National Health and Nutrition Examination Survey (NHANES), 2005-2006 (ICPSR 25504)¶

Summary¶

The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999 the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The surveys examine a nationally representative sample of approximately 5,000 persons each year. These persons are located in counties across the United States, 15 of which are visited each year. For NHANES 2005-2006, there were 10,348 persons selected for the sample, 10,122 of those were interviewed (79.3 percent) and 9,643 (75.6 percent) were examined in the mobile examination centers (MEC). Many of the NHANES 2005-2006 questions were also asked in NHANES II 1976-1980, Hispanic HANES 1982-1984, NHANES III 1988-1994, and NHANES 1999-2004. New questions were added to the survey based on recommendations from survey collaborators, NCHS staff, and other interagency work groups. As in past health examination surveys, data were collected on the prevalence of chronic conditions in the population. Estimates for previously undiagnosed conditions, as well as those known to and reported by survey respondents, are produced through the survey. Risk factors, those aspects of a person's lifestyle, constitution, heredity, or environment that may increase the chances of developing a certain disease or condition, were examined. Data on smoking, alcohol consumption, sexual practices, drug use, physical fitness and activity, weight, and dietary intake were collected. Information on certain aspects of reproductive health, such as use of oral contraceptives and breastfeeding practices, were also collected. The diseases, medical conditions, and health indicators that were studied include: anemia, cardiovascular disease, diabetes and lower extremity disease, environmental exposures, equilibrium, hearing loss, infectious diseases and immunization, kidney disease, mental health and cognitive functioning, nutrition, obesity, oral health, osteoporosis, physical fitness and physical functioning, reproductive history and sexual behavior, respiratory disease (asthma, chronic bronchitis, emphysema), sexually transmitted diseases, skin diseases, and vision. The sample for the survey was selected to represent the United States population of all ages. Special emphasis in the 2005-2006 NHANES was on adolescent health and the health of older Americans. To produce reliable statistics for these groups, adolescents aged 15-19 years and persons aged 60 years and older were over-sampled for the survey. African Americans and Mexican Americans were also over-sampled to enable accurate estimates for these groups. Several important areas in adolescent health, including nutrition and fitness and other aspects of growth and development, were addressed. Since the United States has experienced dramatic growth in the number of older people during the twentieth century, the aging population has major implications for health care needs, public policy, and research priorities. NCHS is working with public health agencies to increase the knowledge of the health status of older Americans. NHANES has a primary role in this endeavor. In the examination, all participants visit the physician who takes their pulse or blood pressure. Dietary interviews and body measurements are included for everyone. All but the very young have a blood sample taken and see the dentist. Depending upon the age of the participant, the rest of the examination includes tests and procedures to assess the various aspects of health listed above. Usually, the older the individual, the more extensive the examination. Some persons who are unable or unwilling to come to the examination center may be given a less extensive examination in their homes. Demographic data file variables are grouped into three broad categories: (1) Status Variables: provide core information on the survey participant. Examples of the core variables include interview status, examination status, and sequence number. (Sequence number is a unique ID assigned to each sample person and is required to match the information on this demographic file to the rest of the NHANES 2005-2006 data). (2) Recoded Demographic Variables: these variables include age (age in months for persons through age 19 years, 11 months; age in years for 1- to 84-year-olds, and a top-coded age group of 85 years of age and older), gender, a race/ethnicity variable, current or highest grade of education completed, (less than high school, high school, and more than high school education), country of birth (United States, Mexico, or other foreign born), Poverty Income Ratio (PIR), income, and a pregnancy status variable (adjudicated from various pregnancy related variables). Some of the groupings were made due to limited sample sizes for the two-year dataset. (3) Interview and Examination Sample Weight Variables: sample weights are available for analyzing NHANES 2005-2006 data. For a complete listing of survey contents for all years of the NHANES see the document -- Survey Content -- NHANES 1999-2010.¶

Link¶

https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25504 ¶

Data:¶

In [6]:

%matplotlib inline
import matplotlib.pyplot as plt
import ipywidgets as widgets
import pandas as pd

In [7]:

from IPython.display import display, clear_output
from ipywidgets import Button, HBox, VBox
from collections import Counter

In [16]:

df = pd.read_csv('25504-0014-Data.tsv', sep= '\t', low_memory = False)
df.head(10)

Out[16]:

