biostatistics assignment

  

Biostatistics assignment that involves 5 Q and excel sheet
bio.docx

data_for_q4.xlsx

bright_et_al_2015_community_dentistry_and_oral_epidemiology.pdf

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Monday, April 3, 2017
Instructions for questions 1-3
• Include your name and the CI, Hypothesis Test or Sample Size assignment that you were given; I don’t want to
have to look-up the HT you were assigned.
• Pick 2 from questions 1, 2, and 3 to answer. For example, you can pick to do a Hypothesis Test and a Confidence
interval that you were assigned, or a Confidence interval and a sample size, …. 2 out of the 3.
• Your write up for 2 of the 3 questions 1-3 should be no more than 1 page total; I won’t grade anything after the
1st page.
• The scenarios used in your write ups do not have to be real but should match the type of data for the hypothesis
test that you are assigned. For example, if you are assigned a one sample mean HT, you should come up with an
example that uses continuous data.
Assignments for questions 1-3 (pick 2)
Write a different example for each assignment that you pick. You do not need to make up data, but you do need to
make up statistics and use these for the templates or sample size calculations. For example, if you are assigned a 1sample mean for a confidence interval, you have to pick an example (could be related to your research, nor not), pick a
sample size, mean, and standard deviation. With these calculate a 95% confidence interval and report all of this using
mostly words – you do not need to include the template. Be sure to tell me what assignment your write up reflects –
this is really important. Don’t use examples from class notes – use your imagination and if that doesn’t help, look for
some published journal articles and grab some appropriate numbers and statistics from there.
Last Name
Alsulaiman
Q1: hypothesis test
Q2: Confidence interval
the mean of a difference
Q3: Sample size
Hypothesis test 2 proportions
Goodness of fit
4. The data file contains 62 observations and the variables age, male, hypo, hyper, and linWidth. This comes from the
same data set that we used in class 2 weeks ago, but again, it is only some of the original data. Calculate summary
statistics for each column (you decide what statistics are appropriate and informative) and then list some tests and
confidence intervals that you would do if you are interest in whether linWidth is associated by hypo/hyper classifications
and also whether age and male affect linWidth
5. Using your own words (and not the words of the journal article), interpret the results in Table 2 of this article for the
1st outcome, condition of the teeth is fair or poor for the unadjusted model and for the adjusted model but only down
through the income variable (so include number of ACEs, age, sex, and income) in your write up.
Bright, M. A., Alford, S. M., Hinojosa, M. S., Knapp, C., & Fernandez-Baca, D. E. (2015). Adverse childhood experiences
and dental health in children and adolescents. Community Dent Oral Epidemiol, 43, 193-199.
(thanks to Saurabh Mankotia for finding this article!)
To summarize: You need to turn in answers to 2 of the 3 personal assignments for Q1-Q3. I will only grade up to 1 page
of write up for these. Next, you need to do summary statistic calculations and report them for Q4 and then write about
what methods you would use to analyze the data further. This should take about .5 page. And then for Q5, get the
article and write a brief discussion of the results for the 1 outcome as described in Q5. This could be another .5 page.
age
male
16
15
31
15
38
29
62
50
50
49
49
49
45
44
44
43
41
37
36
32
29
27
26
17
18
34
18
25
52
35
33
61
60
56
17
15
57
56
50
48
44
40
39
30
27
18
hypo
1
0
1
0
1
0
0
0
0
0
0
0
1
1
0
0
0
1
0
0
1
1
1
1
0
0
0
0
1
1
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
hyper
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
linWidth
23,2
25,4
31,8
27,1
32
21,8
31,4
32
30,2
21,8
25,9
27,6
24,7
30,2
20,7
33,9
26,7
24,3
30,7
29,2
28,1
33,2
32,6
27,5
29,9
29,4
30,4
25,7
16,4
34
29,5
21,8
22,2
26,5
25,5
28,3
15,1
29,4
23,2
27,9
25,3
27,7
24,1
21,9
19,9
28,9
22
56
30
60
14
30
13
40
25
27
60
31
43
17
22
28
1
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
21,3
22,3
25,3
33,8
28,1
18,9
24,6
28,6
28,5
23,6
31,2
22,5
20,9
28,2
35,5
28,4
Community Dent Oral Epidemiol 2015; 43; 193–199
All rights reserved
Ó 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Adverse childhood experiences
and dental health in children
and adolescents
Melissa A. Bright1, Shannon M. Alford1,
Melanie S. Hinojosa2, Caprice Knapp3
and Daniel E. Fernandez-Baca1
1
Institute for Child Health Policy, University
of Florida, Gainesville, FL, USA,
2
Department of Sociology, University of
Central Florida, Orlando, FL, USA,
3
Department of Health Policy and
Administration, The Pennsylvania State
University, State College, PA, USA
Bright MA, Alford SM, Hinojosa MS, Knapp C, Fernandez-Baca DE. Adverse
childhood experiences and dental health in children and adolescents.
