Archive for the ‘Epidemiology’ Category

The Metropolitan Environment and Health: The Impact of Income Inequality, Racial Segregation and Urban Sprawl on the Risk of Physical Inactivity

Thursday, February 14th, 2013

Background

 

Physical inactivity is a risk factor for obesity, cardiovascular disease and premature mortality. But despite health warnings, many Americans remain inactive.

 

Methods

 

The data source was the 2001 Behavioral Risk Factor Surveillance System, a telephone survey of US adults.  Respondents living in all metropolitan areas defined in 2000 were included.  This study used multilevel analysis combining metropolitan level factors:  income inequality (GINI Index), Black-White residential segregation (Dissimilarity Index), urban sprawl (the Urban Affairs Review Sprawl Index) and per capita income along with individual level factors:  sex, age, income, race/ethnicity and education.  The risk of being physically inactive was compared to meeting current CDC guidelines for physical activity.

 

Results

 

The final sample consisted of 121,894 adults in 315 metropolitan areas.  In the full multivariate analysis, increased levels of income inequality (odds ratio: 1.052, 95% CI = 1.033, 1.072), segregation (1.008, 95% CI = 1.005, 1.012) and urban sprawl (1.006, 95% CI = 1.003, 1008) were associated with an increased risk of physical activity.

 

Implications

 

Addressing the physical activity, and its health consequences, may require attention to the structural characteristics of the metropolitan environment.  While recent research highlights the role of the built environment as affecting inactivity, this study suggests that the social environment is also an important predictor of inactivity.

The Relationship Between Rural Status, Individual Characteristics, and Self-Rated Health in the Behavioral Risk Factor Surveillance System

Monday, July 9th, 2012

The Journal of Rural Health

 

The Relationship Between Rural Status, Individual Characteristics, and Self-Rated Health in the Behavioral Risk Factor Surveillance System

 

  1. Traci N. Bethea PhD1,
  2. Russell P. Lopez DSc2,
  3. Yvette C. Cozier DSc1,3,
  4. Laura F. White PhD4,
  5. Michael D. McClean ScD5

 

Article first published online: 31 MAY 2012

 

Abstract

 

Keywords:

  • Epidemiology;
  • health disparities;
  • obesity;
  • self-rated health;
  • social determinants of health

 

Abstract Purpose: To examine rural status and social factors as predictors of self-rated health in community-dwelling adults in the United States.

 

Methods: This study uses multinomial logistic and cumulative logistic models to evaluate the associations of interest in the 2006 US Behavioral Risk Factor Surveillance System, a cross-sectional survey of 347,709 noninstitutionalized adults.

 

Findings: Self-rated health was poorer among rural residents, compared to urban residents (OR = 1.77, 95% CI: 1.54, 1.90). However, underlying risk factors such as obesity, low income, and low educational attainment were found to vary by rural status and account for the observed increased risk (OR = 1.03, 95% CI: 0.94, 1.12). There was little evidence of effect modification by rural status, though the association between obesity and self-rated health was stronger among urban residents (OR = 2.50, 95% CI: 2.38, 2.64) than among rural residents (OR = 2.18, 95% CI: 2.03, 2.34).

 

Conclusions: Our findings suggest that differences in self-rated health by rural status were attributable to differential distributions of participant characteristics and not due to differential effects of those characteristics.

Breat Cancer Disparities

Monday, June 4th, 2012

The goal of this graph is to make you angry:

 

 

 

 

 

 

 

 

 

 

 

 

What this chart suggests is that while there has been about a 20% reduction in White female breast cancer death  rates since the 1970s, Black female breast cancer death rates have increased. Why?

 

 

Here are some of the risk factors for breast cancer:  Age – but the rates are age adjusted so that shouldn’t be a factor here.  Later date of first pregnancy – but the age of Blacks and whites at first pregnancy are about the same, as are overall fertility rates.  Mammography rates – these are also now about the same (thank you to everyone who worked on reducing this disparity).  Obesity  – Black female obesity rates are higher, but isn’t this more of a symptom of disparity than a cause?  Genetics – no genetic factors have been identified that would account for these disparities.

So we are left with a mystery.  And a disgrace.

Built Environment Text Book

Thursday, October 7th, 2010

If all goes to plan, in the Summer of 2011, I will have a textbook published on the built environment and public health by Wiley/Jossey-Bass.  This book surveys the broad field of the built environment. It takes as its premise that there are profound health impacts on how buildings, neighborhoods, cities, and societies are built; and it uses historical analysis, epidemiology and public health research, and urban planning examples and public policy analysis, as well as case studies highlighting successful efforts to mitigate the health impacts of the built environment to analyze issues and develop provide the basis for programmatic responses. The goal is to empower students and readers to understand conditions around them and begin to address these health and environmental impacts. The book emphasizes science and solutions. The book was developed through my experience in teaching courses on the built environment and urban environmental health at the Boston University School of Public Health. It is based on the model curriculum suggested by Botchway and colleagues (of which I was a contributor).

The chapters of the book are:

1.         Introduction

2.         History

3.         Planning and urban design

4.         Transportation

5.         Healthy housing and housing assistance programs

6.         Infrastructure and natural disasters

7.         Assessment tools and data sources

8.         Indoor and outdoor air quality

9.         Water

10.       Food, nutrition and food security

11.       Vulnerable populations

12.       Mental health, stressors, and health care environments

13.       Social capital

14.       Environmental justice

15.       Health policies

16.       Sustainability

So people lie about their height and weight. What’s the problem?

Thursday, May 27th, 2010

Public health  researchers and epidemiologists have little tolerance for inaccurate data.  Their fear is that these inaccuracies could potentially impact the outcomes of research.  They could lead to inaccurate point estimates of effect or somehow produce results whose inaccuracies are impossible to detect.  An example of this potential issue is the ongoing concern regarding problems with self reported height and weight.  This issue illustrates how modern epidemiology analyzes data.

Height and weight are used to compute a person’s body mass index.  BMI , in turn is used to determine whether a person is overweight or obese – a measure that has problems of its own.  Inaccurate height and weight can produce inaccurate measures of BMI.

The easiest and cheapest way to determine height and weight is to ask him or her how tall they are and what they weigh.  No special equipment is needed, no personnel are needed, etc.  What could be easier?

But when researchers compared self reported height and weight to  measured values from data in the National Health and Nutrition Examination Study (NHANES), they found that these self reports were not accurate.  Overall, men said they were taller than they were measured, women self reported lower weights.  Furthermore, the inaccuracies were greater for whites than blacks, black women were the most accurate, Black men were actually more likely to say they were shorter than they were measured and weigh less.

Note that from this data we cannot know the reasons behind these inaccuracies.  People could truly believe they are being accurate or they could be lying.  Who knows?

The problem is that not everyone is inaccurate in the same way.  Overall, the inaccuraciess skew the data in a certain way, but this says nothing about the accuracy of any one individual’s self report.

What should researchers do?  Some suggest that self-reported height/weight data be adjusted to account for these group inaccuracies.  But that makes many researchers uneasy.  How do you know your adjustments would b e appropriate for this particular dataset?

The unknown effects on research outcomes keep researchers (or some of them) up all night.  Are the errors irrelevant?  Are they causing results to appear to be statistically significant when they are really not?    Are  they masking statistically significant associations?  Are they making the point estimates of effect inaccurate?  No one can say at this time.  Also frightening, is this problem going to lead to some skeptic to call for the wholesale rejection of all studies that use self reported height/weight data?   This is not paranoia,  this is a problem that has affected climate change research.

So we watch and we worry and we hedge our findings when we report them.