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Population-based cancer survival data, a key indicator for monitoring progress against cancer, are reported from 27 population-based cancer registries in 14 countries in Africa, Asia, the Caribbean and Central America. In China, Singapore, the Republic of Korea, and Turkey, the 5-year age-standardized relative survival ranged from 76-82% for breast, 63-79% for cervical, 71-78% for bladder, and 44-60% for large-bowel cancer. Survival did not exceed 22% for any cancer site in The Gambia, or 13% for any cancer site except breast (46%) in Uganda. For localized cancers of the breast, large bowel, larynx, ovary, urinary bladder and for regional diseases at all sites, higher survival rates were observed in countries with more rather than less developed health services. Inter- and intra-country variations in survival imply that the levels of development of health services and their efficiency to provide early diagnosis, treatment and clinical follow-up care have a profound impact on survival from cancer. These are reliable baseline summary estimates to evaluate improvements in cancer control and emphasise the need for urgent investment to improve awareness, population-based cancer registration, early detection programmes, health-services infrastructure, and human resources in these countries in the future.

Adequate and complete follow-up is a prerequisite for the conduct of any survival study. Passive follow-up relies on routine availability of mortality data through unique data linkage possibilities, while active follow-up supplements mortality ascertainment, for which there are a variety of methods. Cox proportional-hazard model was employed to test whether censoring was random in presence of loss to follow-up. Absolute survival probability was estimated by the actuarial method following semi-complete approach for all registries, and the period approach was also used wherever possible. Expected survival probability for registries was estimated from the respective country-, age- and sex-specific abridged life tables. Relative survival, as the ratio of absolute to expected survival, was calculated to exclude the effect arising from different background mortalities. To account for the differences in the age structure of the cancer cases, relative survival was adjusted for age and reported as age-standardized relative survival. Estimated incident cancer cases from less-developed countries together for every classified cancer site served as the standard population. Weights were assigned to individual patients, depending on their age, and standardization was carried out using weighted individual data. Analyses were done using the publicly available macros in SAS software.

The Hong Kong cancer registry was established in 1963, and cancer registration is done by passive and active methods. The registry contributed data on 45 cancer sites or types registered during 1996-2001 for this survival study. Follow-up has been carried out by passive methods with median follow-up ranging from 4-60 months. The proportion of cases with histologically verified cancer diagnosis ranged from 38-100%; death certificates only (DCOs) ranged from 0-11%; 83-99% of total registered cases were included for survival analysis. The 5-year age-standardized relative survival exceeded 100% for lip and non-melanoma skin followed by thyroid (94%) and testicular (92%) cancers. The corresponding survival for common cancers were breast (90%), colon (61%), liver and Lung (22%), nasopharynx (70%), rectum (59%) and stomach (39%). The 5-year relative survival by age group showed a decreasing trend with increasing agegroups for most cancers. A decreasing survival with increasing clinical extent of disease was noted.

The Tianjin cancer registry was established in 1978, and registration of cases is done by the active method. The registry contributed data on 51 cancer sites or types registered during 1991-1999 for this survival study. Follow-up has been a mixture of both active and passive methods, with median follow-up ranging from 5-77 months. The proportion with histologically verified diagnosis for various cancers ranged from 21-95% and 97-100% of total registered cases were included for survival analysis. The top-ranking cancers by 5-year age-standardized relative survival (%) were renal pelvis (101%), lip (99%), corpus uteri (91%), penis and nonmelanoma skin (90%) and thyroid (89%). The corresponding survival for common cancers were lung (31%), stomach (41%), Liver (25%) and breast (82%). The 5-year relative survival by age group reveals an inverse relationship for a few cancers and fluctuated for most cancers. Period survival closely predicted the survival experience of cancer cases diagnosed in that period, with the 5-year relative survival in 1991-1995 by period approach being more or less similar to survival by cohort approach in 1996-1999 for most cancers.

Measurement error in a biomarker refers to the error of a biomarker measure applied in a specific way to a specific population, versus the true (etiologic) exposure. In epidemiologic studies, this error includes not only laboratory error, but also errors (variations) introduced during specimen collection and storage, and due to day-to-day, month-to-month, and year-to-year within-subject variability of the biomarker. Validity and reliability studies that aim to assess the degree of biomarker error for use of a specific biomarker in epidemiologic studies must be properly designed to measure all of these sources of error. Validity studies compare the biomarker to be used in an epidemiologic study to a perfect measure in a group of subjects. The parameters used to quantify the error in a binary marker are sensitivity and specificity. For continuous biomarkers, the parameters used are bias (the mean difference between the biomarker and the true exposure) and the validity coefficient (correlation of the biomarker with the true exposure). Often a perfect measure of the exposure is not available, so reliability (repeatability) studies are conducted. These are analysed using kappa for binary biomarkers and the intraclass correlation coefficient for continuous biomarkers. Equations are given which use these parameters from validity or reliability studies to estimate the impact of nondifferential biomarker measurement error on the risk ratio in an epidemiologic study that will use the biomarker. Under nondifferential error, the attenuation of the risk ratio is towards the null and is often quite substantial, even for reasonably accurate biomarker measures. Differential biomarker error between cases and controls can bias the risk ratio in any direction and completely invalidate an epidemiologic study.

