Differences In Disease Rates Research Articles

A Community-Defined Approach to Address Trauma Among Cambodian Immigrants and Refugees.

Asian American and Pacific Islanders are one of the fastest growing and most diverse groups in the United States. Yet, they are often aggregated as a single group, masking within-group differences in rates of disease and demographic characteristics commonly associated with elevated health risk. While more than four decades have passed since the Khmer Rouge genocide, Cambodians continue to experience trauma-related psychiatric disorders, including post-traumatic stress disorder and major depression. Funded by the California Department of Public Health Office of Health Equity, the Community Wellness Program (CWP) aimed to reduce mental health disparities among Cambodians in Long Beach and Santa Ana, California, using community-defined approaches. The 6-month program comprised community outreach, educational workshops, strengths-based case management, and social and spiritual activities. Our study aimed to examine the effects of the CWP on trauma symptoms. Program evaluation followed an incomplete stepped wedge waitlist design with two study arms. A linear mixed models analysis revealed that participants reported fewer trauma symptoms as a result of participation in the CWP and that participants experienced fewer symptoms over time. This is an especially important finding, as trauma can lead to long-term individual health effects and to social and health repercussions on an entire cultural group by way of intergenerational trauma. As the number of refugees and displaced individuals continues to grow, there is an urgent need for programs such as the CWP to prevent the lasting effects of trauma.

Disease-Specific Health Disparities: A Targeted Review Focusing on Race and Ethnicity.

Background: Wide disparities in health status exist in the United States across race and ethnicity, broadly driven by social determinants of health—most notably race and ethnic group differences in income, education, and occupational status. However, disparities in disease frequency or severity remain underappreciated for many individual diseases whose distribution in the population varies. Such information is not readily accessible, nor emphasized in treatment guidelines or reviews used by practitioners. Specifically, a summary on disease-specific evidence of disparities from population-based studies is lacking. Our goal was to summarize the published evidence for specific disease disparities in the United States so that this knowledge becomes more widely available “at the bedside”. We hope this summary stimulates health equity research at the disease level so that these disparities can be addressed effectively. Methods: A targeted literature review of disorders in Pfizer’s current pipeline was conducted. The 38 diseases included metabolic disorders, cancers, inflammatory conditions, dermatologic disorders, rare diseases, and infectious targets of vaccines under development. Online searches in Ovid and Google were performed to identify sources focused on differences in disease rates and severity between non-Hispanic Whites and Black/African Americans, and between non-Hispanic Whites and Hispanics. As a model for how this might be accomplished for all disorders, disparities in disease rates and disease severity were scored to make the results of our review most readily accessible. After primary review of each condition by one author, another undertook an independent review. Differences between reviewers were resolved through discussion. Results: For Black/African Americans, 29 of the 38 disorders revealed a robust excess in incidence, prevalence, or severity. After sickle cell anemia, the largest excesses in frequency were identified for multiple myeloma and hidradenitis suppurativa. For Hispanics, there was evidence of disparity in 19 diseases. Most notable were metabolic disorders, including non-alcoholic steatohepatitis (NASH). Conclusions: This review summarized recent disease-specific evidence of disparities based on race and ethnicity across multiple diseases, to inform clinicians and health equity research. Our findings may be well known to researchers and specialists in their respective fields but may not be common knowledge to health care providers or public health and policy institutions. Our hope is that this effort spurs research into the causes of the many disease disparities that exist in the United States.

Revealing the Complex Relationships Between Ethnicity and Health: 29 Outcomes From the Scottish Health and Ethnicity Linkage Study (SHELS) Cohort

