Surveillance Systems

INTRODUCTION

The Epidemiology of Obesity
Even within national borders, the difficulty of quantifying the benefits of surveillance systems for individual communities leads to neglect by local authorities, providing the economic rationale for funding by the national government. Competent epidemiologists and surveillance staff members are not a luxury in developing countries; they are a necessity for rational planning, implementation, and intervention Narasimhan and others Thacker , Mark A. Email alerts New issue alert. Developing nations share surveillance needs with the rest of the world, yet they are challenged by economic limitations, weak public health infrastructure, and the overwhelming challenges of poverty and disease. Life-course socioeconomic position and obesity in African American women: Elements in Establishing and Maintaining a Surveillance System.

Disease Control Priorities in Developing Countries. 2nd edition.

Guidelines for Evaluating Surveillance Systems

Overall, the prevalence of obesity increased in all SES groups of men and women since the s, but the patterns of the trends in SES disparities are complex Among White men, the prevalence of obesity in the low-SES group decreased between — and —, while, during this period, the prevalence increased at a much higher rate among low-SES Black men compared with other SES groups. Among Black women, obesity increased at a faster pace in the high- and medium-SES groups compared with the low-SES group between — and — The BRFSS data show considerable differences in the prevalence of obesity across states figure 4.

In general, states in the southeastern United States have higher prevalence rates than states on the West Coast, in the Midwest, and on the northeast coast. In , only four states Colorado, Hawaii, Vermont, and Connecticut had obesity prevalence rates of less than 20 percent, while 17 states had prevalence rates of 25 percent or higher; in three of those states Louisiana, Mississippi, and West Virginia , prevalence was 30 percent or higher 26 , 27 , 44 , 49 , Self-reported body mass index data were used.

The regional differences became clearer over time between and figure 4. In , the regional difference was not clear, but, unmistakably in , western and northeastern states had a lower prevalence 10—14 percent compared with that in the other states 15—19 percent.

In and , the burden of obesity shifted toward southern and eastern regions. In , only five states had obesity prevalence rates of 15—19 percent, and none had rates at or above 20 percent. In , obesity prevalence in all 50 states was less than 20 percent. In , 28 states had obesity prevalence rates of less than 20 percent. In , only four states had rates of 20 percent or higher, while 17 states had a prevalence rate of 25 percent or more, and three had a prevalence of 30 percent or higher 26 , 27 , 44 , 49 , As shown in figure 5 , there are large gender and ethnicity differences.

Prevalence was the highest among Black women Data on mean waist circumference by gender and ethnicity are not available for — Between and , the prevalence of central obesity and mean waist circumference increased steadily figure 5. Mean waist circumference increased by 10 cm in men and 17 cm in women. The prevalence of central obesity increased by approximately 10 percentage points, with the highest increase among women aged 20—29 years However, in all surveys, waist circumference linearly increased with age, except in the age group 70—79 years, in which waist circumference decreased slightly.

In general, Mexican-American women ranked first in prevalence in — but in — were surpassed by non-Hispanic Black women 52 , Prevalence was similar among older children and adolescents Similar patterns across age and gender were observed for — compared with — analyses 2.

In — and —, the prevalence of both outcomes showed a larger gender gap among non-Hispanic Blacks and Mexican-American children and adolescents compared with non-Hispanic Whites.

Trends data are shown in figures 6 and 7. In all age groups, the prevalence of overweight had increased since the s. Between — and —, the average annual rate of increase was approximately 0. This rate was slower for young children.

During this period, the prevalence of overweight among children aged 2—5 years increased from 7. Among adolescents aged 12—19 years, it more than tripled, increasing from 5. The pace of increases in mean BMI was slower than that of the prevalence, suggesting that more of the increase is attributable to the upper tail of the distribution. Non-Hispanic White children and adolescents had the lowest prevalence compared with their non-Hispanic Black and Mexican-American counterparts.

For example, combined prevalence was Among boys, Mexican Americans aged 6—11 years had the highest combined prevalence and prevalence of overweight Among girls, non-Hispanic Black adolescents aged 12—19 years had the highest prevalence and non-Hispanic Whites had the lowest prevalence The prevalence of at risk for overweight was Data on self-reported weight and height were collected for 14, waves I and II adolescents aged 12—19 years.

The overall prevalence was 5. The prevalence among American native Indian children was higher than the national average.

Data collected in the PATHWAY study from 1, schoolchildren in grades 2 and 3 in 41 schools from seven American Indian communities show that half of them were at risk of overweight or overweight Although there was a wide range in BMI across study sites, prevalence was consistently higher than the national averages in all seven communities and among both girls and boys The prevalence of at risk for overweight among children and adolescents increased from The Youth Risk Behavior Surveillance System data enabled us to examine the trends based on self-reported weight and height since the early s.

