM, 175 378; non-GDM, 3 784 142) individuals. During the follow-up of 2 to 20 years, 106 560 cases of hypertension were reported. We found that GDM was associated with a higher risk of hypertension (pooled risk ratio, 1.78 [95% CI, 1.47, 2.17]). The risk ratio was lower among cohorts assessing incident (1.58 [95% CI, 1.29, 1.95]) than prevalent hypertension (2.60 [95% CI, 2.40, 2.83]). However, other subgroup analyses showed no differences. The quantitative bias analysis revealed that if the uncontrolled confounder of antenatal psychological stress was additionally adjusted, the positive association between GDM and hypertension would attenuate slightly (≤18%) but remains positive. CONCLUSIONS: Limitations of this study included residual confounding and discrepancies in GDM and hypertension ascertainments. Our findings indicate that GDM is positively associated with hypertension after the index pregnancy.

Genetically Determined Circulating Lactase/Phlorizin Hydrolase Concentrations and Risk of Colorectal Cancer: A Two-Sample Mendelian Randomization Study.

Previous research has found that milk is associated with a decreased risk of colorectal cancer (CRC). However, it is unclear whether the milk digestion by the enzyme lactase-phlorizin hydrolase (LPH) plays a role in CRC susceptibility. Our study aims to investigate the direct causal relationship of CRC risk with LPH levels by applying a two-sample Mendelian Randomization (MR) strategy. Genetic instruments for LPH were derived from the Fenland Study, and CRC-associated summary statistics for these instruments were extracted from the FinnGen Study, PLCO Atlas Project, and Pan-UK Biobank. Primary MR analyses focused on a cis-variant (rs4988235) for LPH levels, with results integrated via meta-analysis. MR analyses using all variants were also undertaken. This analytical approach was further extended to assess CRC subtypes (colon and rectal). Meta-analysis across the three datasets illustrated an inverse association between genetically predicted LPH levels and CRC risk (OR: 0.92 [95% CI, 0.89-0.95]). Subtype analyses revealed associations of elevated LPH levels with reduced risks for both colon (OR: 0.92 [95% CI, 0.89-0.96]) and rectal cancer (OR: 0.92 [95% CI, 0.87, 0.98]). Consistency was observed across varied analytical methods and datasets. Further exploration is warranted to unveil the underlying mechanisms and validate LPH's potential role in CRC prevention.

A comparison of quasi-experimental methods with data before and after an intervention: an introduction for epidemiologists and a simulation study.

BACKGROUND: As the interest in and use of quasi-experimental methods to evaluate impacts of health policies have dramatically increased in the epidemiological literature, we set out this study to (i) systematically compare several quasi-experimental methods that use data before and after an intervention and contrast their performance within a simulation framework while providing a brief overview of the methods; and (ii) discuss challenges that could arise from using these methods as well as directions for future research in the context of epidemiological applications. METHODS: We considered single-group designs [pre-post and interrupted time series (ITS)] and multiple-group designs [controlled interrupted time series/difference-in-differences, synthetic control methods (SCMs): traditional SCMs and generalized SCMs]. We assessed performance based on bias and root mean squared error. RESULTS: We identified settings in which each method failed to provide unbiased estimates. We found that, among the methods investigated, when data for multiple time points and for multiple control groups are available (multiple-group designs), data-adaptive methods such as the generalized SCM were generally less biased than other methods evaluated in our study. In addition, when all of the included units have been exposed to treatment (single-group designs) and data for a sufficiently long pre-intervention period are available, then the ITS performs very well, provided the underlying model is correctly specified. CONCLUSIONS: When using a quasi-experimental method using data before and after an intervention, epidemiologists should strive to use, whenever feasible, data-adaptive methods that nest alternative identifying assumptions including relaxing the parallel trend assumption (e.g. generalized SCMs).

Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach.

Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999-2002 and 2003-2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL). Three models included BMI, BMI2, age, gender, education, income, and interaction terms and produced R-squared values of 0.87 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was -0.005. Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.