A Bayesian Circadian Hidden Markov Model to Infer Rest-Activity Rhythms Using 24-hour Actigraphy Data

24-hour actigraphy data collected by wearable devices offer valuable insights into physical activity types, intensity levels, and rest-activity rhythms (RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period, are regulated by the body's circadian system, synchronizing physiological processes with external cues like the light-dark cycle. Disruptions to these rhythms, such as irregular sleep patterns, daytime drowsiness or shift work, have been linked to adverse health outcomes including metabolic disorders, cardiovascular disease, depression, and even cancer, making RARs a critical area of health research. In this study, we propose a Bayesian Circadian Hidden Markov Model (BCHMM) that explicitly incorporates 24-hour circadian oscillators mirroring human biological rhythms. The model assumes that observed activity counts are conditional on hidden activity states through Gaussian emission densities, with transition probabilities modeled by state-specific sinusoidal functions. Our comprehensive simulation study reveals that BCHMM outperforms frequentist approaches in identifying the underlying hidden states, particularly when the activity states are difficult to separate. BCHMM also excels with smaller Kullback-Leibler divergence on estimated densities. With the Bayesian framework, we address the label-switching problem inherent to hidden Markov models via a positive constraint on mean parameters. From the proposed BCHMM, we can infer the 24-hour rest-activity profile via time-varying state probabilities, to characterize the person-level RAR. We demonstrate the utility of the proposed BCHMM using 2011-2014 National Health and Nutrition Examination Survey (NHANES) data, where worsened RAR, indicated by lower probabilities in low-activity state during the day and higher probabilities in high-activity state at night, is associated with an increased risk of diabetes

Associations Between Rest-Activity Rhythms and Liver Function Tests: the Us National Health and Nutrition Examination Survey, 2011-2014

Liver functions are regulated by the circadian rhythm; however, whether a weakened circadian rhythm is associated with impaired liver function is unclear. This study aims to investigate the association of characteristics of rest-activity rhythms with abnormal levels of biomarkers of liver function. Data were obtained from the National Health and Nutrition Examination Survey 2011-2014. Seven rest-activity rhythm parameters were derived from 24 h actigraphy data using the extended cosine model and non-parametric methods. Multiple logistic regression and multiple linear regression models were used to assess the associations between rest-activity rhythm parameters and alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transaminase (GGT), albumin and bilirubin. Weakened overall rhythmicity characterized by a lower F statistic was associated with higher odds of abnormally elevated ALP (O

The association Between Overnight Fasting and Body Mass index in Older adults: the interaction Between Duration and Timing

BACKGROUND: Circadian rhythms play an important role in the regulation of eating and fasting, and mistimed dietary intakes may be detrimental to metabolic health. Extended overnight fasting has been proposed as a strategy to better align the eating-fasting cycle with the internal circadian clock, and both observational and experimental studies have linked longer overnight fasting with lower body weight. However, it remains unclear if the timing of overnight fasting modifies the relationship between fasting duration and weight outcomes. METHODS: The current study included 495 men and 499 women age 50-74 years. Dietary intake over 12 months was assessed by 24-h dietary recalls every two months, and body-mass index was measured at the beginning, middle and end of the study. Logistic regression was used to estimate the relationship between overnight fasting duration and the likelihood of being overweight or obesity adjusted for multiple confounders, and assessed whether the relationship was modified by the timing of overnight fasting, measured as the midpoint of the fasting period. RESULTS: Among participants with early overnight fasting (midpoint \u3c 02:19 am), a longer fasting duration was associated with lower odds of overweight and obesity; while among those with late fasting (≥02:19 am), longer fasting was associated with higher odds of overweight and obesity. Specifically, when compared to the shortest quintile of overnight fasting duration, the longest quintile was associated with a 53% reduction in the odds of overweight and obesity in the early fasting group (OR = 0.47, 95% CI = 0.23, 0.97), but a 2.36-fold increase in the late fasting group (OR = 3.36, 95% CI = 1.48, 7.62). Additionally adjusting for dietary intakes during morning and late evening periods did not affect the observed associations. CONCLUSIONS: Longer overnight fasting was associated with a reduced likelihood of being overweight or obese, but only among those with an early timing of fasting

Association of Long-Term Trajectories of Neighborhood Socioeconomic Status With Weight Change in Older adults

