Individual and area level factors associated with the breast cancer diagnostic-treatment interval in Queensland, Australia.

BACKGROUND: Delays to breast cancer treatment can lead to more aggressive and extensive treatments, increased expenses, increased psychological distress, and poorer survival. We explored the individual and area level factors associated with the interval between diagnosis and first treatment in a population-based cohort in Queensland, Australia. METHODS: Data from 3216 Queensland women aged 20 to 79, diagnosed with invasive breast cancer (ICD-O-3 C50) between March 2010 and June 2013 were analysed. Diagnostic dates were sourced from the Queensland Cancer Registry and treatment dates were collected via self-report. Diagnostics-treatment intervals were modelled using flexible parametric survival methods. RESULTS: The median interval between breast cancer diagnosis and first treatment was 15 days, with an interquartile range of 9-26 days. Longer diagnostic-treatment intervals were associated with a lack of private health coverage, lower pre-diagnostic income, first treatments other than breast conserving surgery, and residence outside a major city. The model explained a modest 13.7% of the variance in the diagnostic-treatment interval [Formula: see text]. Sauerbrei's D was 0.82, demonstrating low to moderate discrimination performance. CONCLUSION: Whilst this study identified several individual- and area-level factors associated with the time between breast cancer diagnosis and first treatment, much of the variation remained unexplained. Increased socioeconomic disadvantage appears to predict longer diagnostic-treatment intervals. Though some of the differences are small, many of the same factors have also been linked to screening and diagnostic delay. Given the potential for accumulation of delay at multiple stages along the diagnostic and treatment pathway, identifying and applying effective strategies address barriers to timely health care faced by socioeconomically disadvantaged women remains a priority.

Geographical and spatial disparities in the incidence and survival of rare cancers in Australia.

Rare cancers collectively account for around a quarter of cancer diagnoses and deaths. However, epidemiological studies are sparse. We describe spatial and geographical patterns in incidence and survival of rare cancers across Australia using a population-based cancer registry cohort of rare cancer cases diagnosed among Australians aged at least 15 years, 2007 to 2016. Rare cancers were defined using site- and histology-based categories from the European RARECARE study, as individual cancer types having crude annual incidence rates of less than 6/100 000. Incidence and survival patterns were modelled with generalised linear and Bayesian spatial Leroux models. Spatial heterogeneity was tested using the maximised excess events test. Rare cancers (n = 268 070) collectively comprised 22% of all invasive cancer diagnoses and accounted for 27% of all cancer-related deaths in Australia, 2007 to 2016 with an overall 5-year relative survival of around 53%. Males and those living in more remote or more disadvantaged areas had higher incidence but lower survival. There was substantial evidence for spatial variation in both incidence and survival for rare cancers between small geographical areas across Australia, with similar patterns so that those areas with higher incidence tended to have lower survival. Rare cancers are a substantial health burden in Australia. Our study has highlighted the need to better understand the higher burden of these cancers in rural and disadvantaged regions where the logistical challenges in their diagnosis, treatment and support are magnified.

Mapping the prevalence of cancer risk factors at the small area level in Australia.

BACKGROUND: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. METHODS: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. RESULTS: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. CONCLUSIONS: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.

Supportive care needs and psychosocial outcomes of rural versus urban women with breast cancer.

OBJECTIVE: To identify whether supportive care needs vary according to remoteness and area-level socio-economic status and to identify the combinations of socio-demographic, area-level and health factors that are associated with poorer quality of life, psychological distress and severity of unmet supportive care needs. METHODS: Cross sectional data was collected from women with a breast cancer diagnosis (n = 2635) in Queensland, Australia, through a telephone survey including socio-demographic, health, psychosocial and supportive care needs measures. Hierarchical regression and cluster analyses were applied to assess the predictors of unmet need and psychosocial outcomes and to identify socio-demographic and health status profiles of women, comparing their level of unmet needs and psychosocial outcomes. RESULTS: Women living in outer regional areas reported the highest severity of unmet need in the patient care domain. Greater unmet need for health systems and information and patient care was also evident for those in moderately and most disadvantaged areas. Three clusters were identified reflecting (1) older women with poorer health and lower education (19%); (2) younger educated women with better health and private insurance (61%); and (3) physically active women with localised cancer who had completed treatment (20%). Poorer outcomes were evident in the first two of these clusters. CONCLUSIONS: This better understanding of the combinations of characteristics associated with poorer psychosocial outcomes and higher unmet need can be used to identify women with higher supportive care needs early and to target interventions.

Factors associated with being diagnosed with high severity of breast cancer: a population-based study in Queensland, Australia.

