James C. Doidge

Attributable Risk Analysis Reveals Potential Healthcare Savings from Increased Consumption of Dairy Products

With rising burdens of obesity and chronic disease, the role of diet as a modifiable risk factor is of increasing public health interest. There is a growing body of evidence that low consumption of dairy products is associated with elevated risk of chronic metabolic and cardiovascular disorders. Surveys also suggest that dairy product consumption falls well below recommended targets for much of the population in many countries, including the USA, UK, and Australia. We reviewed the scientific literature on the health effects of dairy product consumption (both positive and negative) and used the best available evidence to estimate the direct healthcare expenditure and burden of disease [disability-adjusted life years (DALY)] attributable to low consumption of dairy products in Australia. We implemented a novel technique for estimating population attributable risk developed for application in nutrition and other areas in which exposure to risk is a continuous variable. We found that in the 2010–2011 financial year, AUD

Risk factors for child maltreatment in an Australian population-based birth cohort

2.0 billion (USD

Most Australians do not meet recommendations for dairy consumption: findings of a new technique to analyse nutrition surveys

2.1 billion, €1.6 billion, or ∼1.7% of direct healthcare expenditure) and the loss of 75,012 DALY were attributable to low dairy product consumption. In sensitivity analyses, varying core assumptions yielded corresponding estimates of AUD

Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random:

1.1–3.8 billion (0.9–3.3%) and 38,299–151,061 DALY lost. The estimated healthcare cost attributable to low dairy product consumption is comparable with total spending on public health in Australia (AUD

A guide to evaluating linkage quality for the analysis of linked data

2.0 billion in 2009–2010). These findings justify the development and evaluation of cost-effective interventions that use dairy products as a vector for reducing the costs of diet-related disease.

Demystifying probabilistic linkage

Child maltreatment and other adverse childhood experiences adversely influence population health and socioeconomic outcomes. Knowledge of the risk factors for child maltreatment can be used to identify children at risk and may represent opportunities for prevention. We examined a range of possible child, parent and family risk factors for child maltreatment in a prospective 27-year population-based birth cohort of 2443 Australians. Physical abuse, sexual abuse, emotional abuse, neglect and witnessing of domestic violence were recorded retrospectively in early adulthood. Potential risk factors were collected prospectively during childhood or reported retrospectively. Associations were estimated using bivariate and multivariate logistic regressions and combined into cumulative risk scores. Higher levels of economic disadvantage, poor parental mental health and substance use, and social instability were strongly associated with increased risk of child maltreatment. Indicators of child health displayed mixed associations and infant temperament was uncorrelated to maltreatment. Some differences were observed across types of maltreatment but risk profiles were generally similar. In multivariate analyses, nine independent risk factors were identified, including some that are potentially modifiable: economic disadvantage and parental substance use problems. Risk of maltreatment increased exponentially with the number of risk factors experienced, with prevalence of maltreatment in the highest risk groups exceeding 80%. A cumulative risk score based on the independent risk factors allowed identification of individuals at very high risk of maltreatment, while a score that incorporated all significant risk and protective factors provided better identification of low-risk individuals.

New Australian Dietary Guidelines for consumption of dairy products: are they really evidence-based and does anyone meet them?

Objective: To describe the pattern of dairy consumption in Australians aged 12 years and over, and assess the extent to which the population meets national recommendations.

Economic predictors of child maltreatment in an Australian population-based birth cohort

Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants’ responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random.

Adverse childhood experiences, non-response and loss to follow-up: Findings from a prospective birth cohort and recommendations for addressing missing data

Abstract Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.

Determining the determinants: Is child abuse and neglect the underlying cause of the socio-economic gradient in health?

Abstract Many of the distinctions made between probabilistic and deterministic linkage are misleading. While these two approaches to record linkage operate in different ways and can produce different outputs, the distinctions between them are more a result of how they are implemented than because of any intrinsic differences. In the way they are generally applied, probabilistic and deterministic procedures can be little more than alternative means to similar ends—or they can arrive at very different ends depending on choices that are made during implementation. Misconceptions about probabilistic linkage contribute to reluctance for implementing it and mistrust of its outputs. We aim to explain how the outputs of either approach can be tailored to suit the intended application, but also to highlight the ways in which probabilistic linkage is generally more flexible, more powerful and more informed by the data. This is accomplished by examining common misconceptions about probabilistic linkage and its difference from deterministic linkage, highlighting the potential impact of design choices on the outputs of either approach. We hope that better understanding of linkage designs will help to allay concerns about probabilistic linkage, and help data linkers to select and tailor procedures to produce outputs that are appropriate for their intended use.