This document summarizes a study that evaluated a health indicator developed by the State Environmental Health Indicators Collaborative (SEHIC) to identify populations vulnerable to heat and air quality emergencies in the Twin Cities metropolitan area. The study aimed to determine if the SEHIC indicator accurately accounts for demographic and vulnerability inequities, and if adding chronic lower respiratory disease data provides evidence of errors in the SEHIC calculation methods. Results showed the SEHIC template overrepresented populations below the poverty line compared to elderly individuals living alone. Recommendations were made to improve the SEHIC indicator, such as calculating vulnerability scores using z-scores rather than percentages. When combined with the recommended changes, overlaying chronic lower respiratory
Bearing the Burden - Health Implications of Environmental Pollutants in Our B...v2zq
Bearing the Burden - Health Implications of Environmental Pollutants in Our Bodies - Resources for Healthy Children www.scribd.com/doc/254613619 - For more information, Please see Organic Edible Schoolyards & Gardening with Children www.scribd.com/doc/254613963 - Gardening with Volcanic Rock Dust www.scribd.com/doc/254613846 - Double Food Production from your School Garden with Organic Tech www.scribd.com/doc/254613765 - Free School Gardening Art Posters www.scribd.com/doc/254613694 - Increase Food Production with Companion Planting in your School Garden www.scribd.com/doc/254609890 - Healthy Foods Dramatically Improves Student Academic Success www.scribd.com/doc/254613619 - City Chickens for your Organic School Garden www.scribd.com/doc/254613553 - Huerto Ecológico, Tecnologías Sostenibles, Agricultura Organica www.scribd.com/doc/254613494 - Simple Square Foot Gardening for Schools - Teacher Guide www.scribd.com/doc/254613410 - Free Organic Gardening Publications www.scribd.com/doc/254609890 ~
Epidemiology, Triad of epidemiology, Brief epidemiology, Terminology used in Epidemiology, Epidemiology, traid, modes of disease transmission, disease control and prevention, Basic epidemiology, John Snow and Cholera with Epidemiology
A case-control study of injuries arising from the earthquake in Armenia, 1988
H.K. Armenian, E.K. Noji, & A.P. Oganesian.
Bulletin of the World Health Organization, 70(2): 251-257 (1992)
The study attempts to identify predictors of injuries among persons who were hospitalized following the Armenian earthquake of 7 December 1988. A total of 189 such individuals were identified through neighbourhood polyclinics in the city of Leninakan and 159 noninjured controls were selected from the same neighbourhoods. A standardized interview questionnaire was used. Cases and controls shared many social and demographic characteristics; however, 98% of persons who were hospitalized with injuries were inside a building at the time of the earthquake, compared with 83% of the controls (odds ratio = 12.20, 95% confidence interval (Cl) = 3.62-63.79). The odds ratio of injuries for individuals who were in a building that had five or more floors, compared with those in lower buildings, was 3.65 (95% Cl = 2.12-6.33). Leaving buildings after the first shock of the earthquake was a protective behaviour. The odds ratio for those staying indoors compared with those who ran out was 4.40 (95% Cl = 2.24-8.71).
Bearing the Burden - Health Implications of Environmental Pollutants in Our B...v2zq
Bearing the Burden - Health Implications of Environmental Pollutants in Our Bodies - Resources for Healthy Children www.scribd.com/doc/254613619 - For more information, Please see Organic Edible Schoolyards & Gardening with Children www.scribd.com/doc/254613963 - Gardening with Volcanic Rock Dust www.scribd.com/doc/254613846 - Double Food Production from your School Garden with Organic Tech www.scribd.com/doc/254613765 - Free School Gardening Art Posters www.scribd.com/doc/254613694 - Increase Food Production with Companion Planting in your School Garden www.scribd.com/doc/254609890 - Healthy Foods Dramatically Improves Student Academic Success www.scribd.com/doc/254613619 - City Chickens for your Organic School Garden www.scribd.com/doc/254613553 - Huerto Ecológico, Tecnologías Sostenibles, Agricultura Organica www.scribd.com/doc/254613494 - Simple Square Foot Gardening for Schools - Teacher Guide www.scribd.com/doc/254613410 - Free Organic Gardening Publications www.scribd.com/doc/254609890 ~
Epidemiology, Triad of epidemiology, Brief epidemiology, Terminology used in Epidemiology, Epidemiology, traid, modes of disease transmission, disease control and prevention, Basic epidemiology, John Snow and Cholera with Epidemiology
A case-control study of injuries arising from the earthquake in Armenia, 1988
H.K. Armenian, E.K. Noji, & A.P. Oganesian.
