Engaging With Health Consumers in Scientific Conferences-As Partners not Bystanders.

INTRODUCTION: It is now widely recognised that engaging consumers in research activities can enhance the quality, equity and relevance of the research. Much of the commentary about consumer engagement in research focuses on research processes and implementation, rather than dissemination in conference settings. This article offers reflections and learnings from consumers, researchers and conference organisers on the 12th Health Services Research Conference, a biennial conference hosted by the Health Services Research Association of Australia and New Zealand (HSRAANZ). METHOD: We were awarded funds via a competitive application process by Bellberry Limited, a national not-for-profit agency with a focus on improving research quality, to incorporate consumer engagement strategies in conference processes and evaluate their impact. FINDINGS: Strategies included consumer scholarships, a buddy system, designated quiet space and consumer session co-chairs; the reflections explored in this paper were collected in the funded, independent evaluation. Our insights suggest a need for more structured consumer involvement in conference planning and design, as well as the development of specific engagement strategies. CONCLUSION: To move toward active partnership in scientific conference settings, our experience reinforces the need to engage consumers as members in designing and conducting research and in presenting research and planning conference content and processes. PUBLIC CONTRIBUTION: Consumer engagement in research dissemination at conferences is the focus of this viewpoint article. Consumers were involved in the conception of this article and have contributed to authorship at all stages of revisions and edits.

Differences in weight status among Australian children and adolescents from priority populations: a longitudinal study.

BACKGROUND AND SIGNIFICANCE: Australia has a high level of cultural and linguistic diversity, including Aboriginal and Torres Strait Islander peoples. Children from specific cultural and ethnic groups may be at greater risk of overweight and obesity and may bear the additional risk of socioeconomic disadvantage. Our aim was to identify differences in body-mass index z-score (zBMI) by: (1) Cultural and ethnic groups and; (2) Socioeconomic position (SEP), during childhood and adolescence. SUBJECTS/METHODS: We used data from the Longitudinal Study of Australian children (n = 9417) aged 2-19 years with 50870 longitudinal measurements of zBMI. Children were classified into 9 cultural and ethnic groups, based on parent and child's country of birth and language spoken at home. These were: (1) English-speaking countries; (2) Middle East & North Africa; (3) East & South-East Asia; (4) South & Central Asia; (5) Europe; (6) Sub-Saharan Africa; (7) Americas; (8) Oceania. A further group (9) was defined as Aboriginal and Torres Strait Islander from self-reported demographic information. Longitudinal cohort analyses in which exposures were cultural and ethnic group and family socioeconomic position, and the outcome was zBMI estimated using multilevel mixed linear regression models. We stratified our analyses over three periods of child development: early childhood (2-5 years); middle childhood (6-11 years); and adolescence (12-19 years). RESULTS: Across all three periods of child development, children from the Middle East and North Africa, the Americas and Oceania were associated with higher zBMI and children from the two Asian groups were associated with lower zBMI, when compared to the referent group (English). zBMI was socioeconomically patterned, with increasingly higher zBMI associated with more socioeconomic disadvantage. CONCLUSIONS: Our findings identified key population groups at higher risk of overweight and obesity in childhood and adolescence. Prevention efforts should prioritize these groups to avoid exacerbating inequalities in healthy weight in childhood.

Psychometric Evaluation of the PedsQL GCS and CHU9D in Australian Children and Adolescents with Common Chronic Health Conditions.