Objectives: To present a data visualization approach based on epidemiological principles that can be used to illustrate the complexity of health inequalities by ethnicity. Methods: We provide an example of our technique by presenting results from the Scottish Health and Ethnicity Linkage Study (SHELS) on 29 different health outcomes across ten ethnic groups. SHELS is a census-based retrospective cohort study of the general population of Scotland (4.62 million people) linking ethnicity data to health records. We employed different colors to show health advantage, disadvantage or equivalence; different color shades to represent degree of certainty, combining effect size and precision of estimate; and different font sizes for absolute rates, to highlight more common conditions. We ranked health conditions by age-adjusted rate within each ethnic group to identify differences in burden of disease. Results: Data visualization clearly demonstrates that ethnic differences in health vary substantially depending on outcome, sex and ethnic group. Inequalities were present for every outcome but were not uniformly worse for non-White populations. The patterns are also complex within each ethnic group such as the Pakistani population where strong advantage for some outcomes (e.g. RR=0.52 in men for all-cause mortality) coexists with strong disadvantage for others (e.g. RR=1.80 in women for cardiovascular disease). Despite large differences in relative disease rates, ranking conditions within ethnic groups showed that most ethnic groups have similar disease priorities. Conclusions: Our approach demonstrates the complexity of ethnic differences in health. The use of these simple data visualization techniques can facilitate interpretation of this complexity and avoid misleading generalizations that the health of ethnic minorities is worse or better than majority populations. Using absolute rates of disease and ranking conditions within ethnic groups is also useful as large relative differences in rates of disease between ethnic groups may not always translate into different disease priorities.

Financial Incentives to Increase Stool Collection Rates for Microbiome Studies in Adult Bone Marrow Transplant Patients

Introduction: Dysbiosis of the gut microbiome during hematopoietic stem cell transplantation (HCT) is associated with adverse post-transplant outcomes such as graft-versus-host disease, bloodstream infections, and mortality. In order to learn more about the role of the microbiome in HCT in adverse clinical outcomes, researchers collect stool samples from patients at various time points throughout HCT. However, unlike blood samples or skin swabs, stool collection requires active subject participation, particularly in the outpatient setting, and may be limited by patient aversion to handling stool. By providing study participants with compensation for their stool samples, we hypothesize that we can significantly increase stool collection rates. Methods: We performed a prospective cohort study on the impact of financial incentives on stool collection rates for microbiome studies. The intervention group consisted of allogeneic (allo)-HCT patients from 05/2017-05/2018 who were compensated with a $10 gas gift card for each stool sample. The intervention group was compared to a historical control group consisting of allo-HCT patients from 11/2016-05/2017 who provided stool samples before the incentive was implemented. To control for potential changes in collections over time, we also compared a contemporaneous control group of autologous (auto)-HCT patients from 05/2017-05/2018 with a historical control group of auto-HCT patients from 11/2016-05/2017; neither auto-HCT groups were compensated. Allo-HCT patients were required to give samples at pre-HCT, day 0 (the day of HCT), and days 7, 14, 21, 30, 60, and 90 post-HCT. Auto-HCT patients were required to give samples at pre-HCT and days 7, 14, and 90 post-HCT. Collection rates were defined as the number of samples provided divided by the number of time points for which we attempted to obtain samples. Patient characteristics were summarized by proportions for categorical variables and median with interquartile ranges for continuous variables. Chi-square tests or Fisher's exact tests were used to compare categorical variables, as appropriate, and Wilcoxon Rank Sum tests or t-tests were used to compare continuous variables, as appropriate. This study was approved by the Duke Institutional Review Board, and informed consent was obtained from all patients. Results: There were 35 allo-HCT patients in the intervention group, 19 allo-HCT patients in the historical control group, 142 auto-HCT patients in the contemporaneous control group, and 75 auto-HCT patients in the historical control group. Groups were similar with regard to baseline demographics such as age, race, and gender. While allo-HCT patients were more likely to have leukemia and auto-HCT patients were more likely to have lymphoma and multiple myeloma, there were no differences in disease rates across the study periods. Allo-HCT patients in the intervention group had significantly higher average overall collection rates when compared to the historical control group allo-HCT patients (80% vs 37%, p<0.001), as well has significantly higher average outpatient collection rates (84% vs 23%, p<0.001) and average inpatient collection rates (71% vs 46%, p=0.04). In contrast, there were no significant differences in overall average collection rates between the auto-HCT patients in the contemporaneous control and historical control group (36% vs 32%, p=0.28), as well as the average outpatient collection rates (30% vs 28%, p=0.54) and the average inpatient collection rates (46% vs 59%, p=0.25). Discussion: Our results demonstrate that even a modest incentive can significantly increase collection rates. Use of a contemporaneous control group to account for potential differences in stool collection rates over time strengthens our finding that financial incentives increase stool collection rates. Furthermore, the significant increase in collection rates in the outpatient setting highlights the role of the incentive when patient participation is needed, as opposed to the inpatient setting in which the nurse assists with collection. While this study uses a specialized HCT patient population, these results may be generalizable to future studies and aid other researchers in obtaining stool samples needed for future microbiome studies. Disclosures Peled: Seres Therapeutics: Other: IP licensing fees, Research Funding. van den Brink:Acute Leukemia Forum (ALF): Consultancy, Honoraria; Juno Therapeutics: Other: Licensing; Merck & Co, Inc.: Consultancy, Honoraria; Seres Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Therakos: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Flagship Ventures: Consultancy, Honoraria; Evelo: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Magenta and DKMS Medical Council: Membership on an entity's Board of Directors or advisory committees. Sung:Novartis: Research Funding; Merck: Research Funding; Seres: Research Funding.