PURPOSE: This study explores factors that are associated with the severity of breast cancer (BC) at diagnosis. METHODS: Interviews were conducted among women (n = 3326) aged 20-79 diagnosed with BC between 2011 and 2013 in Queensland, Australia. High-severity cancers were defined as either Stage II-IV, Grade 3, or having negative hormone receptors at diagnosis. Logistic regression models were used to estimate odds ratios (ORs) of high severity BC for variables relating to screening, lifestyle, reproductive habits, family history, socioeconomic status, and area disadvantage. RESULTS: Symptom-detected women had greater odds (OR 3.38, 2.86-4.00) of being diagnosed with high-severity cancer than screen-detected women. Women who did not have regular mammograms had greater odds (OR 1.78, 1.40-2.28) of being diagnosed with high-severity cancer than those who had mammograms biennially. This trend was significant in both screen-detected and symptom-detected women. Screen-detected women who were non-smokers (OR 1.77, 1.16-2.71), postmenopausal (OR 2.01, 1.42-2.84), or employed (OR 1.46, 1.15-1.85) had greater odds of being diagnosed with high-severity cancer than those who were current smokers, premenopausal, or unemployed. Symptom-detected women being overweight (OR 1.67, 1.31-2.14), postmenopausal (OR 2.01, 1.43-2.82), had hormone replacement therapy (HRT) < 2 years (OR 1.60, 1.02-2.51) had greater odds of being diagnosed with high-severity cancer than those of healthy weight, premenopausal, had HRT > 10 years. CONCLUSION: Screen-detected women and women who had mammograms biennially had lower odds of being diagnosed with high-severity breast cancer, which highlighted the benefit of regular breast cancer screening. Women in subgroups who are more likely to have more severe cancers should be particularly encouraged to participate in regular mammography screening.

Does Self-Esteem Have an Interpersonal Imprint Beyond Self-Reports? A Meta-Analysis of Self-Esteem and Objective Interpersonal Indicators.

Self-esteem promises to serve as the nexus of social experiences ranging from social acceptance, interpersonal traits, interpersonal behavior, relationship quality, and relationship stability. Yet previous researchers have questioned the utility of self-esteem for understanding relational outcomes. To examine the importance of self-esteem for understanding interpersonal experiences, we conducted systematic meta-analyses on the association between trait self-esteem and five types of interpersonal indicators. To ensure our results were not due to self-esteem biases in perception, we focused our meta-analyses to 196 samples totaling 121,300 participants wherein researchers assessed interpersonal indicators via outsider reports. Results revealed that the association between self-esteem and the majority of objective interpersonal indicators was small to moderate, lowest for specific and distal outcomes, and moderated by social risk. Importantly, a subset of longitudinal studies suggests that self-esteem predicts later interpersonal experience. Our results should encourage researchers to further explore the link between self-esteem and one's interpersonal world.

Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model.

This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.

Beyond the urban-rural divide: Exploring spatial variations in breast cancer outcomes in Queensland, Australia.

BACKGROUND: Breast cancer is the most commonly diagnosed cancer among women worldwide. While previous studies have reported urban and rural differences in breast cancer outcomes, the level of heterogeneity within these broad regions is currently unknown. METHODS: Population-level data from Queensland Cancer Register including 58,679 women aged at least 20 years who were diagnosed with breast cancer in Queensland, Australia, 2000-2019 were linked to BreastScreen Queensland and Queensland Hospital Admitted Patients Data Collection to estimate five breast cancer outcomes: incidence, proportion of localised disease and screen-detected cases (via public-funded program), surgical rates, and 5-year survival. Bayesian spatial models were used to smooth outcomes across 512-517 small areas in Queensland. RESULTS: The incidence of breast cancer was not proportionally distributed, with urban regions having higher rates. Less than half (47 %) of women were diagnosed with localised disease, 91 % had surgery, with five-year relative survival of 92 %. There was no evidence of geographic variation in the proportion of localised disease, surgical rates, or survival over Queensland. Publicly-funded screening detected 38 % of cases, with lower proportion of screen-detected cases observed in Queensland's urbanised south-east corner. CONCLUSION: Although the disparities in health outcomes faced by Australians living in rural areas have received increased attention, this study found limited evidence for spatial variation in breast cancer outcomes along the continuum of care across Queensland. These results suggest the detection and management practices for breast cancer may provide an achievable benchmark for other cancer types in reducing the geographical disparity in cancer outcomes.

A Review of the Application of Spatial Survival Methods in Cancer Research: Trends, Modeling, and Visualization Techniques.