BACKGROUND: Generic instruments such as the Pediatric Quality of Life Inventory™ v4.0 Generic Core Scales (PedsQL GCS) and Child Health Utility 9D (CHU9D) are widely used to assess health-related quality of life (HRQOL) of the general childhood population, but there is a paucity of information about their psychometric properties in children with specific health conditions. This study assessed psychometric properties, including acceptability, reliability, validity, and responsiveness, of the PedsQL GCS and the CHU9D in children and adolescents with a range of common chronic health problems. METHODS: We used data from the Longitudinal Study of Australian Children (LSAC), for children aged 10-17 years with at least one of the following six parent-reported health conditions: asthma, anxiety/depression, attention deficit hyperactivity disorder (ADHD), autism/Asperger's, epilepsy, and type 1 diabetes mellitus. The LSAC used parent proxy-reported PedsQL GCS and child self-reported CHU9D assessments. The performance of each instrument (PedsQL GCS and CHU9D) for each psychometric property (acceptability, reliability, validity, and responsiveness) was assessed against established criteria. RESULTS: The study sample included 7201 children and adolescents (mean age = 14 years; range 10.1-17.9 years; 49% female) with 15,568 longitudinal observations available for analyses. Across the six health conditions, acceptability of the PedsQL GCS was high, while acceptability for the CHU9D was mixed. Both the PedsQL GCS and CHU9D showed strong internal consistency (Cronbach's alpha range: PedsQL GCS = 0.70-0.95, CHU9D = 0.76-0.84; item-total correlations range: PedsQL GCS = 0.35-0.84, CHU9D = 0.32-0.70). However, convergent validity for both the PedsQL GCS and CHU9D was generally weak (Spearman's correlations ≤ 0.3). Known group validity was strong for the PedsQL GCS (HRQOL differences were detected for children with and without asthma, anxiety/depression, ADHD, autism/Asperger's, and epilepsy). CHU9D was only able to discriminate between children with and without anxiety/depression, ADHD, and autism/Asperger's. The responsiveness of both the PedsQL GCS and CHU9D was variable across the six conditions, and most of the estimated effect sizes were relatively small (< 0.5). CONCLUSION: This study expands the evidence base of psychometric performance of the PedsQL GCS and CHU9D and can aid in appropriate HRQOL instrument selection for the required context by researchers and clinicians.

Modelled Distributional Cost-Effectiveness Analysis of Childhood Obesity Interventions: A Demonstration.

OBJECTIVE: To demonstrate how distributional cost-effectiveness analyses of childhood obesity interventions could be conducted and presented for decision makers. METHODS: We conducted modelled distributional cost-effectiveness analyses of three obesity interventions in children: an infant sleep intervention (POI-Sleep), a combined infant sleep, food, activity and breastfeeding intervention (POI-Combo) and a clinician-led treatment for primary school-aged children with overweight and obesity (High Five for Kids). For each intervention, costs and socioeconomic position (SEP)-specific effect sizes were applied to an Australian child cohort (n = 4898). Using a purpose-built microsimulation model we simulated SEP-specific body mass index (BMI) trajectories, healthcare costs and quality-adjusted life years (QALYs) from age 4 to 17 years for control and intervention cohorts. We examined the distribution of each health outcome across SEP and determined the net health benefit and equity impact accounting for opportunity costs and uncertainty due to individual-level heterogeneity. Finally, we conducted scenario analyses to test the effect of assumptions about health system marginal productivity, the distribution of opportunity costs and SEP-specific effect sizes. The results of the primary analyses, uncertainty analyses and scenario analyses were presented on an efficiency-equity impact plane. RESULTS: Accounting for uncertainty, POI-Sleep and High Five for Kids were found to be 'win-win' interventions, with a 67% and 100% probability, respectively, of generating a net health benefit and positive equity impact compared with control. POI-Combo was found to be a 'lose-lose' intervention, with a 91% probability of producing a net health loss and a negative equity impact compared with control. Scenario analyses indicated that SEP-specific effect sizes were highly influential on equity impact estimates for POI-Combo and High Five for Kids, while health system marginal productivity and opportunity cost distribution assumptions primarily influenced the net health benefit and equity impact of POI-Combo. CONCLUSIONS: These analyses demonstrated that distributional cost-effectiveness analyses using a fit-for-purpose model are appropriate for differentiating and communicating the efficiency and equity impacts of childhood obesity interventions.

Reliability, acceptability, validity and responsiveness of the CHU9D and PedsQL in the measurement of quality of life in children and adolescents with overweight and obesity.