In general, they suggest similar patterns The dramatic decline in prevalence between and among Mexican-American adolescents may be due to sampling problems. In general, the patterns for US children and adolescents and for adults share some similarities, but some features are unique. The patterns of the SES disparities in obesity are presented in table 6. Among adolescents, no consistent association was found between SES and overweight for boys, but low-SES adolescent girls had a much higher prevalence than their medium- and high-SES counterparts This difference is mainly due to the strong inverse association between SES and overweight among White adolescent girls 3.

Trend in ratios during — to — and — to — We found no consistent patterns for an SES difference in the increasing trend. Between and and between and , the low- to high-SES ratio for prevalence increased from 0.

Tertiles of family per capita income assessed by using poverty income ratio were used to define low-, medium-, and high-SES groups. To our knowledge, limited studies have examined the regional differences in overweight among US children and adolescents.

In children aged 6—9 years, the combined prevalence was higher in urban than in rural areas Among adolescents aged 10—18 years, whereas the combined prevalence was slightly higher in rural than in urban areas A recent study based on the Add Health study — baseline data examined the differences in US adolescents' risk of obesity and in their physical activity patterns according to neighborhood characteristics Study participants were grouped into six categories: Compared with US adolescents living in newer suburbs, those living in rural working-class, exurban, and mixed-ethnicity urban areas were approximately 30 percent more likely to be overweight, independent of individual SES, age, and ethnicity.

These findings illustrate important effects of the neighborhood on health and the inherent complexity of assessing residential landscapes across the United States. Simple classic urban-suburban-rural measures may mask the important complexities. Unlike for adults, and because national guidelines for classifying central obesity are lacking, little is known about the status of central obesity in US children and adolescents.

In fact, compared with their counterparts, Mexican-Americans boys and non-Hispanic Black girls aged 18 years had the highest waist circumference values in the 90th percentile Ethnic differences in waist circumference cm and gender, age-, and ethnicity-specific percentiles among US children and adolescents, National Health and Nutrition Examination Survey III, — In the two keys, ages are given in parentheses.

A large number of studies have shown the tracking of BMI and obesity status from childhood to adulthood 62—67 , providing additional support for early prevention. Overall, it is estimated that about one third of obese preschool children and about one half of obese school-age children become obese adults, although findings from different studies varied considerably. For example, when longitudinal data collected from 2, children initially aged 5—14 years over 17 years from childhood to adulthood were used 62 , the tracking of childhood BMI was stronger in Blacks than in Whites.

Among overweight children, 65 percent of White girls versus 84 percent of Black girls became obese adults; among boys, the corresponding figures were 71 percent versus 82 percent. Projections based on these models indicate that by , the prevalence of obesity among adults will reach The projection is even more alarming for the prevalence of overweight. Overall, the prevalence will be In some of the projections, the last available data were for the period — These projections include adult, gender-specific obesity for all ethnic groups, and adult gender-specific overweight for each ethnic group and all ethnic groups.

A similar pattern is observed for adolescents aged 12—19 years. Currently, more than two thirds of US adults and approximately one third of US children and adolescents are overweight or obese, and some minority and low-SES groups are disproportionally affected. The prevalence of obesity and overweight among US children and adults has more than doubled since the s, and the rate continues to rise.

Numerous studies have shown that obesity increases morbidity and mortality Obesity has become the second leading preventable cause of disease and death in the United States, second only to tobacco use 1.

Obesity is likely to continue to increase and soon become the leading cause if no effective approaches to controlling it can be implemented. On the other hand, some minority groups such as Asian Americans have a lower prevalence of obesity. Of great concern, our analysis shows that the prevalence of obesity and overweight has increased at an average annual rate of approximately 0. If a similar increase in trend is assumed, by , the majority of US adults 75 percent: Some population groups will be more seriously affected.

For example, by , However, current available data are limited and do not enable us to examine the trends in other minority groups or to understand the factors that have led to the current obesity epidemic. A good understanding of underlying causes that triggered the increase in obesity prevalence in the United States over the past three decades and the factors that have contributed to the disparities across groups is critical in fighting this growing public health crisis and achieving an important national priority to eliminate health disparities.

