Infertility, medical advice and treatment with fertility hormones and/or in vitro fertilisation: a population perspective from the Australian Longitudinal Study on Women's Health

OBJECTIVE: To identify the factors associated with infertility, seeking advice and treatment with fertility hormones and/or in vitro fertilisation (IVF) among a general population of women. METHODS: Participants in the Australian Longitudinal Study on Women's Health aged 28-33 years in 2006 had completed up to four mailed surveys over 10 years (n=9,145). Parsimonious multivariate logistic regression was used to identify the socio-demographic, biological (including reproductive histories), and behavioural factors associated with infertility, advice and hormonal/IVF treatment. RESULTS: For women who had tried to conceive or had been pregnant (n=5,936), 17% reported infertility. Among women with infertility (n=1031), 72% (n=728) sought advice but only 50% (n=356) used hormonal/IVF treatment. Women had higher odds of infertility when: they had never been pregnant (OR=7.2, 95% CI 5.6-9.1) or had a history of miscarriage (OR range=1.5-4.0) than those who had given birth (and never had a miscarriage or termination). CONCLUSION: Only one-third of women with infertility used hormonal and/or IVF treatment. Women with PCOS or endometriosis were the most proactive in having sought advice and used hormonal/IVF treatment. IMPLICATIONS: Raised awareness of age-related declining fertility is important for partnered women aged approximately 30 years to encourage pregnancy during their prime reproductive years and reduce the risk of infertility.

Contraceptive changes after reproductive events among Australian women born in 1973 to 1978: A longitudinal study from 1996 to 2009

Background Women change contraception as they try to conceive, space births, and limit family size. This longitudinal analysis examines contraception changes after reproductive events such as birth, miscarriage or termination among Australian women born from 1973 to 1978 to identify potential opportunities to increase the effectiveness of contraceptive information and service provision. Methods Between 1996 and 2009, 5,631 Australian women randomly sampled from the Australian universal health insurance (Medicare) database completed five self-report postal surveys. Three longitudinal logistic regression models were used to assess the associations between reproductive events (birth only, birth and miscarriage, miscarriage only, termination only, other multiple events, and no new event) and subsequent changes in contraceptive use (start using, stop using, switch method) compared with women who continued to use the same method. Results After women experienced only a birth, or a birth and a miscarriage, they were more likely to start using contraception. Women who experienced miscarriages were more likely to stop using contraception. Women who experienced terminations were more likely to switch methods. There was a significant interaction between reproductive events and time indicating more changes in contraceptive use as women reach their mid-30s. Conclusion Contraceptive use increases after the birth of a child, but decreases after miscarriage indicating the intention for family formation and spacing between children. Switching contraceptive methods after termination suggests these pregnancies were unintended and possibly due to contraceptive failure. Women’s contact with health professionals around the time of reproductive events provides an opportunity to provide contraceptive services.

A longitudinal study of grieving in family caregivers of people with dementia

A longitudinal study of grieving in family caregivers of people with dementia Recent research into dementia has identified the long term impact that the role of care giving for a relative with dementia has on family members This is largely due to the cognitive decline that characterises dementia and the losses that can be directly attributed to this. These losses include loss of memories, relationships and intimacy, and are often ambiguous so that the grief that accompanies them is commonly not recognised or acknowledged. The role and impact of pre-death or anticipatory grief has not previously been widely considered as a factor influencing health and well-being of family caregivers. Studies of grief in caregivers of a relative with dementia have concluded that grief is one of the greatest barriers to care giving and is a primary determinant of caregiver well-being. The accumulation of losses, in conjunction with experiences unique to dementia care giving, place family caregivers at risk of complicated grief. This occurs when integration of the death does not take place following bereavement and has been associated with a range of negative health outcomes. The aim of this research was to determine the influence of grief, in addition to other factors representing both positive and negative aspects of the role, on the health related quality of life of family caregivers of people with dementia, prior to and following the death of their relative with dementia. An exploratory research project underpinned by a conceptual framework of caregivers’ adaptation in the context of subjective appraisal of the strains and gains in their role was undertaken. The research comprised three studies. Study 1 was a scoping study that involved a series of semi-structured interviews with thirteen participants who were family caregivers of people with severe dementia or whose relative with dementia had died in the previous twelve months. The results of this study in conjunction with factors identified in the literature informed data collection for the further studies. Study 2 was a cross sectional survey of fifty caregivers recruited when their relative was in the moderate to severe stage of dementia. This study provided the baseline data for Study 3, a prospective cohort follow up study. Study 3 consisted of seventeen participants followed up at two time points after the death of their relative with dementia: six weeks and then six months following the death of the relative with dementia. The scoping study indicated that differences in appraisal of the care giving role and encounters with health professionals were related to levels of grief of caregivers prior to and following the death of the relative with dementia. This was supported in the baseline and follow up studies. In the baseline study, after adjusting for all variables in multivariate regression models, subjective appraisal of burden was found to make a significant contribution (p<.05) to mental health related quality of life. The two dependent variables, anticipatory grief and mental health related quality of life, were significantly (p<.01) correlated at a bivariate level. In the follow up study, linear mixed modelling and multiple regression analysis of data found that subjective appraisal of burden and resilience were significantly associated (p<.05 and p<.01, respectively) with mental health related quality of life over time. In addition, bereavement and complicated grief were significantly associated (p<.05) with mental health following the death of the relative. In this study social support and satisfaction with end of life care were found to be statistically associated (p<.05) with physical health related quality of life over time. The strong relationship between grief of caregivers and their health related quality of life over the entire care giving trajectory and period following the death of their relative highlights the urgent need for further research and interventions in this area. Overall results indicate that addressing the risk and protective factors including subjective appraisal of their care giving role, resilience, social support and satisfaction with end of life care of their relative, has the potential to both ameliorate negative health outcomes and to promote improved health for these caregivers. This research provides important information for development of targeted and appropriate interventions that aim to promote resilience and reduce the personal burden on caregivers of people with dementia.