BACKGROUND: The Paediatric Quality of life Inventory (PedsQLTM) Generic Core Scales and the Child Health Utilities 9 Dimensions (CHU9D) are two paediatric health-related quality of life (HRQoL) measures commonly used in overweight and obesity research. However, no studies have comprehensively established the psychometric properties of these instruments in the context of paediatric overweight and obesity. The aim of this study was to assess the reliability, acceptability, validity and responsiveness of the PedsQL and the CHU9D in the measurement of HRQoL among children and adolescents living with overweight and obesity. SUBJECTS/METHODS: Subjects were 6544 child participants of the Longitudinal Study of Australian Children, with up to 3 repeated measures of PedsQL and CHU9D and aged between 10 and 17 years. Weight and height were measured objectively by trained operators, and weight status determined using World Health Organisation growth standards. We examined reliability, acceptability, known group and convergent validity and responsiveness, using recognised methods. RESULTS: Both PedsQL and CHU9D demonstrated good internal consistency reliability, and high acceptability. Neither instrument showed strong convergent validity, but PedsQL appears to be superior to the CHU9D in known groups validity and responsiveness. Compared with healthy weight, mean (95%CI) differences in PedsQL scores for children with obesity were: boys -5.6 (-6.2, -4.4); girls -6.7 (-8.1, -5.4) and differences in CHU9D utility were: boys -0.02 (-0.034, -0.006); girls -0.035 (-0.054, -0.015). Differences in scores for overweight compared with healthy weight were: PedsQL boys -2.2 (-3.0, -1.4) and girls -1.3 (-2.0, -0.6) and CHU9D boys: no significant difference; girls -0.014 (-0.026, -0.003). CONCLUSION: PedsQL and CHU9D overall demonstrated good psychometric properties, supporting their use in measuring HRQoL in paediatric overweight and obesity. CHU9D had poorer responsiveness and did not discriminate between overweight and healthy weight in boys, which may limit its use in economic evaluation.

Economic evaluation of a mobile phone text-message intervention for Australian adults with type 2 diabetes.

Background: The rising prevalence of type 2 diabetes in Australia is a public health concern, contributing to significant disease burden and economic costs. Text-message programs have been shown to improve health outcomes for people with type 2 diabetes, however they remain underutilized, and no evidence exists on their cost-effectiveness or costs of scale up to a population level in Australia. This study aimed to determine the cost-effectiveness and cost-utility of a 6-month text-message intervention (DTEXT) to improve glycated hemoglobin (HbA1c) and self-management behaviors for Australian adults with type 2 diabetes. Methods: A within-trial economic evaluation was conducted on the DTEXT randomized controlled trial. Incremental cost-effectiveness ratios (ICERs) were determined per 11 mmol/mol (1%) reduced HbA1c and per quality adjusted life year (QALY) gained, compared to usual care. Cost-effectiveness acceptability curves (CEAC) determined the probability of the intervention being cost-effective over a range of willingness to pay thresholds. A scenario analysis was conducted to determine how cost-effectiveness was impacted by using current implementation costs. Results: The DTEXT intervention cost AU$36 (INT$24) per participant, with an ICER of AU$311 (INT$211) per 11 mmol/mol (1%) reduced HbA1c. Based on HbA1c outcomes, DTEXT had a 33% probability of being effective and cost-saving. Based on the QALY outcomes, the intervention had only a 24% probability of being cost-effective. Scenario analysis indicated costs per participant of AU$13 (INT$9) to deliver the intervention, with a reduced incremental cost effectiveness ratio of AU$151 (INT$103) per 11 mmol/mol (1%) reduced HbA1c and a 38% probability of being effective and cost-saving. Conclusions: DTEXT was low cost and potentially scalable, but only had a low to moderate probability of being effective and cost saving. Further research should determine more targeted approaches that may improve cost-effectiveness. Trial Registration: ACTRN12617000416392.

Is the cost-effectiveness of an early-childhood sleep intervention to prevent obesity affected by socioeconomic position?

OBJECTIVE: This study aimed to determine whether the cost-effectiveness of an infant sleep intervention from the Prevention of Overweight in Infancy (POI) trial was influenced by socioeconomic position (SEP). METHODS: An SEP-specific economic evaluation of the sleep intervention was conducted. SEP-specific intervention costs and effects at age 5 years, derived from the trial data, were applied to a representative cohort of 4,898 4- to 5-year-old Australian children. Quality-adjusted life years and health care costs were simulated until age 17 years using a purpose-built SEP-specific model. Incremental cost-effectiveness ratios and acceptability curves were derived for each SEP group. RESULTS: The incremental cost-effectiveness ratios, in Australian dollars per quality-adjusted life year gained, were smaller in the low- ($23,010) and mid-SEP ($18,206) groups compared with the high-SEP group ($31,981). The probability that the intervention was cost-effective was very high in the low- and mid-SEP groups (92%-100%) and moderately high in the high-SEP group (79%). CONCLUSIONS: An infant sleep intervention is more cost-effective in low- and mid-SEP groups compared with high-SEP groups. Targeting this intervention to low-SEP groups would not require trade-offs between efficiency and equity.