Community Dent Oral Epidemiol 2015; 43: 193–199. © 2014 John Wiley & Sons
A/S. Published by John Wiley & Sons Ltd
Abstract – Objective: This study seeks to explore the how specific toxic
stressors, specifically adverse childhood experiences (ACEs), and their
frequencies may be associated with tooth condition and the presence of caries.
Methods: Data from the 2011–12 National Survey for Child Health (NSCH), a
nationally representative survey of child health, were used in this study.
Pediatric dental health was measured using parent report of two
characteristics: condition of teeth and having a toothache, decayed teeth, and/
or unfilled cavities in the past 12 months. ACEs were measured by asking
about a child’s exposure to the divorce of a parent, parental incarceration,
domestic violence, neighborhood violence, drug and alcohol abuse, mental
illness, and financial hardship. Analyses were adjusted by sociodemographic
characteristics, healthcare access and utilization, and comorbid chronic
conditions. Results: The presence of even one ACE in a child’s life increased the
likelihood of having poor dental health. Additionally, having multiple ACEs
had a cumulative negative effect on the condition of their teeth and the
presence of dental caries (Odds Ratios 1.61–2.55). Adjusted models show that
racial and socioeconomic factors still play a significant role in dental health.
Conclusions: In addition to the known disparities in dental caries, this study
demonstrates that there is significant association between childhood
psychosocial issues and dental health. Preventive dental care should be
considered incorporating the screening of multiple biological stressors,
including ACEs, in routine dental visits as a means of identifying and reducing
dental health inequities.
Poor dental health, characterized by dental caries
(i.e., tooth decay), periodontal disease, unfilled
cavities, missing teeth, or toothache, can have serious implications for overall health. In children,
having untreated dental caries and poor dental
health is linked to lower weight (1), more school
absences (2), poor school performance (3), and
lower quality of life (4). Dental caries is generally
preventable; however, in the United States, they
are the most common pediatric disease. Among
adolescents (aged 14–17), tooth decay is four times
more common than asthma, affecting 50% of children in this age range (5). Additionally, tooth
doi: 10.1111/cdoe.12137
Key words: pediatric dentistry; psychosocial
aspects of oral health; stress
Melissa A. Bright, Institute for Child Health
Policy, University of Florida, PO Box 100177,
Gainesville, FL 32610-0177, USA
Tel.: +1 352-627-9467
Fax: +1 352-265-7221
e-mail: Mbright08@ufl.edu
Submitted 24 March 2014
accepted 14 October 2014
decay affects more than 25% of preschool aged
children (ages 2–5) (6).
Consistent with patterns in general health outcomes, there are significant socioeconomic and
racial disparities in the prevalence of dental caries. Children from racial and ethnic minority
groups, particularly Black and Hispanic children,
are more likely than their counterparts to have
teeth in poor or fair condition (7), dental caries,
untreated disease, and decayed teeth (8, 9). Children from single-parent households (10), as well
as children from families with low household
incomes, are more likely to have unmet dental
193
Bright et al.
needs (11) and less likely to have preventative
dental visits (12). Children who are not enrolled
in dental insurance plans are more likely to
have unmet dental needs than their insured peers
(13, 14).
Socioeconomically disadvantaged youth often
experiences social stressors, such as dysfunctional
family relationships and household dynamics
(15), which may contribute to their increased likelihood for poor dental health. Exposure to social
stressors is associated with increased activity of
the neuroendocrine-immune stress response systems and subsequent increased susceptibility to
disease (16). In asthmatic children, for example,
low socioeconomic status was associated with
increased production of cytokines implicated in
immune responses found in asthma (17). Additionally, compared to high-socioeconomic-status
children (aged 9–18 years), low-SES children
demonstrated an increased production of cortisol,
the primary hormonal output of the hypothalamic–pituitary–adrenal axis, over a two-year
period (18).
Given the prevalence and impact of poor dental health, it is surprising that research is limited.
Experiences such as child abuse/neglect, parental
divorce, domestic violence, caregiver mental illness, caregiver incarceration, exposure to drug/
alcohol abuse, and struggles with family income
have been identified as toxic stressors based on
their association with poor health outcomes.
