Cohort Effect
Cohort Studies
Age Factors
Age Distribution
Incidence
Sex Distribution
Models, Statistical
Poisson Distribution
Sex Factors
Prevalence
Risk Factors
United States
Epidemiologic Methods
Longitudinal Studies
Regression Analysis
Role of intergenerational effects on linear growth. (1/105)
Current knowledge on the role of intergenerational effects on linear growth is reviewed on the basis of a literature search and recent findings from an ongoing study in Guatemala. Fourteen studies were identified, most of which examined the intergenerational relationships in birth weight. Overall, for every 100 g increase in maternal birth weight, her child's birth weight increased by 10-20 g. The study samples were primarily from developed countries, and birth weight data were extracted from hospital records and/or birth registries. Among the few studies that examined associations between the adult heights of parents and their offspring, correlation coefficients of 0.42-0.5 were reported. None of the studies examined intergenerational relationships in birth length or linear growth patterns during early childhood, preadolescence and/or adolescence. Prospectively collected data from long-term studies being carried out in rural Guatemala provide the first evidence of intergenerational relationships in birth size in a developing country setting. Data were available for 215 mother-child pairs. Maternal birth size was a significant predictor (P < 0.05) of child's birth size after adjusting for gestational age and sex of the child and other potential confounders. Child's birth weight increased by 29 g/100 g increase in maternal birth weight which is nearly twice that reported in developed countries. Similarly, child's birth length increased by 0.2 cm for every 1 cm increase in mother's birth length. The effect of maternal birth weight remained significant even after adjusting for maternal adult size. More evidence from developing countries will help explain the underlying mechanisms and identify appropriate interventions to prevent growth retardation. (+info)International trends in rates of hypospadias and cryptorchidism. (2/105)
Researchers from seven European nations and the United States have published reports of increasing rates of hypospadias during the 1960s, 1970s, and 1980s. Reports of increasing rates of cryptorchidism have come primarily from England. In recent years, these reports have become one focus of the debate over endocrine disruption. This study examines more recent data from a larger number of countries participating in the International Clearinghouse for Birth Defects Monitoring Systems (ICBDMS) to address the questions of whether such increases are worldwide and continuing and whether there are geographic patterns to any observed increases. The ICBDMS headquarters and individual systems provided the data. Systems were categorized into five groups based on gross domestic product in 1984. Hypospadias increases were most marked in two American systems and in Scandinavia and Japan. The increases leveled off in many systems after 1985. Increases were not seen in less affluent nations. Cryptorchidism rates were available for 10 systems. Clear increases in this anomaly were seen in two U.S. systems and in the South American system, but not elsewhere. Since 1985, rates declined in most systems. Numerous artifacts may contribute to or cause upward trends in hypospadias. Possible "real" causes include demographic changes and endocrine disruption, among others. (+info)Juvenile hypothyroidism among two populations exposed to radioiodine. (3/105)
We found an epidemic of juvenile hypothyroidism among a population of self-defined "downwinders" living near the Hanford nuclear facility located in southeast Washington State. The episode followed massive releases of 131I. Self-reported data on 60 cases of juvenile hypothyroidism (<20 years of age) among a group of 801 Hanford downwinders are presented, as well as data concerning the thyroid status of approximately 160,000 children exposed to radioiodine before 10 years of age as a result of the 26 April 1986 Chernobyl explosion in the former Soviet Union. These children were residents of five regions near Chernobyl. They were examined by standardized screening protocols over a period of 5 years from 1991 to 1996. They are a well-defined group of 10 samples. Fifty-six cases of hypothyroidism were found among boys and 92 among girls. Body burdens of 137Cs have been correlated with hypothyroidism prevalence rates. On the other hand, the group of juvenile (<20 years of age) Hanford downwinders is not a representative sample. Most of the 77 cases of juvenile hypothyroidism in the Hanford group were diagnosed from 1945 to 1970. However, the ratio of reported cases to the county population under 20 years of age is roughly correlated with officially estimated mean levels of cumulative thyroid 131I uptake in these counties, providing evidence that juvenile hypothyroidism was associated with radioiodine exposures. Because even subtle hypothyroidism may be of clinical significance in childhood and can be treated, it may be useful to screen for the condition in populations exposed to radioiodine fallout. Although radiation exposure is associated with hypothyroidism, its excess among fallout-exposed children has not been previously quantified. (+info)Time trends in the mortality rates for tobacco- and alcohol-related cancers within the oral cavity and pharynx in Japan, 1950-94. (4/105)
Mortality data of oral cancer over 40 years in Japan were analyzed to investigate time trends of the disease site-specifically and discuss the relation between these trends and the changing patterns of consumption of tobacco and alcohol beverages. Mortality rates were adjusted to the world standard population. In the males, overall oral cancer (ICD-9: 141-149) mortality rates have increased consistently from the lowest value of 1.25 (per 100,000 per year) in 1956 to 2.40 in 1992. The rates for females were constantly lower than those for males, and formed a modest peak of 0.96 in 1979. Regarding site-specific mortality rates, tongue cancer (141) presented a decreasing trend, while oro/hypopharyngeal (146, 148) and mouth (143-145) cancers showed increasing patterns, particularly in males. When the changing patterns of male truncated rates for ages 35-64 were compared with those of the annual consumption of cigarette and alcohol per capita, the time trend of oro/hypopharyngeal cancer mortality was analogous to cigarette consumption rather than to alcohol consumption, mouth cancer vice versa, and tongue cancer was not related to tobacco or alcohol consumption. The present findings suggest that tobacco and alcohol have different site-specific effects on the development of cancers within the oral cavity and pharynx. (+info)Social environment and year of birth influence type 1 diabetes risk for African-American and Latino children. (5/105)