Spatial modeling of cancer survival is an important tool for identifying geographic disparities and providing an evidence base for resource allocation. Many different approaches have attempted to understand how survival varies geographically. This is the first scoping review to describe different methods and visualization techniques and to assess temporal trends in publications. The review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline using PubMed and Web of Science databases. Two authors independently screened articles. Articles were eligible for review if they measured cancer survival outcomes in small geographical areas by using spatial regression and/or mapping. Thirty-two articles were included, and the number increased over time. Most articles have been conducted in high-income countries using cancer registry databases. Eight different methods of modeling spatial survival were identified, and there were seven different ways of visualizing the results. Increasing the use of spatial modeling through enhanced data availability and knowledge sharing could help inform and motivate efforts to improve cancer outcomes and reduce excess deaths due to geographical inequalities. Efforts to improve the coverage and completeness of population-based cancer registries should continue to be a priority, in addition to encouraging the open sharing of relevant statistical programming syntax and international collaborations.

Geographical and spatial variations in bowel cancer screening participation, Australia, 2015-2020.

BACKGROUND: Participation in bowel cancer screening programs remains poor in many countries. Knowledge of geographical variation in participation rates may help design targeted interventions to improve uptake. This study describes small-area and broad geographical patterns in bowel screening participation in Australia between 2015-2020. METHODS: Publicly available population-level participation data for Australia's National Bowel Cancer Screening Program (NBCSP) were modelled using generalized linear models to quantify screening patterns by remoteness and area-level disadvantage. Bayesian spatial models were used to obtain smoothed estimates of participation across 2,247 small areas during 2019-2020 compared to the national average, and during 2015-2016 and 2017-2018 for comparison. Spatial heterogeneity was assessed using the maximized excess events test. RESULTS: Overall, screening participation rates was around 44% over the three time-periods. Participation was consistently lower in remote or disadvantaged areas, although heterogeneity was evident within these broad categories. There was strong evidence of spatial differences in participation over all three periods, with little change in patterns between time periods. If the spatial variation was reduced (so low participation areas were increased to the 80th centile), an extra 250,000 screens (4% of total) would have been conducted during 2019-2020. CONCLUSIONS: Despite having a well-structured evidence-based government funded national bowel cancer screening program, the substantial spatial variation in participation rates highlights the importance of accounting for the unique characteristics of specific geographical regions and their inhabitants. Identifying the reasons for geographical disparities could inform interventions to achieve more equitable access and a higher overall bowel screening uptake.

Severity and risk factors of interval breast cancer in Queensland, Australia: a population-based study.

BACKGROUND: Interval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC. METHODS: Telephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010-2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation. RESULTS: Compared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9-4.3), high-grade (OR = 2.36, 1.9-2.9) and triple-negative cancers (OR = 2.55, 1.9-3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6-0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2-2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1-1.7), received hormone replacement therapy (2-10 years: OR = 1.33, 1.0-1.7; > 10 years: OR = 1.55, 1.1-2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2-2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2-2.0). CONCLUSION: These results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.

Disease mapping: Geographic differences in population rates of interventional treatment for prostate cancer in Australia.

BACKGROUND: Treatment decisions for men diagnosed with prostate cancer depend on a range of clinical and patient characteristics such as disease stage, age, general health, risk of side effects and access. Associations between treatment patterns and area-level factors such as remoteness and socioeconomic disadvantage have been observed in many countries. OBJECTIVE: To model spatial differences in interventional treatment rates for prostate cancer at high spatial resolution to inform policy and decision-making. METHODS: Hospital separations data for interventional treatments for prostate cancer (radical prostatectomy, low dose rate and high dose rate brachytherapy) for men aged 40 years and over were modelled using spatial models, generalised linear mixed models, maximised excess events tests and k-means statistical clustering. RESULTS: Geographic differences in population rates of interventional treatments were found (p<0.001). Separation rates for radical prostatectomy were lower in remote areas (12.2 per 10 000 person-years compared with 15.0-15.9 in regional and major city areas). Rates for all treatments decreased with increasing socioeconomic disadvantage (radical prostatectomy 19.1 /10 000 person-years in the most advantaged areas compared with 12.9 in the most disadvantaged areas). Three groups of similar areas were identified: those with higher rates of radical prostatectomy, those with higher rates of low dose brachytherapy, and those with low interventional treatment rates but higher rates of excess deaths. The most disadvantaged areas and remote areas tended to be in the latter group. CONCLUSIONS: The geographic differences in treatment rates may partly reflect differences in patients' physical and financial access to treatments. Treatment rates also depend on diagnosis rates and thus reflect variation in investigation rates for prostate cancer and presentation of disease. Spatial variation in interventional treatments may aid identification of areas of under-treatment or over-treatment.

