Abstract Background Cardiovascular disease (CVD) is a leading cause of death in China. Evaluation of risk factors and their impacts on disease burden is important for future public health initiatives ...and policy making. Objectives The study used data from a cohort of the China Health and Nutrition Survey to estimate time trends in cardiovascular risk factors from 1991 to 2011. Methods We applied the comparative risk assessment method to estimate the number of CVD events attributable to all nonoptimal levels (e.g., theoretical-minimum-risk exposure distribution TMRED) of each risk factor. Results In 2011, high blood pressure, high low-density lipoprotein cholesterol, and high blood glucose were associated with 3.1, 1.4, and 0.9 million CVD events in China, respectively. Increase in body mass index was associated with an increase in attributable CVD events, from 0.5 to 1.1 million between 1991 and 2011, whereas decreased physical activity was associated with a 0.7-million increase in attributable CVD events. In 2011, 53.4% of men used tobacco, estimated to be responsible for 30.1% of CVD burden in men. Dietary quality improved, but remained suboptimal; mean intakes were 5.4 (TMRED: 2.0) g/day for sodium, 67.7 (TMRED: 300.0) g/day for fruits, 6.2 (TMRED: 114.0) g/day for nuts, and 25.0 (TMRED: 250.0) mg/day for marine omega-3 fatty acids in 2011. Conclusions High blood pressure remains the most important individual risk factor related to CVD burden in China. Increased body mass index and decreased physical activity were also associated with the increase in CVD burden from 1991 to 2011. High rates of tobacco use in men and unhealthy dietary factors continue to contribute to the burden of CVD in China.
Missing data are common in longitudinal cohort studies and can lead to bias, particularly in studies with informative missingness. Many common methods for handling informatively missing data in ...survey samples require correctly specifying a model for missingness. Although doubly robust methods exist to provide unbiased regression coefficients in the presence of missing outcome data, these methods do not account for correlation due to clustering inherent in longitudinal or cluster‐sampled studies. In this work, we developed a doubly robust method to estimate the regression of an outcome on a predictor in the presence of missing multilevel data on the outcome, which results in consistent estimation of regression coefficients assuming correct specification of either (1) the probability of missingness or (2) the outcome model. This method involves specification of separate hierarchical models for missingness and for the outcome, conditional on observed auxiliary variables and cluster‐specific random effects, to account for correlation among observations. We showed this proposed estimator is doubly robust and derived its asymptotic distribution, conducted simulation studies to compare the method to an existing doubly robust method developed for independent data, and applied the method to data from the China Health and Nutrition Survey, an ongoing multilevel longitudinal cohort study.
Describing correlates of physical activity (PA) and sedentary behavior (SB) among postmenopausal cancer survivors can help identify risk profiles and can be used to support development of targeted ...interventions to improve PA and reduce SB in this population.
To describe PA/SB and identify correlates of PA/SB among cancer and cancer-free post-menopausal women.
Women from the Women's Health Study (N = 16,629) and Women's Health Initiative/Objective Physical Activity and Cardiovascular Health Study (N = 6,079) were asked to wear an accelerometer on the hip for 7 days. Multiple mixed-effects linear regression models were used to identify sociodemographic-, health-, and chronic condition-related correlates (independent variables) associated with PA and SB (dependent variables) among women with (n = 2,554) and without (n = 20,154) a history of cancer. All correlates were mutually adjusted for each other.
In unadjusted analyses, women with a history of cancer took fewer mean daily steps (4,572 (standard deviation 2557) vs 5,029 (2679) steps/day) and had lower mean moderate-to-vigorous PA (74.9 (45.0) vs. 81.6 (46.7) minutes/day) than cancer-free women. In adjusted analyses, for cancer and cancer-free women, age, diabetes, overweight, and obesity were inversely associated with all metrics of PA (average vector magnitude, time in moderate-to-vigorous PA, step volume, time at ≥40 steps/minutes, and peak 30-minute step cadence). In unadjusted analyses, mean SB was similar for those with and without cancer (529.7 (98.1) vs. 521.7 (101.2) minutes/day). In adjusted analyses, for cancer and cancer-free women, age, diabetes, cardiovascular disease, current smoking, overweight, and obesity were positive correlates of SB, while Black or Hispanic race/ethnicity, weekly/daily alcohol intake, and excellent/very good/good self-rated health were inverse correlates of SB.
Several sociodemographic, health, and chronic conditions were correlates of PA/SB for postmenopausal women with and without cancer. Future studies should examine longitudinal relationships to gain insight into potential determinants of PA/SB.
Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been ...systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.
Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.
Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217-7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.
This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.
BackgroundLittle attention has been paid to how the association between urbanisation and abdominal adiposity changes over the course of economic development in low-income and middle-income ...countries.MethodsData came from the China Health and Nutrition Survey waves 1993–2011 (seven waves). A mixed linear model was used to investigate the association between community-level urbanisation with waist-to-height ratio (WHtR; an indicator of abdominal adiposity). We incorporated interaction terms between urbanisation and study waves to understand how the association changed over time. The analyses were stratified by age (children vs adults).ResultsAdult WHtR was positively associated with urbanisation in earlier waves but became inversely associated over time. More specifically, a 1 SD increase in the urbanisation index was associated with higher WHtR by 0.002 and 0.005 in waves 1993 and 1997, while it was associated with lower WHtR by 0.001 in 2011. Among child participants, the increase in WHtR over time was predominantly observed in more urbanised communities.ConclusionOur study suggests a shift in adult abdominal adiposity from more urbanised communities to less urbanised communities over a time of rapid economic development in China. Children living in more urbanised communities had higher increase in abdominal obesity with urbanisation over time relative to children living in less urbanised communities.
Abstract
Background
There is a close relationship between weight status and cognitive impairment in older adults. This study examined the association between weight status and the trajectory of ...cognitive decline over time in a population-based cohort of older adults in China.
Methods
We used data from adults aged ≥55 years participating in the China health and nutrition survey (1997–2018). Underweight (body mass index BMI ≤ 18.5 kg/m2), normal weight (18.5–23 kg/m2), overweight (23–27.5 kg/m2), and obesity (BMI ≥ 27.5 kg/m2) were defined using the World Health Organization Asian cutpoints. Global cognition was estimated every 2–4 years through a face-to-face interview using a modified telephone interview for cognitive status (scores 0–27). The association between BMI and the rate of global cognitive decline, using a restricted cubic spline for age and age category, was examined with linear mixed-effects models accounting for correlation within communities and individuals.
Results
We included 5 992 adults (53% female participants, mean age of 62 at baseline). We found differences in the adjusted rate of global cognitive decline by weight status (p = .01 in the cubic spline model). Models were adjusted for sex, marital status, current employment status, income, region, urbanization, education status, birth cohort, leisure activity, smoking status, and self-reported diagnosis of hypertension, diabetes, or Myocardial Infarction (MI)/stroke. In addition, significant declines by age in global cognitive function were found for all weight status categories except individuals with obesity.
Conclusions
In a cohort of adults in China, cognitive decline trajectory differed by weight status. A slower rate of change was observed in participants classified as having obesity.
Abstract
Growth in early infancy is hypothesized to affect chronic disease risk factors later in life. To date, most reports draw on European-ancestry cohorts with few repeated observations in early ...infancy. We investigated the association between infant growth before 6 months and lipid levels in adolescents in a Hispanic/Latino cohort. We characterized infant growth from birth to 5 months in male (n = 311) and female (n = 285) infants from the Santiago Longitudinal Study (1991–1996) using 3 metrics: weight (kg), length (cm), and weight-for-length (g/cm). Superimposition by translation and rotation (SITAR) and latent growth mixture models (LGMMs) were used to estimate the association between infant growth characteristics and lipid levels at age 17 years. We found a positive relationship between the SITAR length velocity parameter before 6 months of age and high-density lipoprotein cholesterol levels in adolescence (11.5, 95% confidence interval; 3.4, 19.5), indicating higher high-density lipoprotein cholesterol levels occurring with faster length growth. The strongest associations from the LGMMs were between higher low-density lipoprotein cholesterol and slower weight-for-length growth, following a pattern of associations between slower growth and adverse lipid profiles. Further research in this window of time can confirm the association between early infant growth as an exposure and adolescent cardiovascular disease risk factors.
Objective
In adulthood, excess BMI is associated with cardiovascular disease (CVD); it is unknown whether risk differs by BMI trajectories from adolescence to adulthood.
Design and Methods
The ...National Longitudinal Study of Adolescent Health, a nationally representative, longitudinal adolescent cohort (mean age: 16.9 years) followed into adulthood (mean age: 28.8 years) n = 13,984 individuals (41,982 observations) was examined. Separate logistic regression models for diabetes, hypertension, and inflammation were used to examine odds of risk factors at given adult BMI according to varying BMI trajectories from adolescence to adulthood.