Visualising the complex relationships between ethnic group and health in Scotland: 2001-2013

Abstract Background With growing numbers of migrants worldwide, accurate data are needed to assess the health of migrants and ethnic minorities, highlight inequalities and evaluate relevant policies and actions. To summarise and reveal the complexity of the findings, we developed data visualisation techniques based on epidemiological principles. Methods We used published results from the Scottish Health and Ethnicity Linkage Study (SHELS), a retrospective cohort of 4.62 million people linking census ethnicity data to health service and death records during 2001-2013. In tables mainly using rate ratios, we employed different colours to show health advantage, disadvantage or equivalence; different colour shades to represent degree of certainty, combining effect size and precision of estimate; and different font sizes for absolute rates, to highlight more common conditions. We ranked health conditions by age-adjusted rate within each ethnic group to show differences in burden of disease and disease priorities. Results Using 30 health outcomes for up to 11 ethnic groups, we show that ethnic differences vary greatly depending on outcome, sex and ethnic group. The patterns are complex with some ethnic groups showing strong advantages for some outcomes and strong disadvantages for others. Using absolute rates highlighted differences in common conditions such as myocardial infarction, COPD, and falls. Ranking conditions within ethnic groups showed that most ethnic groups have largely similar disease priorities. Conclusions Our approach helps reveal and interpret the complexity of ethnic health differences. Simplistic generalisations that the health of migrants or ethnic minorities is worse or better than majority populations are often misleading and best avoided. Using absolute rates and ranking conditions within ethnic groups are useful as large relative differences in disease rates between ethnic groups may not translate into different disease priorities. Key messages Statements that the health of migrants or ethnic minorities is worse or better than majority populations are often misleading and best avoided. Large relative differences in disease rates between ethnic groups may not translate into different disease priorities.

Abstract A71: Modification of obesity associations with prostate cancer grade by area-level factors

