CTFL-PT health - ISTQB Certified Tester Foundation Level-Performance Testing Updated: 2024

Data Title:      Behavioral Risk Factor Surveillance System (BRFSS) Survey
Year(s):           1984-2016
Level:              Individual
Source:           Centers for Disease Control and Prevention
File Format:   ASCII, .sas
Description:  BRFSS provides telephone survey data on six individual-level behavioral health risk factors associated with the leading causes of premature mortality and morbidity among adults: 1) cigarette smoking, 2) alcohol use, 3) physical activity, 4) diet, 5) hypertension, and 6) safety belt use.  Some of the variables included in BRFSS are: state, private residence, college housing, 18 or older, number of adults in the household, number of adult men in household, number of adult women, general health rating, general mental health rating, health care coverage, sleeping habits, presence of various medical conditions, marital status, veteran status, education level, income level, weight, height, internet usage, sex, cigarette consumption, alcohol consumption, dieting habits, exercise habits, blood pressure, seatbelt usage, and vaccination use.
Links:
Website:          BRFSS Annual Survey Data Overview
Data Files:
2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998, 1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987, 1986, 1985, 1984

Data Title:      Youth Risk Behavior Surveillance System (YBRSS) Survey
Year(s):           1991-2015 (biannual)
Level:              Individual
Source:           Center for Disease Control and Prevention YRBSS
File Format:   ASCII, .sas, SPSS, Access
Description:  YBRSS monitors six categories of priority health-risk behaviors, plus obesity and asthma, among adolescents at the national, state, territorial, tribal, and local levels.  Some of the variables included are: age, sex, grade, race, height, weight, seatbelt usage, driving habits, weapon possession, fighting frequency, bullying, depression, cigarette consumption, alcohol consumption, drug consumption, sexual activity, and dietary habits.
Links:
Website/Data Files:     YRBSS Data Files and Methods
Example papers:
The Impact of State Physical Education Requirements on Youth Physical Activity and Overweight

Multiple Measures

Data Title:      County Health Rankings
Level:              County
Year(s):           2010-2017
Source:           County Health Rankings
Description:  Data provide information on the health rankings of each state’s counties.  Variables are divided into four sections: health behaviors, clinical care, social and economic factors, and physical environment.  Each section contains variables of interest:

Health behaviors: adult smoking, adult obesity, food environment index, physical inactivity, access to exercise opportunities, excessive drinking, alcohol-impaired driving deaths, sexually transmitted infections, and teen births 

Clinical care: uninsured, primary care physicians, dentists, mental health providers, preventable hospital stays, diabetic monitoring, and mammography screening 

Social and economic factors: high school graduation, some college, unemployment, children in poverty, income inequality, children in single-parent households, social associations, violent crime, and injury deaths 

Physical environment: air pollution – particulate matter, drinking water violations, severe housing problems, driving alone to work, and long commute – driving alone.
Links:
Website:          County Health Rankings
Data Files:       State Rankings Data (select each state)

Data Title:      Virginia Atlas Data
Year(s):           Depends on indicator (see website for details)
Level:              County
Source:           Virginia Atlas of Community Health
File Format:   .csv
Description:  The Virginia Atlas of Community Health provides health data for the counties within Virginia.  The data are separated into Health Planning Districts (HPD).  The data is further categorized into profiles, which include:  Designations, Health Demographic Profile (2013), Maternal and Infant Health Profile (2012), Mortality Profile (2012), Health Coverage Profile (2013), Prevention Quality Indicator Hospital Discharge Profile (2012), Behavioral Health Hospital Discharge Profile (2012), Adult Health Risk Profile (2013), Youth Health Risk Profile (2013), Communicable Disease Profile (2013), Cancer Profile (2011), and Diabetes Profile (various years).

Below are some of the variables included in each profile:

Health Demographic Profile: population, square miles, racial density, gender density, and median household income. 

Maternal and Infant Health Profile: pregnancy rate per 1,000 females age 15-44, number of induced terminations of pregnancy, number of natural fetal deaths, number of total live births, birth rate per 1,000 population, and total teenage pregnancies.