These events, often termed adverse childhood
experiences (ACEs), are relatively prevalent in
50–65% of adults (19) and 90% of adolescents
who are at risk for maltreatment (20), reporting
at least one ACE in their lifetime. The health and
behavior implications of ACEs have been well
documented (19, 21). Compared to individuals
who report no ACEs, adults who report experiencing at least one ACE are more likely to engage
in high-risk behaviors (i.e., drug use, risky sexual
behavior) and to suffer from mental (e.g., alcoholism, depression) and physical (e.g., liver disease,
chronic lung disease) disorders (21–23). Additionally, there is a cumulative effect whereas a higher
number of ACEs are associated with greater likelihood for poor health outcomes (19). Similar
results have been found in studies of adolescents
(24, 25). Compared to adolescents who reported
no ACEs, adolescents who reported one or more
ACE were more likely to have an injury that
required a doctor, poor health, and experience
somatic concerns (26).
194
Given the prevalence of poor dental health
and social stressors among low-income children,
and the link between social stressors and etiology of disease, the association between social
stressors and pediatric dental health warrants
examination. In this study, we investigate the
association between parent-reported ACEs and
pediatric dental health outcomes. Our specific
objectives are to examine the following: (i) frequency of poor dental health, as measured by (a)
having teeth in fair or poor condition and (b)
having a toothache, decayed teeth, and/or
unfilled cavities in the past 12 months, in children and adolescents, (ii) frequency of ACEs for
children with poor dental health, and (iii) association between number of adverse childhood
experiences and poor dental health outcomes in
children.
Method
Sample
Data from the 2011–2012 National Survey for
Child Health (NSCH) were used (27). A project
of the Child and Adolescent Measurement Initiative (CAHMI), this parent-report survey drawn
from a random-digit-dial sample of landline and
cellular telephone numbers. Eligible households
included at least one resident child between 0
and 17 years of age. When there were more than
one eligible child in the household, only one
child was chosen. Respondents were required to
be a parent or guardian with knowledge of the
health and health care of the target child. In this
sample, 68.6% of respondents were mothers (biological, step, foster, or adoptive), 24.2% were
fathers, and 7.2% were other relatives or guardians.1 A total of 95 677 interviews were conducted across the United States with at least 1800
interviews being conducted per state and the
District of Columbia. For purposes of this study,
children must have been between 1 and 17 years
of age and have natural teeth.2 These eligibility
criteria resulted in 90 555 children in the current
analyses.
1
Frequencies provided by the 011-2012 National Survey of Children’s Health Frequently Asked Questions.
2
Sixty-two (0.1%) children were excluded from the
sample for having no natural teeth.
Adverse childhood experiences and pediatric dental health
Measurement
Two items measuring dental health were used as
outcome variables. One item measured overall condition of the child’s teeth: ‘How would you describe
the condition of [CHILD’s NAME] teeth?’ Response
options for this item included excellent, very good,
good, fair, or poor. The second item measured more
specific dental health: ‘During the past 12 months,
did [CHILD’S NAME] have a toothache, decayed
teeth, and/or unfilled cavities?’ Response options
for this item were yes or no; responses of yes were
used to indicate poor dental health.
The primary predictor variables included seven
items capturing ACEs. The adverse experiences
included divorce of a parent, exposure to domestic
violence, exposure to drug and alcohol abuse, exposure to mental illness, having a parent in jail, witnessing or being a victim of neighborhood violence,
and household financial hardship. Respondents
were asked whether their child had experienced
each of these ACEs and could respond yes or no.
Each item was coded such that affirmative responses
indicated presence of the stressor. Details of these
questions can be found on the Data Resource Center
for Child and Adolescent Health website.
Several additional items were added as covariates in adjusted models: sociodemographic characteristics, healthcare access and utilization, and
potentially comorbid special healthcare conditions.
Sociodemographic variables included child age,
sex, race/ethnicity, maternal education level, family structure (two parent – adoptive or biological,
two parent – step family, single mother – no father
present, or other family type), and income based
on federal poverty level (FPL).
Healthcare access and utilization items assessed
recent dental care (‘During the past 12 months, did
[CHILD’S NAME] see a dentist for any kind of dental care, including checkups, dental cleanings, Xrays, or filling cavities?’). Health insurance coverage
was determined by two questions: ‘Does [CHILD’S
NAME] have any kind of healthcare coverage,
including health insurance, prepaid plans such as
HMOs, or government plans such as Medicaid?’
and ‘If yes, is that coverage Medicaid or the Children’s Health Insurance Program, CHIP?’ Children
who are insured but do not have public insurance
were coded as having private insurance coverage.
