Annette Jane Dobson

Aims/hypothesis: Menopause hormone therapy (MHT) may have effects on metabolic conditions. This study was conducted to systematically review the literature, to summarise and quantify the effect of MHT on glucose or insulin levels and on new-onset type 2 diabetes among women without type 2 diabetes and to summarise and quantify the effect of MHT on glucose, insulin, lipid and blood pressure levels among women with type 2 diabetes. Methods: Data from six electronic databases (PubMed, Embase, Cochrane, CINAHL, PsyclNFO and MEDLINE) from inception to 29 July 2025 were searched for randomised controlled trials (RCTs) and cohort studies comparing MHT users with non-users among postmenopausal women or women undergoing the menopausal transition. Eligible studies reported the effects of MHT on glucose metabolism, lipid levels or blood pressure in women with or without type 2 diabetes. The risk of bias in RCTs and cohort studies was assessed with version 2 of the Cochrane risk-of-bias for randomised trials (RoB 2) tool and the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tool, respectively. Certainty of evidence (CoE) was assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Random-effects models were used to estimate the pooled effects. Results: In total, 49 articles (33 reporting on RCTs and 16 on cohort studies) from 15 countries were identified, involving 1,472,277 participants. The study durations ranged from 1 week to 13.2 years for RCTs and from 6 months to 17 years for cohort studies. Seven of these were assessed as having a low risk of bias. Among women without type 2 diabetes, MHT use was associated with small reductions in fasting insulin (11 studies: N =4262, mean difference [MD] −5.90 pmol/l [95% confidence interval (CI) −8.03, −3.77 pmol/l]; high CoE) and in homeostasis model assessment of insulin resistance score (four studies: N =2883, MD −0.21 [95% CI −0.32, −0.09]; moderate CoE), as well as a lower risk of new-onset type 2 diabetes (14 studies: N =1,450,134, risk ratio 0.83 [95% CI 0.75, 0.92]; very low CoE], compared with non-MHT users. Among women with type 2 diabetes, MHT was associated with small reductions in fasting plasma glucose (16 studies: N =1379, MD −1.07 mmol/l [95% CI −1.86, −0.28 mmol/l]; very low CoE), haemoglobin A _1c (17 studies: N =15,720, MD −5.47 mmol/mol [−0.50%] [95% CI −8.32, −2.62 mmol/mol (−0.76, −0.24%)]; very low CoE), cholesterol (15 studies: N =907, MD −0.37 mmol/l [95% CI −0.50, −0.24 mmol/l]; low CoE), low-density lipoprotein cholesterol (16 studies: N =961, MD −0.33 mmol/l [95% CI −0.54, −0.13 mmol/l]; low CoE) and systolic blood pressure (nine studies: N =630, MD −3.43 mmHg (−0.46 kPa) [95% CI −4.88, −1.98 mmHg]; low CoE), compared with non-MHT users. Conclusions/interpretation: MHT showed small beneficial associations with glucose metabolism among women with and without type 2 diabetes, while the effect size was generally too small to be clinically significant. Future studies are needed to shed more light on the effect of MHT by initiation age. Study limitations include the predominantly single-reviewer screening and assessment, the high risk of bias of the included studies and restricted geographical representation. Registration: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251230387). Funding: This work was supported by a National Health and Medical Research Council investigator grant (APP2009577).

Purpose: We examined the temporal association between the number of chronic conditions and health-related quality of life (HRQOL) among women of reproductive age and assessed how individual conditions affected specific HRQOL dimensions. Methods: Data from 9323 women born between 1973 and 1978 who participated in the Australian Longitudinal Study on Women’s Health and had no chronic health condition at the baseline survey were analysed. Linear mixed-effects models examined the temporal association between the number of conditions and SF-36 HRQOL and also compared HRQOL between women with and without six conditions: musculoskeletal disorders, diabetes, asthma, endometriosis, uterine fibroids, and cancer. Results: Women who remained free of conditions during ages 18 to 49 maintained stable physical component scores (PCS) and improved mental component scores (MCS). Accumulating conditions was associated with declining scores, more in PCS than MCS. The largest and smallest decline per an additional condition was observed in role physical (6.5 points; 95% CI 6.0, 7.0) and mental health (1.2 points; 0.9, 1.5). Within-group analysis found that all conditions were associated with PCS decline. Effects on MCS varied by condition and outcome. Between-group comparisons revealed that women with musculoskeletal disorders, diabetes, or asthma had lower PCS and MCS scores than women without those conditions. Endometriosis, uterine fibroids, and cancer showed smaller and more dimension-specific reductions. Conclusion: Women who did not develop chronic conditions maintained their PCS scores and had improvements in MCS scores. This underscores the value of preventing chronic conditions in early and mid-adulthood for maintaining physical and mental well-being.