IMPORTANCE: Studying long-term changes in neighborhood socioeconomic status (SES) may help to better understand the associations between neighborhood exposure and weight outcomes and provide evidence supporting neighborhood interventions. Little previous research has been done to examine associations between neighborhood SES and weight loss, a risk factor associated with poor health outcomes in the older population. OBJECTIVE: to determine whether improvements in neighborhood SES are associated with reduced likelihoods of excessive weight gain and excessive weight loss and whether declines are associated with increased likelihoods of these weight outcomes. DESIGN, STUDY, AND PARTICIPANTS: This cohort study was conducted using data from the National Institutes of Health-AARP (formerly known as the American Association of Retired Persons) Diet and Health study (1995-2006). The analysis included a cohort of 126 179 adults (aged 50-71 years) whose neighborhoods at baseline (1995-1996) were the same as at follow-up (2004-2006). All analyses were performed from December 2018 through December 2020. EXPOSURES: Living in a neighborhood that experienced 1 of 8 neighborhood SES trajectories defined based on a national neighborhood SES index created using data from the US Census and American Community Survey. The 8 trajectory groups, in which high, or H, indicated rankings at or above the sample median of a specific year and low, or L, indicated rankings below the median, were HHH (ie, high in 1990 to high in 2000 to high in 2010), or stable high; HLL, or early decline; HHL, or late decline; HLH, or transient decline; LLL, or stable low; LHH, or early improvement; LLH, or late improvement; and LHL, or transient improvement. MAIN OUTCOMES AND MEASURES: Excessive weight gain and loss were defined as gaining or losing 10% or more of baseline weight. RESULTS: Among 126 179 adults, 76 225 (60.4%) were men and the mean (SD) age was 62.1 (5.3) years. Improvements in neighborhood SES were associated with lower likelihoods of excessive weight gain and weight loss over follow-up, while declines in neighborhood SES were associated with higher likelihoods of excessive weight gain and weight loss. Compared with the stable low group, the risk was significantly reduced for excessive weight gain in the early improvement group (odds ratio [OR], 0.87; 95% CI, 0.79-0.95) and for excessive weight loss in the late improvement group (OR, 0.89; 95% CI, 0.80-1.00). Compared with the stable high group, the risk of excessive weight gain was significantly increased for the early decline group (OR, 1.19; 95% CI, 1.08-1.31) and late decline group (OR, 1.13; 95% CI, 1.04-1.24) and for excessive weight loss in the early decline group (OR, 1.15; 95% CI, 1.02-1.28). The increases in likelihood were greater when the improvement or decline in neighborhood SES occurred early in the study period (ie, 1990-2000) and was substantiated throughout the follow-up (ie, the early decline and early improvement groups). Overall, we found a linear association between changes in neighborhood SES and weight outcomes, in which every 5 percentile decline in neighborhood SES was associated with a 1.2% to 2.4% increase in the risk of excessive weight gain or loss (excessive weight gain: OR, 1.01; 95% CI, 1.00-1.02 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; excessive weight loss: OR, 1.02; 95% CI, 1.01-1.03 for women; OR, 1.02; 95% CI, 1.01-1.03 for men; P for- trend \u3c .0001). CONCLUSIONS AND RELEVANCE: These findings suggest that changing neighborhood environment was associated with changes in weight status in older adults

The Aassociation Between Rest-Activity Rhythms and Glycemic Markers: the Us National Health and Nutrition Examination Survey, 2011-2014