Abstract Purpose: Area-level characteristics have been associated with differences in rates of disease among populations. Specific factors, such as neighborhood socioeconomic status (SES), may influence overall exposure to risk factors and modify disease outcomes. The role of obesity in prostate cancer (PCa) progression has been confirmed with most studies showing a relationship to advanced disease and increased risk for biochemical failure and mortality. Although obesity and prostate cancer rates vary geographically, it is unclear if the relationship between obesity and PCa outcomes is modified by neighborhood factors. We evaluated the association of obesity with prostate cancer grade by area-level factors in a patient cohort recruited between 1995 and 2011. Materials and Methods: A total of 949 patients were studied to evaluate whether the association of obesity with prostate tumor grade at diagnosis varied by residential neighborhood and county characteristics. Residential characteristics were obtained using PolicyMap data sources including the US Census, CDC data, and a regional survey collected biannually (CHDB). There were 680 non-obese and 269 obese patients. High tumor grade was defined as a Gleason Score ≥7. We analyzed the relationship between obesity and PCa grade using logistic regression models, adjusted for age and race. Similar models stratified by residential characteristics were assessed. Results: Obese patients were more likely to have a high PCa grade compared to the non-obese patients (OR=1.47, 95% CI= 1.10-1.96). Associations between obesity and PCa grade were also observed for individuals residing in areas with higher rates of poverty (OR=1.57, 95% CI= 1.02-2.42), lower income (OR=1.56, 95% CI= 1.01-2.41), and lower education (OR=1.68, 95% CI= 1.12-2.52). The odds ratio was increased in areas with high rates of burglary (OR=1.55, 95% CI= 1.04-2.30) and murder (OR=1.51, 95% CI= 1.01-2.25) and low rates of robbery (OR=1.63, 95% CI= 1.03-2.58). In addition, obese men in areas with low rates of prostate cancer incidence (OR=1.84, 95% CI= 1.20-2.82) and low rates of obesity (OR=1.64, 95% CI= 1.02-2.63) also were likely to have a high grade PCa. Conclusions: Associations between obesity and prostate cancer vary significantly by area-level SES, crime, and health indicators. These results suggest specific neighborhood characteristics that may help identify high risk groups and pathways for interventions. Citation Format: Renyta Scales, Elaine Spangler, Knashawn Morales, Karen Glanz, Timothy Rebbeck, Charnita Zeigler-Johnson. Modification of obesity associations with prostate cancer grade by area-level factors. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A71.

Virus-Specific Differences in Rates of Disease during the 2010 Dengue Epidemic in Puerto Rico

BackgroundDengue is a potentially fatal acute febrile illness (AFI) caused by four mosquito-transmitted dengue viruses (DENV-1–4) that are endemic in Puerto Rico. In January 2010, the number of suspected dengue cases reported to the passive dengue surveillance system exceeded the epidemic threshold and an epidemic was declared soon after.Methodology/Principal FindingsTo characterize the epidemic, surveillance and laboratory diagnostic data were compiled. A suspected case was a dengue-like AFI in a person reported by a health care provider with or without a specimen submitted for diagnostic testing. Laboratory-positive cases had: (i) DENV nucleic acid detected by reverse transcriptase-polymerase chain reaction (RT-PCR) in an acute serum specimen; (ii) anti-DENV IgM antibody detected by ELISA in any serum specimen; or (iii) DENV antigen or nucleic acid detected in an autopsy-tissue specimen. In 2010, a total of 26,766 suspected dengue cases (7.2 per 1,000 residents) were identified, of which 46.6% were laboratory-positive. Of 7,426 RT-PCR-positive specimens, DENV-1 (69.0%) and DENV-4 (23.6%) were detected more frequently than DENV-2 (7.3%) and DENV-3 (<0.1%). Nearly half (47.1%) of all laboratory-positive cases were adults, 49.7% had dengue with warning signs, 11.1% had severe dengue, and 40 died. Approximately 21% of cases were primary DENV infections, and 1–4 year olds were the only age group for which primary infection was more common than secondary. Individuals infected with DENV-1 were 4.2 (95% confidence interval [CI]: 1.7–9.8) and 4.0 (95% CI: 2.4–6.5) times more likely to have primary infection than those infected with DENV-2 or -4, respectively.Conclusions/SignificanceThis epidemic was long in duration and yielded the highest incidence of reported dengue cases and deaths since surveillance began in Puerto Rico in the late 1960's. This epidemic re-emphasizes the need for more effective primary prevention interventions to reduce the morbidity and mortality of dengue.