Children with special healthcare needs (CSHCN)
are those who require prescription medications to
manage their condition, need or use specialized
services or therapies, and/or experience of one or
more functional limitations. The CSHCN screener
(28) used to identify these children in this sample
included 19 items to assess these needs. Children
were categorized as either having a special healthcare need based on one or more of the aforementioned criteria or not having a special heathcare
need.
Analyses
Observations were weighted using complex sampling specifications provided in the NSCH dataset
(27) with state and telephone phone type (cellular or
landline) as stratum identifiers and unique household identifier as the primary sampling unit. Resulting
estimates
are
representative
of
all
noninstitutionalized children aged 0–17 years in the
United States and in each state. All analyses were
conducted using complex design techniques in SPSS
version 22.0 (Armonk, NY, 2013). Univariate analyses were first conducted to determine the number of
children with poor dental health. Next, bivariate
analyses were conducted to test the association
between number of ACEs and the two dental health
outcomes. Finally, multivariate logistic regression
analyses were conducted to determine the likelihood
of (i) having teeth in fair or poor condition and (ii)
having a toothache, decayed teeth, and/or unfilled
cavities in the past 12 months. Two models for each
outcome were tested, the first was unadjusted and
included only ACEs, the second adjusted for sociodemographic characteristics, healthcare access and
utilization, and potentially comorbid medical conditions for a total of 4 logistic regressions analyses.
Results
Sample characteristics
Descriptive statistics for sample characteristics are
outlined in Table 1. The average age of children
was 8.59 years (Standard Error = 0.04). Approximately, 46% of parents rated their children’s teeth
excellent, 26% very good, 21% good, 6% fair, and
2% poor. In these analyses, responses of fair and
poor were combined to indicate poor dental health.
A little over 18% of caregivers reported that their
child had a toothache, decayed teeth, and/or
unfilled cavities in the past 12 months. Most caregivers reported that their (73%) child visited a dentist within the previous 12 months (Table 1). In
regards to ACEs, slightly more half (54%) of parents reported their children having no ACEs.
195
Bright et al.
Table 1. Descriptive statistics of sample as reported by
parents of children aged 1–17 years with natural teeth
Variable
Dental health and practices
Condition of child’s teeth is fair/poor
Had toothache, decayed teeth, and/
or unfilled cavities in past 12 months
Visited a dentist in past 12 months
Sex: Female
Income
0–99% FPL
100–199%FPL
200–399% FPL
400%+ FPL
Race/Ethnicity
Hispanic
Non-hispanic black
Non-hispanic white
Other
Maternal education: less than high
school diploma
Family structure
Two parent – biological or adoptive
Two parent – step family
Single mother – no father present
Other family type
Insurance coverage
Private insurance
Public insurance
Uninsured
Has special healthcare need
Adverse childhood experiences
Divorce of a parent
Parent spent time in jail
Exposure to domestic violence
Witness to or victim of neighborhood
violence
Exposure to drug and alcohol abuse
Lived with someone who was mentally ill,
suicidal, depressed
Hard to get by on family income
Number of ACEs
0 adverse childhood experiences
1 adverse childhood experiences
2 adverse childhood experiences
≥3 adverse childhood experiences
n
%
4,858
14,736
7.6
18.7
72,777
43,853
73.1
48.8
13,901
16,247
27,545
32,862
22.4
21.5
28.2
27.8
11,469
8,446
58,244
9,810
6,250
23.0
13.7
53.0
10.4
14.3
62,079
6,534
14,389
6,460
65.6
8.8
19.0
6.7
60,034
25,516
3,922
19,458
57.4
37.1
5.6
19.8
16,967
5,572
5,861
8,410
20.1
6.9
7.3
8.6
9,941
8,410
10.7
8.6
18,870
25.7
54,392
20,924
7,948
8,591
54.4
25.2
9.9
10.5
Raw values are unweighted; percentages are weighted
based on the specifications for complex samples FPL,
federal poverty level.
Approximately, a quarter (25%) of children had
one ACE, while a tenth (10%) had two ACEs, and
another tenth (10%) had three or more.
Bivariate analyses
A higher proportion of children experiencing any
ACE had teeth in fair/poor condition compared to
children who did not experience an ACE. The same
was true for all ACEs and having a toothache,
decayed teeth, and/or unfilled cavities. Similar patterns were found for number of ACEs.
196
Multivariate analyses
Condition of teeth is f …
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