INTRODUCTION: Administrative datasets can be used to calculate population incidence and prevalence of dementia. It is unclear how changes in data sources may affect these estimates. METHODS: We obtained linked administrative health data for individuals 60 years of age or older in Western Australia for 1989–2019 ( n = 893,243) including hospital admissions, emergency department, cause‐of‐death, aged care assessment (from April 2003), and mental health services. RESULTS: There was a marked increase in prevalence and incidence estimates over time. There appeared to be two phases: an initial increase attenuated by 1995–1999 and a rapid increase since 2000–2004 corresponding to inclusion of aged care assessments. There was a decrease in 2015–2019 coinciding with the unavailability of aged care assessment data. DISCUSSION: An apparent secular change in rates of dementia over 31 years may be a product of increased propensity to record dementia diagnosis and availability of additional aged care data. Consistent comprehensive data coverage is required.

BACKGROUND In 2016/17, the Mothers and their Children’s Health (MatCH) study produced a detailed snapshot of the health and development of 5,842 children aged 0-12 years, from 3,039 women participating in the Australian Longitudinal Study on Women’s Health. Data on up to three children were collected from each woman. To further advance understanding of child and adolescent health and development within an intergenerational and family focussed framework, and inform more effective family-centred health strategies, a follow-up study is underway. OBJECTIVE The specific aims of the MatCH 2 study are to follow up participants in the first wave, tracking outcomes over an eight to nine-year period, as well as collecting data on children not in the initial wave. MatCH 2 will focus on the transition into adolescence and identify the maternal, health, social, behavioural, economic, family, and environmental factors associated with child and adolescent health and adverse outcomes. METHODS Participants in the Australian Longitudinal Study on Women’s Health who were born in 1973-78 and have children will complete surveys about their children aged up to 12 years while adolescents aged 13-19 years will complete their own surveys. The age specific data being collected on children includes health conditions and symptoms, diet, anthropometric measures, childcare, screen time, physical activity, sleep, temperament, behaviour, language development, motor development, health service utilisation, as well as household and environmental factors. Additional topics being collected in the adolescent surveys include body image and body development (including pubertal status), menstruation and menstrual issues, contraceptive use, sexual and reproductive health, medication use, substance use, participation in sport, violence and abuse (including bullying), disruptive events and resilience. Following consent, data will be linked to national and State/Territory administrative databases on health, health service use and educational outcomes. RESULTS Data collection for MatCH 2 began in January 2025 and will continue throughout 2025. To October 2025, surveys have been completed for 753 children aged 0-12 years, and 720 adolescents. CONCLUSIONS The MatCH 2 study will combine the detailed history of almost three decades of maternal and family data with detailed data on children and adolescents together with extensive record linkage for health service use and educational outcomes. The new data will allow the in-depth examination of associations between child/adolescent health and a range of biological, psychological, social and lifestyle factors.

Summary Diabetes increases fracture risk, but whether this risk varies by age at diagnosis is unclear. In a large cohort of Australian women, younger age at diabetes diagnosis was linked to substantially higher fracture risk. Age at diagnosis may enhance fracture risk assessment and guide targeted prevention. Purpose The purpose is to examine whether age at diabetes diagnosis modifies the association between diabetes and fracture risk in women. Methods We used data from 12,170 participants in the 1946–1951 cohort of the Australian Longitudinal Study on Women’s Health, linked with administrative health records. Women were followed from age 27 to 76 years. Diabetes status, age at diagnosis, and fracture events were identified from survey and administrative data. Poisson regression was used to compare fracture rates between women with and without diabetes, stratified by age at diagnosis, and adjusted for age and time-varying BMI. Results A total of 2251 women (18.5%) had diabetes and 3761 (30.9%) sustained at least one fracture. Compared to women without diabetes, those with diabetes had a higher fracture risk (RR 1.13; 95% CI 1.02–1.24). The excess risk varied by age at diagnosis: RR 1.61 (95% CI 1.23–2.12) for diagnosis at age 35, declining to RR 1.02 (95% CI 0.90–1.16) for diagnosis at age 60. Conclusions Diabetes is associated with increased fracture risk, but this risk varies by age at diagnosis. Women diagnosed at younger ages face substantially higher risk, while those diagnosed later show little excess risk. Age at diagnosis may improve fracture risk assessment and inform prevention.