Bulletin of the World Health Organization, 70(2): 251-257 (1992)
The study attempts to identify predictors of injuries among persons who were hospitalized following the Armenian earthquake of 7 December 1988. A total of 189 such individuals were identified through neighbourhood polyclinics in the city of Leninakan and 159 noninjured controls were selected from the same neighbourhoods. A standardized interview questionnaire was used. Cases and controls shared many social and demographic characteristics; however, 98% of persons who were hospitalized with injuries were inside a building at the time of the earthquake, compared with 83% of the controls (odds ratio = 12.20, 95% confidence interval (Cl) = 3.62-63.79). The odds ratio of injuries for individuals who were in a building that had five or more floors, compared with those in lower buildings, was 3.65 (95% Cl = 2.12-6.33). Leaving buildings after the first shock of the earthquake was a protective behaviour. The odds ratio for those staying indoors compared with those who ran out was 4.40 (95% Cl = 2.24-8.71).
Introduction to Epidemiology
At the end of this session the participants will be able to:
Discuss the historical evolution of epidemiology
Explain the usage of epidemiology
List the core epidemiological functions
Explain types of epidemiological studies
Introduction to Epidemiology
At the end of this session the participants will be able to:
Discuss the historical evolution of epidemiology
Explain the usage of epidemiology
List the core epidemiological functions
Explain types of epidemiological studies
S4C Colloquium Aveiro 2016
https://scientistsforcyclingaveiro2016.wordpress.com/
University of Aveiro (Portugal),
Region of Aveiro (CIRA), ABIMOTA/Portugal Bike Value
and the European Cyclists’ Federation (ECF)
with its global network Scientists for Cycling (S4C)
Bad for Enterprise: Attacking BYOD Enterprise Mobile Security SolutionsVincent Tan
The global market for Bring Your Own Device (BYOD) and enterprise mobility is expected to quadruple in size over the next four years, hitting $284 billion by 2019. BYOD software is used by some of the largest organizations and governments around the world. Barclays, Walmart, AT&T, Vodafone, United States Department of Homeland Security, United States Army, Australian Department of Environment and numerous other organizations, big and small, all over the world. Enterprise Mobile Security (EMS) is a component of BYOD solutions that promises data, device and communications security for enterprises. Amongst others, it aims to solve Data Loss, Network Privacy and jailbreaking/rooting of devices.
Using the Good Technology EMS suite as an example, my talk will show that EMS solutions are largely ineffective and in some cases can even expose an organization to unexpected risks. I will show attacks against EMS protected apps on jailbroken and non-jailbroken devices, putting to rest the rebuttal that CxOs and solution vendors often give penetration testers, "We do not support jailbroken devices." I will also introduce a groundbreaking tool, Swizzler, to help penetration testers confronted with apps wrapped into EMS protections. The tool conveniently automates a large amount of attacks that allows pen-testers to bypass each of the protections that Good and similar solutions implement. In a live demonstration of Swizzler I will show how to disable tampering detection mechanisms and application locks, intercept & decrypt encrypted data, and route "secure" HTTP requests through BURP into established Good VPN tunnels to attack servers on an organization's internal network. Swizzler will be released to the world along with my talk at Blackhat USA. Whether you are a CxO, administrator or user, you can't afford not to understand the risks associated with BYOD.
This article, "Casting a Wider Net in Behavioral Health Screening in Primary Care" found that the ORS identified more clients for behavioral healthcare consultation than the PHQ-9. A first step toward the upcoming RCT with PCOMS in an integrated setting.
Note; you didn’t corrected this question network models and netw.docxcurwenmichaela
Note; you didn’t corrected this question network models and network theories is a sure Comment by Vetter-Smith, Molly J: Again, you need to be more specific about which model or theory you are employing for your intervention.
Note: You didn’t corrected References Comment by Vetter-Smith, Molly J: Your references are not in correct APA format. Review APA style.