The Promise of Primary Care–Based Screening for Diabetic Retinopathy

The Promise of Primary Care–Based Screening for Diabetic Retinopathy

Birth Cohort Effects in Neurological Diseases: Amyotrophic Lateral Sclerosis, Parkinson’s Disease and Multiple Sclerosis

Background: Generational differences in disease rates are the main subject of age-period-cohort (APC) analysis, which is mostly applied in cancer and suicide research. This study applied APC analysis to selected neurological diseases: amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and multiple sclerosis (MS). Methods: The analyses were based on Swiss mortality data. Age-stratified data has been available for MS, PD and ALS since 1901, 1921, and 1942, respectively. APC analysis was performed within the framework of logit models. Main effect models were extended by implementing nested effects, i.e. age effects nested in subperiods, in order to account for the fact that age profiles may change for reasons other than generational influences. Results: In preliminary analyses, APC analysis yielded noteworthy birth cohort effects in all three diseases. After implementing nested effects, the birth cohort effects disappeared in ALS, and smoothed out in PD, where they were greater for the generations born before the 1920s. In MS, the birth cohort effects remained stable, and exhibited a peak in cohorts born in the 1910s and 1920s. Conclusions: APC analysis yielded some evidence for birth cohort effects, i.e. predisposing risk factors that may change in historical terms, in MS and PD, but probably not in ALS.

Group Medical Claims as a Source of Information on Worker Health and Potentially Work-Related Diseases

To help address underrecognition of occupational illnesses and support planning of workplace health initiatives. Data from Highmark Inc., a health care insurer headquartered in Pittsburgh and Camp Hill, Pennsylvania, was used to calculate age and gender-adjusted rates of 15 diseases by industry and broad industry sector. Significant industry differences in disease rates were observed, some corresponding to known differences in workplace risk factors. Group medical claims offer timely, relatively low cost, longitudinal data on rates of physician-diagnosed cases and costs of many diseases for large populations. Limitations of medical claims data include inaccuracies in industry coding, lack of occupation codes, and lack of key variables that affect health. Yet, some elevated industry rates suggest potential new targets for wellness programs and evaluation of possible workplace health risks.

Pulmonary Disease Associated with Nontuberculous Mycobacteria, Oregon, USA

To the Editor: Nontuberculous mycobacteria (NTM) are environmental organisms ubiquitous in soil and water, including municipal water supplies. When inhaled, these organisms cause chronic, severe lung disease in susceptible persons (1). Recent epidemiologic studies suggest NTM pulmonary disease is increasingly prevalent in North America, with annual incidence rates of 13 cases per 100,000 population in persons >50 years of age and 2–4-fold higher in older age groups (2–4). The current distribution of pulmonary NTM disease has been poorly characterized with regard to environment, climate, and other factors. We recently performed a statewide NTM surveillance project in Oregon, United States, where we documented higher pulmonary disease rates within the moister, temperate western regions of the state. Oregon is bisected north-south by mountains into 2 distinct climate zones. Western Oregon, where 87% of the state’s population lives, is temperate and wet; eastern Oregon is primarily rural, with an arid, high desert climate. Our goal was to evaluate whether disease clustering within the state could be explained by population density. For all Oregon residents who had newly diagnosed and existing pulmonary NTM disease during 2005 and 2006, we used case-patient home ZIP code and county of residence to construct statewide disease maps (4). We obtained state ZIP code and county-level census data for 2005 and 2006 from the Portland State University Population Research Center and used Oregon Office of Rural Health criteria to designate ZIP codes as urban or rural and counties as rural (nonmetropolitan), micropolitan, or metropolitan (5,6). Unlike ZIP code data, which lacked age information, county census data were age stratified and consisted of population numbers aggregated in 5-year age groups (e.g., 0–4 years, 5–9 years). Because nearly all pulmonary NTM disease occurred in persons >50 years of age, we calculated age-adjusted disease prevalence rates (by using 95% Poisson exact confidence intervals) for patients >50 years of age in the county census data. We used the Cochran-Armitage test for trend to evaluate differences in rates by rural, micropolitan, and metropolitan county designations. Statewide, 385 (94%) of 411 NTM cases occurred among residents of western Oregon, and the crude rate of annual disease prevalence was significantly higher in western than in eastern Oregon (6.0 vs. 2.7/100,000; p 50 years of age were similar (7.6 cases/100,000 population) to those in rural counties within western Oregon (6.5/100,000). Table Prevalence of pulmonary nontuberculous mycobacterial disease, by geographic region and population density, Oregon, USA, 2005 and 2006* In Oregon, where most pulmonary NTM disease is caused by Mycobacterium avium complex (MAC), our findings suggest that the higher rates of disease in the wet western portion of the state are best explained by differences in population density (4). Disease rates there were highly correlated with increasing population density, and in rural areas of western Oregon, disease rates were similar to those in the arid, primarily rural eastern portion of the state. Humans presumably are exposed to NTM daily through showering, bathing, and other activities where water or soil is aerosolized (7). Previous environmental studies suggest that persons living in urban areas could potentially have greater NTM exposure during these activities because NTM is more prevalent in piped networks of municipal water systems than in well-water systems primarily used in rural regions (8). A study in Japan in the 1980s found a similar association of pulmonary NTM disease (primarily MAC) with urban and wet environments compared with arid and rural regions in our study but unlike our study was not able to evaluate differences in disease rates between urban and rural areas independent of climate differences (9). A 1979 Texas study found an association of pulmonary NTM with rural living, although this result was driven by M. kansasii disease, and rates of MAC were actually higher in rural areas (10). These and other similar studies were conducted decades ago when the epidemiology of NTM was substantially different (i.e., predominantly a disease of male patients) and before the formulation of the 2007 American Thoracic Society/Infectious Diseases Society of America pulmonary NTM disease criteria (1). We were limited in drawing firm conclusions about why pulmonary NTM is more common in urban areas because we were not able to evaluate patients or regional water systems within our study. Persons living rurally might be less likely to seek medical care and thus have NTM diagnosed, which would account for the differences in our study. However, given the reasonably close proximity of western Oregon’s rural regions to major medical centers, we believe this scenario is unlikely. Our findings suggest that pulmonary NTM disease is closely associated with urban living. We suspect the difference in disease rates between urban and rural areas might reflect differences in host exposure to these pathogens. Further studies should be undertaken to elucidate the environmental exposures associated with pulmonary NTM.