The Friends-to-Lovers Pathway to Romance: Prevalent, Preferred, and Overlooked by Science.

There is more than one pathway to romance, but relationship science does not reflect this reality. Our research reveals that relationship initiation studies published in popular journals (Study 1) and cited in popular textbooks (Study 2) overwhelmingly focus on romance that sparks between strangers and largely overlook romance that develops between friends. This limited focus might be justified if friends-first initiation was rare or undesirable, but our research reveals the opposite. In a meta-analysis of seven samples of university students and crowdsourced adults (Study 3; N = 1,897), two thirds reported friends-first initiation, and friends-first initiation was the preferred method of initiation among university students (Study 4). These studies affirm that friends-first initiation is a prevalent and preferred method of romantic relationship initiation that has been overlooked by relationship science. We discuss possible reasons for this oversight and consider the implications for dominant theories of relationship initiation.

Spatial disparities in the reported incidence and survival of myeloproliferative neoplasms in Australia.

Myeloproliferative neoplasms (MPNs) are an uncommon group of blood cancers that, if untreated, result in an increased risk of haemorrhagic event or thrombosis. Unlike other cancer types, diagnosis of MPNs requires a combination of microscopic, clinical and genetic evidence, which provide unique challenges given the typical notification processes of cancer registries. This, and the relatively recent advances in diagnosis and revision of the World Health Organization diagnostic criteria, may result in under-diagnosis or under-reporting of MPNs. We used population-based cancer registry data from the Australian Cancer Database and modelled the incidence and survival of MPNs between 2007 and 2016 using generalised linear models and Bayesian spatial Leroux models. Substantial evidence was found of spatial heterogeneity in the incidence of MPNs and significant differences in incidence and survival by state or territory. States with lower incidence tended to have poorer survival, suggesting that some less severe cases may not be diagnosed or notified to the registries in those states. Population rates of genetic testing and percentages of records diagnosed using bone marrow biopsies did not explain the differences in incidence by state and territory. It is important to determine the key drivers of these geographical patterns, including the need to standardise diagnosis and reporting of MPNs.

Geographic distribution of malignant mesothelioma incidence and survival in Australia.

OBJECTIVES: To understand the geographic distribution of and area-level factors associated with malignant mesothelioma incidence and survival in Australia. MATERIALS AND METHODS: Generalised linear models and Bayesian spatial models were fitted using population registry data. Area-level covariates were socioeconomic quintile, remoteness category and state or territory. The maximised excess events test was used to test for spatial heterogeneity. RESULTS: There was strong evidence of spatial differences in standardised incidence rates for malignant mesothelioma but survival was uniformly poor. Incidence rates varied by state or territory and were lower in remote areas. Patterns in the geographic distribution of modelled incidence counts for malignant mesothelioma differed substantially from patterns of standardised incidence rates. CONCLUSIONS: Geographic variation in the modelled incidence counts of malignant mesothelioma demonstrates varying demand for diagnostic and management services. The long latency period for this cancer coupled with migration complicates any associations with patterns of exposure, however some of the geographic distribution of diagnoses can be explained by the location of historical mines and asbestos-related industries.

Projections of the future burden of cancer in Australia using Bayesian age-period-cohort models.

BACKGROUND: Accurate forecasts of cancer incidence, with appropriate estimates of uncertainty, are crucial for planners and policy makers to ensure resource availability and prioritize interventions. We used Bayesian age-period-cohort (APC) models to project the future incidence of cancer in Australia. METHODS: Bayesian APC models were fitted to counts of cancer diagnoses in Australia from 1982 to 2016 and projected to 2031 for seven key cancer types: breast, colorectal, liver, lung, non-Hodgkin lymphoma, melanoma and stomach. Aggregate cancer data from population-based cancer registries were sourced from the Australian Institute of Health and Welfare. RESULTS: Over the projection period, total counts for these cancer types increased on average by 3 % annually to 100 385 diagnoses in 2031, which is a 50 % increase over 2016 numbers, although there is considerable uncertainty in this estimate. Counts for each cancer type and sex increased over the projection period, whereas decreases in the age-standardized incidence rates (ASRs) were projected for stomach, colorectal and male lung cancers. Large increases in ASRs were projected for liver and female lung cancer. Increases in the percentage of colorectal cancer diagnoses among younger age groups were projected. Retrospective one-step-ahead projections indicated both the incidence and its uncertainty were successfully forecast. CONCLUSIONS: Increases in the projected incidence counts of key cancer types are in part attributable to the increasing and ageing population. The projected increases in ASRs for some cancer types should increase motivation to reduce sedentary behaviour, poor diet, overweight and undermanagement of infections. The Bayesian paradigm provides useful measures of the uncertainty associated with these projections.