Mortality Profile: total number of deaths, number of heart disease deaths, number of Alzheimer’s disease deaths, and number of deaths by suicide

Health Coverage Profile: non-elderly population (age 0-64), rate of uninsured nonelderly, rate of insured nonelderly, adult population (age 19-64), rate of uninsured adult, rate of insured adult, child population (age 0-18), rate of uninsured children, rate of insured children, elderly Medicaid enrollees (age 65+), adult Medicaid enrollees (age 18-64), and child Medicaid enrollees (age 0-17)

Prevention Quality Indicator Hospital Discharge Profile: Prevention quality indicator (PQI) hospitalization discharge rate, PQI discharge rate for asthma in younger adults, PQI discharge rate for angina, PQI discharge rate for diabetes, PQI discharge rate for congestive heart failure, and PQI discharge rate for perforated appendix

Behavioral Health Hospital Discharge Profile: total behavioral health (BH) hospitalization discharge rate, BH hospitalization discharge rate for affective psychoses, BH hospitalization discharge rate for alcohol dependence syndrome, BH hospitalization discharge rate for depressive disorder, BH hospitalization discharge rate for drug dependence disorder, BH hospitalization discharge rate for neurotic disorders, and BH hospitalization discharge rate for schizophrenic disorders 

Adult Health Risk Profile: estimated total population age 18+, percent of adults (age 18+) that are overweight and obese, percent of adults at risk for binge drinking, percent of adults unable to see doctor due to cost, percent of adults that are smokers, percent of adults that are in fair or poor health status, percent of adults with diabetes, percent of adults with high blood pressure, and percent of adults with high cholesterol

Youth Health Risk Profile: estimated youth age 14-19, estimated youth age 0-17, percent of high school-aged youth (age 14-19) who ate vegetables less than three times per day during the seven days before the survey, percent of high school-aged youth who watched television three or more hours per day on an average school day, percent of high school-aged youth who had at least one drink of alcohol on at least one day during the 30 days before the survey, percent of high school-aged youth who felt sad or hopeless almost every day for two or more weeks in a row so that they stopped doing some usual activities, percent of high-school aged youth who were bullied on school property during the 12 months before the survey, percent of high school-aged youth who are overweight or obese, and percent of high school-aged youth who were ever told by doctor or nurse that they had asthma and still have asthma

Communicable Disease Profile: number of newly diagnosed cases of HIV disease, total number of early syphilis diagnoses, number of chlamydia diagnoses, number of gonorrhea diagnoses, rate of newly diagnoses cases of HIV disease, rate of total early syphilis diagnoses, rate of chlamydia diagnoses, and rate of gonorrhea diagnoses

The variables included in the Cancer Profile deliver information on the cancer incidence (both in total number and age-adjusted rate) as well as the total number and the percent of the cancers diagnosed at the local stage for each type of cancer.  The types of cancer included are: breast, cervical, colorectal, lung and bronchus, melanoma, oral cavity, ovarian, prostate, and neoplasms.  The Cancer Profile also provides information on the five-year total (2008-2012) of cancer deaths (both in total number and age-adjusted rate), for each type of cancer.  There is also information on blood stool tests, sigmoidoscopies or colonoscopies, mammograms, pap tests, and PSA tests.

The Diabetes Profile provides information both on total number and age-adjusted rates (or percentages).  The variables included are deaths from diabetes mellitus, PQI hospital discharges for diabetes, estimated adults age 18+ who are overweight and obese, estimated adults age 18+ with diabetes, and estimated high school-aged youth who are overweight or obese.

To obtain the data, click on the website/data file link below, click on the health planning district of interest, select the indicator(s) of interest, click “Download Data” on the top right side of the page.
Links:
Website/Data Files:     Atlas Data

Mortality Measures

Data Title:      Linked Birth/Infant Death Records
Year(s):           1995-2015
Level:              State and county
Source:           CDC Wonder
Description:   This data collection provides counts and rates for deaths of children under 1 year of age, occurring within the United States. Information from death certificates has been linked to corresponding birth certificates. Data are available by county of mother's residence, child's age, underlying cause of death, gender, birth weight, birth plurality, birth order, gestational age at birth, period of prenatal care, maternal race and ethnicity, maternal age, maternal education and marital status. The data are produced by the National Center for Health Statistics.  To access the data, agree to the terms and conditions.  Then use the search function to select which variables for which you wish to obtain data.  After sending the request, the data should appear in a table, which can then be exported into a text file.
Links:
Website:
Linked Birth/Infant Death Records Overview
Data Files:
Linked Birth/Infant Death Records, 2007-2015 with ICD 10 codes
Linked Birth/Infant Death Records, 2003-2006 with ICD 10 codes
Linked Birth/Infant Death Records, 1999-2002 with ICD 10 codes
Linked Birth/Infant Death Records, 1995-1998 with ICD 9 codes
Linked Birth/Infant Death Records Data Description

Data Title:      Multiple Cause of Death
Year(s):           1999-2016
Level:              State and county
Source:           CDC Wonder
Description:   In discussing mortality data from the National Vital Statistics System, the CDC notes:  “Cause-of-death data are traditionally presented in terms of one underlying cause for each death. However, underlying-cause data can be augmented with additional information on the other conditions that the medical certifier reported as contributing to death…Because several chronic conditions are often reported, multiple-cause data may be important in chronic disease surveillance.”

