Body iron stores and the risk of cancer

Innovative strategies using SUDAAN for analysis of health surveys with complex samples

Large-scale health surveys provide a wealth of information for addressing problems in health sciences research. Designed for multiple purposes, these surveys frequently have large sample sizes and extensive measurements of demographic and socioeconomic characteristics, risk factors, disease outcomes and health care service use and costs. Complex features of the sampling design typically employed to select the survey sample, coupled with the vast amount of information available from the survey database, underlie issues that must be addressed during data processing and analysis. Numerous articles in the literature have focused on the debate of whether or not, and how, to control for features of the sample design during data analysis. Traditional statistical methods for simple random samples and the software that accompanies them have historically not had the capacity to account for the survey design. Recent advancements in statistical methodology for survey data analysis have greatly expanded the analytical tools available to the survey analyst. Commercial software packages that incorporate these methods offer the analyst convenient ways for applying such tools to large survey databases in an easy and efficient manner. We present an overview of analysis strategies for survey data and illustrate their application via the SUDAAN software system. Examples for analyses are provided through data from two large US health surveys, the National Health Interview Survey and the Longitudinal Study of Aging. Questions of both a cross-sectional and longitudinal nature are addressed. The examples involve logistic regression, time-to-event analysis, and repeated measures analysis.

Iron disorders can mimic anything, so always test for them

Routinely measuring iron status is necessary because not only are about 6% of Americans in significant negative iron balance, but about 1% have iron overload. Serum ferritin is in equilibrium with body iron stores, and is the only blood test that measures them. Barring inflammation, each one ng (0.0179 pmol) ferritin/ml of serum indicates approximately 10 mg (0.179 mmol) of body iron stores. Very early Stage I positive balance is best recognized by measuring saturation of iron binding capacity. Conversely, serum ferritin best recognizes early (Stage I and II) negative balance. Deviations from normal are: 1. Both stages of iron depletion (i.e. low stores, no dysfunction). Negative iron balance Stage I is reduced iron absorption producing moderately depleted iron stores. Stage II is severely depleted stores, without dysfunction. These stages include over half of all cases of negative iron balance. Treated with iron, they never progress to dysfunction, i.e. to disease. 2. Both stages of iron deficiency. Deficiency is inadequate iron for normal function, i.e. dysfunction, disease. Negative balance Stage III is dysfunction without anemia; Stage IV is with anemia. 3. Positive iron balance: Stage I is a multi-year period without dysfunction. Supplements of iron and/or vitamin C promote progression to dysfunction (disease). Iron removal prevents progression. Stage II is iron overload disease, encompassing years of insidiously progressive damage to tissues and organs from iron overload. Iron removal arrests progression.

Epidemiologic studies utilizing surveys: accounting for the sampling design

BACKGROUND

Since large-scale health surveys usually have complicated sampling schemes, there is often a question as to whether the sampling design must be considered in the analysis of the data. A recent disagreement concerning the analysis of a body iron stores-cancer association found in the first National Health and Nutrition Examination Survey and its follow-up is used to highlight the issues.

METHODS

We explain and illustrate the importance of two aspects of the sampling design: clustering and weighting of observations. The body iron stores-cancer data are reanalyzed by utilizing or ignoring various aspects of the sampling design. Simple formulas are given to describe how using the sampling design of a survey in the analysis will affect the conclusions of that analysis.

RESULTS

The different analyses of the body iron stores-cancer data lead to very different conclusions. Application of the simple formulas suggests that utilization of the sample clustering in the analysis is appropriate, but that a standard utilization of the sample weights leads to an uninformative analysis. The recommended analysis incorporates the sampling weights in a nonstandard way and the sample clustering in the standard way.

CONCLUSIONS

Which particular aspects of the sampling design to use in the analysis of complex survey data and how to use them depend on certain features of the design. We give some guidelines for when to use the sample clustering and sample weights in the analysis.