	SEQN	PEASCST1	PEASCTM1	PEASCCT1	BPXCHR	BPQ150A	BPQ150B	BPQ150C	BPQ150D	BPAARM	...	FIAPROXY	FIAINTRP	MIALANG	MIAPROXY	MIAINTRP	AIALANG	WTINT2YR	WTMEC2YR	SDMVPSU	SDMVSTRA
0	31127	1	41		100						...	2	2					6434.950248	6571.396373	2	44
1	31128	1	401			2	2	2	2	1	...	2	2	1	2	2	1	9081.700761	8987.041810	1	52
2	31129	1	664			1	2	2	2	1	...	2	2	1	2	2	1	5316.895215	5586.719481	1	51
3	31130	2		4		2	2	2	2	1	...	2	2					29960.839509	34030.994786	2	46
4	31131	1	827			2	2	2	2	1	...	2	2	1	2	2	1	26457.708180	26770.584605	1	48
5	31132	1	730			2	2	2	2	1	...	2	2	1	2	2	1	32961.509920	35315.538900	2	52
6	31133	1	612			2	2	2	2	1	...	2	2	1	2	2	1	5635.221296	5920.617679	1	51
7	31134	1	844			2	2	2	2	1	...	2	2	1	2	2	1	43718.506372	44231.167252	2	48
8	31135	1	61		120						...	2	2					15711.544398	16143.815753	2	52
9	31137	1	819			2	2	2	2	1	...	2	2	1	2	2	1	5576.468045	5576.194780	2	51

10 rows × 70 columns

Graph Template:¶

In [15]:

style = {'description_width': 'initial'}

# set up initial text box and button
graphs_num = widgets.IntText(value = 1, description = "Number of Graphs", style = style)
graphs_ex = widgets.Button(description = "Execute")

# set up the graph widgets
x_axis = []
x_filter = []
x_filter_num = []
y_axis = []
y_filter = []
y_filter_num = []
graph_type = []

# create graph button
run_graph = widgets.Button(description = "Graph!")

# display setup buttons
display(graphs_num)
display(graphs_ex)
x_axis.append(widgets.Dropdown(options = list(df), description = "X Variable", style = style))
x_filter.append(widgets.Dropdown(options = ["No Filter", "=", "!=", ">", ">=", "<", "<="], description = "Filter:", style = style))
x_filter_num.append(widgets.IntText(value = 0))
y_axis.append(widgets.Dropdown(options = list(df), description = "Y Variable", style = style))
y_filter.append(widgets.Dropdown(options = ["No Filter", "=", "!=", ">", ">=", "<", "<="], description = "Filter:", style = style))
y_filter_num.append(widgets.IntText(value = 0))
graph_type.append(widgets.Dropdown(options = ["Bar (Sum)", "Line (Trends)", "Pie (Percent)"], description = "Type:", style = style))
x_group = HBox([x_axis[0], x_filter[0], x_filter_num[0]])
y_group = HBox([y_axis[0], y_filter[0], y_filter_num[0]])
x_and_y = VBox([x_group, y_group]), VBox([x_group, y_group])
print ("Graph 1:")
display(x_and_y[0])
display(graph_type[0])
display(run_graph)

print_x = []
print_y = []
xvar = []

# create graphs
def publish_graph(p):
    clear_output()
    display(graphs_num)
    display(graphs_ex)
    
    for i in range(graphs_num.value):     
        x_group = HBox([x_axis[i], x_filter[i], x_filter_num[i]])    
        y_group = HBox([y_axis[i], y_filter[i], y_filter_num[i]])
        x_and_y = VBox([x_group, y_group]), VBox([x_group, y_group])
        print ("Graph %d:" %(i+1))
        display(x_and_y[0])
        display(graph_type[i])
    display(run_graph)
    
    for i in range(graphs_num.value):
        if x_filter[i].value == 'No Filter':
            for j in range(len(df)):
                xvar.append(j)        
        elif x_filter[i].value == "=":
            for j in range(len(df)):
                if df[x_axis[i].value][j] == x_filter_num[i].value:
                    xvar.append(j)
        elif (x_filter[i].value == "!="):
            for j in range(len(df)):
                if df[x_axis[i].value][j] != x_filter_num[i].value:
                    xvar.append(j)     
        elif x_filter[i].value == ">":
            for j in range(len(df)):
                if df[x_axis[i].value][j] > x_filter_num[i].value:
                    xvar.append(j)          
        elif x_filter[i].value == ">=":
            for j in range(len(df)):
                if df[x_axis[i].value][j] >= x_filter_num[i].value:
                    xvar.append(j)           
        elif x_filter[i].value == "<":
            for j in range(len(df)):
                if df[x_axis[i].value][j] < x_filter_num[i].value:
                    xvar.append(j)          
        elif x_filter[i].value == "<=":
            for j in range(len(df)):
                if df[x_axis[i].value][j] <= x_filter_num[i].value:
                    xvar.append(j) 
                    