The Multiple Cause of Death dataset contains information on both the underlying cause of death as well as other contributing causes of death for U.S. counties.  Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to 20 additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates and 95% confidence intervals for death rates can be obtained by cause of death (4 digit ICD-10 codes, 113 selected causes of death, 130 selected causes of infant death, drug and alcohol related causes of death, injury intent and injury mechanism categories), place of residence (national, region, division, state, and county), age (single-year-of age, 5-year age groups, 10-year age groups and infant age groups), race (American Indian or Alaskan Native, Asian/Pacific Islander, Black or African American, White), Hispanic ethnicity, gender and year. Data are also available by place of death, month and week day of death, and whether an autopsy was performed.

To access the data, agree to the terms and conditions.  Then, use the search function to select the variables of interest.  After sending the request, the data should appear in a table, which can then be exported into a text file.
Links:
Website/Data Files:
Multiple Cause of Death Overview
Multiple Cause of Death, 1999-2016
Multiple Cause of Death Data Description

Data Title:      Underlying Cause of Death (Detailed Mortality)
Level:              State and county
Year(s):           1999-2016
Source:           CDC Wonder
File Format:   .txt
Description:   The National Vital Statistics Reports notes:  “Cause-of-death statistics are based on the underlying cause of death.”  Unlike the dataset immediately preceding this one, this database provides detailed information only on the Underlying Cause of Death (and does not include multiple causes of death) for residents in U.S. counties.  Data are based on death certificates.  Each death certificate identifies a single underlying cause of death and demographic data. The number of deaths, crude death rates or age-adjusted death rates, and 95% confidence intervals and standard errors for death rates can be obtained by place of residence (total U.S., region, state and county), age group (single-year-of age, 5-year age groups, 10-year age groups and infant age groups), race, Hispanic ethnicity, gender, year, cause-of-death (4-digit ICD-10 code or group of codes), injury intent and injury mechanism, drug/alcohol induced causes and urbanization categories. Data are also available for place of death, month and week day of death, and whether an autopsy was performed. 

To access the data, first agree to the terms and conditions.  Then, use the search function to select which the variables of interest.  After sending the request, the data should appear in a table, which can then be exported into a text file.
Links:
Website/Data Files: Underlying Cause of Death, 1999-2016
Full data description and instructions: Detailed Mortality Data Description

Healthcare Spending

Data Title:      Medicare Spending
Year(s):           2003-2014
Level:              State and county
Source:           Dartmouth Atlas of Health Care
File Format:   .xls
Description:   Data on Medicare spending are claims-based and adjusted for price, age, sex, and race (20% demo for 2003-2009, 100% demo for 2010-2013). The variables included are: state, number of Medicare enrollees, total Medicare reimbursements per enrollee, hospital and skilled nursing facility reimbursements per enrollee, physician reimbursements per enrollee, outpatient facility reimbursements per enrollee, home health agency reimbursements per enrollee, hospice reimbursements per enrollee, and durable medical equipment per enrollee.
Links:
Website:          Medicare Spending
Data Files:
State-level:      2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003
County-level:  2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003

Data on post-acute care provide information on individuals upon being discharged from hospitals.  The data include 30-day readmission rates, emergency room visits within 30 days of discharge, and follow-up visits within 14 days of discharge. 
Links:
Website/Data Files:     Hospital Discharges and Post-acute Care
Example papers:
Health System Characteristics and Rates of Readmission After Acute Myocardial Infarction in the United States