Economic evaluation of the Communicating Healthy Beginnings Advice by Telephone trial for early childhood obesity prevention.

OBJECTIVE: This study aimed to conduct an economic evaluation of the Communicating Healthy Beginnings Advice by Telephone (CHAT) trial to prevent childhood obesity. METHODS: Cost-effectiveness analyses were conducted for the telephone and short message service (SMS) delivery of Healthy Beginnings advice, compared with usual care, which included child health services unrelated to Healthy Beginnings. Costs were valued in 2018 Australian dollars, and costs and outcomes were discounted at 5% per year. The costs of upscaling both delivery modes to all yearly births in New South Wales, Australia, were estimated and compared with the original Healthy Beginnings home-visiting intervention. RESULTS: At child age 2 years, the SMS delivery was more cost-effective ($5154 per unit BMI and $979 per 0.1 BMI z score units avoided) than the telephone delivery ($10,665 per unit BMI and $2017 per 0.1 BMI z score units avoided). The costs of upscaling the SMS ($7.64 million) and the telephone delivery modes ($37.65 million) were lower than the home-visiting intervention ($108.45 million). CONCLUSIONS: SMS delivery of Healthy Beginnings advice was more cost-effective than telephone delivery but less cost-effective than the original home-visiting approach ($4230 per unit BMI avoided, as calculated in an earlier study). Both the SMS and telephone interventions were more affordable than the home-visiting approach.

Cost-effectiveness of scaling up a whole-of-community intervention: The Romp & Chomp early childhood obesity prevention intervention.

BACKGROUND: Given the high prevalence of early childhood overweight and obesity, more evidence is required to better understand the cost-effectiveness of community-wide interventions targeting obesity prevention in children aged 0-5 years. OBJECTIVES: To assess the cost-effectiveness of the Romp & Chomp community-wide early childhood obesity prevention intervention if delivered across Australia in 2018 from a funder perspective, against a no-intervention comparator. METHODS: Intervention costs were estimated in 2018 Australian dollars. The annual Early Prevention of Obesity in Childhood micro-simulation model estimated body mass index (BMI) trajectories to age 15 years, based on end of trial data at age 3.5 years. Results from modelled cost-effectiveness analyses were presented as incremental cost-effectiveness ratios (ICERs): cost per BMI unit avoided, and cost per quality-adjusted life year (QALY) gained at age 15 years. RESULTS: All Australian children aged 0-5 years (n = 1 906 075) would receive the intervention. Total estimated intervention cost and annual cost per participant were AUD178 million and AUD93, respectively, if implemented nationally. The ICERs were AUD1 126 per BMI unit avoided and AUD26 399 per QALY gained (64% probability of being cost-effective measured against a AUD50 000 per QALY threshold). CONCLUSIONS: Romp & Chomp has a fair probability of being cost-effective if delivered at scale.

Investigating socioeconomic inequalities in BMI growth rates during childhood and adolescence.

BACKGROUND: Many countries report socioeconomic inequalities in childhood obesity, but when they develop is not well-characterised. Studies rarely isolate BMI growth rates from overall BMI, perhaps overlooking an important precursor to the observed inequalities in obesity. The objective of this study was to determine the age at which inequalities in BMI growth rates develop in children and whether they are similar across the BMI spectrum. METHODS: Using the Longitudinal Study of Australian Children (n = 9024), a cohort study, we measured socioeconomic inequalities in annual BMI growth from age 2 to 17 years by age, sex and weight status. We fit a linear model using generalised estimating equations (GEE) to estimate simultaneously the effects of age and weight status on inequalities in BMI growth rate. RESULTS: The slope (SII) and relative (RII) indexes of inequality for annual BMI growth were greatest in middle childhood (age 4-11 years) (SII 0.25, RII 1.83 (boys) 1.78 (girls)) and were moderate during adolescence (age 10-17 years) (SII 0.11, RII 1.16 [boys] 1.15 [girls]). In early childhood, there was little evidence of inequality in annual BMI growth except in children with obesity. In middle childhood and adolescence, inequalities were greater at higher weight status. The GEE indicated that both weight status (P < 0.001) and age period (P < 0.001) affected inequalities in BMI growth rates. CONCLUSIONS: Inequalities in annual BMI growth were strongest in middle childhood, and widest in children at the upper end of the BMI spectrum. This could signify a key age bracket to intervene clinically and at a public health level and improve inequalities in childhood obesity.