OBJECTIVES: Previous studies conducted in mostly homogeneous sociodemographic samples have reported a relationship between weakened and/or disrupted rest-activity patterns and metabolic dysfunction. This study aims to examine rest-activity rhythm characteristics in relation to glycemic markers in a large nationally representative and diverse sample of American adults. METHODS: This study used data from the National Health and Nutrition Examination Survey 2011-2014. Rest-activity characteristics were derived from extended cosine models using 24-hour actigraphy. We used multinomial logistic regression and multiple linear regression models to assess the associations with multiple glycemic markers (i.e., glycated hemoglobin, fasting glucose and insulin, homeostatic model assessment of insulin resistance, and results from the oral glucose tolerance test), and compared the results across different categories of age, gender, race/ethnicity, and body mass index. RESULTS: We found that compared to those in the highest quintile of F statistic, a model-fitness measure with higher values indicating a stronger cosine-like pattern of daily activity, participants in the lowest quintile (i.e, those with the weakest rhythmicity) were 2.37 times more likely to be diabetic (OR Q1 vs. Q5 2.37 (95% CI 1.72, 3.26), p-trend \u3c .0001). Similar patterns were observed for other rest-activity characteristics, including lower amplitude (2.44 (1.60, 3.72)), mesor (1.39 (1.01, 1.91)), and amplitude:mesor ratio (2.09 (1.46, 2.99)), and delayed acrophase (1.46 (1.07, 2.00)). Results were consistent for multiple glycemic biomarkers, and across different sociodemographic and BMI groups. CONCLUSIONS: Our findings support an association between weakened and/or disrupted rest-activity rhythms and impaired glycemic control among a diverse US population

Cross-Sectional and Prospective associations of Rest-Activity Rhythms With Body Mass index in Older Men: a Novel analysis Using Harmonic Hidden Markov Models

Growing evidence supports a role for rest-activity rhythms (RARs) in metabolic health. Epidemiological studies in adolescents and young adults showed that RAR characteristics consistent with weakened rhythmicity were associated with obesity. However, studies in older adults are lacking. The objective of this study was to examine the cross-sectional and prospective associations between RAR and obesity in older men using the Harmonic Hidden Markov Model (HHMM), a novel analytical approach with several advantages over conventional methods for characterizing RAR. The analysis included nearly 3,000 participants in the Osteoporotic Fractures in Men study with 5-day 24-h actigraphy data. The strength of RAR was measured by rhythmic index (RI), a scaled value between 0 and 1 with higher values indicating better RAR. Multiple linear and logistic regression adjusting for multiple confounders were performed to examine the RI in relation to body mass index (BMI) and obesity status at baseline and after ~3.5 years of follow-up. We showed that the HHMM can derive both meaningful visual profile and quantifier of RAR. A lower RI was associated with higher BMI and obesity at baseline, and an elevated likelihood for developing obesity over follow-up. Specifically, when compared with men in the highest quartile of RI, those in the lowest quartile on average had a higher BMI (β [95% confidence interval (CI)], 1.76 [1.39, 2.13]) and were more likely to be obese at baseline (odds ratio (OR) [95% CI], 2.63 [2.03, 3.43]). Moreover, among nonobese men at baseline, those in the lowest quartile of RI were 2.06 times (OR [95% CI], 2.06 [1.02, 4.27]) more likely to develop obesity over follow-up when compared with those in the highest quartile. In conclusion, our study demonstrated the utility of HHMM in characterizing RAR and showed that rhythmicity strength was associated with BMI and risk of obesity in older men

Climate Patterns and Mosquito-Borne Disease Outbreaks in South and Southeast Asia

Background: Vector-borne infectious diseases, particularly mosquito-borne, pose a substantial threat to populations throughout South and Southeast Asia. Outbreaks have affected this region several times during the early years of the 21st century, notably through outbreaks of Chikungunya and Dengue. These diseases are believed to be highly prevalent at endemic levels in the region as well. With a changing global climate, the impacts of changes in ambient temperatures and precipitation levels on mosquito populations are important for understanding the effects on risk of mosquito-borne disease outbreaks. This study aims to make use of a large data set to determine how risk of mosquito-borne infectious disease outbreaks relates to the highest monthly average temperature and precipitation for each year in South and Southeast Asia. Methods: Generalized additive models were used in a marked point process to fit nonlinear trends relating temperature and precipitation to outbreak risk, fitting splines for temperature and precipitation. Confounding factors for nation affluence, climate type, and ability to report outbreaks were also included. Results: Parabolic trends for both temperature and precipitation were observed relating to outbreak risk. The trend for temperature, which was significant, showed that outbreak risk peaks near 33.5 °C as the highest monthly average temperature. Though not significant, a trend for precipitation was observed showing risk peaking when the highest monthly average precipitation is 650 mm. Conclusions: Peak levels of temperature and precipitation were identified for outbreak risk. These findings support the notion of a poleward shift in the distribution of mosquitoes within this region rather than a poleward expansion in geographic range. Keywords: Mosquito, Infectious disease, Temperature, Precipitation, Asi