Admixture mapping comes of age.

Admixture mapping is based on the hypothesis that differences in disease rates between populations are due in part to frequency differences in disease-causing genetic variants. In admixed populations, these genetic variants occur more often on chromosome segments inherited from the ancestral population with the higher disease variant frequency. A genome scan for disease association requires only enough markers to identify the ancestral chromosome segments; for recently admixed populations, such as African Americans, 1,500-2,500 ancestry-informative markers (AIMs) are sufficient. The method was proposed over 50 years ago, but the AIM panels and statistical methods required have only recently become available. Since the first admixture scan in 2005, the genetic bases for a range of diseases/traits have been identified by admixture mapping. Here, we provide a historical perspective, review AIM panels and software packages, and discuss recent successes and unexpected insights into human diseases that exhibit disparate rates across human populations.

The Science of Soy: What Do We Really Know?

A stroll through nearly any American grocery store or pharmacy yields ample proof of the soybean’s increasing role in the U.S. diet. Food packaging offers statements about products’ soy content and the purported associated health benefits. Products such as tofu, soy milk, soy-based infant formula, and meatless “texturized vegetable protein” burgers are widely available. Shelves of dietary supplements and nutraceuticals are stocked with isoflavones, naturally occurring estrogenic compounds found in soy. The general impression is one of certainty that both soy and soy isoflavones deliver many health benefits, including prevention of cardiovascular disease, cancer, and osteoporosis, as well as treatment of menopausal symptoms. The science is less absolute, however, and still evolving. Soy provides a complete source of dietary protein, meaning that, unlike most plant proteins, it contains all the essential amino acids. According to the American Soybean Association, 3.14 billion bushels (85.5 million metric tons) of soybeans were harvested in the United States in 2004. Approximately half of the harvest was exported, and most of the remainder was crushed to produce oil and protein meal for domestic use. An April 2006 report from the USDA Economic Research Service indicates that only a small amount of whole soybeans are used to produce soy foods, and just 2% of soy protein meal is used for human consumption; the rest is used for animal feed. The Soyfoods Association of North America reports that U.S. sales of soy foods reached $3.9 billion in 2003, continuing an 11-year trend of 15% average annual increases. According to the United Soybean Board’s 2004–2005 Consumer Attitudes About Nutrition report, 25% of Americans consume soy foods or beverages at least once per week, and 74% view soy products as healthy. Nevertheless, Americans as a whole still consume very little soy protein. Based on 2003 data from the UN Food and Agriculture Organization, per-capita soy protein consumption is less than 1 gram (g) per day in most European and North American countries, although certain subpopulations such as vegetarians, Asian immigrants, and infants fed soy-based formula consume more. The Japanese, on the other hand, consume an average 8.7 g of soy protein per day; Koreans, 6.2–9.6 g; Indonesians, 7.4 g; and the Chinese, 3.4 g. Traditional soy foods include tofu, which is produced by pureeing cooked soybeans and precipitating the solids, and miso and tempeh, which are made by fermenting soybeans with grains. “Second generation” soy products involve chemical extractions and other processing, and include soy protein isolate and soy flour. These products become primary ingredients in items such as meatless burgers, dietary protein supplements, and infant formula, and are also used as nonnutritive additives to improve the characteristics of processed foods.

Make it HuGE: human genome epidemiology reviews, population health, and the IJE