Biomarkers can be used to measure the presence of a wide variety of parent compounds and metabolites in body fluids and excreta, and serve as biomarkers of internal dose. Chemical-macromolecular adducts formed in blood and tissue or excreted in urine serve as biomarkers of exposure as well, and in many instances reflect both exposure and additional relevant biological processes. An assortment of analytical techniques have been developed to identify and measure parent compounds, metabolites, chemical-DNA and protein adducts. This chapter will discuss many analytical techniques that measure biomarkers in molecular epidemiologic studies, including biological, physical, chemical and immunological methods.

This chapter presents formulae that methodologically adjust for losses, and gives examples describing magnitude of bias in survival estimates without such adjustment. Loss-adjusted survival is estimated under the assumption that survival of patients Lost to follow-up is the same as that for patients with known follow-up time and similar characteristics of different prognostic factors at first entry. The observed number of Losses to follow-up is then relocated into expected numbers of death and survivors on this basis. Standard methods, such as the actuarial one, are then applied with the sum of observed and expected outcome events. A total of 336 hospital series of treated new breast cancer cases from Mumbai with 24% lost to follow-up revealed a substantial bias of 7 per cent units for 3-year survival estimated with (54%) and without (61%) loss-adjustment. Stepwise adjustment of losses established that increasing the number of prognostic factors explained the bias better. Population-based series comprising 13 371 cases of top ranking cancers from Chennai, with loss to follow-up ranging from 7-24%, revealed negligible bias, ranging from 0-2% in 5-year survival by the loss-adjusted approach for different cancers. Data source seems to affect the need for loss-adjustment, and the loss-adjusted approach is recommended when hospital-based cancer registry data of a low- or medium-resource country are used to evaluate the outcome of cancer patients.

The Seoul cancer registry was established in 1991. Cancer is a notifiable disease, and registration of cases is done by passive and active methods. The registry contributed survival data for 56 cancer sites or types registered during 1993-1997. Follow-up information has been gleaned predominantly by passive methods with median follow-up ranging between 5-82 months for various cancers. The proportion with histologically verified diagnosis for different cancers ranged between 23-99%; death certificates only (DCOs) comprised 0-67%; 33-100% of total registered cases were included for survival analysis. The top-ranking cancers on 5-year age-standardized relative survival rates were testis and placenta (95%), thyroid (93%), non-melanoma skin (93%), corpus uteri (79%), renal pelvis (77%), cervix (76%), Hodgkin lymphoma (75%), breast (74%) and prostate (74%). Five-year relative survival by age group showed a decreasing trend with increasing age groups for cancers of the small intestine, colon, gall bladder, cervix, corpus uteri, ovary, kidney, urinary bladder and thyroid, or was fluctuating for other cancers.

Degenerative diseases of the nervous system impose substantial medical and public health burdens on populations throughout the world. Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) are three of the major neurodegenerative diseases. The prevalence and incidence of these diseases rise dramatically with age; thus the number of cases is expected to increase for the foreseeable future as life spans in many countries continue to increase. Causal contributions from genetic and environmental factors are, with some exceptions, poorly understood. Nonetheless, molecular epidemiology approaches have proven valuable for improving disease diagnoses, characterizing disease prognostic factors, identifying high-risk genes for familial neurodegenerative diseases, investigating common genetic variants that may predict susceptibility for the non-familial forms of these diseases, and for quantifying environmental exposures. Incorporation of molecular techniques, including genomics, proteomics, and measurements of environmental toxicant body burdens into epidemiologic research, offer considerable promise for enhancing progress on characterizing pathogenesis mechanisms and identifying specific risk factors, especially for the non-familial forms of these diseases. In this chapter, brief overviews are provided of the epidemiologic features of PD, AD, and ALS, as well as illustrative examples in which molecular epidemiologic approaches have advanced knowledge on underlying disease mechanisms and risk factors that might lead to improved medical management and ultimately disease prevention. The chapter concludes with some recommendations for future molecular epidemiology research.

Molecular tools have enhanced our understanding of the epidemiology of infectious diseases by describing the transmission system, including identifying novel transmission modes and reservoirs, identifying characteristics of the infectious agent that lead to transmission and pathogenesis, identifying potential vaccine candidates and targets for therapeutics, and recognizing new infectious agents. Applications of molecular fingerprinting to public health practice have enhanced outbreak investigation by objectively confirming epidemiologic evidence, and distinguishing between time-space clusters and sporadic cases. Clinically, moleculartools are used to rapidly detect infectious agents and predict disease course. Integration of molecular tools into etiologic studies has identified infectious causes of chronic diseases, and characteristics of the agent and host that modify disease risk. The combination of molecular tools with epidemiologic methods provides essential information to guide clinical treatment, and to design and implement programmes to prevent and control infectious diseases. However, incorporating molecular tools into epidemiologic studies of infectious diseases impacts study design, conduct, and analysis.