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The prevalence of diabetes, heart disease, and stroke multimorbidity (co-occurrence of two or three of these conditions) has increased rapidly. Little is known about how the three conditions progress from one to another sequentially through the life course. We aimed to delineate this progression in middle-aged women and to determine the roles of common risk factors in the accumulation of diabetes, heart disease, and stroke multimorbidity. We used data from 13,714 women aged 45-50 years without a history of any of the three conditions. They were participants in the Australian Longitudinal Study on Women's Health (ALSWH), enrolled in 1996, and surveyed approximately every 3 years to 2016. We characterized the longitudinal progression of the three conditions and multimorbidity. We estimated the accumulation of multimorbidity over 20 years of follow-up and investigated their association with both baseline and time-varying predictors (sociodemographic factors, lifestyle factors, and other chronic conditions). Over 20 years, 2,511 (18.3%) of the women progressed to at least one condition, of whom 1,420 (56.6%) had diabetes, 1,277 (50.9%) had heart disease, and 308 (12.3%) had stroke; 423 (16.8%) had two or three of these conditions. Over a 3-year period, the age-adjusted odds of two or more conditions was approximately twice that of developing one new condition compared to women who did not develop any new conditions. For example, the odds for developing one new condition between Surveys 7 and 8 were 2.29 (95% confidence interval [CI], 1.93-2.72), whereas the odds for developing two or more conditions was 6.51 (95% CI, 3.95-10.75). The onset of stroke was more strongly associated with the progression to the other conditions (i.e., 23.4% [95% CI, 16.3%-32.2%] of women after first onset of stroke progressed to other conditions, whereas the percentages for diabetes and heart disease were 9.9% [95% CI, 7.9%-12.4%] and 11.4% [95% CI, 9.1%-14.4%], respectively). Being separated, divorced, or widowed; being born outside Australia; having difficulty managing on their available income; being overweight or obese; having hypertension; being physically inactive; being a current smoker; and having prior chronic conditions (i.e., mental disorders, asthma, cancer, osteoporosis, and arthritis) were significantly associated with increased odds of accumulation of diabetes, heart disease, and stroke multimorbidity. The main limitations of this study were the use of self-reported data and the low number of events. Stroke was associated with increased risk of progression to diabetes or heart disease. Social inequality, obesity, hypertension, physical inactivity, smoking, or having other chronic conditions were also significantly associated with increased odds of accumulating multimorbidity. Our findings highlight the importance of awareness of the role of diabetes, heart disease, and stroke multimorbidity among middle-aged women for clinicians and health-promotion agencies.

Cigarette smoking is associated with earlier menopause, but the impact of being a former smoker and any dose-response relationships on the degree of smoking and age at menopause have been less clear. If the toxic impact of cigarette smoking on ovarian function is irreversible, we hypothesized that even former smokers might experience earlier menopause, and variations in intensity, duration, cumulative dose, and age at start/quit of smoking might have varying impacts on the risk of experiencing earlier menopause. A total of 207,231 and 27,580 postmenopausal women were included in the cross-sectional and prospective analyses, respectively. They were from 17 studies in 7 countries (Australia, Denmark, France, Japan, Sweden, United Kingdom, United States) that contributed data to the International collaboration for a Life course Approach to reproductive health and Chronic disease Events (InterLACE). Information on smoking status, cigarettes smoked per day (intensity), smoking duration, pack-years (cumulative dose), age started, and years since quitting smoking was collected at baseline. We used multinomial logistic regression models to estimate multivariable relative risk ratios (RRRs) and 95% confidence intervals (CIs) for the associations between each smoking measure and categorised age at menopause (<40 (premature), 40-44 (early), 45-49, 50-51 (reference), and ≥52 years). The association with current and former smokers was analysed separately. Sensitivity analyses and two-step meta-analyses were also conducted to test the results. The Bayesian information criterion (BIC) was used to compare the fit of the models of smoking measures. Overall, 1.9% and 7.3% of women experienced premature and early menopause, respectively. Compared with never smokers, current smokers had around twice the risk of experiencing premature (RRR 2.05; 95% CI 1.73-2.44) (p < 0.001) and early menopause (1.80; 1.66-1.95) (p < 0.001). The corresponding RRRs in former smokers were attenuated to 1.13 (1.04-1.23; p = 0.006) and 1.15 (1.05-1.27; p = 0.005). In both current and former smokers, dose-response relationships were observed, i.e., higher intensity, longer duration, higher cumulative dose, earlier age at start smoking, and shorter time since quitting smoking were significantly associated with higher risk of premature and early menopause, as well as earlier menopause at 45-49 years. Duration of smoking was a strong predictor of age at natural menopause. Among current smokers with duration of 15-20 years, the risk was markedly higher for premature (15.58; 11.29-19.86; p < 0.001) and early (6.55; 5.04-8.52; p < 0.001) menopause. Also, current smokers with 11-15 pack-years had over 4-fold (4.35; 2.78-5.92; p < 0.001) and 3-fold (3.01; 2.15-4.21; p < 0.001) risk of premature and early menopause, respectively. Smokers who had quit smoking for more than 10 years had similar risk as never smokers (1.04; 0.98-1.10; p = 0.176). A limitation of the study is the measurement errors that may have arisen due to recall bias. The probability of earlier menopause is positively associated with intensity, duration, cumulative dose, and earlier initiation of smoking. Smoking duration is a much stronger predictor of premature and early menopause than others. Our findings highlight the clear benefits for women of early smoking cessation to lower their excess risk of earlier menopause.

