1. Data Rocks!
Epidemiology 101
Epidemiologists

2. Summary Measures
Ratios
Proportions
Rates

3. Ratio Comparison of any two numbers
Calculated by dividing one quantity by the other
The numerator and denominator are separate and distinct quantities (neither is included in the other)
Often see ratios written in the form X:Y. Teacher to student ratio. Teachers and students are distinct, mutually exclusive groups.
Student
Teacher 8 1
= 8

4. Ratio Example
What is the ratio of abortions to live births in Montana?
1,800 induced abortions
12,000 live births
Ratio = 1,800/12,000 = .15
Montana’s abortion ratio is 150 abortions per 1,000 live births (or 15 abortions per 100 live births)

5. Only 4 counties meet the national school to nurse ratio standard: Mineral, Glacier, Park, and Sweet Grass

6. Proportion Terminology Comparing two numbers
Proportion = Percent = Prevelance
Comparing two numbers
Numerator is part of the denominator
Styles
Percent: 10%
Decimal: .10 4 6
= .667
= 66.7%

7. Causes of Unintentional Injuries in Montana, 2010-2013
Other/
Undetermined
28%
All of the categories represented here are considered ‘Injury Deaths’. Each individual category (numerator) is included in the denominator.

8. Rates
A rate measures the number of events that occur in a defined population, with respect to some unit of time.

9. Crude Rates The numerator is part of the denominator
Numerator: count of events in a time period
Denominator: population at risk during that same time period
There are other types of rates (miles per hour, etc.). We are usually interested in tracking number of occurrences to some population of interest.

10. Crude Rates Asthma Emergency Department (ED) Visits in Montana in 2016
Count: 2,100 cases
Population at Risk: 85,000 people with asthma
Constant: 10,000
Rate = times 10,000
= 247 ED visits
per 10,000 persons with asthma
per year

11. Proportions, Rates, and Ratios
Is the numerator included in the denominator?
Yes No
Is time included in the denominator?
RATE
PROPORTION
RATIO
What is the ratio of females to males in the room?
What is the proportion of females?

12. Applying Summary Measures to Disease
Incidence
Prevalence
Mortality

14. Incidence
Measures NEW cases of disease among a population at risk of disease over a period of time
Examples of diseases/events measured with incidence
Cancer
Influenza, pertussis, or other notifiable disease
Hospitalizations
Often reported as a NUMBER or a RATE

15. Prevalence
Measures existing cases of a disease at a particular point in time or over a period of time
Often reported as a PERCENTAGE
We use point prevalence
Ex: The prevalence of current adult smokers in Montana is 19.0%.
We deal mainly with prevalence, specifically point prevalence.
We collect data from surveys that are cross-sectional; they are asking questions at one specific point in time. ATS, BRFSS, YRBS, etc

16. Mortality
Measures DEATH due to a particular cause among a population over a period of time.
Often reported as a NUMBER or a RATE.

17. Age-adjusted rates Disease or death is often associated with age.
Age-adjusted rates are a way to make more fair comparisons between groups with different age distributions.
Use when:
Comparing geographic areas
Ex: MT to U.S.; or MT to counties; or County A to County B
Comparing time periods
Ex: to

18. Why age-adjust?

19. Why age-adjust? Number of cancer deaths 1,923 41,467
Mortality rate (crude) per 100,000 people
194.1
220.1

20. Why age-adjust? Number of cancer deaths 1,923 41,467
Mortality rate (crude)
194.1
220.1
Mortality rate
(age-adjusted)
159.8
163.9

21. Measures of Uncertainty
Statistics (from samples) estimate the true value of a parameter (from population).
There is unavoidable variability because of this called sampling variability.
Confidence intervals are most common in health data.
Generally what are they? why are they important? what factors influence them?

22. Confidence Intervals
We can’t ask all one million Montanans about their health habits. So we sample.
BRFSS sample n=9,300
2015 – 24.6% obese (23.4 – 25.8)
If we look at all of the different samples of 9,300 people, and we produced an interval estimate for each sample, then 95% of those intervals would contain the true estimate.

23. Absolute obesity prevalence = 24.3%
(If we were able to ask ALL Montanans)
24.3
23.8 (22.6 – 24.9)
24.6 ( )

24. Confidence Intervals 2015 – 24.6% obese Margin of error: 1.2%
= 23.4
24.6%+1.2 = 25.8
95% confidence interval (23.4% – 25.8%)

25. Why do they matter?
Confidence intervals are one method to tell if two estimates are significantly different or not
Overlapping confidence intervals indicate that there is no significant difference in the two estimates
Non-overlapping confidence intervals indicate that there is a significant difference between the two estimates

26. Confidence Intervals Are these significantly different?
11.3% ( ) of youth in MT use smokeless tobacco versus 7.3% ( ) of youth nationwide
88.2% ( ) of mothers in Flathead county initiate breastfeeding versus 76.5% ( ) of mothers in Dawson county.
11.3%
7.3%
YES 6 8 10 12 14 NO
88.2%
76.5% 75 80 85 90 95

27. Confidence Intervals BIGGER ≠ BETTER
To your confidence interval:
Increase sample size
decrease standard error
confidence level (from 95% to 90%)
Want confidence intervals to be as small as possible.
Increasing sample size is always acceptable. Sometimes, have a statistician do a power calculation to determine the margin of error.
Standard error, sometimes can reduce measurement (experiments, matched pairs).
Decreasing confidence is a trade-off. Not often recommended.

