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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%). [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Background: The complex multifactorial nature of childhood obesity makes community interventions difficult to evaluate using traditional approaches; innovative methods are needed. Objective: To evaluate the impact of various interventions targeting childhood obesity‐related behaviours, and classified as using a micro‐level (e.g., home visitation programs) or macro‐level (e.g., business practices) strategy, on obesity among children enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Methods: We simulated a population of 1500 children enrolled in WIC, with specific diet, physical activity, breastfeeding behaviours and body mass index z‐scores (BMIz), following them from age 2 to 5 years. Results: Combined interventions targeting breastfeeding appeared to be moderately effective, reducing BMIz by 0.03 (95% CI −005, −0.01). Two strategy‐specific interventions, home visitation programs and business practices targeting obesity‐related behaviours, appeared to be moderately effective at reducing BMIz by 0.04 (95% CI −0.06, −0.02) and 0.02 (95% CI −0.04, 0.00), respectively. Contrary to expectation, combining all micro and macro interventions appeared to have no impact or moderately increased the proportion of obesity/overweight among children. Conclusion: Interventions targeting breastfeeding behaviour were most effective when both micro and macro strategies were implemented. Interventions targeting obesity‐related behaviours in general were effective for two strategies, home visitation and business practices. [ABSTRACT FROM AUTHOR]

Background: We recently estimated that 36.9% of Alzheimer's disease and related dementias (ADRD) cases in the US may be attributable to modifiable risk factors, but it is not known whether national estimates generalize to specific states or regions.Objective: To compare national estimates of modifiable risk factors of ADRD to California, overall and by sex and race/ethnicity, and to estimate number of cases potentially preventable by reducing the prevalence of key risk factors by 25%.Methods: Adults ≥18 years who participated in the Behavioral Risk Factor Surveillance Survey in California (n = 9,836) and the US (n = 378,615). We calculated population attributable risks (PARs) for eight risk factors (physical inactivity, current smoking, depression, low education, diabetes mellitus, midlife obesity, midlife hypertension, and hearing loss) and compared estimates in California and the U.S.Results: In California, overall, 28.9% of ADRD cases were potentially attributable to the combination of risk factors, compared to 36.9% in the U.S. The top three risk factors were the same in California and the U.S., although their relative importance differed (low education [CA:14.9%; U.S.:11.7% ], midlife obesity [CA:14.9%; U.S.:17.7% ], and physical inactivity [CA:10.3%; U.S.:11.8% ]). The number of ADRD cases attributable to the combined risk factors was 199,246 in California and 2,287,683 in the U.S. If the combined risk factors were reduced by 25%, we could potentially prevent more than 40,000 cases in California and 445,000 cases in the U.S.Conclusion: Our findings highlight the importance of examining risk factors of ADRD regionally, and within sex and race/ethnic groups to tailor dementia risk reduction strategies. [ABSTRACT FROM AUTHOR]

Simulation methods are a powerful set of tools that can allow researchers to better characterize phenomena from the real world. As such, the ability to simulate data represents a critical set of skills that epidemiologists should use to better understand epidemiologic concepts and ensure that they have the tools to continue to self-teach even when their formal instruction ends. Simulation methods are not always taught in epidemiology methods courses, whereas causal directed acyclic graphs (DAGs) often are. Therefore, this paper details an approach to building simulations from DAGs and provides examples and code for learning to perform simulations. We recommend using very simple DAGs to learn the procedures and code necessary to set up a simulation that builds on key concepts frequently of interest to epidemiologists (e.g. mediation, confounding bias, M bias). We believe that following this approach will allow epidemiologists to gain confidence with a critical skill set that may in turn have a positive impact on how they conduct future epidemiologic studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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. 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. These findings highlight the importance of improving support from family, hospitals and workplaces for breastfeeding mothers to reduce racial/ethnic disparities in breastfeeding. • Breastfeeding rates at 6 months differed by race/ethnicity • Access to breastfeeding support explained a substantial portion of the difference • Support from family, hospitals and workplaces is essential to promote breastfeeding • Causal mediation analysis is used to estimate the effect of breastfeeding support [ABSTRACT FROM AUTHOR]