Adopting the outbreak investigation using the network models and network theories is a sure way to prevent food-borne threats compared to the standard public strategies or procedures that use tracings along the food shipping chains and case-control studies. These methods or interventions are biased in data collection and time-consuming. The network in this intervention program will capture the different transportation routes or transmission pathways that are the major points along the food production chain identified to result in food poisoning (Meyers, Newman, Martin et al., 2003). We have learned in the earlier sections that the best approach to preventing food-borne illness is understanding the mechanisms of food poisoning and developing strategies that can control such points along the chain of production. The technique employed will only require spatial information on the case reports that are regularly collected by the public health institutions. Therefore, the self-report survey will be analyzed in this case. Also important will be the model used for the food distribution networks. The approach that is based on the concept of replacing the geographic distance (conventional) with effective distance efficiently identifies the most probable epicenters that are the origins of the food-borne illness outbreaks. Comment by Vetter-Smith, Molly J: Again, you need to be more specific about which model or theory you are employing for your intervention. Comment by Vetter-Smith, Molly J: Explain this in more detail of what you mean by spatial information Comment by Vetter-Smith, Molly J: What type of questions will be asked on this self-report survey?
Conclusion
Epidemiology still finds a lot of challenges in handling infectious diseases such as the food-borne diseases. What makes the matter worse is the fact that these diseases are primarily caused by pathogens that evolve overtime into new generations and thus making it difficult to establish a conventional vaccine to prevent the outbreak of food-borne illness. While several interventions have been brought forward to prevent the outbreak of food-borne diseases, most of the approaches have not been effective enough in identifying the outbreak origin and then acting immediately to control any potential spread of the disease. Also, most of the methods adopted have been time-consuming. Adopting the outbreak investigation in the prevention of food-borne disease outbreak is more efficient than the other methods when there is a focus on the network models and networks theory, ...
Environmental Pollutants and Disease in American: Children: Estimates of Morbidity, Mortality, and Costs for Lead Poisoning, Asthma, Cancer, and Developmental Disabilities
RESEARCH Open AccessExposures to fine particulate air poll.docxronak56
RESEARCH Open Access
Exposures to fine particulate air pollution and
respiratory outcomes in adults using two national
datasets: a cross-sectional study
Keeve E Nachman1* and Jennifer D Parker2
Abstract
Background: Relationships between chronic exposures to air pollution and respiratory health outcomes have yet
to be clearly articulated for adults. Recent data from nationally representative surveys suggest increasing disparity
by race/ethnicity regarding asthma-related morbidity and mortality. The objectives of this study are to evaluate the
relationship between annual average ambient fine particulate matter (PM2.5) concentrations and respiratory
outcomes for adults using modeled air pollution and health outcome data and to examine PM2.5 sensitivity across
race/ethnicity.
Methods: Respondents from the 2002-2005 National Health Interview Survey (NHIS) were linked to annual kriged
PM2.5 data from the USEPA AirData system. Logistic regression was employed to investigate increases in ambient
PM2.5 concentrations and self-reported prevalence of respiratory outcomes including asthma, sinusitis and chronic
bronchitis. Models included health, behavioral, demographic and resource-related covariates. Stratified analyses
were conducted by race/ethnicity.
Results: Of nearly 110,000 adult respondents, approximately 8,000 and 4,000 reported current asthma and recent
attacks, respectively. Overall, odds ratios (OR) for current asthma (0.97 (95% Confidence Interval: 0.87-1.07)) and
recent attacks (0.90 (0.78-1.03)) did not suggest an association with a 10 μg/m3 increase in PM2.5. Stratified analyses
revealed significant associations for non-Hispanic blacks [OR = 1.73 (1.17-2.56) for current asthma and OR = 1.76
(1.07-2.91) for recent attacks] but not for Hispanics and non-Hispanic whites. Significant associations were observed
overall (1.18 (1.08-1.30)) and in non-Hispanic whites (1.31 (1.18-1.46)) for sinusitis, but not for chronic bronchitis.
Conclusions: Non-Hispanic blacks may be at increased sensitivity of asthma outcomes from PM2.5 exposure.
Increased chronic PM2.5 exposures in adults may contribute to population sinusitis burdens.