28. Activity
Female
smoker
Female
Female
Male
Male
Male smoker

29. Public Health Data Sources
Data Rocks!
Public Health Data Sources
Part 1

30. Outline Population Estimates Vital Statistics
Hospital and Emergency Department Discharge Data
IBIS
In Part I, we will be discussing the National Center for Health Statistics Population Estimates, Vital Statistics, and Hospital and Emergency Department Discharge Data. Data from these sources are available on Montana’s Indicator Based Information Systems, also know as IBIS. Furthermore, over the next year, more data will be added to IBIS. Therefore, we will close out Part I by walking through IBIS to explore how you can immediate access to this data.

31. National Center for Health Statistics (NCHS) Population Estimates
First up, we have Population Estimates, which are provided by the National Center for Health Statistics
National Center for Health Statistics (NCHS) Population Estimates

32. NCHS Population Estimates
Derived from Census or American Community Survey estimates
Provided by the National Center for Health Statistics
Bridged Race Population Estimates
Single year data for age and county level population estimates
No economic information
Available via Montana Indicator-Based Information System (IBIS)
The National Center for Health Statistics population estimates are derived from Census or American Community Survey data. These estimates included single year data for age and county level data, but they do not contain economic information, just demographic information.

33. NCHS Population Estimates
Age
Single Year though 85, 85+
Race
4 Categories:
White
Black
American Indian / Alaska Native
Asian / Pacific Islander
Hispanic Ancestry (Hispanic / Non-Hispanic)
County of residence
Sex
Single year ( )
Population Estimates Data include:
Age
Race
County of residence
Sex

34. Accessing Population Estimates
To access the Population Estimates go to
Select Data Queries from the tool bar
On the next page, on a tool bar on the left side of the page, select data sets to explore
That will take you to a new page that lists all of the available data sets, select population data

35.
Select Query Builder for County and Region Estimates
On the next page, on the left side tool bar, select Modify Query Definition
(may have to select ok on pop up data use agreement)
Then the query builder will walk you through step by step to obtain your desired population estimates

36. NCHS Population Estimates
Cody Custis,
If IBIS cannot answer your questions, the point of contact for this data set is Cody Custis.
His contact information is provided here.

37. Vital Statistics

38. Death Data
Data on all resident deaths and deaths that occurred in state
Cause of death
underlying vs. multiple causes
Location of residence
down to county
Location of death
hospital, home, nursing home, or other
Demographic information
Did tobacco use contribute to death
yes, no, probably, unknown
Manner of death

39. Birth Data
Data on all resident births and births that occurred in state
Parental characteristics
Location of birth
hospital, birthing center, home etc.
Prenatal care
Smoking/alcohol use during pregnancy
Method of delivery
Birth outcomes
weight, apgar score, abnormal conditions
Breastfeeding status at discharge
Mention upcoming PRAMS data.

40. Accessing Vital Statistics
CDC Wonder
National Vital Statistics
Montana IBIS

41. Accessing Vital Statistics
Again, to access Vital Statistics data, you can go to the Montana IBIS website:
Select Data Queries from the tool bar
On the next page, on a tool bar on the left side of the page, select data sets to explore
That will take you back to the exorable data sets page, select vital records birth and death data
A drop down will appear with birth data and mortality data, select the data set that you are interested in

42. Assuming we selected the Birth data, that will bring us to Birth Query Module Configuration Selection
Let’s say we are interested in Percentage with no prenatal care, so we select that data, which opens a new page: Query Results
On the left side of this page, under related links, select Modify Query Definition

43. Selecting Modify Query Definition will bring us to the Query Builder, and walk us through the steps to obtain the specific data we are interested in
Once you’ve ran your query, on the left side tool bar, under related links, there are several features available that may be useful to you
For example, you can look through different chart options for displaying the queried data
You can also save your query definition, access your saved query definitions, and output the data to excel, among other things.

44. Vital Statistics Montana Vital Statistics Office
Todd Koch,
Again, if you have any further questions on Vital Statistics, the point of contact is Todd Koch.