OBJECTIVE: Credible epidemiological data, primarily from European-origin populations, indicate that environmental factors play an important role in the incidence of type 1 diabetes. RESEARCH DESIGN AND METHODS: A population-based registry of incident cases of type 1 diabetes among African-American and Latino children in Chicago was used to explore the influence of individual and neighborhood characteristics on diabetes risk. New cases of insulin-treated diabetes in African-American and Latino Chicagoans aged 0-17 years for 1985-1990 (n = 400) were assigned to one of 77 community areas based on street address. Census tables provided denominators, median household income, percentage of adults > or = 25 years old who had completed high school and college, and a crowding variable for each community area individual-level data were birth cohort, sex, and ethnicity. Outcomes in Poisson regression were sex-, ethnic-, and birth cohort-specific incidence rates. RESULTS: Significant univariate associations between diabetes risk and ethnicity, birth cohort, crowding, and the percentage of adults in each community area who had completed high school and college were observed. African-Americans had a relative risk (RR) of 1.42 (95% CI, 1.14-1.76) compared with Latinos. Risk varied significantly by birth cohort in both ethnic groups. For every 10% increase in the proportion of adults who completed college, the RR for diabetes increased by 25% (RR, 1.25 [95% CI, 1.09-1.44]). Social class variables were significant determinants of risk for African Americans, but not for Latinos. CONCLUSIONS: The strong birth cohort and social class associations observed in this study implicate an infectious exposure linked with age. (+info)Food safety knowledge and practice among elderly people living at home. (6/105)
OBJECTIVE: To assess the food storage knowledge and practice of elderly people living at home. METHODS: Three phase survey data collection: face to face interviews; dietary diaries with a food frequency questionnaire; and follow up interviews. SETTING: Urban Nottingham. PARTICIPANTS: 809 elderly people (aged 65+) randomly selected from general practitioner lists. MAIN OUTCOME MEASURES: Respondent's refrigerator temperature; knowledge of freezer star rating; understanding of "use by" and "sell by" dates; reported ability to read food product safety labels. RESULTS: From a weighted total of 645 refrigerators measured, 451 (70%) were too warm for the safe storage of food (> or = 6 degrees Celsius). Only 41% of respondents (n = 279) knew the star rating of their freezer. Within a smaller sub-sample knowledge of the "use by" and "sell by" dates was good, but 45% of these respondents reported difficulty reading food labels. The storage of foods at inappropriate temperatures was not independent of socioeconomic or demographic status, and tended to be more likely among the poorer and those not living alone. CONCLUSIONS: Food storage practices among the majority of elderly people interviewed in this study do not meet recommended safety standards to minimise the risk of food poisoning. (+info)A review of the healthy worker effect in occupational epidemiology. (7/105)
This review article aims to anatomize sources of the healthy worker effect (HWE) and to summarize advantages and limitations of several approaches frequently proposed to eliminate the HWE. Although the HWE is frequently addressed in the context of selection bias, our review suggests that the selection of occupational cohorts with advantageous health status would preferably be addressed as a source of confounding biases. The authors also conclude that the exclusion of unhealthy workers at employment and the study of active workers are the two main sources of HWE, and that the use of the general population as a comparison group in occupational epidemiology should be avoided if possible. The authors encourage investigators to make distinctions between the underlying factors related to the use of the general population as the comparison group in occupational epidemiology. (+info)Increase in cervical cancer mortality in Spain, 1951-1991. (8/105)
BACKGROUND: The trend in cervical cancer mortality in Spain from 1951 to 1991 is examined. METHODS: Analysis of national mortality statistics calculating age standardised mortality rates and an age-period cohort analysis. A fit to the Gompertz function was made to estimate the influence of the environmental factors on the mortality rates evolution. MAIN RESULTS: The age standardised mortality rate in Spain is lower than in other developed countries (USA or Estonia) and equal to Norwegian and Finland rates; but whereas in these countries the trend is to decrease, the Spanish rate has increased during this period, because of a cohort effect. A misclassification bias could be responsible for the trend in women aged 40 and older but the increasing trend in younger women could not be interpreted as espurious. The Gompertzian analysis suggests an increase in environmental factors causing cervical cancer. CONCLUSIONS: Cervical cancer mortality rates are increasing in Spain because of environmental factors. (+info)A "cohort effect" refers to a phenomenon where individuals who belong to the same generation or group, born during the same period, share similar experiences, exposures, and behaviors that can influence their health outcomes differently from other generations. These shared experiences and exposures can include historical events, societal trends, technological advancements, and changes in public policy that occur during their formative years and beyond.