Keywords: Particulate matter, Asthma, Sinusitis, Air pollution, National Health Interview Survey (NHIS)
Background
Relationships between exposure to particulate air pollu-
tion and a variety of adverse effects, including cardiovas-
cular and respiratory diseases, birth outcomes, genetic
polymorphisms, as well as mortality and life expectancy
have been studied [1-8]. A number of studies have
investigated the influence of exposure to particulate
matter on development of respiratory outcomes, though
the majority focus on children [9-13]; a limited number
of published reports exist documenting of the effects of
chronic exposures on non-cancer respiratory outcomes
in adults [14-17].
National prevalence data for several respiratory condi-
tions are available from the National Center for Health
Statistics (NCHS) of the Ce ...
Assignment 2 Defining the Problem and Research MethodsSecdesteinbrook
Assignment 2: Defining the Problem and Research Methods
Sections 1 and 2 of Major Assessment 7: Using an Epidemiological Approach to Critically Analyze a Population Health Problem
How do culture and environment influence health? What role does personality play in health outcomes? How do stressful life events influence disease? As a health care professional, you have most likely witnessed the influence of psychosocial factors on individual health. These factors also have a significant impact on population health. Chronic conditions such as high blood pressure and heart disease, as well as degenerative diseases, can be studied at the population level through the use of epidemiologic methods (Friis, 2014). The insights gained from this type of research can then positively impact health outcomes locally, nationally, and globally.
As you continue working on Assignment 2, which is due
by Thursday 04/05/2018 Day 5 of this week
, consider how psychosocial factors influence your population and population health issue.
To complete:
In 5–6 pages, APA format with a minimum of five (5) scholarly references (see list of required readings below), write the following sections of your paper:
Section 1: The Problem
1) Introduction (ending with a purpose statement: “the purpose of this paper is…)
2) A brief outline of the environment you selected (i.e., home, workplace, school)
3) A summary of your selected population health problem in terms of person, place, and time, and the magnitude of the problem based on data from appropriate data resources (Reference the data resources you used.)
4) Research question/hypothesis (same as the one in assignment 1. I’m including an attachment of assignment 1 you did for me).
Section 2: Research Methods
1) The epidemiologic study design you would use to assess and address your population health problem
2) Assessment strategies (i.e., if you were conducting a case-control study, how would you select your cases and controls? Regarding the methods and tools, you would use to make these selections, how is it convenient for you as the researcher or as the investigator to use this tool?)
3) Summary of the data collection activities (i.e., how you would collect data—online survey, paper/pen, mailing, etc.)
4) Conclusion of the whole paper.
Required Readings
Friis, R. H., & Sellers, T. A. (2014). Epidemiology for public health practice (5th ed.). Sudbury, MA: Jones & Bartlett.
Chapter 10, “Data Interpretation Issues”
Chapter 15, “Social, Behavioral, and Psychosocial Epidemiology”
Appendix A – Guide to the Critical Appraisal of an Epidemiologic/Public Health Research Article
In Chapter 10, the authors describe issues related to data interpretation and address the main types of research errors that need to be considered when conducting epidemiologic research, as well as when analyzing published results. It also presents techniques for reducing bias. Chapter 15 features psychosocial, behavioral, ...
A comparative risk assessment of burden of disease and injury attributable to...Chuco Diaz
Background Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time.
3. Sara Dunlap June 11, 2009
MPH Thesis
3
Table of Contents
Introduction: ..................................................................................................................................................4
Statement of the Problem: ........................................................................................................................4
Study Objectives:........................................................................................................................................5
Study Population Demographics:...............................................................................................................6
Literature Review:......................................................................................................................................6
Conceptual Framework: Public Health Geography..................................................................................17
Research Questions..................................................................................................................................19
Methodology:...............................................................................................................................................19
Study Design.............................................................................................................................................21
Data Collection:........................................................................................................................................22
Results:.........................................................................................................................................................23
Individual Vulnerable Population Analysis:.............................................................................................32
Recommendations’ to SEHIC: Adjusted SEHIC Indicator:.......................................................................35
Case Study: Chronic Lower Respiratory Disease: ....................................................................................38
Discussion:....................................................................................................................................................44
Study Limitations:.....................................................................................................................................44
Bias:..........................................................................................................................................................46
Conclusions: .................................................................................................................................................47
References....................................................................................................................................................50
24. Sara Dunlap June 11, 2009
MPH Thesis
24
Table 1: ELA and BPL Averages (Census, 2008)
Averages ELA % BPL %
U.S. 6.15% 12.37%
Minn. 5.75% 7.94%
7 County 4.35% 6.91%
Anoka Co. 3.04% 4.19%
Hennepin
Co.