Data Title:      Selected Measures of Primary Care Access and Quality
Year(s):           2003-2014
Level:              State and county
Source:           Dartmouth Atlas of Health Care
File Format:   .xls
Description:  Data include measures of primary care utilization, quality of care for diabetes, mammography, leg amputation, and preventable hospitalizations for different age groups and different races.  All variables have three categories: total (indicating all individuals), black (indicating individuals who are African-American), and white (indicating individuals who are white).  The variables included are state/county, number of health care beneficiaries (Part B eligible), average annual percent of Medicare enrollees having at least one ambulatory visit to a primary care clinician, number of diabetic Medicare enrollees age (65-75), average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c test, average annual percent of diabetic Medicare enrollees age 65-75 having eye examination, average annual percent of diabetic Medicare enrollees age 65-75 having blood lipids (LDL-C) test, number of female Medicare enrollees age 67-69, average percent of female Medicare enrollees age 67-69 having at least one mammogram over a two-year period, number of Medicare beneficiaries (Part A eligible), leg amputations per 1,000 Medicare enrollees, discharges for ambulatory care sensitive conditions per 1,000 Medicare enrollees.
Links:
Website:          Selected Measures of Primary Care and Quality
Data Files:
State-level:      2014 2013 2012 2011 2010 2009 2008 2003-07
County-level:  2014 2013 2012 2011 2010 2009 2008 2003-07

AN NBA PLAYER is many things: an athlete, a performer, a brand, a star. He’s likely a millionaire and definitely in superior physical shape. He seems to have everything going for him. But he also suffers the same slings and arrows that send us mere mortals to the therapist’s couch: anxiety, depression, workplace tension. And just as these issues nibble away at our mental health when left unaddressed, they can harm players—on and off the court.

Recognizing this, the league recommended in 2018 that all its teams employ a mental-health professional. That recommendation became a rule a year later, giving rise to the NBA’s groundbreaking Mind Health program, led by

The program, she explains, was born after NBA stars like DeMar DeRozan and Kevin Love publicly shared their emotional struggles, helping destigmatize vulnerability. The teams’ mental-health and performance professionals span the landscape of mental-health care and include psychologists, psychiatrists, and more. We asked these mind coaches how they help their players face tough but common situations. Their game plans can be yours, too.

The expert: Ronald Kimmons, Psy.D., Vice President of Player Wellness, Utah Jazz

As in many professions, the situations and expectations change and evolve for NBA players throughout their careers. You’re going to see 19-year-olds with 19-year-old problems, all the way up to a guy that might be in his 30s and is Studying to retire, often with a lot of anxiety. Understandably, their identity is wrapped up in their profession.

To help them with the transition to what’s next, we talk a lot about not retiring from something but retiring to something. We talk about what the future might hold for them outside of the league. The same thing applies in the league, too. Of course they have to perform on the court. How can they do that when, perhaps, they’re no longer at their physical peak?

First, it’s about finding the ways they can contribute to the team. Maybe they can move into the role of a mentor to younger players. That helps flip the script from a rookie coming to take your job to something more harmonious.

Even if they might not be putting up the numbers they did when they were younger, they’re still helping the team win. That’s a hard lesson, but thankfully, easier for a veteran to understand than a rookie.

When You’re Going Through a Rough Patch 

The expert: Chantelle Green, Ph.D., Director of Mind Health and Wellness, Portland Trail Blazers

If a player is feeling sad or disappointed because they lost the game, that’s okay. But it’s important to know your happy medium, what I call your baseline. You don’t want to stray too far from that, either sink too low or run too high. The first step in avoiding that is to evaluate what your baseline is by keeping track of when you feel like your best self—you have mental clarity, peace, and moments of joy. The second is having tools and strategies that help you get back to that baseline. Those can be everything from spending time with friends, family, and teammates to meditating, deep breathing, enjoying nature, listening to music, practicing your faith, playing video games, and connecting with a therapist. The more strategies, the better. If one doesn’t work, go down the list.

When you’re feeling below that baseline, it can be hard to stay motivated. Just like there’s a repertoire of coping mechanisms to return you to your baseline happiness, there’s a repertoire of things that return your motivation, too. You may think, I’m motivated by making the playoffs. Well, when that’s not an option, you need to rethink things. It might be more like: I’m motivated by making sure I keep my contract. I’m motivated to be able to provide for my family. I’m motivated by my kids. I’m motivated by being a good role model. Something on that list will be a motivator. It’s not always going to be wins and losses.

When Your Anxiety Is Paralyzing

The expert: Corey Yeager, Ph.D., Psychotherapist working with organizations including the Detroit Pistons, and author of How Am I Doing?: 40 Conversations to Have With Yourself

The overarching complaint that I hear from players and coaches is anxiety. Anxiety is best described as being thinking or ruminating about what’s going to happen in the future. It’s not always negative—some anxiety can push you to work out more or study more—but I’m talking about the paralyzing kind: anxiety about being cut from the team, or the next contract, or who is going to want more financial support from you amongst your family and friends. I try to help players see that anxiety takes place in a futuristic world. The moment about which you’re feeling anxious has yet to occur. You are spinning your wheels on something that isn’t real, burning your energy about something that may never happen.