Little is known about patterns of associative multimorbidity and their aetiology. We aimed to identify patterns of associative multimorbidity among mid-aged women and the lifestyle and socioeconomic factors associated with their development. Participants were from the Australian Longitudinal Study on Women's Health. We included 4896 women born 1946-51, without multimorbidity in 1998. We identified multimorbidity patterns at survey 6 (2010) using factor analysis, and related these patterns to baseline lifestyle and socioeconomic factors using logistic regression. We dichotomised factor scores and determined odds ratios (ORs) with 95% confidence intervals (CIs) for associations between characteristics and odds of a high versus low factor score. We identified five multimorbidity patterns: psychosomatic; musculoskeletal; cardiometabolic; cancer; and respiratory. Overweight and obesity were respectively associated with increased odds of having a high score for the musculoskeletal (adjusted ORs 1.45 [95% CI 1.23, 1.70] and 2.14 [95% CI 1.75, 2.60]) and cardiometabolic (adjusted ORs 1.53 [95% CI 1.31, 1.79] and 2.46 [95% CI 2.02, 2.98]) patterns. Physical inactivity was associated with increased odds of a high score for the psychosomatic, musculoskeletal and cancer patterns (adjusted ORs 1.41 [95% CI 1.13, 1.76]; 1.39 [95% CI 1.11, 1.74]; and 1.35 [95% CI 1.08, 1.69]). Smoking was associated with increased odds of a high score for the respiratory pattern. Education and ability to manage on income were associated with increased odds of a high score for the psychosomatic pattern (adjusted ORs 1.34 [95% CI 1.03, 1.75] and 1.73 [95% CI 1.37, 1.28], respectively) and musculoskeletal pattern (adjusted ORs 1.43 [95% CI 1.10, 1.87] and 1.38 [1.09, 1.75], respectively). Distinct multimorbidity patterns can be identified among mid-aged women. Social inequality, physical activity and BMI are risk factors common to multiple patterns and are appropriate targets for reducing the risk of specific multimorbidity groups in mid-life women.