Objective: It is unclear whether relatively low glycated haemoglobin (HbA1c) levels are beneficial or harmful for the long-term health outcomes among people without diabetes. We aimed to investigate the association between low HbA1c levels and mortality among the US general population.Methods: This study includes a nationally representative sample of 39 453 US adults from the National Health and Nutrition Examination Surveys 1999-2014, linked to mortality data through 2015. We employed the parametric g-formula with pooled logistic regression models and the ensemble machine learning algorithms to estimate the time-varying risk of all-cause and cardiovascular mortality by HbA1c categories (low, 4.0 to <5.0%; mid-level, 5.0 to <5.7%; prediabetes, 5.7 to <6.5%; and diabetes, ≥6.5% or taking antidiabetic medication), adjusting for 72 potential confounders including demographic characteristics, lifestyle, biomarkers, comorbidities and medications.Results: Over a median follow-up of 7.5 years, 5118 (13%) all-cause deaths, and 1116 (3%) cardiovascular deaths were observed. Logistic regression models and machine learning algorithms showed nearly identical predictive performance of death and risk estimates. Compared with mid-level HbA1c, low HbA1c was associated with a 30% (95% CI, 16 to 48) and a 12% (95% CI, 3 to 22) increased risk of all-cause mortality at 5 years and 10 years of follow-up, respectively. We found no evidence that low HbA1c levels were associated with cardiovascular mortality risk. The diabetes group, but not the prediabetes group, also showed an increased risk of all-cause mortality.Conclusions: Using the US national database and adjusting for an extensive set of potential confounders with flexible modelling, we found that adults with low HbA1c were at increased risk of all-cause mortality. Further evaluation and careful monitoring of low HbA1c levels need to be considered. [ABSTRACT FROM AUTHOR]

In order to develop effective public health initiatives aimed at promoting healthy weight development, identifying the interventions/combination of interventions with the highest beneficial effect on body weight is vital. The study aimed to estimate the mean BMI at age 13 under hypothetical interventions targeting dietary behavior, physical activity and screen time at age 11. We used data from a school-based cohort study of 530 participants followed between the ages of 11 and 13. We used g-computation, a causal modeling method, to estimate the impact of single and combined hypothetical behavioral interventions at age 11 on BMI at age 13. Of the hypothetical interventions, the one with the largest population mean difference in BMI was the one combining all interventions (dietary behavior, physical activity and screen time interventions) and assuming 100% intervention adherence, with a population mean differences of − 0.28 (95% CI − 0.59, 0.07). Isolated behavioral interventions had a limited impact on BMI. This study demonstrated that a combination of healthy dietary behavior and physical activity promotion, as well as screen time reduction interventions at age 11 could have the highest beneficial effect on the reduction of BMI at age 13, although the change in BMI was small. The findings highlight the importance of a systems approach to obesity prevention focusing on multicomponent interventions. [ABSTRACT FROM AUTHOR]

