Emerging Themes in Epidemiology最新文献

Background: The issue of poor response rates to population surveys has existed for some decades, but few studies have explored methods to improve the response rate in follow-up population cohort studies.

Methods: A sample of 100,000 adults from the 45 and Up Study, a large population cohort in Australia, were followed up 3.5 years after the baseline cohort was assembled. A pilot mail-out of 5000 surveys produced a response rate of only 41.7 %. This study tested methods of enhancing response rate, with three groups of 1000 each allocated to (1) receiving an advance notice postcard followed by a questionnaire, (2) receiving a questionnaire and then follow-up reminder letter, and (3) both these strategies.

Results: The enhanced strategies all produced an improved response rate compared to the pilot, with a resulting mean response rate of 53.7 %. Highest response was found when both the postcard and questionnaire reminder were used (56.4 %) but this was only significantly higher when compared to postcard alone (50.5 %) but not reminder alone (54.1 %). The combined approach was used for recruitment among the remaining 92,000 participants, with a resultant further increased response rate of 61.6 %.

Conclusions: Survey prompting with a postcard and a reminder follow-up questionnaire, applied separately or combined can enhance follow-up rates in large scale survey-based epidemiological studies.

Background: In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology.

Analysis: There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery.

Conclusion: This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice.

Background: Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration.

Methods: We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life.

Results: Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation.

Conclusions: Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.