In medical research, a cohort study is an observational study design where a group of individuals who share a common characteristic or exposure are followed up over time to examine the incidence and prevalence of specific health outcomes. When these studies focus on comparing health outcomes across different birth cohorts, they aim to identify cohort effects that may influence disease risk, morbidity, and mortality.
Examples of cohort effects include the impact of historical smoking patterns on lung cancer rates, the influence of changes in vaccination policies on infectious disease incidence, or the effect of technological advancements on sedentary behavior and obesity prevalence. Understanding cohort effects is essential for developing targeted public health interventions and prevention strategies that consider the unique experiences and exposures of different generations.
A cohort study is a type of observational study in which a group of individuals who share a common characteristic or exposure are followed up over time to determine the incidence of a specific outcome or outcomes. The cohort, or group, is defined based on the exposure status (e.g., exposed vs. unexposed) and then monitored prospectively to assess for the development of new health events or conditions.
Cohort studies can be either prospective or retrospective in design. In a prospective cohort study, participants are enrolled and followed forward in time from the beginning of the study. In contrast, in a retrospective cohort study, researchers identify a cohort that has already been assembled through medical records, insurance claims, or other sources and then look back in time to assess exposure status and health outcomes.
Cohort studies are useful for establishing causality between an exposure and an outcome because they allow researchers to observe the temporal relationship between the two. They can also provide information on the incidence of a disease or condition in different populations, which can be used to inform public health policy and interventions. However, cohort studies can be expensive and time-consuming to conduct, and they may be subject to bias if participants are not representative of the population or if there is loss to follow-up.
"Age factors" refer to the effects, changes, or differences that age can have on various aspects of health, disease, and medical care. These factors can encompass a wide range of issues, including:
1. Physiological changes: As people age, their bodies undergo numerous physical changes that can affect how they respond to medications, illnesses, and medical procedures. For example, older adults may be more sensitive to certain drugs or have weaker immune systems, making them more susceptible to infections.
2. Chronic conditions: Age is a significant risk factor for many chronic diseases, such as heart disease, diabetes, cancer, and arthritis. As a result, age-related medical issues are common and can impact treatment decisions and outcomes.
3. Cognitive decline: Aging can also lead to cognitive changes, including memory loss and decreased decision-making abilities. These changes can affect a person's ability to understand and comply with medical instructions, leading to potential complications in their care.
4. Functional limitations: Older adults may experience physical limitations that impact their mobility, strength, and balance, increasing the risk of falls and other injuries. These limitations can also make it more challenging for them to perform daily activities, such as bathing, dressing, or cooking.
5. Social determinants: Age-related factors, such as social isolation, poverty, and lack of access to transportation, can impact a person's ability to obtain necessary medical care and affect their overall health outcomes.
Understanding age factors is critical for healthcare providers to deliver high-quality, patient-centered care that addresses the unique needs and challenges of older adults. By taking these factors into account, healthcare providers can develop personalized treatment plans that consider a person's age, physical condition, cognitive abilities, and social circumstances.
"Age distribution" is a term used to describe the number of individuals within a population or sample that fall into different age categories. It is often presented in the form of a graph, table, or chart, and can provide important information about the demographic structure of a population.