So I tell them to stay in the current moment, not to engage in futuristic thinking. When you step to the line, go through the routine. Do your breathing. If you have a saying, say it. Center yourself, then shoot the free throw like you’ve done a million times before. We know that we can control how we act and react in this moment. We won’t be as concerned or thinking about what is coming. That’s liberation from anxiety.

When You See Yourself  Differently  Than Your  Boss Does 

The expert: Joe Carella, Psy.D., Sport Psychology Consultant, Orlando Magic

Anyone who gets drafted feels like they’re going to be an all-star with a long career ahead of them. Perhaps you think of yourself as a primary scorer, the guy you deliver the ball to at the end of the game to make the bucket to win. The coach, however, sees you primarily as a defensive player. You can either fight or accept that.

I work with the players to accept their coach’s vision and to develop the skills to excel in that role. Having done so, they are significantly more likely to earn the opportunity to expand to a new role that is more closely aligned with their vision of themselves. If you don’t take advantage of the opportunity you’re given, you may regret it for a long time. Interestingly, this is much less of a problem with veteran players. When you’re a rookie who might not want to recognize or accept your limitations, it’s hard. Unfortunately, the guys who don’t develop greater self-awareness are more likely to resist change, and their NBA careers are shorter and don’t match their potential. But the players who find a way to be dependable while embracing the challenge of changing perceptions are the ones with long, fulfilling careers.

Joshua David Stein has written for publications including _The New York Times, Fatherly, Esquire, and The Guardian.

The first thing to consider when deciding on a diet is: What’s my goal? Am I trying to lose weight or body fat? Or am I trying to Excellerate a specific aspect of my health or my life? A 2014 study in Health Psychology and Behavioral Medicine found that examining the intersection of life goals and dietary goals can have an impact on your ability to achieve and maintain diet-related changes . Once you know what your desired outcome is, it’s time to delve into the details.

Dr. Cheskin says to determine if you’re likely to stick with a diet, it’s important to “know yourself—the more you can be introspective, the better.” After all, a 2018 study in JAMA Network found people achieved similar weight loss results on a healthy low-fat diet and a healthy low-carbohydrate diet. So the diet that’s likely to work for you is the one you’re most likely to stick with .

To that end, ask yourself the following questions:

• Does the diet have foods I like to eat?
• What is it about my habits and preferences that might make this particular approach work for me?
• What am I going to change to help me lose weight or lower my cholesterol or my _____?
• Are the foods on this diet affordable?
• Do I have time to shop for and prepare the recommended meals?

“The practicality of what you’re choosing is really important because there are still only 24 hours in a day,” says Bonci.

It’s also wise to consider your dieting history, including what has worked for you and what hasn’t—and why. “There are very few people in this world who haven’t been through this a few times before,” Bonci says.

There may be valuable lessons in your previous experiences. If you were tired and miserable on a low-carb approach in the past, you should probably look at a different one. On the other hand, if you were successful with a plan that included mini meals throughout the day, that approach might be worth trying again.

Also, think about what’s realistic for your lifestyle. While a rigid, calorie-cutting plan may be appealing initially because it takes the guesswork out of what to eat, it may be hard to stick with it for an extended period of time.

“If there isn’t some flexibility built in, it probably won’t work for you in the long term because life throws us curveballs,” says Dr. Cheskin. “It should be adaptable to different situations and personalities.” In other words, it needs to be a plan you can live with.

It’s also important to consider a particular diet’s safety and effectiveness. For example, is there research or science behind the diet? Or is it based on unproven assumptions? Look at statistics or clinical studies to gauge its success for other people, Dr. Cheskin advises. In general, experts say that a healthy, sustainable weight loss plan should include:

• A healthy number of daily calories. That means no less than around 1,500 for women, or 1,800 for men—although that number varies based on factors like your weight and activity level.
• A variety of foods from different food groups. Think fruits, vegetables, whole grains, legumes, nuts, seeds, lean protein and healthy fats, says Dr. Cheskin. The diet should include appropriate proportions of macronutrients (carbohydrates, proteins and fats) to provide your body with energy, as well as sufficient micronutrients (like vitamins and minerals) for optimal function. It shouldn’t rely on supplements to provide these nutrients, says Dr. Cheskin, because that suggests the plan is nutritionally unsound and not sustainable.
• An afternoon snack. Snacks “keep people fulfilled,” says Dr. Cheskin. “Part of eating is not just to fill the fuel tank; it’s also the pleasure of food.”