Although obesity is caused by many factors, in most persons, weight gain results from a combination of excess calorie consumption and inadequate physical activity. To maintain a healthy weight, there must be a balance between energy consumption through dietary intake and energy expenditure through metabolic and physical activity A number of individual-, population-, and international-level factors and environmental determinants might have played a role in the obesity trends, such as changes in people's eating behaviors, physical activity and inactivity patterns, occupation, development of technology, culture exchange, and global trade 16 , 17 , The NHANES data show a dramatic increase in the prevalence of overweight and obesity across all population groups and a declining disparity of obesity across SES groups over the past two decades.

This finding indicates that individual characteristics are not the dominant factor to which the rising obesity epidemic is ascribed. Social environmental factors might have a more profound effect in influencing individuals' body weight status than do individuals' characteristics such as SES.

A growing consensus is that environmental factors have played a pivotal role in influencing people's lifestyles and fueling the obesity epidemic in the United States and worldwide 17 , 68 , The current society provides Americans with abundant food at a relatively low cost and numerous opportunities to reduce energy expenditure at work and at home, which facilitates sedentary behaviors.

Nationally representative survey data examining trends in people's eating patterns between and the s have indicated several patterns likely to put people in the United States at increased risk of obesity, such as increased consumption of total energy, soft drink, and snack foods; more frequent eating at fast-food and other restaurants; and inadequate consumption of vegetables and fruits compared with dietary recommendations 70— The increase in portion size in the United States over the past three decades probably is an important contributor to overconsumption of food and has fueled the growing obesity epidemic.

Although our current understanding of the underlying complex causes of the disparities in obesity between population groups in the United States e. At the community level, disadvantage may constrain people's ability to acquire and maintain healthy diet and exercise behaviors. Differential rates of available local area physical fitness facilities, restaurants, and types of food stores by neighborhood characteristics may help explain why obesity does not affect all population groups equally 79 , A recent study shows significant disparities in the availability of food stores.

African-American and Hispanic neighborhoods had fewer chain supermarkets compared with White and non-Hispanic neighborhoods, by about 50 percent and 70 percent, respectively The availability of supermarkets has been associated with more healthful diets, higher vegetable and fruit consumption, and lower rates of obesity 82 , Shopping at supermarkets versus independent groceries has been associated with more frequent vegetable and fruit consumption The Add Health study shows that lower-SES and minority population groups had less access to physical activity facilities, which in turn was associated with decreased physical activity and increased overweight Population-based policies and programs that emphasize environmental changes are most likely to be successful.

Strategies to tackle obesity need to be incorporated into other existing health promotion programs, particularly those preventing chronic diseases by promoting healthful eating and physical activity. Childhood and adolescence are key times for persons to form lifelong eating and physical activity habits. Overweight children are likely to remain obese as adults. Thus, obesity prevention in schoolchildren is a public health priority.

In addition, because the majority of children spend many of their waking hours in schools, schools should be key partners in the prevention of childhood obesity. It is crucial to tailor treatment and prevention efforts to each particular ethnicity group's specific situation and needs. Government agencies, industry, public health professionals, and individual persons all need to play an active role in the growing national efforts to combat the obesity epidemic.

The surveys were designed by using stratified multistage probability samples. In each survey, standardized protocols were used for all interviews and examinations. Data on weight and height were collected for each person through direct physical examination in a mobile examination center. Recumbent length was measured in children younger than age 4 years and stature in children aged 2 years or older.

BRFSS is the world's largest ongoing telephone health survey system, tracking health conditions and risk behaviors in the United States yearly since Conducted by the 50 state health departments as well as those in the District of Columbia, Puerto Rico, Guam, and the US Virgin Islands, with support from the Centers for Disease Control and Prevention, this system uses standard procedures to collect data through a series of monthly telephone interviews with US adults.

BRFSS provides state-specific information about issues such as obesity, asthma, diabetes, health care access, alcohol use, hypertension, cancer screening, nutrition and physical activity, and tobacco use; that is, it enables geographic differences to be examined The YRBSS was developed in ; the first survey was started in to monitor priority health risk behaviors that contribute markedly to the leading causes of death, disability, and social problems among youth and adults in the US.

YRBSS collected information on risk behaviors e. Add Health is a nationally representative, school-based study of youths grades 7—12, approximately aged 12—17 years followed up with multiple interview waves into young adulthood approximately aged 18—26 years. The study used a multistage, stratified, school-based, clustered sampling design. A stratified sample of 80 high schools and feeder middle schools was selected with probability proportional to size. Wave I — included 20, adolescents aged 12—19 years and their parents.

Wave II included 14, wave I adolescents including school dropouts and excluding graduating seniors. Wave III — included 15, wave I adolescents, now aged 18—26 years and entering the transition to adulthood 76 percent response rate. In waves I and II, information on self-reported weight and height, and in wave III direct measured weight and height, was collected.