AbstractObjectiveTo examine the associations of infertility, recurrent miscarriage, and stillbirth with the risk of first non-fatal and fatal stroke, further stratified by stroke subtypes.DesignIndividual participant pooled analysis of eight prospective cohort studies.SettingCohort studies across seven countries (Australia, China, Japan, Netherlands, Sweden, the United Kingdom, and the United States) participating in the InterLACE (International Collaboration for a Life Course Approach to Reproductive Health and Chronic Disease Events) consortium, which was established in June 2012.Participants618 851 women aged 32.0-73.0 years at baseline with data on infertility, miscarriage, or stillbirth, at least one outcome event (non-fatal or fatal stroke), and information on covariates were included; 93 119 women were excluded. Of the participants, 275 863 had data on non-fatal and fatal stroke, 54 716 only had data on non-fatal stroke, and 288 272 only had data on fatal stroke.Main outcome and measuresNon-fatal strokes were identified through self-reported questionnaires, linked hospital data, or national patient registers. Fatal strokes were identified through death registry data.ResultsThe median follow-up for non-fatal stroke and fatal stroke was 13.0 years (interquartile range 12.0-14.0) and 9.4 years (7.6-13.0), respectively. A first non-fatal stroke was experienced by 9265 (2.8%) women and 4003 (0.7%) experienced a fatal stroke. Hazard ratios for non-fatal or fatal stroke were stratified by hypertension and adjusted for race or ethnicity, body mass index, smoking status, education level, and study. Infertility was associated with an increased risk of non-fatal stroke (hazard ratio 1.14, 95% confidence interval 1.08 to 1.20). Recurrent miscarriage (at least three) was associated with higher risk of non-fatal and fatal stroke (1.35, 1.27 to 1.44, and 1.82, 1.58 to 2.10, respectively). Women with stillbirth were at 31% higher risk of non-fatal stroke (1.31, 1.10 to 1.57) and women with recurrent stillbirth were at 26% higher risk of fatal stroke (1.26, 1.15 to 1.39). The increased risk of stroke (non-fatal or fatal) associated with infertility or recurrent stillbirths was mainly driven by a single stroke subtype (non-fatal ischaemic stroke and fatal haemorrhagic stroke), while the increased risk of stroke (non-fatal or fatal) associated with recurrent miscarriages was driven by both subtypes.ConclusionA history of recurrent miscarriages and death or loss of a baby before or during birth could be considered a female specific risk factor for stroke, with differences in risk according to stroke subtypes. These findings could contribute to improved monitoring and stroke prevention for women with such a history.

Background Obtaining population-level estimates of the incidence and prevalence of dementia is challenging due to under-diagnosis and under-reporting. We investigated the feasibility of using multiple linked datasets and capture-recapture techniques to estimate rates of dementia among women in Australia. Methods This work is based on the Australian Longitudinal Study on Women's Health. A random sample of 12,432 women born in 1921-1926 was recruited in 1996. Over 16 years of follow-up records of dementia were obtained from five sources: three-yearly self-reported surveys; clinical assessments for aged care assistance; death certificates; pharmaceutical prescriptions filled; and, in three Australian States only, hospital in-patient records. Results A total of 2534 women had a record of dementia in at least one of the data sources. The aged care assessments included dementia records for 79.3% of these women, while pharmaceutical data included 34.6%, death certificates 31.0% and survey data 18.5%. In the States where hospital data were available this source included dementia records for 55.8% of the women. Using capture-recapture methods we estimated an additional 728 women with dementia had not been identified, increasing the 16 year prevalence for the cohort from 20.4 to 26.0% (95% confidence interval [CI] 25.2, 26.8%). Conclusions This study demonstrates that using routinely collected health data with record linkage and capture-recapture can produce plausible estimates for dementia prevalence and incidence at a population level.

Background National mortality statistics are only based on the underlying cause of death, which may considerably underestimate the effects of some chronic conditions. Methods The sensitivity, specificity, and positive and negative predictive values for diabetes (a common precursor to multimorbidity), dementia (a potential accelerant of death) and cancer (expected to be well-recorded) were calculated from death certificates for 9 056 women from the 1921–26 cohort of the Australian Longitudinal Study on Women’s Health. Log binomial regression models were fitted to examine factors associated with the sensitivity of death certificates with these conditions as underlying or contributing causes of death. Results Among women who had a record of each of these conditions in their lifetime, the sensitivity was 12.3% (95% confidence interval, 11.0%, 13.7%), 25.2% (23.7%, 26.7%) and 57.7% (55.9%, 59.5%) for diabetes, dementia and cancer, respectively, as the underlying cause of death, and 40.9% (38.8%, 42.9%), 52.3% (50.6%, 54.0%) and 67.1% (65.4%, 68.7%), respectively, if contributing causes of death were also taken into account. In all cases specificity (> 97%) and positive predictive value (> 91%) were high, and negative predictive value ranged from 69.6% to 84.6%. Sensitivity varied with age (in different directions for different conditions) but not consistently with the other sociodemographic factors. Conclusions Death rates associated with common conditions that occur in multimorbidity clusters in the elderly are underestimated in national mortality statistics, but would be improved if the multiple causes of death listed on a death certificate were taken into account in the statistics.