The age distribution of a population can be influenced by a variety of factors, including birth rates, mortality rates, migration patterns, and aging. Public health officials and researchers use age distribution data to inform policies and programs related to healthcare, social services, and other areas that affect the well-being of populations.
For example, an age distribution graph might show a larger number of individuals in the younger age categories, indicating a population with a high birth rate. Alternatively, it might show a larger number of individuals in the older age categories, indicating a population with a high life expectancy or an aging population. Understanding the age distribution of a population can help policymakers plan for future needs and allocate resources more effectively.
In epidemiology, the incidence of a disease is defined as the number of new cases of that disease within a specific population over a certain period of time. It is typically expressed as a rate, with the number of new cases in the numerator and the size of the population at risk in the denominator. Incidence provides information about the risk of developing a disease during a given time period and can be used to compare disease rates between different populations or to monitor trends in disease occurrence over time.
Mortality, in medical terms, refers to the state or condition of being mortal; the quality or fact of being subject to death. It is often used in reference to the mortality rate, which is the number of deaths in a specific population, divided by the size of that population, per a given time period. This can be used as a measure of the risk of death among a population.
"Sex distribution" is a term used to describe the number of males and females in a study population or sample. It can be presented as a simple count, a percentage, or a ratio. This information is often used in research to identify any differences in health outcomes, disease prevalence, or response to treatment between males and females. Additionally, understanding sex distribution can help researchers ensure that their studies are representative of the general population and can inform the design of future studies.
Statistical models are mathematical representations that describe the relationship between variables in a given dataset. They are used to analyze and interpret data in order to make predictions or test hypotheses about a population. In the context of medicine, statistical models can be used for various purposes such as:
1. Disease risk prediction: By analyzing demographic, clinical, and genetic data using statistical models, researchers can identify factors that contribute to an individual's risk of developing certain diseases. This information can then be used to develop personalized prevention strategies or early detection methods.
2. Clinical trial design and analysis: Statistical models are essential tools for designing and analyzing clinical trials. They help determine sample size, allocate participants to treatment groups, and assess the effectiveness and safety of interventions.
3. Epidemiological studies: Researchers use statistical models to investigate the distribution and determinants of health-related events in populations. This includes studying patterns of disease transmission, evaluating public health interventions, and estimating the burden of diseases.
4. Health services research: Statistical models are employed to analyze healthcare utilization, costs, and outcomes. This helps inform decisions about resource allocation, policy development, and quality improvement initiatives.
5. Biostatistics and bioinformatics: In these fields, statistical models are used to analyze large-scale molecular data (e.g., genomics, proteomics) to understand biological processes and identify potential therapeutic targets.
In summary, statistical models in medicine provide a framework for understanding complex relationships between variables and making informed decisions based on data-driven insights.
I'm sorry for any confusion, but Poisson Distribution is actually a statistical concept rather than a medical term. Here's a general definition:
Poisson Distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, as long as these events occur with a known average rate and independently of each other. It is often used in fields such as physics, engineering, economics, and medical research to model rare events or low-probability phenomena.
In the context of medical research, Poisson Distribution might be used to analyze the number of adverse events that occur during a clinical trial, the frequency of disease outbreaks in a population, or the rate of successes or failures in a series of experiments.
"Sex factors" is a term used in medicine and epidemiology to refer to the differences in disease incidence, prevalence, or response to treatment that are observed between males and females. These differences can be attributed to biological differences such as genetics, hormones, and anatomy, as well as social and cultural factors related to gender.
For example, some conditions such as autoimmune diseases, depression, and osteoporosis are more common in women, while others such as cardiovascular disease and certain types of cancer are more prevalent in men. Additionally, sex differences have been observed in the effectiveness and side effects of various medications and treatments.
It is important to consider sex factors in medical research and clinical practice to ensure that patients receive appropriate and effective care.
Prevalence, in medical terms, refers to the total number of people in a given population who have a particular disease or condition at a specific point in time, or over a specified period. It is typically expressed as a percentage or a ratio of the number of cases to the size of the population. Prevalence differs from incidence, which measures the number of new cases that develop during a certain period.
Medical Definition:
"Risk factors" are any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. They can be divided into modifiable and non-modifiable risk factors. Modifiable risk factors are those that can be changed through lifestyle choices or medical treatment, while non-modifiable risk factors are inherent traits such as age, gender, or genetic predisposition. Examples of modifiable risk factors include smoking, alcohol consumption, physical inactivity, and unhealthy diet, while non-modifiable risk factors include age, sex, and family history. It is important to note that having a risk factor does not guarantee that a person will develop the disease, but rather indicates an increased susceptibility.