Objective: We examined the association between BMI change and risk of multimorbidity among women of reproductive age and estimated annual weight gain before and after diagnosis of chronic conditions. Methods: Data were from 8895 women in the Australian Longitudinal Study on Women's Health who had no chronic conditions at baseline. BMI from Survey 1 (ages 18–23) and Survey 3 (ages 25–30) defined BMI change categories. Linear mixed models estimated annual weight gain from Survey 1 to Survey 9 (ages 43–49). Results: Compared to stable normal BMI, stable obesity and increasing BMI were associated with a higher risk of incident multimorbidity (OR = 1.79 [95% CI: 1.11, 2.90] and 1.34 [1.10, 1.63]). Women who remained free of chronic conditions gained 0.50 kg (0.48, 0.52) annually. Women with one condition gained 0.54 kg (0.52, 0.56): 0.55 (0.53, 0.57) before and 0.54 (0.51, 0.57) after diagnosis. Those with multimorbidity gained 0.65 kg (0.63, 0.67): 0.75 kg (0.70, 0.80) before the first, 0.60 kg (0.56, 0.64) between the first and second, and 0.57 kg (0.53, 0.61) after the second condition. Conclusions: Although weight gain declined after diagnosis, it remained higher than among women without conditions, underscoring the need for improved post‐diagnosis weight management.

Background: Accurately measuring dementia prevalence and incidence (including Alzheimer's disease) is important for service planning and chronic condition management. Linked administrative data present an accessible and inexpensive method to estimate dementia prevalence and incidence. Objective: To examine the accuracy of identifying people with dementia in linked administrative data through comparison with adjudicated cases of dementia from a community-based clinical trial. Methods: We created a comprehensive linked administrative dataset containing people with dementia in Australia and used it to develop algorithms that identified people with dementia. These algorithms were tested against adjudicated cases of dementia (both confirmed and not confirmed) in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial (Number: ISRCTN83772183, Registration date: 14/07/2005) to help determine optimal sensitivity and specificity. Results: The algorithm that provided the highest sensitivity and positive predictive value estimates on balance without excluding ASPREE trial participants with suspected (but not confirmed) dementia was having at least one record for dementia in a minimum of two sources or two records with dementia in one source based on truncating the timeframe of randomization in the ASPREE trial to one year from the date that the first participant was randomized. This algorithm produced a sensitivity of 70.7%, specificity of 98.2%, positive predictive value of 72.6% and negative predictive value of 98.1%. Conclusions: Linked administrative data can identify people with dementia with moderate accuracy. This is promising but requires enhancement before it can be used for estimating dementia prevalence and incidence. Clinical trial name and URL: ASPirin in Reducing Events in the Elderly; ISRCTN - ISRCTN83772183: ASPirin in Reducing Events in the Elderly ( https://www.isrctn.com/ISRCTN83772183 )

Background: Low tumor stage is associated with improved prognosis following diagnosis of primary melanoma. This study evaluated the associations between patient socioeconomic status (SES) and location of residence on tumor stage at melanoma diagnosis. Methods: Patients with a new diagnosis of histologically confirmed melanoma of stages T1b to T4b in Queensland, Australia, were recruited between 2010 and 2014. Logistic regression models were used to assess whether SES or location of residence were associated with lower (T1b–T3a) versus higher (≥T3b) tumor stage. Results: Of 700 study participants with melanoma, those living in lower SES areas (n = 358) had a higher likelihood of being diagnosed with ≥T3b-stage disease compared with patients in higher SES areas [n = 342; adjusted OR = 1.65, 95% confidence interval (CI), 1.04–2.62; P = 0.03]. Relative to patients living in urban areas (n = 389), patients living in inner regional areas (n = 209) were more likely to be diagnosed with a ≥T3b-stage melanoma (OR = 1.65; 95% CI, 1.11–2.46; P = 0.01); however, this finding was not significant following adjustment for SES (adjusted OR = 1.17; 95% CI, 0.72–1.91; P = 0.53). Conclusions: Patients residing in areas with lower average socioeconomic advantage were more frequently diagnosed with a locally advanced melanoma than patients in high-SES areas. Geographical remoteness was not independently associated with tumor stage at diagnosis in this cohort. Impact: Our study highlights a disparity in early melanoma detection across socioeconomic classes. Population groups who are disproportionately more likely to be diagnosed with a thicker primary melanoma should be considered for targeted screening initiatives and healthcare accessibility programs.