Results
CVD risk factor prevalence at follow‐up ranged from 5.5% (diabetes) to 26.4% (hypertension) and 31.3% (inflammation); risk differed across BMI trajectories. For example, relative to men aged 27 years (BMI = 23 kg/m2 maintained over full study period), odds for diabetes were comparatively higher for men of the same age and BMI ≈ 30 kg/m2 with ≈8 BMI unit gain between 15 and 20 years (OR = 2.35; 95% CI, 1.51, 3.66) or in those who maintained BMI ≈ 30 kg/m2 across the study period (OR = 2.33; 1.92, 2.83) relative to the same ≈8 BMI unit gain, but between 20 and 27 years (OR = 1.44; 1.10, 1.87).
Conclusions
Specific periods and patterns of weight gain in the transition from adolescence to adulthood might be critical for CVD preventive efforts.
Background
Few studies have examined accelerometer‐measured physical activity and incident breast cancer (BC). Thus, this study examined associations between accelerometer‐measured vector magnitude ...counts per 15 seconds (VM/15s) and average daily minutes of light physical activity (LPA), moderate‐to‐vigorous PA (MVPA), and total PA (TPA) and BC risk among women in the Women's Health Accelerometry Collaboration (WHAC).
Methods
The WHAC comprised 21,089 postmenopausal women (15,375 from the Women's Health Study WHS; 5714 from the Women's Health Initiative Objective Physical Activity and Cardiovascular Health Study OPACH). Women wore an ActiGraph GT3X+ on the hip for ≥4 days and were followed for 7.4 average years to identify physician‐adjudicated in situ (n = 94) or invasive (n = 546) BCs. Multivariable stratified Cox regression estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for tertiles of physical activity measures in association with incident BC overall and by cohort. Effect measure modification was examined by age, race/ethnicity, and body mass index (BMI).
Results
In covariate‐adjusted models, the highest (vs. lowest) tertiles of VM/15s, TPA, LPA, and MVPA were associated with BC HRs of 0.80 (95% CI, 0.64–0.99), 0.84 (95% CI, 0.69–1.02), 0.89 (95% CI, 0.73–1.08), and 0.81 (95% CI, 0.64–1.01), respectively. Further adjustment for BMI or physical function attenuated these associations. Associations were more pronounced among OPACH than WHS women for VM/15s, MVPA, and TPA; younger than older women for MVPA; and women with BMI ≥30 than <30 kg/m2 for LPA.
Conclusion
Greater levels of accelerometer‐assessed PA were associated with lower BC risk. Associations varied by age and obesity and were not independent of BMI or physical function.
In a study of over 20,000 US postmenopausal women, higher levels of accelerometer‐measured physical activity were associated with lower risk of breast cancer. Associations varied by age and obesity and were not independent of body mass index or physical function.
Dramatic increases in child overweight have occurred in China. A comprehensive look at trends in physical activity and sedentary behaviors among Chinese youth is needed. The study aimed to examine ...trends in domain-specific physical activity and sedentary behaviors, explore mean and distributional changes in predicted behaviors over time, and investigate how behaviors vary by residence.
Using 2004-2011 China Health and Nutrition Survey data, adjusted means for MET-hours/week from physical activity and hours/week from sedentary behaviors were determined for school children (6-18 years), stratifying by gender, age group, and residence. Physical activity domains included in-school physical activity, active leisure (out-of-school physical activity), active travel (walking or biking), and domestic activity (cooking, cleaning, and child care). For each physical activity domain, the MET-hours/week measure was determined from the total weekly time spent (hours) in domain-specific activities and corresponding MET-values using the Compendium of Energy Expenditures for Youth. Sedentary behaviors included television, computer use, homework, and other behaviors (board games, toys, extracurricular reading and writing). For each sedentary behavior, the hours/week measure was determined from total weekly time spent in specific sedentary behaviors. Residence groups included megacities (population ≥ 20million), cities/towns (300,000 ≤ population < 20million), and rural/suburban areas (population < 300,000). Repeated measure linear mixed and quantile regression models were used to predict adjusted means.
Little change in physical activity behaviors occurred over time, with the exception of statistically significant trends toward increased domestic activity among male children (p < .05). Across all gender and age groups, statistically significant trends over time toward an average increase in computer use were seen (p < .01); these increases were largely driven by those ≥50th percentile on the distribution. Children living in megacities (versus rural areas) reported higher levels of physical activity, homework, and computer use.
Intensified, systematic intervention and policy efforts promoting physical activity and reducing sedentary behaviors among children are needed.
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