To report rates of perinatal mental health screening from 2000 to 2017 and investigate factors associated with not being screened both antenatally and postnatally more recently (2013–2017). A longitudinal community‐based study of self‐reported perinatal mental health screening with a national sample of 7,566 mothers from the Australian Longitudinal Study on Women's Health reporting on 9,384 children. The main outcome measure was whether mothers were asked about their emotional wellbeing by a health professional, including completing a questionnaire. From 2000 to 2017, the percentage of women not screened decreased from 40.6% to 1.7%. The percentage of women screened both antenatally and postnatally increased from 21.3% to 79.3%. From 2013 to 2017, women who were older (aOR, 0.65; 95%CI, 0.52–0.81) or had reported emotional distress (aOR, 0.77; 95%CI, 0.60–0.99) were less likely to have been screened both antenatally and postnatally. Despite improvements, perinatal mental health screening is not yet universal. One‐in‐five women are not screened both antenatally and postnatally, including women in high‐risk populations such as those who have reported emotional distress. Women are in regular contact with health professionals in the perinatal period. This opportunity to detect women at risk of perinatal mental health issues is too important to be missed.

Current evidence on the association between body mass index (BMI) and age at menopause remains unclear. We investigated the relationship between BMI and age at menopause using data from 11 prospective studies. A total of 24,196 women who experienced menopause after recruitment was included. Baseline BMI was categorised according to the WHO criteria. Age at menopause, confirmed by natural cessation of menses for ≥ 12 months, was categorised as < 45 years (early menopause), 45-49, 50-51 (reference category), 52-53, 54-55, and ≥ 56 years (late age at menopause). We used multinomial logistic regression models to estimate multi variable relative risk ratios (RRRs) and 95% confidence intervals (CI) for the associations between BMI and age at menopause. The mean (standard deviation) age at menopause was 51.4 (3.3) years, with 2.5% of the women having early and 8.1% late menopause. Compared with those with normal BMI (18.5-24.9 kg/m²), underweight women were at a higher risk of early menopause (RRR 2.15, 95% CI 1.50-3.06), while overweight (1.52, 1.31-1.77) and obese women (1.54, 1.18-2.01) were at increased risk of late menopause. Overweight and obesity were also significantly associated with around 20% increased risk of menopause at ages 52-53 and 54-55 years. We observed no association between underweight and late menopause. The risk of early menopause was higher among obese women albeit not significant (1.23, 0.89-1.71). Underweight women had over twice the risk of experiencing early menopause, while overweight and obese women had over 50% higher risk of experiencing late menopause.

Purpose This study aimed to validate a 6-item 1-factor global measure of social support developed from the Medical Outcomes Study Social Support Survey (MOSS-SSS) for use in large epidemiological studies. Methods Data were obtained from two large population-based samples of participants in the Australian Longitudinal Study on Women’s Health. The two cohorts were aged 53–58 and 28–33 years at data collection (N = 10,616 and 8,977, respectively). Items selected for the 6-item 1-factor measure were derived from the factor structure obtained from unpublished work using an earlier wave of data from one of these cohorts. Descriptive statistics, including polychoric correlations, were used to describe the abbreviated scale. Cronbach’s alpha was used to assess internal consistency and confirmatory factor analysis to assess scale validity. Concurrent validity was assessed using correlations between the new 6-item version and established 19-item version, and other concurrent variables. Results In both cohorts, the new 6-item 1-factor measure showed strong internal consistency and scale reliability. It had excellent goodness-of-fit indices, similar to those of the established 19-item measure. Both versions correlated similarly with concurrent measures. Conclusion The 6-item 1-factor MOS-SSS measures global functional social support with fewer items than the established 19-item measure.

Background Network analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statistics. Our aim was to examine the robustness of the results to the choice of weight. Methods Data used on 27 chronic conditions listed on Australian death certificates for women aged 85+. Five statistics were calculated to measure the association between 351 possible pairs: odds ratio (OR), lift, phi correlation, Salton cosine index (SCI), and normalised-joint frequency of pairs (NF). Network analysis was performed on the 10% of pairs with the highest weight according to each definition, the ‘top pairs’. Results Out of 56 ‘top pairs’ identified, 13 ones were consistent across all statistics. In networks of OR and lift, three of the conditions which did not join communities were among the top five most prevalent conditions. Networks based on phi and NF had one or two conditions not part of any community. For the SCI statistics, all three conditions which did not join communities had prevalence below 3%. Low prevalence conditions were more likely to have high degree in networks of OR and lift but not SCI. Conclusion Use of different statistics to estimate weights leads to different networks. For exploratory purposes, one may apply alternative weights to identify a large list of pairs for further assessment in independent studies. However, when the aim is to visualise the data in a robust and parsimonious network, only pairs which are selected by multiple statistics should be visualised.