4.93% 8.27
Solution: Minnesota Averages Set as Standard for Establishing Census Tract Vulnerability
Therefore, this study uses averages of below poverty line and elderly living alone individuals
based on the 2000 Minnesota averages. In 2000, the average number of below poverty line individuals
was 7.94% (Government, 2009). Elderly individuals living alone accounted for 5.75% of the population
(Government, 2009). Using the Minnesota averages, the extracted SEHIC equation is:
SEHIC Total vulnerability score %= ∑ 7.94 ‐ [(tract BPL population / total tract population) *100] + 5.75 ‐
[(tract ELA population / occupied housing units) *100]
Calculating Census Tract Vulnerability Scores Using the SEHIC Indicator:
To effectively demonstrate the calculations, each group is calculated and explained separately.
Below Poverty Line Calculations:
Census Tract BPL % = 7.94‐ [(tract BPL population / total tract population) x 100]
The measured below poverty line risk in each census tract equals the tract 1999 BPL population
per tract divided by the total tract population in 1999, multiplied by 100. The result is the census tract’s
percent of below poverty line individuals. The calculation numerator equals the number of Minnesotans
per census tract identified by the 2000 U.S. Census Bureau Summary File 3 data series P087002 as
28. Sara Dunlap June 11, 2009
MPH Thesis
28
the SEHIC template by subtracting the tract vulnerability percentages for both groups from their
associated standards.
Second, to identify the mean of the deviated scores, sum the squared deviated scores to equal
the sum of variances:
Third, the standard error ( of the seven county population data is calculated by taking the
square root of the sum of variances: The standard error is the same for each census
tract since it a calculation involving all census tracts.
Fourth, to find the corresponding z‐score for each census tract, the equation equals the original
SEHIC score ( minus the mean (µ), divided by the standard error ( :
(Gravetter, 2000). An example of using the Z‐score calculation to determine the vulnerability level of
ELA in a tract includes: Z‐score = ( 5.75 ‐ tract ELA %) / (
The significance of the standard error models is that it relates the vulnerability of each tract
according to the SEHIC template.
Table 3: Original SEHIC Scores Standardized by Z‐Score
Census
Tract
Total
Pop
Total
BPL
Pop BPL %
Risk
Factor
1
Total
ELA
Total
Housing
Units ELA %
Risk
Factor
2: Total
Vuln.
Score
Total
Vuln
Score
Vul. Z-
Score
BPL
pop/total
pop
7.94-
BPL %
ELA/House
Units
5.75-%
ELA
R.F 1 +
R.F 2
SEHIC σ
= 9.52
x µ x µ (µ-x)/σ
1 1721 703 40.85 -32.91 0 464 0 -5.75 -38.66 -4.06092
2 2008 109 5.43 2.51 95 1182 8.04 -2.29 0.22 0.023109
38. Sara Dunlap June 11, 2009
MPH Thesis
38
Adjusted SEHIC
(A.S)
Below Poverty Line Elderly Living Alone Total
Tracts 142/235 (60%) of A.S. 139/235 (59%) of A.S 235 (34%) of 7 Co. tracts
Population 106,285 (83%) of A.S 21,926 (17%) of A.S 128,211 (57%) of all V.Pop
High Risk Areas
46/235 (20%)
20,708 (80%) of High Risk 5,058 (20%) of High Risk 25,766 (20%) of total A.S
In the spatial analysis, 235 census tracts were identified as vulnerable in the Adjusted SEHIC
analysis. Of those, 142 were BPL areas, a decrease of 11 from the original template. In contrast, elderly
living alone tracts accounted for 139 of the 235 tracts, an increase of 60 tracts from the original SEHIC
template. The nearly equal number of tracts associated with the vulnerable population is significant
because it equally represents the vulnerable areas and accommodates for the overwhelming burden of
below poverty line individuals against elderly individuals.