A systematic review and meta-analysis on international studies of prevalence, mortality and survival due to coal mine dust lung disease.

BACKGROUND: Coal mine dust lung disease comprises a group of occupational lung diseases including coal workers pneumoconiosis. In many countries, there is a lack of robust prevalence estimates for these diseases. Our objective was to perform a systematic review and meta-analysis of published contemporary estimates on prevalence, mortality, and survival for coal mine dust lung disease worldwide. METHODS: Systematic searches of PubMed, EMBASE and Web of Science databases for English language peer-reviewed articles published from 1/1/2000 to 30/03/2021 that presented quantitative estimates of prevalence, mortality, or survival for coal mine dust lung disease. Review was conducted per PRISMA guidelines. Articles were screened independently by two authors. Studies were critically assessed using Joanna Briggs Institute tools. Pooled prevalence estimates were obtained using random effects meta-analysis models. Heterogeneity was measured using the I2 statistics and publication bias using Egger's tests. RESULTS: Overall 40 studies were included, (31 prevalence, 8 mortality, 1 survival). Of the prevalence estimates, fifteen (12 from the United States) were retained for the meta-analysis. The overall pooled prevalence estimate for coal workers pneumoconiosis among underground miners was 3.7% (95% CI 3.0-4.5%) with high heterogeneity between studies. The pooled estimate of coal workers pneumoconiosis prevalence in the United States was higher in the 2000s than in the 1990s, consistent with published reports of increasing prevalence following decades of declining trends. Sub-group analyses also indicated higher prevalence among underground miners, and in Central Appalachia. The mortality studies were suggestive of reduced pneumoconiosis mortality rates over time, relative to the general population. CONCLUSION: The ongoing prevalence of occupational lung diseases among contemporary coal miners highlights the importance of respiratory surveillance and preventive efforts through effective dust control measures. Limited prevalence studies from countries other than the United States limits our understanding of the current disease burden in other coal-producing countries.

Peer groups for organisational learning: Clustering with practical constraints.

Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considerations. Additionally, statistical peer groups are constructed from many different variables, and can be difficult to understand, especially for non-statistical audiences. We developed methodology to apply business constraints to clustering solutions and allow the decision-maker to choose the balance between statistical goodness-of-fit and conformity to business constraints. Several tools were utilised to identify complex distinguishing features in peer groups, and a number of visualisations are developed to explain high-dimensional clusters for non-statistical audiences. In a case study where peer group size was required to be small (≤ 100 members), we applied constrained clustering to a noisy high-dimensional data-set over two subsequent years, ensuring that the clusters were sufficiently stable between years. Our approach not only satisfied clustering constraints on the test data, but maintained an almost monotonic negative relationship between goodness-of-fit and stability between subsequent years. We demonstrated in the context of the case study how distinguishing features between clusters can be communicated clearly to different stakeholders with substantial and limited statistical knowledge.

Singlehood and Attunement of Self-Esteem to Friendships.

Romantic relationships activate a process of psychological attunement whereby self-esteem becomes responsive to the romantic bond, thereby potentially benefitting relationship quality and bolstering self-esteem. Yet some people are romantically single, raising the question: Do single people also exhibit psychological attunement? In a 2-year longitudinal study of young adults (N = 279), we test whether singles psychologically attune to their friendships. Multilevel modeling revealed that within-person fluctuations in friendship quality predicted within-person fluctuations in self-esteem, and this association was stronger for singles than for partnered people. A cross-sectional mediation analysis also revealed that singles invested more in their friendships than partnered people, and greater friendship investment predicted greater friendship quality and self-esteem later on. Finally, singles maintain their friendship quality over time while partnered people experience declines. Taken together, these results suggest that singles are psychologically attuned to their friendships, and such attunement may benefit their belongingness and self-esteem.

Deconstructing the "reign of error": interpersonal warmth explains the self-fulfilling prophecy of anticipated acceptance.

People's expectations of acceptance often come to create the acceptance or rejection they anticipate. The authors tested the hypothesis that interpersonal warmth is the behavioral key to this acceptance prophecy: If people expect acceptance, they will behave warmly, which in turn will lead other people to accept them; if they expect rejection, they will behave coldly, which will lead to less acceptance. A correlational study and an experiment supported this model. Study 1 confirmed that participants' warm and friendly behavior was a robust mediator of the acceptance prophecy compared to four plausible alternative explanations. Study 2 demonstrated that situational cues that reduced the risk of rejection also increased socially pessimistic participants' warmth and thus improved their social outcomes.