Highly prevalagent hearing and vision sensory impairments among older people may contribute to the risk of cognitive decline and pathological impairments including dementia. This study aims to determine whether single and dual sensory impairment (hearing and/or vision) are independently associated with cognitive decline among older adults and to describe cognitive trajectories according to their impairment pattern. we used data from totals of 13,123, 11,417 and 21,265 respondents aged 50+ at baseline from the Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA) and the Survey of Health, Ageing and Retirement in Europe (SHARE), respectively. We performed growth curve analysis to identify cognitive trajectories, and a joint model was used to deal with attrition problems in longitudinal ageing surveys. respondents with a single sensory impairment had lower episodic memory score than those without sensory impairment in HRS (β = −0.15, P < 0.001), ELSA (β = −0.14, P < 0.001) and SHARE (β = −0.26, P < 0.001). The analysis further shows that older adults with dual sensory impairment in HRS (β = −0.25, P < 0.001), ELSA (β = −0.35, P < 0.001) and SHARE (β = −0.68, P < 0.001) remembered fewer words compared with those with no sensory impairment. The stronger associations between sensory impairment and lower episodic memory levels were found in the joint model which accounted for attrition. hearing and/or vision impairments are a marker for the risk of cognitive decline that could inform preventative interventions to maximise cognitive health and longevity. Further studies are needed to investigate how sensory markers could inform strategies to improve cognitive ageing.

Evening-oriented sleep timing preferences have been associated with risk of diabetes, cardiovascular diseases, obesity, psychiatric disorders, and increased mortality. This research aims to explore the relationship between diurnal preferences (chronotype), daily habits, metabolic health, and mortality, using longitudinal data from The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age (6375 participants at inception, recruited in the North of England) with a long follow-up period (up to 35.5 years). Mixed models were used to investigate the influence of aging, socio-demographic, and seasonal factors on sleep timing. Results show that sleep timing shifted towards earlier time with aging. Test seasons influence chronotype of older adults but working schedules challenge seasonality of sleep timing. Moreover, the season of birth may set chronotype in adulthood. Individual chronotype trajectories were clustered using latent class analysis and analyzed against metabolic health and mortality. We observed a higher risk of hypertension in the evening-type cluster compared to morning-type individuals (Odds ratio = 1.88, 95%CI = 1.02/3.47, p = .04). Evening-type cluster was also associated with traits related to lower health such as reduced sport participation, increased risk of depression and psychoticism personality, late eating, and increased smoking and alcohol usage. Finally, Cox regression of proportional hazards was used to study the effects of chronotype on longevity after adjusting for sleep duration, age, gender, smoking, alcohol usage, general health, and social class. The survival analysis (82.6% censored by death) revealed that evening-type chronotype increased the likelihood of mortality (Hazard ratio = 1.15, 95%CI = 1.04/1.26, p = .005). Taken together, chronotype is influenced by aging and seasonal effects. Evening-type preference may have detrimental outcomes for human well-being and longevity.

Immunization is one of the most cost-effective public health interventions to prevent children from contracting vaccine-preventable diseases. Indonesia launched the Expanded Program for Immunization (EPI) in 1977. However, immunization coverage remains far below the United Nations International Children’s Emergency Fund (UNICEF) and World Health Organization (WHO) target of 80%. This study aims to investigate the determinants of complete immunization status among children aged 12–23 months in Indonesia. We used three waves of the Indonesian National Socioeconomic Survey (2008, 2011, and 2013) and national village censuses from the same years. Multilevel logistic regression was used to conduct the analysis. The number of immunized children increased from 47.48% in 2008 to 61.83% in 2013. The presence of health professionals, having an older mother, and having more educated mothers were associated with a higher probability of a child’s receiving full immunization. Increasing the numbers of hospitals, village health posts, and health workers was positively associated with children receiving full immunization. The MOR (median odds ratio) showed that children’s likelihood of receiving complete immunization varied significantly among districts. Both household- and district-level determinants were found to be associated with childhood immunization status. Policy makers may take these determinants into account to increase immunization coverage in Indonesia.

Difference in life expectancy between males and females has been suggested to rest on sex difference in physiological dysregulation. But allostatic load, a physiological index, has not been carefully examined for an extended period beyond middle age. We aim to draw longitudinal trajectories of allostatic load in a national sample of older Americans and Britons; also to examine sex-based trajectories and factors behind their differences. We studied men and women aged ≥50 years participating in the Health and Retirement Study Waves 8–11, 2006–2012 (N = 15,583 person-years) and the English Longitudinal Study of Ageing Waves 2, 4 and 6, 2004–2012 (N = 14,765 person-years). Because of the difference in provenance, we included different number of biomarkers to calculate allostatic load in HRS and ELSA. In HRS we used 8 biomarkers (systolic and diastolic blood pressure, haemoglobin A1c, high-density lipoprotein, total cholesterol, waist circumference, cystatin C, and C-reactive protein), while ELSA allostatic load was constructed from 10 biomarkers (systolic and diastolic blood pressure, haemoglobin A1c, high-density lipoprotein, total cholesterol, waist circumference, BMI, triglyceride, fibrinogen and C-reactive protein). A growth curve model was fitted to repeated observations of allostatic load, demographic characteristics, socioeconomic position, comorbidities and health behaviours (smoking, drinking, and physical exercise). To account for attrition, a joint model was applied. The analysis showed that allostatic load increases linearly with age in the U.S. However, there are different levels for males and females. In England allostatic load follows such different paths that their trajectories cross in later life. Sex-based trajectories of allostatic load showed distinct female advantage and are mostly consistent with female advantage in life expectancy.

The relationships between older age and sleep efficiency have traditionally been assessed using cross‐sectional studies that ignore changes within individuals as they age. This research examines the determinants of sleep efficiency, the heterogeneity in an individual's sleep efficiency trajectory across a period of up to 27 years in later life and its associations with health. The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age cohort (n = 6,375; age 42–94 years) was used in this study. Depression and health data were collected using self‐report validated instruments (Cornell Medical Index, Beck Depression Inventory and Geriatric Depression Scale). Longitudinal sleep and sociodemographic data were collected using a study‐specific self‐report questionnaire. A mixed‐effect model was performed for sleep efficiency with adjustments for time‐invariant and time‐variant predictors. Latent class analysis was used to demonstrate subgroups of sleep efficiency trajectories and associations between sleep efficiency clusters and health history of the participants were investigated. Older adults have decreased sleep efficiency over time, with 18.6% decline between 40 and 100 years of age. Three sleep efficiency trajectory clusters were identified: high (32%), medium (50%) and low sleep efficiency (18%). Belonging to the high sleep efficiency cluster was associated with having lower prevalence of hypertension, circulatory problems, general arthritis, breathing problems and recurrent episodes of depression compared to the low efficiency cluster. Overall, ageing decreases sleep efficiency. However, there are detectable subgroups of sleep efficiency that are related to prevalence of different diseases.