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The department of health-Rizal cancer registry (DOH-RCR) was the first population-based cancer registry in the Philippines, established in 1974. Even though cancer is reportable by legislation, cancer registration is pursued by active methods. Data on survival from cancer of the breast registered in 1996-1997 are reported. Followup was carried out by passive and active methods. The proportion of cases with a histological confirmation of cancer diagnosis was 90%; death certificates only (DCOs) constituted 6%; 81% of the total registered were included for the survival analysis. Complete follow-up at five years from the incidence date was 30%. Relative survival rates at one, three and five years were 89%, 56% and 37%, respectively. Five-year age-standardized relative survival was 35%. Five-year relative survival by age group did not display any pattern or trend, and was fluctuating. A majority of cases were diagnosed with a regional spread of disease (44%) followed by localized stage (17%). Five-year absolute survival ratesby extent of disease were localized (65%), regional (35%), distant metastasis (12%) and unknown (35%). Thetrend of 5-year survival for breast cancer decreased from 46% in 1987 to 37% in 1996-1997.

The Bhopal population-based cancer registry was established in 1986 under the national cancer registry programme to investigate the after-effect of a gas leak in 1984. Cancer registration is done entirely by active methods. The registry is contributing data on survival for 16 cancer sites or types registered during 1991-1995. Follow-up of cases was done by active methods with median follow-up time ranging between 8-44 months for different cancers. The proportion with histologically verified diagnosis for various cancers ranged between 61-100%; death certificates only (DCOs) comprised 0-2%; 50-92% of total registered cases were included for survival analysis. The 5-year age-standardized relative survival rates for common cancers were mouth (34%), cervix (31%), breast (25%), tongue (12%), oesophagus (3%) and lung (1%). The 5-year relative survival by age group showed that survival was the highest in the youngest age group (45 years and below) for a majority of cancers. A decreasing survival with increasing clinical extent of disease was noted for most cancers studied.

The hormonal milieu has been hypothesized to play a role in a range of human diseases, and therefore has been a topic of much epidemiologic investigation. Hormones of particular interest include: sex steroids; growth hormones; insulin-like growth factors; stress hormones, such as cortisol; and hormones produced by the adipose tissue, termed adipokines. Depending on the hormone, levels may be measured in plasma or serum, urine, saliva, tissue, or by assessing genetic variation in the hormone or hormone metabolizing genes. Sample collection, processing, and storage requirements vary according to the type of sample collected (e.g. blood or urine) and the hormone of interest. Laboratory analysis of hormones is frequently complex, and the technology used to conduct the assays is constantly evolving. For example, direct or indirect radioimmunoassay, bioassay or mass spectrometry can be used to measure sex steroids, each having advantages and disadvantages. Careful attention to laboratory issues, including close collaboration with laboratory colleagues and ongoing quality control assessments, is critical. Whether a single hormone measurement, as is frequently collected in epidemiologic studies, is sufficient to characterize the hormonal environment of interest (e.g. long-term adult hormone exposure) is also an important issue. While the assessment of hormones in epidemiologic studies is complex, these efforts have, and will continue to, add importantly to our knowledge of the role of hormones in human health.

The adverse effects of obesity support the use of biomarkers to help elucidate disease mechanism, therapeutic interventions, and preventive strategies. Emerging biomarkers for obesity-associated cardiovascular disease (CVD), type 2 diabetes and cancer play diverse roles in biological pathways including immune modulation and fat metabolism. Animal and in vitro data support the association of these biomarkers with obesity-associated diseases, but evidence in humans is still lacking. In humans, plasma levels of biomarkers are widely used to determine risk, but many studies are limited by ethnicity/race, gender or sample size. In this chapter, the use of biomarkers in obesity research and in the context of CVD, type 2 diabetes and cancer will be discussed. Markers of exposure (adipokines), effect (resulting metabolic abnormalities), and susceptibility (genetic determinants for obesity and related disorders) are covered for each of the three diseases.