Relationship between obesity and school absenteeism in Australian children: Implications for carer productivity.

BACKGROUND: Economic evaluations of childhood obesity interventions are often used to assist decision making when presented with alternative course of action. Including indirect costs related to productivity losses is recommended; in children this would include school absenteeism. Our aim was to determine the association between school absenteeism and weight status among Australian children and estimate the indirect costs of this. METHODS: We used data from a nationally representative sample of 8551 Australian children in the Longitudinal study of Australian Children (LSAC) with follow-up between 2006 and 2018. A mixed-effects negative binomial regression model was used to investigate the relationship between school absenteeism and weight status, controlling for age, sex, socio-economic position, indigenous status, rural/remote status and long-term medical conditions. We used average daily wages for the year 2018 to value the indirect costs of school absenteeism (through caregiver lost productivity). RESULTS: Australian children with obesity aged 6-13 years missed on average an extra day of school annually compared to children of a healthy weight (p = 0.004), while adolescents with obesity aged 14-17 years missed on average an extra 0.69 days of school annually (p = 0.006). The estimated national cost for children with obesity aged 6-13 years was approximately $64 million AUD ($43 million USD) or $338 AUD ($230 USD) per child through caregiver lost productivity in 2018. CONCLUSIONS: There is a small but significant association between school absenteeism and childhood obesity in Australia which is estimated to generate a considerable national cost through caregiver productivity losses. Our results will assist health economists evaluating childhood obesity interventions capture the full extent of the associated costs with this condition.

Utility Decrements Associated with Adult Overweight and Obesity in Australia: A Systematic Review and Meta-Analysis.

OBJECTIVE: The aim was to conduct a systematic review and meta-analysis of health state utility decrements associated with overweight and obesity in adults 18 years and over, for use in modelled economic evaluations in Australia. METHODS: A systematic review was conducted in nine databases to identify studies that reported health state utility values by weight status. Random-effects meta-analysis was used to synthesise average utility decrements (from healthy weight) associated with overweight, all obesity and obesity classes 1, 2 and 3. Heterogeneity surrounding utility decrements was assessed via sub-group analysis, random-effects meta-regression and sensitivity analyses. RESULTS: Twelve studies were found for which data were used to synthesise utility decrements, estimated as overweight = 0.020 (95% confidence interval 0.010-0.030), all obesity = 0.055 (0.034-0.076), obesity class 1 = 0.047 (0.017-0.077), class 2 = 0.072 (0.028-0.116) and class 3 = 0.084 (0.039-0.130). There was considerable heterogeneity in our results, which could be accounted for by the different ages and utility instruments used in the contributing studies. CONCLUSIONS: Our results demonstrate that elevated weight status is associated with small but statistically significant reductions in utility compared with healthy weight, which will result in reduced quality-adjusted life years when extrapolated across time and used in economic evaluations.

Looking backwards and forwards: tracking and persistence of weight status between early childhood and adolescence.

BACKGROUND/OBJECTIVE: Many studies have shown that child BMI or weight status tracks over time, but the demographic predictors of high tracking have not been investigated. Our objective was to identify demographic predictors of persistence (duration) of healthy weight and overweight/obesity throughout childhood, and to examine whether tracking was age dependent. METHODS: We conducted secondary data analysis of 4606 children from the Birth cohort and 4983 children from the Kindergarten cohort of the Longitudinal Study of Australian Children with follow-up to age 12/13 and 16/17 years, respectively. Retrospective and prospective tracking were examined descriptively. Time-to-event analysis determined demographic predictors of persistence of healthy weight and overweight/obesity beyond age 4-5 years, after controlling for child BMI z-score. Weight status was determined using WHO methods. RESULTS: Tracking of healthy weight was consistently higher than that of overweight/obesity, and incident overweight was equally likely throughout childhood and adolescence. Tracking of overweight was lower for children under 7 years than in middle childhood and adolescence (2-year probability 65%, compared with 80%; 2-year resolution of overweight 35 and 20%). Children of lower socioeconomic position, those from culturally and linguistically diverse backgrounds, and girls were more likely to move into overweight (hazard ratios [95%CI] for incident overweight: 1.39 [1.26-1.52], 1.16 [1.02-1.31] and 1.12 [1.02-1.23], respectively) and less likely to resolve their overweight (hazard ratios for resolution of overweight/obesity: 0.77 [0.69-0.85], 0.8 [0.69-0.92] and 0.79 [0.71-0.81], respectively) during childhood. However, persistence of weight status was not significantly affected by rurality or Indigenous status (P > 0.05). CONCLUSIONS: Lowest tracking and highest natural resolution of overweight in children under 7 years suggests this may be an opportune time for interventions to reduce overweight. Primary and secondary prevention programmes during the school years should be designed with special consideration for lower socioeconomic communities, for culturally and linguistically diverse populations and for girls.