Violence in and around entertainment districts : a longitudinal analysis of the impact of late-night lockout legislation

Violence in entertainment districts is a major problem across urban landscapes throughout the world. Research shows that licensed premises are the third most common location for homicides and serious assaults, accounting for one in ten fatal and nonfatal assaults. One class of interventions that aims to reduce violence in entertainment districts involves the use of civil remedies: a group of strategies that use civil or regulatory measures as legal “levers” to reduce problem behavior. One specific civil remedy used to reduce problematic behavior in entertainment districts involves manipulation of licensed premise trading hours. This article uses generalized linear models to analyze the impact of lockout legislation on recorded violent offences in two entertainment districts in the Australian state of Queensland. Our research shows that 3 a.m. lockout legislation led to a direct and significant reduction in the number of violent incidents inside licensed premises. Indeed, the lockouts cut the level of violent crime inside licensed premises by half. Despite these impressive results for the control of violence inside licensed premises, we found no evidence that the lockout had any impact on violence on streets and footpaths outside licensed premises that were the site for more than 80 percent of entertainment district violence. Overall, however, our analysis suggests that lockouts are an important mechanism that helps to control the level of violence inside licensed premises but that finely grained contextual responses to alcohol-related problems are needed rather than one-size-fits-all solutions.

Survival models of community tenure and length of hospital stay for the seriously mentally ill : a 10-year perspective

Objective: To examine the effects of personal and community characteristics, specifically race and rurality, on lengths of state psychiatric hospital and community stays using maximum likelihood survival analysis with a special emphasis on change over a ten year period of time. Data Sources: We used the administrative data of the Virginia Department of Mental Health, Mental Retardation, and Substance Abuse Services (DMHMRSAS) from 1982-1991 and the Area Resources File (ARF). Given these two sources, we constructed a history file for each individual who entered the state psychiatric system over the ten year period. Histories included demographic, treatment, and community characteristics. Study Design: We used a longitudinal, population-based design with maximum likelihood estimation of survival models. We presented a random effects model with unobserved heterogeneity that was independent of observed covariates. The key dependent variables were lengths of inpatient stay and subsequent length of community stay. Explanatory variables measured personal, diagnostic, and community characteristics, as well as controls for calendar time. Data Collection: This study used secondary, administrative, and health planning data. Principal Findings: African-American clients leave the community more quickly than whites. After controlling for other characteristics, however, race does not affect hospital length of stay. Rurality does not affect length of community stays once other personal and community characteristics are controlled for. However, people from rural areas have longer hospital stays even after controlling for personal and community characteristics. The effects of time are significantly smaller than expected. Diagnostic composition effects and a decrease in the rate of first inpatient admissions explain part of this reduced impact of time. We also find strong evidence for the existence of unobserved heterogeneity in both types of stays and adjust for this in our final models. Conclusions: Our results show that information on client characteristics available from inpatient stay records is useful in predicting not only the length of inpatient stay but also the length of the subsequent community stay. This information can be used to target increased discharge planning for those at risk of more rapid readmission to inpatient care. Correlation across observed and unobserved factors affecting length of stay has significant effects on the measurement of relationships between individual factors and lengths of stay. Thus, it is important to control for both observed and unobserved factors in estimation.

Associations between early shared music activities in the home and later child outcomes : findings from the Longitudinal Study of Australian Children

The benefits of early shared book reading between parents and children have long been established,yet the same cannot be said for early shared music activities in the home. This study investigated the parent–child home music activities in a sample of 3031 Australian children participating in Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) study. Frequency of shared home music activities was reported by parents when children were 2–3 years and a range of social, emotional,and cognitive outcomes were measured by parent and teacher report and direct testing two years later when children were 4–5 years old. A series of regression analyses (controlling for a set of important socio-demographic variables) found frequency of shared home music activities to have a small significant partial association with measures of children’s vocabulary, numeracy, attentional and emotional regulation, and prosocial skills. We then included both book reading and shared home music activities in the same models and found that frequency of shared home music activities maintained small partial associations with measures of prosocial skills, attentional regulation, and numeracy. Our findings suggest there may be a role for parent-child home music activities in supporting children’s development.