######## BAR GRAPH ############################################################
######## PIE CHART ############################################################

        if graph_type[i].value == "Bar (Sum)" or graph_type[i].value == "Pie (Percent)":         
            if y_filter[i].value == "No Filter":
                for k in range(len(xvar)):
                    print_y.append(df[y_axis[i].value][xvar[k]])
         
            elif (y_filter[i].value == "="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] == y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]])   
        
            elif (y_filter[i].value == "!="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] != y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]]) 
       
            elif (y_filter[i].value == ">"):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] > y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]]) 
                        
            elif (y_filter[i].value == ">="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] >= y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]]) 
                        
            elif (y_filter[i].value == "<"):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] < y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]]) 
                        
            elif (y_filter[i].value == "<="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][xvar[k]] <= y_filter_num[i].value):
                        print_y.append(df[y_axis[i].value][xvar[k]])
                        
            D = Counter(print_y)
                        
######## LINE GRAPH ############################################################

        else:
            if (y_filter[i].value == "No Filter"):
                for k in range(len(xvar)):
                    print_x.append(df[x_axis[i].value][xvar[k]])
                    print_y.append(df[y_axis[i].value][xvar[k]])           
            elif (y_filter[i].value == "="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] == y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])          
            elif (y_filter[i].value == "!="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] != y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])  
            elif (y_filter[i].value == ">"):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] > y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])             
            elif (y_filter[i].value == ">="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] >= y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])            
            elif (y_filter[i].value == "<"):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] < y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])            
            elif (y_filter[i].value == "<="):
                for k in range(len(xvar)):
                    if (df[y_axis[i].value][k] <= y_filter_num[i].value):
                        print_x.append(df[x_axis[i].value][xvar[k]])
                        print_y.append(df[y_axis[i].value][xvar[k]])  
########## Print Graph ########################################################                    
        #print(print_x)
        #print(print_y)
        plt.figure()
        if (graph_type[i].value == "Bar (Sum)"):
            plt.bar(range(len(D)), list(D.values()), align='center')
            plt.xticks(range(len(D)), list(D.keys()))
            
        elif (graph_type[i].value == "Pie (Percent)"):
            plt.pie([float(v) for v in D.values()], labels=[float(k) for k in D], autopct='%1.1f%%')
        else:
            plt.plot(print_x, print_y)
            
        print_x.clear()
        print_y.clear()
        xvar.clear()
            
def run_setup(r):
    clear_output()
    display(graphs_num)
    display(graphs_ex)
    for i in range(graphs_num.value):
        x_axis.append(widgets.Dropdown(options = list(df), description = "X Variable", style = style))
        x_filter.append(widgets.Dropdown(options = ["No Filter", "=", "!=", ">", ">=", "<", "<="], description = "Filter:", style = style))
        x_filter_num.append(widgets.IntText(value = 0))
        y_axis.append(widgets.Dropdown(options = list(df), description = "Y Variable", style = style))
        y_filter.append(widgets.Dropdown(options = ["No Filter", "=", "!=", ">", ">=", "<", "<="], description = "Filter:", style = style))
        y_filter_num.append(widgets.IntText(value = 0))
        graph_type.append(widgets.Dropdown(options = ["Bar (Sum)", "Line (Trends)", "Pie (Percent)"], description = "Type:", style = style))
        
        x_group = HBox([x_axis[i], x_filter[i], x_filter_num[i]])
        y_group = HBox([y_axis[i], y_filter[i], y_filter_num[i]])
        x_and_y = VBox([x_group, y_group]), VBox([x_group, y_group])
        print ("Graph %d:" %(i+1))
        display(x_and_y[0])
        display(graph_type[i])
    display(run_graph)
    
graphs_ex.on_click(run_setup)
run_graph.on_click(publish_graph)

A Jupyter Widget

A Jupyter Widget

Graph 1:

A Jupyter Widget

A Jupyter Widget

A Jupyter Widget

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-15-dee2ac45eac2> in publish_graph(p)
    113             elif (y_filter[i].value == "<"):
    114                 for k in range(len(xvar)):
--> 115                     if (df[y_axis[i].value][xvar[k]] < y_filter_num[i].value):
    116                         print_y.append(df[y_axis[i].value][xvar[k]])
    117 

TypeError: '<' not supported between instances of 'str' and 'int'

National Health and Nutrition Examination Survey (NHANES), 2005-2006 (ICPSR 25504)¶

Summary¶

Link¶

https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25504¶

Data:¶

Graph Template:¶

https://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25504 ¶