Weight status and health-related quality of life during childhood and adolescence: effects of age and socioeconomic position.

BACKGROUND: Overweight and obesity in children is associated with poor health-related quality of life (HRQoL), but the nuances of this relationship across different age and socio-demographic groups are not well-established. The aim of this study is to examine how the association between weight status and HRQoL changes with age and socioeconomic position (SEP) throughout childhood and adolescence. METHODS: We used data from the Longitudinal Study of Australian Children (LSAC), a cohort study in which children were interviewed biennially from ages 4 to 17 years over seven waves of data. Measurements of HRQoL (using PedsQLTM), body mass index (BMI), and socio-demographic characteristics were collected at each interview. Of the 4983 children recruited into the study, we included data from 4083 children (a total of 24,446 observations). We used generalised estimating equations to assess whether age and SEP modified the association between weight status and HRQoL, after controlling for sex, long-term medical condition, language spoken to child and maternal smoking status. RESULTS: Age was a significant modifier of the association between weight status and HRQoL, with adjustment for known predictors of HRQoL (P < 0.001). At age 4, children with obesity had, on average, a 0.99 (95% CI 0.02-1.96) point lower PedsQL total score than children at healthy weight. This difference became clinically important by age 9 at 4.50 (95% CI 3.86-5.13) points and increased to 6.69 (95% CI 5.74-7.64) points by age 17. There was no evidence that SEP modified the relationship between weight status and HRQoL (P > 0.05). CONCLUSIONS: Our results demonstrate that the relationship between overweight and obesity status and poor HRQoL is strengthened with increasing age through childhood and adolescence, but is not affected by SEP. Paediatricians, researchers and carers of children with obesity should acknowledge HRQoL outcomes, particularly for older children and adolescents.

Estimating Age- and Sex-Specific Utility Values from the CHU9D Associated with Child and Adolescent BMI z-Score.

OBJECTIVE: Our objective was to identify age- and sex-specific utilities for children and adolescents by body mass index (BMI) z-score. METHODS: We used data from 6822 participants and 12,094 observations from two cohorts and two waves of interviews from the Longitudinal Study of Australian Children. We fit linear models using generalised estimating equations to investigate associations between Child Health Utility 9D and BMI z-score in girls and boys aged 10-17 years. We initially fit models for each sex, fully adjusted for known predictors of health-related quality of life, including socioeconomic position, long-term medical condition and maternal smoking status and also included an interaction between age and BMI z-score to examine age-specific effects. Finally, we derived a minimal model for each sex by eliminating interaction terms with P > 0.01 and predictors with P > 0.05. RESULTS: Our adjusted results show different utility patterns in girls and boys. In girls, utility decrements for each unit increase in BMI z-score changed with age (P < 0.01 for interaction between age and BMI z-score). At age 10 years, the mean utility decrement for each unit increase in BMI z-score was 0.002 (95% confidence interval [CI] 0.011 decrement to 0.006 increment), but, by age 17 years, this utility decrement was 0.023 (95% CI 0.013 to 0.032). In boys, small non-significant decrements were found in utility for each unit increase in BMI z-score, with no observable change with age. CONCLUSION: Our analyses demonstrated that age and sex should be considered when attributing utility values and decrements to BMI z-scores.

Socioeconomic inequalities in obesity: modelling future trends in Australia.