Background Pertussis is a contagious respiratory disease. Maternal tetanus–diphtheria–acellular pertussis vaccination during pregnancy has been recommended by the United States Centres for Disease Control (US CDC) Advisory Committee on Immunization Practices (ACIP) for unvaccinated pregnant women since October 2011 to prevent infection among infants; in 2012, ACIP extended this recommendation to every pregnancy, regardless of previous vaccination status. The population-level effect of these recommendations on infant pertussis is unknown. This study aimed to examine the impact of the 2011/2012 ACIP pertussis recommendation on pertussis incidence and mortality among US infants. Methods We used monthly data on pertussis deaths among infants aged <1 year between January 2005 and December 2017 in the CDC Death Data and yearly infant pertussis incidence data from the CDC National Notifiable Disease Surveillance System to perform an interrupted time series analysis, accounting for the passage of the Affordable Care Act. Results This study included 156 months of data. A potential decline in trend in infant pertussis incidence was noted during the post-recommendations period. No appreciable differences in trend were found in population-level infant pertussis mortality after the guideline changes in both adjusted and unadjusted models. Results were similar for all mortality sensitivity analyses. Conclusions The 2011/2012 ACIP maternal pertussis vaccination recommendations were not associated with a population-level change in the trend in mortality, but were potentially associated with a decrease in incidence in the USA between 2005 and 2017. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: Little is known about what interventions worked or did not work in slowing the obesity epidemic. The long-term comparative effectiveness of environmental and behavioral public health interventions for obesity and type 2 diabetes prevention over an individual's life course is relatively unexplored. The potential impact and long-term collective effectiveness of environmental and behavioral interventions on obesity and type 2 diabetes throughout the life course was evaluated.Methods: The Virtual Los Angeles Obesity Model developed in 2016 was used to estimate the incidence and prevalence of obesity and type 2 diabetes under current and hypothetical interventions among 98,000 individuals born in 2009 and followed from birth to age 65 years. Analyses were performed in 2016 and completed in 2018.Results: The 48-year risk of type 2 diabetes was 0.533 (95% CI=0.446, 0.629) under the natural course, 0.451 (95% CI=0.334, 0.570) under the physical activity intervention, and 0.443 (95% CI=0.389, 0.495) under the fast-food intervention. The 64-year risk of obesity was 0.892 (95% CI=0.879, 0.903) under the natural course, 0.876 (95% CI=0.850, 0.899) under the physical activity intervention, and 0.864 (95% CI=0.856, 0.873) under the fast-food intervention. The other interventions had little or no long-term effects. When all the interventions were applied, the population risk ratios were 0.942 (95% CI=0.914, 0.967) and 0.634 (95% CI=0.484, 0.845) for obesity and type 2 diabetes, respectively.Conclusions: Implementing health interventions continuously throughout the life span and in combination with other interventions could substantially halt the obesity and the type 2 diabetes epidemics. [ABSTRACT FROM AUTHOR]

Background It is difficult to evaluate the effectiveness of interventions aimed at reducing early childhood obesity using randomized trials. Objective To illustrate how observational data can be analysed using causal inference methods to estimate the potential impact of behavioural 'interventions' on early childhood adiposity. Methods We used longitudinal data from 1054 children 1-5 years old enrolled in the Special Supplemental Nutrition Program for Women, Infants and Children and followed (WIC) from 2008 to 2010 for a mean duration of 23 months. The data came from a random sample of WIC families living in Los Angeles County in 2008. We used the parametric g-formula to estimate the impact of various hypothetical behavioural interventions. Results Adjusted mean weight-for-height Z score at the end of follow-up was 0.73 (95% CI 0.65, 0.81) under no intervention and 0.63 (95% CI 0.38, 0.87) for all interventions given jointly. Exclusive breastfeeding for 6 months or longer was the most effective intervention [population mean difference = −0.11 (95% CI −0.22, 0.01)]. Other interventions had little or no effect. Conclusions Compared with interventions promoting healthy eating and physical activity behaviours, breastfeeding was more effective in reducing obesity risk in children aged 1-5 years. When carefully applied, causal inference methods may offer viable alternatives to randomized trials in etiologic and evaluation research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Maternal solvent exposure has been suspected to increase offspring cancer risk. The study aimed to evaluate the associations between maternal residential exposure to solvents from industrial pollution during pregnancy and childhood cancer. The present study included 15,744 cancer cases (aged 0–19 years at diagnosis) identified from California Cancer Registry and 283,141 controls randomly selected from California Birth Registry (20:1 frequency-matched by birth year: 1998–2016). We examined industrial releases of tetrachloroethylene and 1,1,1-trichloroethane within 3 km of the birth address, while we used a 5 km buffer for carbon disulfide. We calculated the total exposure from all linked Toxic Release Inventory sites during each index pregnancy and assigned "ever/never" and "high/low exposed/unexposed" exposure, using median values. We performed quadratic decay models to estimate cancer risks associated with maternal solvent exposure in pregnancy. 1,1,1-Trichloroethane was associated with rhabdomyosarcoma (adjusted Odds Ratio (aOR): 1.96; 95% Confidence Interval (CI): 1.16, 3.32) in the "ever exposed" group. Ever exposure to carbon disulfide was associated with increased risks of medulloblastoma (OR = 1.85, 95% CI 1.01, 3.40) and ependymoma (OR = 1.63, 95% CI 0.97, 2.74). Overall, our findings suggested maternal residential exposure to solvents from industrial sources might be associated with elevated childhood cancer risks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes and depression are risk factors for cardiovascular disease, but the evidence about their interaction effect on long-term health outcomes among Latinos is lacking. We aimed to investigate the joint association of diabetes and subsequent depressive symptoms with mortality among older Latinos, an understudied racial/ethnic group with high prevalence of diabetes.Methods: This study included 1,495 adults from the Sacramento Area Latino Study on Aging. We employed Cox proportional-hazards models to estimate the adjusted hazard ratios [aHRs] for cardiovascular and all-cause mortality according to diabetes status at enrollment and depressive symptoms a year after the enrollment. We used marginal structural models to adjust for time-varying confounders.Results: The mean age (standard deviation) of participants was 70 (6.6) years. Over follow-up (median 7.7 years), diabetes and depressive symptoms were individually associated with increased risk of cardiovascular mortality (diabetes, aHR[95% CI]=2.13[1.60-2.84]; depressive symptoms, aHR[95% CI]=1.62[1.09-2.39]) and all-cause mortality (diabetes, aHR[95% CI]=1.92[1.53-2.41]; depressive symptoms, aHR[95% CI]=1.41[1.02-1.94]). After adjusting for time-varying confounders, we found a multiplicative interaction between diabetes and subsequent depressive symptoms for cardiovascular mortality (aHR[95% CI]=2.94[1.07-8.39]), but not all-cause mortality (aHR[95% CI]=1.80[0.81-4.35]).Conclusions: Using a longitudinal cohort of community-dwelling older Latinos, we found that diabetes and subsequent depressive symptoms were jointly associated with increased risk of cardiovascular mortality. [ABSTRACT FROM AUTHOR]