Diabetes mellitus and the risk of depressive and anxiety disorders in Australian women: A longitudinal study

Background Longitudinal studies examining the risk of depressive and anxiety disorders associated with diabetes are limited. This study examined the association between diabetes and the risk of depressive and anxiety disorders in Australian women using longitudinal data. Methods Datawere froma sample of women who were part of anAustralian pregnancy and birth cohort study. Data comprised self-reported diabetes mellitus and the subsequent reporting of depressive and anxiety disorders. Mood disorders were assessed according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, obtained from participants using Composite International Diagnostic Interview (CIDI)-Auto (WHO WMH-CIDI CAPI, version 21.1.3). Multiple regression models with adjustment for important covariates were used. Results Women with diabetes had a higher lifetime prevalence of any depressive and/or anxiety disorder than women without diabetes. About 3 in 10 women with diabetes experienced a lifetime event of any depressive disorder, while 1 in 2 women with diabetes experienced a lifetime event of any anxiety disorder. In prospective analyses, diabetes was only significantly associated with a 30-day episode of any anxiety disorder (odds ratio [OR] 1.53, 95% confidence interval [CI] 1.09–2.15). In the case of lifetime disorders, diabetes was significantly associated with any depressive disorder (OR 1.37, 95% CI 1.03–1.84), major depressive disorder (OR 1.36, 95% CI 1.01–1.85), and posttraumatic stress disorder (OR 1.42, 95% CI 1.01–2.02). Conclusions The findings suggest that the presence of diabetes is a significant risk factor for women experiencing current anxiety disorders. However, in the case of depression, the association with diabetes only held for women who had experienced past episodes, there was no association with current depression. This suggests that the evidence is not strong enough to support a direct effect of diabetes as a cause of mood disorders.

Association between aneurysm shoulder stress and abdominal aortic aneurysm expansion: A longitudinal follow-up study

Background Aneurysm expansion rate is an important indicator of the potential risk of abdominal aortic aneurysm (AAA) rupture. Stress within the AAA wall is also thought to be a trigger for its rupture. However, the association between aneurysm wall stresses and expansion of AAA is unclear. Methods and Results Forty-four patients with AAAs were included in this longitudinal follow-up study. They were assessed by serial abdominal ultrasonography and computed tomography scans if a critical size was reached or a rapid expansion occurred. Patient-specific 3-dimensional AAA geometries were reconstructed from the follow-up computed tomography images. Structural analysis was performed to calculate the wall stresses of the AAA models at both baseline and final visit. A nonlinear large-strain finite element method was used to compute the wall-stress distribution. The relationship between wall stresses and expansion rate was investigated. Slowly and rapidly expanding aneurysms had comparable baseline maximum diameters (median, 4.35 cm [interquartile range, 4.12 to 5.0 cm] versus 4.6 cm [interquartile range, 4.2 to 5.0 cm]; P=0.32). Rapidly expanding AAAs had significantly higher shoulder stresses than slowly expanding AAAs (median, 300 kPa [interquartile range, 280 to 320 kPa] versus 225 kPa [interquartile range, 211 to 249 kPa]; P=0.0001). A good correlation between shoulder stress at baseline and expansion rate was found (r=0.71; P=0.0001). Conclusion A higher shoulder stress was found to have an association with a rapidly expanding AAA. Therefore, it may be useful for estimating the expansion of AAAs and improve risk stratification of patients with AAAs.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Smooth bootstrap methods for analysis of longitudinal data

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.

A modified pseudolikelihood approach for analysis of longitudinal data

We consider the analysis of longitudinal data when the covariance function is modeled by additional parameters to the mean parameters. In general, inconsistent estimators of the covariance (variance/correlation) parameters will be produced when the "working" correlation matrix is misspecified, which may result in great loss of efficiency of the mean parameter estimators (albeit the consistency is preserved). We consider using different "Working" correlation models for the variance and the mean parameters. In particular, we find that an independence working model should be used for estimating the variance parameters to ensure their consistency in case the correlation structure is misspecified. The designated "working" correlation matrices should be used for estimating the mean and the correlation parameters to attain high efficiency for estimating the mean parameters. Simulation studies indicate that the proposed algorithm performs very well. We also applied different estimation procedures to a data set from a clinical trial for illustration.

Robust estimating functions and bias correction for longitudinal data analysis

Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.

Effects of variance-function misspecification in analysis of longitudinal data

The approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.