OBJECTIVES: To develop a model to predict future socioeconomic inequalities in body mass index (BMI) and obesity. DESIGN: Microsimulation modelling using BMI data from adult participants of Australian Health Surveys, and published data on the relative risk of mortality in relation to BMI and socioeconomic position (SEP), based on education. SETTING: Australia. PARTICIPANTS: 74 329 adults, aged 20 and over from Australian Health Surveys, 1995-2015. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were BMI trajectories and obesity prevalence by SEP for four birth cohorts, born 10 years apart, centred on 1940, 1950, 1960 and 1970. RESULTS: Simulations projected persistent or widening socioeconomic inequality in BMI and obesity over the adult life course, for all birth cohorts. Recent birth cohorts were predicted to have greater socioeconomic inequality by middle age, compared with earlier cohorts. For example, among men, there was no inequality in obesity prevalence at age 60 for the 1940 birth cohort (low SEP 25% (95% CI 17% to 34%); high SEP 26% (95% CI 19% to 34%)), yet for the 1970 birth cohort, obesity prevalence was projected to be 51% (95% CI 43% to 58%) and 41% (95% CI 36% to 46%) for the low and high SEP groups, respectively. Notably, for more recent birth cohorts, the model predicted the greatest socioeconomic inequality in severe obesity (BMI >35 kg/m2) at age 60. CONCLUSIONS: Lower SEP groups and more recent birth cohorts are at higher risk of obesity and severe obesity, and its consequences in middle age. Prevention efforts should focus on these vulnerable population groups in order to avoid future disparities in health outcomes. The model provides a framework for further research to investigate which interventions will be most effective in narrowing the gap in socioeconomic disparities in obesity in adulthood.

Impact of overweight, obesity and severe obesity on life expectancy of Australian adults.

BACKGROUND/OBJECTIVES: Adult obesity has been shown to substantially heighten the risk of adverse health outcomes but its impact on life expectancy (LE) has not been quantified in Australia. Our aim is to estimate reductions in LE and years of life lost (YLL) associated with overweight and obesity, relative to those at a healthy weight for Australian adults aged 20-69 years. SUBJECTS/METHODS: We used a microsimulation model of obesity progression in Australia that integrates annual change in BMI based on age and sex, with Australian life-table data and published relative risk of all-cause mortality for different BMI categories. Remaining LE and YLL compared to healthy weight were estimated using 10-year cohorts, by sex. A nationally representative sample of 12,091 adults aged 20-69 from the 2014/15 Australian National Health Survey were used to represent the input population of 14.9 million. RESULTS: Estimated remaining years of LE for healthy weight men and women aged 20-29 years was approximately 57.0 (95% CI 56.7-57.4) and 59.7 (95% CI 59.4-60.0) years, respectively. YLL associated with being overweight at baseline was approximately 3.3 years. For those obese and severely obese the loss in LE was predicted to be 5.6-7.6 years and 8.1-10.3 years for men and women aged 20-29 years, respectively. Across men and women, excess BMI in the adult population is responsible for approximately 36.3 million YLLs. Men stand to lose 27.7% more life years compared to women. CONCLUSIONS: Overweight and obesity is associated with premature mortality at all ages, for both men and women. Adults aged 20-39 years with severe obesity will experience the largest YLL, relative to healthy weight. More needs to be done in Australia to establish a coherent, sustained, cost-effective strategy to prevent overweight and obesity, particularly for men in early adulthood.

A Common Cancer Risk-Associated Allele in the hTERT Locus Encodes a Dominant Negative Inhibitor of Telomerase.

The TERT-CLPTM1L region of chromosome 5p15.33 is a multi-cancer susceptibility locus that encodes the reverse transcriptase subunit, hTERT, of the telomerase enzyme. Numerous cancer-associated single-nucleotide polymorphisms (SNPs), including rs10069690, have been identified within the hTERT gene. The minor allele (A) at rs10069690 creates an additional splice donor site in intron 4 of hTERT, and is associated with an elevated risk of multiple cancers including breast and ovarian carcinomas. We previously demonstrated that the presence of this allele resulted in co-production of full length (FL)-hTERT and an alternatively spliced, INS1b, transcript. INS1b does not encode the reverse transcriptase domain required for telomerase enzyme activity, but we show here that INS1b protein retains its ability to bind to the telomerase RNA subunit, hTR. We also show that INS1b expression results in decreased telomerase activity, telomere shortening, and an increased telomere-specific DNA damage response (DDR). We employed antisense oligonucleotides to manipulate endogenous transcript expression in favor of INS1b, which resulted in a decrease in telomerase activity. These data provide the first detailed mechanistic insights into a cancer risk-associated SNP in the hTERT locus, which causes cell type-specific expression of INS1b transcript from the presence of an additional alternative splice site created in intron 4 by the risk allele. We predict that INS1b expression levels cause subtle inadequacies in telomerase-mediated telomere maintenance, resulting in an increased risk of genetic instability and therefore of tumorigenesis.