Some other studies published since the early s have also examined the complex relation between gender, ethnicity, SES, and obesity among US adults and children. For example, earlier data collected in the CARDIA study from 5, Black men and women and White men and women aged 18—30 years suggested that the association of education with obesity was negative among White women and positive among Black men, with no significant association noted among White men and Black women Another study assessed the contribution of SES in explaining ethnic disparities in obesity among adult women; it concluded that Black ethnicity was an independent SES risk factor for obesity However, patterns of obesity were shown to differ by educational attainment within ethnic groups, which has implications for the segmentation of risk reduction programs When Whites were compared with Hispanics, a matched-pair design study found the highest prevalence of overweight among the least educated Hispanic women In a multiple regression model, the higher body mass index levels of Hispanic women and men relative to their White counterparts were not explained by age, gender, education, city of residence, time of survey, or language spoken A study of cardiovascular disease risk factors, including obesity, based on several national surveys found that for men, the highest prevalence of obesity Black women with or without a high school education had a higher prevalence of obesity Another study showed that socioeconomic deprivation in childhood was a strong predictor of adulthood obesity in African-American women, and the findings were consistent with both critical-period and cumulative-burden models of life-course socioeconomic deprivation and long-term risk for obesity Regarding young people, the — baseline data from the Add Health study show that overweight prevalence decreased with increasing SES among White females and remained elevated and even increased among higher SES African-American females.

Among males, disparity was lowest at the average SES level The Growth and Health Study of the National Heart, Lung, and Blood Institute collected data from younger children aged 9—10 years and showed that higher-SES White girls had a lower prevalence of obesity, but there was no clear relation among Black girls Another study of a nationwide sample of preschool children drawn from 20 large US cities showed that the higher prevalence of obesity among Hispanics relative to Blacks and Whites was not explained by ethnic differences in maternal education, household income, or food security A study compared current portions of food products in the United States with past portions, concluding that the sizes of current marketplace foods almost universally exceed those offered in the past.

The trend toward larger portion sizes in the United States began in the s, and portion sizes increased sharply in the s and have continued to increase. Study results show that, except for sliced white bread, all of the commonly available food portions exceeded the US Department of Agriculture and Food and Drug Administration standard portions, sometimes to a great extent. For example, the largest excess over US Department of Agriculture standards by percent occurred in the cookie category, while cooked pasta, muffins, steaks, and bagels exceeded standards by percent, percent, percent, and percent, respectively.

For french fries, hamburgers, and soda, the current portion sizes are 2—5 times larger than in the past The influence of growing portion size on people's energy intake is magnified by the fact that more people in the United States increasingly eat meals away from home more often than they did in the past Dietary intake data collected from individuals also support a marked trend toward larger portion sizes in the United States. Based on nationally representative data collected between and , a study reported that the portion sizes of food consumed both at home and outside the home had increased for a large number of foods.

Some of the increases were substantial, very often ranging between 50 kcal and kcal per item for commonly consumed food items such as salty snacks, soft drinks, hamburgers, french fries, and Mexican food.

The potential impact of larger portion sizes on people's overconsumption of energy and weight gain can be remarkable. For example, an added 10 kcal per day of extra calories can result in an extra pound 0. The authors thank Drs. Oxford University Press is a department of the University of Oxford. Nonetheless, passive surveillance is often incomplete because there are few incentives for health workers to report.

This figure is an example of data gathered by passive surveillance from the hospitals run by one organization:. An active surveillance system provides stimulus to health care workers in the form of individual feedback or other incentives. Often reporting frequency by individual health workers is monitored; health workers who consistently fail to report or complete the forms incorrectly are provided specific feedback to improve their performance.

There may also be incentives provided for complete reporting. Active surveillance requires substantially more time and resources and is therefore less commonly used in emergencies. But it is often more complete than passive surveillance.

It is often used if an outbreak has begun or is suspected to keep close track of the number of cases. Community health workers may be asked to do active case finding in the community in order to detect those patients who may not come to health facilities for treatment.

Instead of attempting to gather surveillance data from all health care workers, a sentinel surveillance system selects, either randomly or intentionally, a small group of health workers from whom to gather data. These health workers then receive greater attention from health authorities than would be possible with universal surveillance.

Sentinel surveillance also requires more time and resources, but can often produce more detailed data on cases of illness because the health care workers have agreed to participate and may receive incentives.

It may be the best type of surveillance if more intensive investigation of each case is necessary to collect the necessary data.

For example, sentinel influenza surveillance in the United States collects nasopharyngeal swabs from each patient at selected sites to identify the type of influenza virus. Collection of such data from all health workers would not be possible.

Types of surveillance