Associated with the change in tract distribution the population percentages saw a reduction in
the number of BPL individuals and an increase in ELA. Additionally, an additional 1,500 very high risk
individuals were identified, those who fit census tracts with elevated ELA and BPL scores. Although
more individuals were identified, the distribution did not change. This can be explained from the small
percentage of individuals relative to entire vulnerable population.
Case Study: Chronic Lower Respiratory Disease:
Chronic Lower Respiratory Diseases (CLRD) has historical excess morbidity and mortality levels
among low‐income elderly populations during heat and air quality emergencies. Analyzing CLRD data in
the Twin Cities accomplishes two tasks. First, to incorporate health data into the existing SEHIC
44. Sara Dunlap June 11, 2009
MPH Thesis
44
Analysis of the combined adjusted SEHIC template and CLRD hospitalization records revealed vulnerable
areas in 35.7% of ZCTA’s. In the overlapping areas, population components include 77% BPL, 59% ELA,
and 97.6% CLRD.
Table 7: Analysis of Disease Indicator 1 and 2
CLRD Data SEHIC Data Total
DI 1: ZCTA’s Identified 440/455 (97%) of Total 313/455 (69%) of Total 455/1316 (34.5%) of all
ZCTA’s
DI 2: ZCTA’s Identified 361/469 (97.6%) of
Total
BPL: 361/469 (77%)
ELA: 277/469 (59%)
Each of Total
469/1316 (35.7%) of all
ZCTA’s
Reflection on the outcomes of the DI2 map provide excellent grounds to change the SEHIC
calculation methods from percentages to rates and to use Z‐scores to accurately accommodate for both
vulnerable populations.
Discussion:
Discussion of the SEHIC indicator study includes two areas of analysis; the study limitations of the
SEHIC design, and biases that could impact the results and conclusions of the research study.
Study Limitations:
Limitations in the SEHIC study primarily include unresolved issues with the indicator template.
Because the indicator model is under development, there are several weaknesses in the indicator
methods that detract from the effectiveness and reliability of the indicator. The limitations of the SEHIC
49. Sara Dunlap June 11, 2009
MPH Thesis
49
Appendix 1: SEHIC “How‐To‐Guide”
Indicator: Susceptible Populations to Health
Measure: Heat Vulnerability Index: Percent of Households of Elderly Living Alone and Percent Below the
Poverty Level at the Census Tract
How‐To‐Guide
1. Got to U.S. Census website, http://factfinder.census.gov/home/staff/main.html?_lang=en
2. Select “get data” under “Decennial Census”
3. Select “Census File 3”
4. Select “detailed tables:
5. Select “Census tract” as geographic type; your state and county of interest, and select “all census
tracts”
6. Click on the ‘add’ button. All census tracts in the selected country should appear. Then click
“next”
7. Select “P87 Poverty Status in 1999 by Age” and “H19 Tenure by Housing Type (including living
alone) by Age of Householder.” Click “add” to add each one. Click “show results.”
8. Table of results will appear. Click “print/download” at the top of the page.
9. Select “download” and “Microsoft Excel zip file”
10. Download zip file to a folder you specify on your hard drive.
11. Open the zip file. There will be two excel files. One has geographic information, including census
tract number, and the other file will have the population data. (In the population data file, the
census tract number is also embedded in a variable called “geographic identifier.”
12. In the population excel file, erase all columns except “geography identifier, P087001, P087002,
H019001, H019037, H019038, H019054, and H019055”.
13. Create a new column and calculate percent of population below poverty level by dividing
P087001 by P087002 and multiplying by 100.
14. Compute the mean for the percent of the population below poverty level.
15. Create a new column and center each value of the percent of the population below the poverty
level by census tract by subtracting each value from the mean
16. Add the total male householders living alone (65+) by adding columns H019037 and H019038.
Do the same for females (H019054 and H019055.) Sum the males and females by census tract.
17. Compute the percent of households with elderly living alone by dividing the total in #16 by
H019001.
18. Center the percentages as before in #15
19. Create a final heat vulnerability score by adding the centered values for the two variables.
20. Create a choropleth map by census tract using a standard GIS package. We will need to decide
on standard cut‐points.