I'm sorry for any confusion, but "United States" is a geopolitical entity, specifically the name of the country consisting of 50 states, and it is not a medical term or concept. Therefore, I can't provide a medical definition for it. If you have any questions related to health, medicine, or biology, I would be happy to try to help answer those!
In the field of medicine, "time factors" refer to the duration of symptoms or time elapsed since the onset of a medical condition, which can have significant implications for diagnosis and treatment. Understanding time factors is crucial in determining the progression of a disease, evaluating the effectiveness of treatments, and making critical decisions regarding patient care.
For example, in stroke management, "time is brain," meaning that rapid intervention within a specific time frame (usually within 4.5 hours) is essential to administering tissue plasminogen activator (tPA), a clot-busting drug that can minimize brain damage and improve patient outcomes. Similarly, in trauma care, the "golden hour" concept emphasizes the importance of providing definitive care within the first 60 minutes after injury to increase survival rates and reduce morbidity.
Time factors also play a role in monitoring the progression of chronic conditions like diabetes or heart disease, where regular follow-ups and assessments help determine appropriate treatment adjustments and prevent complications. In infectious diseases, time factors are crucial for initiating antibiotic therapy and identifying potential outbreaks to control their spread.
Overall, "time factors" encompass the significance of recognizing and acting promptly in various medical scenarios to optimize patient outcomes and provide effective care.
Epidemiologic methods are systematic approaches used to investigate and understand the distribution, determinants, and outcomes of health-related events or diseases in a population. These methods are applied to study the patterns of disease occurrence and transmission, identify risk factors and causes, and evaluate interventions for prevention and control. The core components of epidemiologic methods include:
1. Descriptive Epidemiology: This involves the systematic collection and analysis of data on the who, what, when, and where of health events to describe their distribution in a population. It includes measures such as incidence, prevalence, mortality, and morbidity rates, as well as geographic and temporal patterns.
2. Analytical Epidemiology: This involves the use of statistical methods to examine associations between potential risk factors and health outcomes. It includes observational studies (cohort, case-control, cross-sectional) and experimental studies (randomized controlled trials). The goal is to identify causal relationships and quantify the strength of associations.
3. Experimental Epidemiology: This involves the design and implementation of interventions or experiments to test hypotheses about disease prevention and control. It includes randomized controlled trials, community trials, and other experimental study designs.
4. Surveillance and Monitoring: This involves ongoing systematic collection, analysis, and interpretation of health-related data for early detection, tracking, and response to health events or diseases.
5. Ethical Considerations: Epidemiologic studies must adhere to ethical principles such as respect for autonomy, beneficence, non-maleficence, and justice. This includes obtaining informed consent, ensuring confidentiality, and minimizing harm to study participants.
Overall, epidemiologic methods provide a framework for investigating and understanding the complex interplay between host, agent, and environmental factors that contribute to the occurrence of health-related events or diseases in populations.
Longitudinal studies are a type of research design where data is collected from the same subjects repeatedly over a period of time, often years or even decades. These studies are used to establish patterns of changes and events over time, and can help researchers identify causal relationships between variables. They are particularly useful in fields such as epidemiology, psychology, and sociology, where the focus is on understanding developmental trends and the long-term effects of various factors on health and behavior.
In medical research, longitudinal studies can be used to track the progression of diseases over time, identify risk factors for certain conditions, and evaluate the effectiveness of treatments or interventions. For example, a longitudinal study might follow a group of individuals over several decades to assess their exposure to certain environmental factors and their subsequent development of chronic diseases such as cancer or heart disease. By comparing data collected at multiple time points, researchers can identify trends and correlations that may not be apparent in shorter-term studies.
Longitudinal studies have several advantages over other research designs, including their ability to establish temporal relationships between variables, track changes over time, and reduce the impact of confounding factors. However, they also have some limitations, such as the potential for attrition (loss of participants over time), which can introduce bias and affect the validity of the results. Additionally, longitudinal studies can be expensive and time-consuming to conduct, requiring significant resources and a long-term commitment from both researchers and study participants.
Regression analysis is a statistical technique used in medicine, as well as in other fields, to examine the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It allows for the estimation of the average change in the outcome variable associated with a one-unit change in an independent variable, while controlling for the effects of other independent variables. This technique is often used to identify risk factors for diseases or to evaluate the effectiveness of medical interventions. In medical research, regression analysis can be used to adjust for potential confounding variables and to quantify the relationship between exposures and health outcomes. It can also be used in predictive modeling to estimate the probability of a particular outcome based on multiple predictors.