With the two-step gestational diabetes mellitus (GDM) screening approach, hyperglycemic subtypes can be identified. We aimed to investigate racial/ethnic differences in the prevalence of hyperglycemic subtypes and to examine the associations between these subtypes and adverse pregnancy outcomes. In this retrospective cohort, 11,405 pregnancies were screened using the two-step approach. Hyperglycemic subtypes included: pregnancy-impaired glucose intolerance-I (PIGT-I), PIGT-II, GDM-I (abnormal post-load glucose only), and GDM-II (abnormal fasting & post-load glucose). Modified Poisson regressions with robust error variance were used to estimate age-adjusted prevalence ratios (PR) of hyperglycemic subtypes and multivariable-adjusted risk ratios (RR) of adverse pregnancy outcomes. The prevalence of hyperglycemic subtypes was higher in Asians (PIGT-I: 1.51 [95% confidence interval 1.35–1.69]; PIGT-II: 2.18 [1.78–2.68]; GDM-I: 2.55 [2.10–3.10]; GDM-II: 1.55 [1.08–2.21]) and Hispanics (PIGT-I: 1.32 [1.16–1.50]; PIGT-II: 2.07 [1.67–2.57]; GDM-I: 1.69 [1.35–2.13]; GDM-II: 2.68 [1.93–3.71]) than non-Hispanic Whites (NHW). Despite low GDM prevalence, Japanese and Koreans had higher PIGT prevalence than NHW. PIGT-II was positively associated with hypertensive disorders of pregnancy (1.19 [1.02–1.38]), large-for-gestational age (1.73 [1.37–2.18]), and preterm birth (PB, 1.33 [1.05–1.68]). PIGT-I (1.23 [1.04–1.45]) and GDM-I (1.56 [0.87–1.71]) were positively related to PB. The prevalence of hyperglycemic subtypes varies by race/ethnicity and they have distinct health implications. [ABSTRACT FROM AUTHOR]