45. Hospital and Emergency department discharge data
Moving on to Hospital and Emergency Department Discharge Data
Hospital and Emergency department discharge data

46. ED Encounters and Inpatient Admissions
Data on ED and hospital discharges from majority of hospitals in state
Voluntary system through MHA
Does not represent individual people and cannot be de-duplicated
A person may have been transferred to another hospital, which would be counted twice as two discharges
Hospital and Emergency Department discharge data consists of information collected by a majority of hospitals throughout Montana.
This information is collected through a voluntary system run by the Montana Hospital Association or MHA.
Importantly, this data does not represent individual persons and it cannot be de-duplicated.. MEANING:
if an individual was transferred from one hospital to another, they would be counted twice and we would have no way of knowing this or accounting for it

47. ED Encounters and Inpatient Admissions
Age
Sex
Length of stay
Date of admission
Primary diagnosis
Secondary diagnoses
Up to 8 total
Procedure codes
E-codes
Up to 3
Total charges
County of residence
Information that we do obtain from ED encounters and inpatient admissions include the following:
Age, sex, length of stay, date of admission, primary diagnosis, secondary diagnoses (up to 8), procedure codes, E-codes, or external cause of injury, (up to 3), Total charges, and county of residence

48. Accessing Hospital Discharge Data
Again, we can access Hospital Discharge Data on the Montana IBIS website:
Similar to the other data sets, we would:
Select Data Queries from the tool bar
On the next page, on a tool bar on the left side of the page, select data sets to explore
That will take you back to the exorable data sets page, select Hospital Inpatient Discharge Data
Selecting Hospital Inpatient Discharge Data takes us to the Query Module Configuration, including a drop down list for Hospital Discharge Data
Let’s pretend we want to look at age-adjusted rates

49. By selecting age adjusted rates, we are brought to the query builder
We can then make selections based on
Year: (example selects 2014)
Diagnosis: example = 21. Injuries, Poison and Toxic Effect of Drugs
Population: SEX (example = both)
Residency States: Montana resident or non-resident (example = Montana resident)
Geographic area: Health planning region of residence or Urban and Rural Counties of resident (example = all health planning regions, default)
How to display the data: example display by geographic area

50. From that query we would obtain the follow data
Then, if I wanted to go back to the tool bar on the left, we could choose to display the data differently through the chart options.
For example, if we were to select Vertical Bar we would then be given the following:

51. Accessing ED/Admission Data
Montana Hospital Discharge Data Program
Cody Custis,
For additional information, the point of contact for Montana Hospital Discharge Data is Cody Custis

52. Montana’s Indicator Based Information System
An Overview
To wrap up part I, I am going to give a little bit of a overview of Montana's Indicator Bases Information System, commonly referred to as IBIS.

53. This is not to be confused with the bird, ibis

54. MT - IBIS Public Health Data Resource
Instead, this IBIS is a highly useful resource for obtaining public health data

55. MT - IBIS Multiple roles Database query system Indicator query system
Topic query system

56. MT – IBIS Query Database query system
July 2016: Vital statistics (Birth, Death), Population
February 2017: Inpatient admission
May 2017: Emergency department
As a database query system, IBIS currently includes:
Population Estimates
Vital Statistics
and Inpatient Admission and Emergency Department Data
With more data to come

57. MT - IBIS Query Dataset Query System http://ibis.mt.gov/query
If you do not want to go through the IBIS home page every time you want to query data, you can directly link to it using forward slash query in the url
Doing so will bring you to the custom query introduction page, the same page we reached when selecting data queries from the top tool bar
This page includes additional resources such as basic steps on getting started for querying a data set and data set specific resources such as questionnaires and code lists.

58. MT – IBIS Indicator Indicator Query System
The indicator based query system for IBIS can be found at the url provided here.
From the Montana IBIS home page, you would get here by selecting community profiles from the top tool bar (instead of data queries)
Then on the left side tool bar, under explore communities, there are two options
Community Health Assessment and
Community Snapshot Reports
Select Community Snapshot Reports
Then, just like with the database query system, follow the given steps to obtain the indicator information you are interested in

59. Lewis and Clark County: Demographic Characteristics
For example, we could obtain demographic characteristics for Lewis and Clark County (as pictured here) by doing the following:
Select Lewis and Clark County in Step 1 (Select a Community)
Then in Step 2, selected Demographic Characteristics (other option being community health indicators)
Then in Step 3 selected numerator and denominator definitions to be included in the footnotes

60. MT – IBIS Topics Health Topics http://ibis.mt.gov/topic/Index.html
For health topics on IBIS, the URL is:
From the home page: select health topics from the top tool bar
Say we are interested in Mental Health, so we click on that

61. Selecting mental health would bring us to the mental health topic page
This page contains several drop downs that include:
a description or definition of mental health
a brief section on why mental health is important
information about what is known about mental health
who is at risk for mental health disorders
what can be done to reduce the risk of mental health disorders
information on how mental health is tracked
and finally indicator reports related to mental health

62. MT IBIS That is IBIS in a nutshell It is a work in progress
Future Direction(s)
Continue making data more readily
Adding more information and more data sets
That is IBIS in a nutshell
It is a work in progress and something that we are very excited about
We hope to continue making informative data more readily available by adding it to IBIS