Human Papillomavirus Vaccine administration Trends among Commercially insured Us adults aged 27-45 Years Before and after advisory Committee On Immunization Practices Recommendation Change, 2007-2020

IMPORTANCE: In 2019, the Advisory Committee on Immunization Practices (ACIP) recommended patient-clinician shared decision-making for human papillomavirus (HPV) vaccination in adults aged 27 to 45 years. Less is known about the HPV vaccine administration trends in this age group before and after this recommendation update. OBJECTIVE: to examine the association between the ACIP recommendation update and the HPV vaccine administration among US adults aged 27 to 45 years. DESIGN, SETTING, AND PARTICIPANTS: This large commercial claim-based retrospective cohort study used the Optum Clinformatics database for validated claims from January 1, 2007, through December 31, 2020. A total of 22 600 520 US adults aged 27 to 45 years without previous HPV vaccination claims during the study and enrollment period were included. MAIN OUTCOMES AND MEASURES: The first-appearing HPV vaccination claim per individual was defined as a new HPV vaccine administration. Interrupted time-series analyses were conducted to assess the association between the ACIP update and the quarterly vaccine administration rate change. The annual rate trends across race and ethnicity groups and the proportions of vaccination cases by sub-age groups and valent types were also estimated. Vaccine administration trends were assessed by race and ethnicity in this age group because HPV vaccination trends were found to differ by race and ethnicity in the initially eligible population. RESULTS: Among 22 600 520 final study participants, the majority were men (50.9%) and non-Hispanic White (53.4%), and the mean (SD) age when first observed was 34.6 (5.8) years. In women, the ACIP update was associated with an immediate increase in vaccine administration rate (coefficient β2, 40.18 per 100 000 persons; P = .01) and an increased slope (coefficient β3, 9.62 per 100 000 persons per quarter; P = .03) over time postupdate. The ACIP update was only associated with an immediate increase in vaccine administration in men (coefficient β2, 27.54; P \u3c .001). The annual rate trends were similar across race and ethnicity groups. Age at vaccine administration shifted over time (eg, women aged 40-45 years comprised only 4.9% of vaccinations in 2017, then 19.0% in 2019, and 22.7% in 2020). The most administered HPV vaccines in 2020 were 9 valent (women, 97.0%; men, 97.7%). CONCLUSIONS AND RELEVANCE: In this population-based cohort study, there were statistically significant increases in HPV vaccine administration in adults aged 27 to 45 years after the ACIP recommendation update. Patient-clinician shared decision-making may have been the main associated factor for this increase. Further research is warranted to explore the decision-making process in receiving HPV vaccination and to develop effective decision aids to maximize the cancer prevention benefit in this age group

Spatial Epidemiology: an Empirical Framework For Syndemics Research

Syndemics framework describes two or more co-occurring epidemics that synergistically interact with each other and the complex structural social forces that sustain them leading to excess disease burden. The term syndemic was first used to describe the interaction between substance abuse, violence, and AIDS by Merrill Singer. A broader range of syndemic studies has since emerged describing the framework\u27s applicability to other public health scenarios. With syndemic theory garnering significant attention, the focus is shifting towards developing robust empirical analytical approaches. Unfortunately, the complex nature of the disease-disease interactions nested within several social contexts complicates empirical analyses. In answering the call to analyze syndemics at the population level, we propose the use of spatial epidemiology as an empirical framework for syndemics research. Spatial epidemiology, which typically relies on geographic information systems (GIS) and statistics, is a discipline that studies spatial variations to understand the geographic landscape and the risk environment within which disease epidemics occur. GIS maps provide visualization aids to investigate the spatial distribution of disease outcomes, the associated social factors, and environmental exposures. Analytical inference, such as estimation of disease risks and identification of spatial disease clusters, can provide a detailed statistical view of spatial distributions of diseases. Spatial and spatiotemporal models can help us to understand, measure, and analyze disease syndemics as well as the social, biological, and structural factors associated with them in space and time. In this paper, we present a background on syndemics and spatial epidemiological theory and practice. We then present a case study focused on the HIV and HCV syndemic in West Virginia to provide an example of the use of GIS and spatial analytical methods. The concepts described in this paper can be considered to enhance understanding and analysis of other syndemics for which space-time data are available