The International Journal of Epidemiology is concerned with scientific evidence that can ultimately form the basis of strategies for improving population health. Hence, the IJE would be expected to remain cautious about the technological advances heralded by the sequencing of the human genome. The classical epidemiological approaches of examining secular trends in disease risk, changes in risk consequent upon migration, and differences in disease rates between populations indicate that little of the global burden of common disease can be attributed to simple differences in genetically determined risk. It is not surprising that many social epidemiologists and public health practitioners (including, in the past, some of the authors of this editorial) have pointed this out. More surprising, perhaps, is that in the spirit of honest accounting, some geneticists and genetic epidemiologists have also punctured the inflated claims of genetic epidemiology by emphasizing that the population-attributable risk of most common genetic variants will be low and that in any case the influence of genetic factors is not reversible through changing genetic make-up. Thus Terwilliger and Weiss 1 point out that alleles identified as increasing the risk of common diseases ‘tend to be involved in only a small subset of all cases of such diseases’ and that in any case ‘while the concept of attributable risk is an important one for evaluating the impact of removable environmental factors, for non-removable genetic risk factors, it is a moot point’. Bearing these criticisms in mind, should the IJE be excited by the growing Human Genome Epidemiology Network

Cohort Profile: The Kadoorie Study of Chronic Disease in China (KSCDC)

During recent decades China has undergone a rapid transition in the main disease patterns of its population. There has been a substantial decrease in maternal and child mortality and in adult mortality from themain infectious/parasitic diseases, whereas for ischaemic heart disease and some other non-communicable chronic diseases, there has been a moderate increase in mortality rates, at least in some parts of China. Consequently, most of the premature mortality that still remains in China now involves the chronic diseases of middle age, such as cancer, stroke, heart disease, or chronic lung disease. 1–4 In the mid-1970s a retrospective survey of the causes of 20 million deaths in China during 1973–75 showed that for each major disease there was large unexplained variation in the age-specific rates between different parts of China, indicating that there must be some large avoidable causes. 5 This finding was confirmed and extended in the late 1980s by a more careful retrospective survey of the causes of one million deaths during 1986–88 in 69 rural counties and 24 cities (Figure 1). 3,4 These big differences in disease rates between one area and another probably reflect differences in the ways people live rather than genetic differences. Moreover, even within one area there are likely to be substantial differences between individuals in genetic factors, as well as in patterns of chronic infection, personal biochemistry, physical characteristics, lifestyle, etc. that persist for many years and eventually influence the likelihood of particular individuals developing particular diseases. 3,4

Multilevel Analysis of Infectious Diseases

Traditional study designs, such as individual-level studies and ecological studies, are unable to simultaneously examine the effects of individual-level and group-level factors on risk of disease. Multilevel analysis overcomes this limitation by allowing the simultaneous investigation of factors defined at multiple levels. Areas in which multilevel modeling can be applied to sexually transmitted infection (STI) research include examining how both group-level and individual-level factors are related to individual-level STI outcomes, assessing interactions between individual-level and group-level constructs, and exploring how factors at multiple levels contribute to group-to-group differences in rates of disease. In this article, we review the fundamentals of multilevel modeling, the applications of multilevel models for the examination of STIs, and the key challenges associated with using multilevel modeling for infectious-disease research.