This chapter first discusses the urgent need for prevention of childhood diseases that impose a huge and growing burden on families and society. It provides a review of recent research in this area to illustrate both the strengths and limitations of molecular epidemiology in drawing needed links between environmental exposures and illness in children. For illustration, three of the major diseases in children are discussed: asthma, cancer and developmental disorders. All three impose significant difficulties, have increased in recent decades, and are thought to be caused in substantial part by environmental factors, such as toxic exposures due to lifestyle choices (i.e. smoking and diet), pollutants in the workplace, ambient air, water and the food supply. These exogenous exposures can interact with "host" factors, such as genetic susceptibility and nutritional deficits, to cause disease. Molecular epidemiology has provided valuable new insights into the magnitude and diversity of exposures beginning in utero, the unique susceptibility of the young, and the adverse preclinical and clinical effects resulting from the interactions between these factors. However, molecular epidemiology also faces certain constraints and challenges that are specific to studies of the very young, including ethical issues, technical issues due to the limited amount of biological specimens that can be obtained, and communication of results to parents and communities. These challenges are particularly apparent when incorporating the newer epigenetic and "omic" techniques and biomarkers into studies of children's diseases.

This chapter will present some general background material on the cellular, biochemical, and genetic mechanisms of the immune system, then focus on specific examples that illustrate the promise and pitfalls of using immune biomarkers as tools for molecular epidemiologic research and public health practice. Some of the most exciting frontiers in medical science will be discussed: early detection of cancer through autoimmunity; malignancies that arise from the immune system itself; newborn screening for lethal immune deficiencies and latent autoimmune disorders; and neurodevelopmental disabilities that could result from maternal immune responses, which protect the mother but harm the fetus. The chapter concludes with some thoughts about current challenges and future directions.

This chapter will discuss design considerations for epidemiological studies that use biomarkers in the framework of etiologic investigations. The main focus will be on describing the incorporation of biomarkers into the main epidemiologic study designs, including cross-sectional or short-term longitudinal designs to characterize biomarkers, and prospective cohort and case-control studies to evaluate biomarker-disease associations. The advantages and limitations of each design will be presented, and the impact of study design on the feasibility of different approaches to exposure assessment and biospecimen collection and processing will be discussed.

The rapidly growing number of molecular epidemiology studies is providing an enormous, often multidimensional, body of evidence on the association of various disease outcomes and biomarkers. The testing and validation of statistical hypotheses in genetic and molecular epidemiology presents a major challenge requiring methodological rigor and analytical power. The non-replication of many genetic and other biomarker association studies suggests that there may be an abundance of spurious findings in the field. This chapter will discuss ways of combining evidence from different sources using meta-analysis methods. Research synthesis not only aims at producing a summary effect estimate for a specific biomarker, but also offers a unique opportunity for a meticulous attempt to critically appraise a research field, identify substantial differences between or within studies, and detect sources of bias. Systematic reviews and meta-analyses in human genome epidemiology are specifically discussed, as they comprise the bulk of the available evidence in molecular epidemiology where these methods have been applied to date. Considered here are issues regarding validity and interpretation in genetic association studies, as well as strategies for developing and integrating evidence through international consortia. Finally, there is a brief look at how combining data through meta-analysis may be applied in other areas of molecular epidemiology.

This chapter describes basic principles in study design, data analysis, and interpretation of epidemiological studies of genetic polymorphisms and disease risk, including the assessment of gene-environment interactions. The case-control design (hospital-based, population-based or nested within a prospective cohort) is frequently used to study common genetic variants and disease risk. Because of their widespread use, the analysis of case-control data will be the focus of this chapter. Two key considerations in the study design will be addressed: the selection of genetic markers to be evaluated, and sample size considerations to ensure adequate power to detect associations with disease risk. Single nucleotide polymorphisms (SNPs) are the most frequent form of common genetic variation, thus the discussion on data analysis will be based on the evaluation of associations between SNPs and disease risk. This chapter will begin with the evaluation of quality control of genotyping data, which is a critical first step in the analysis of genetic data. A description of statistical methods will follow, aimed at the discovery of genetic susceptibility loci, including analysis of candidate SNPs and genome-wide association studies, haplotype analyses, and the evaluation of gene-gene and gene-environment interactions.