Residential mobility in the California Teachers Study: implications for geographic differences in disease rates

Background: Especially for cancers with long latency periods, such as breast cancer, the issue of residential mobility hinders ecologic analyses seeking to examine the role of environmental contaminants in chronic disease etiology. This study describes and evaluates characteristics associated with residential mobility in a sub-sample of the California Teachers Study (CTS) cohort. Methods: In 2000, lifetime residential histories were collected for a sub-sample of 328 women enrolled in the CTS; women's degree of residential mobility and associated factors were analyzed. Results: While most women moved many times during their lives (average=8.9), the average number of years at their residence when they enrolled in the study was reasonably long (15.1 years). Age strongly predicted duration at current residence but was not related to the number of lifetime residences. After adjusting for age, California-born women and women living in high socioeconomic status (SES) neighborhoods were significantly more residentially stable. Agreement between self-reported urbanization of recent residences and that based on census data of the geocoded residences was very good (80% concordant). Among women currently living in urban areas, an average of 43.3 years, or 77%, of their lifetimes were spent in urban residences; among women currently living in a rural area, an average of 37.3 years, or 67% of their lifetimes were spent in rural residences. Conclusions: This suggests that analyses of incidence rates based on current residence, while not capturing a woman's full exposure history, may reasonably reflect some aspect of longer term chronic exposures, especially those related to urbanization, at least in professional women.

Toward the Last Cohort

Toward the Last Cohort

The epidemiology of paediatric tuberculosis in Europe

Tuberculosis is one of the major infections affecting children world-wide. The epidemiology of paediatric tuberculosis in Europe reflects world-wide trends, with significant differences in rates of disease between resource-poor nations and those with well-funded tuberculosis control programmes. The highest rates of disease occur in Eastern European countries, with increasing rates of disease in children resulting from increasing numbers of infectious adults within communities. Western European nations continue to have low rates of tuberculosis, with a large proportion of disease occurring in recent immigrants from high-burden countries, particularly those in Sub-Saharan Africa.

Pathogenic Interactions of Alternaria crassa and Phenolic Metabolism in Jimsonweed (Datura stramonium L.) Varieties

Alternaria crassa, a mycoherbicide for jimsonweed control, was tested for differential effects on growth, infectivity, and phenolic metabolism of two field-collected jimsonweed varieties. Seeds from field-grown red- and green-stemmed jimsonweed varieties were grown in the greenhouse and spores were applied in aqueous solutions. Infection studies showed no significant differences between the two weed varieties when tested at various growth stages from cotyledonary to 8–leaf stage at 2.5 × 104spores ml−1. Spore dose response tests with A. crassa spores (6.25 to 100 x103spores ml−1) showed no significant varietal differences in disease ratings. Extractable phenylalanine ammonia-lyase activity increased nearly equally (2- to 3-fold above control) in both varieties (8- to 9-leaf stage) 48 to 72 h after treatment with 5 × 104spores ml−1. The only significant differences between treated varieties occurred 72 h after treatment. Exposure to A. crassa lowered total ethanol-soluble phenolic levels in both varieties 72 h after treatment. Spectrophotometric scans (400 to 700 nm) of ethanol extracts (72 h after treatment) indicated that infection caused some absorbance increases, especially in red-stemmed jimsonweed. Anthocyanin content was about 11-fold higher in red-stemmed than green-stemmed jimsonweed, and A. crassa treatment slightly increased the anthocyanin absorption peak of the red-stemmed variety.