Lec Module 1-4 Flashcards
(145 cards)
1
Q
branch of applied statistics directed toward applications in the health sciences and biology.
A
Biostatistics
2
Q
Design experiments and observational studies
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Making hypotheses
3
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Summary of data
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Gathering data
4
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Testing of hypotheses
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Drawing Interferences
5
Q
Application of Biostatistics
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In community medicine and public health
In cancer research
In advanced biomedical technologies
In pharmacology
In ecology
In demography
In population genetics and statistical genetics
In bioinformatics
In systems biology
In agriculture
In genetics
In physiology and anatomy
6
Q
2 Types of Non-Experimental Studies
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- Population based studies
- Individual based studies
7
Q
Population Based Studies
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A. Descriptive study
B. Analytic study
8
Q
This study aims to generate a hypothesis by answering the following questions,
1. What?
2. Who?
3. Where?
4. When?
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Descriptive Study
9
Q
characteristics (age, sex, occupation) of the individuals affected by the outcome
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Person
10
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geography (residence, work, hospital) of the affected individuals
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Place
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when events (diagnosis, reporting; testing) occurred
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Time
12
Q
Newly reported or registered disease cases compared over time, place, or person
Population estimates or other population group totals used as denominators
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Incidence Study
13
Q
This study aims to generate a hypothesis by answering the the “why?” and “how?” questions.
The goal of this study is to measure the association between exposure and outcome.
This type of study uses a comparison group.
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Analytic Study
Example,
Ecological Study
Rates are linked to the level of exposure to some agent for the group as a whole
14
Q
prepared for illustrating novel, unusual, or atypical features identified in patients in medical practice, and they potentially generate new research questions.
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Case Reports
15
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means new and not resembling something formerly known or used.
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novel
16
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Example - Case reports
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Descriptive Study
17
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Examples - Cross-sectional study, Case-control study, and Cohort study
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Analytic Study
18
Q
To learn about the characteristics of a population at one point in time
Does not use a comparison group
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Cross-Sectional Study
19
Q
To study rare diseases
To study multiple exposures that may be related to a single outcome
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Case-Control Study
20
Q
Can be used to find multiple outcomes from a single exposure
A cohort is a well-defined group of individuals who share a common characteristic or experience
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Cohort Study/ Longitudinal Study/ Follow-up Study
21
Q
study in which people are allocated at random (by chance alone) to receive one of several clinical interventions.
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Randomized Controlled Trial (RCT)
22
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The control may be a standard practice, a placebo (“sugar pill”), or no intervention at all. Someone who takes part in a randomized controlled trial (RCT) is called a participant or subject.
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standard of comparison or control.
23
Q
Under this branch is Clinical trial and Control trial
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Randomized Study
24
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Under this branch are Quasi-experimental study, Field trial, and Community trial
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Non-randomized Study
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Shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control.
Quasi-Experimental Research
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Applies preventive interventions to healthy individuals
Field Trial
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Applies intervention to aggregative units
Community Trial
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Aims of Experimental Study
-To provide scientific proof of etiological factors which may permit modification or control of disease
-To provide a method of measuring the effectiveness and efficiency of health services for the prevention, control and treatment of disease and improve the health of the community.
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observations of random variables made on the elements of a population or sample.
Data
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researchers simply collect data based on what is seen and heard and infer based on the data collected. Researchers should not interfere with the subjects or variables in any way.
Observational Data
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researchers produced this by measurement, test method, experimental design. The researcher has control over some variables.
Experimental Data
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The data gathered are presented in paragraph form. Data are written and read. It is a combination of texts and figures.
Textual
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Method of presenting data using the statistical table.
Tabular
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The most effective manner of presenting data since it can be easily understood. Examples are Pie, Barr, Venn, Histogram, Line Diagram and Epidemic curve
Graphical
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consists of table number and title
Table heading
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categories which are found at the left side of the body of the table
Stubs
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the top of the column
Box head
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main part of the table
Body
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any statement or note inserted
Footnotes
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source of the statistics
Source Note
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To examine a relationship between two (non-sequential) variables.
Scatterplot
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where the x- axis represents some sequential variable like time, or distance along a transect (right, and below). In both cases there is an explicit (spatial or temporal) relationship between adjacent points along the x-axis, and the inclusion of the line makes the pattern of this sequence much clearer.
Line Plot
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have two different y-axes, allowing variables with different scales to be plotted on the same graph. Primarily used in the same sorts of situations as line plots, where you want to compare the pattern of change in two different types of variable.
Double Y Plot
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Probably the most widely used type of graph in science. This chart can be presented horizontally or vertically. straightforward to produce, and generally are either used to represent means
Bar Charts
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combine features of line plots and stacked bar charts. They have similar disadvantages to the latter, though since such plots are generally used to show trends in time or space it is often easier to interpret, as the patterns of expansion or contraction of areas on the graph have a logical meaning.
Area Plot
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familiar to everyone, much beloved of business graphics packages and the media, but of relatively limited use for scientific figures.
Pie Chart
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plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data.
Histogram
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method of organizing raw data in a compact form by displaying a series of scores in ascending or descending order, together with their frequencies
Frequency table
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diagram representing mathematical or logical sets pictorially as circles or closed curves within an enclosing rectangle (the universal set), common elements of the sets being represented by the areas of overlap among the circles.
Venn Diagram
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A visual display of the onset of illness among cases associated with an outbreak.
Epidemic curve
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is used to estimate the extent of the disease in the population.
Survey Study
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is designed to monitor or detect specific diseases.
Surveillance Study
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investigate association between an exposure and a disease outcome. They rely on “natural”allocation of individuals to exposed or non-exposed groups.
Observational Study
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also investigate the association between an exposure, often therapeutic treatment, and disease outcome individuals are “intentionally” placed into the treatment groups by the investigators.
Experimental Study
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subset of the population
sample
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Summary measures computed on the sample are used to make statistical inference on the population.
Measures of Central Tendency and Measures of Variability
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all items or individuals of interest.
Population
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A finite subset of statistical individuals obtained from the population.
Sample
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To select a portion from the population.
Sampling
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Reasons for Selecting a Sample
-Complete enumerations are not possible when the population is infinite
-Results are needed in short amount of time
-Survey area is wide
-Limited resources
-Item or unit is destroyed during investigation
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Stages in Sampling Collection
-Define Target Population
-Select sampling frame
-Define if probability or non probability
-Procedure for selecting sampling
-Determine sample size
-Selection of actual sample
-Fieldwork
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Probability of Sampling Techniques
Simple Random
Systematic
Stratified Random
Cluster
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Non-Probability Sampling Technqiue
Quota
Snowball
Self-Selection
Convenience
Purposive**
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Types of Purposive Sampling
Extreme Case
Heterogenous
Homogenous
Critical Case
Typical Case
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The researcher chooses a sample that is readily available in some non-random way.
Convenience Sampling
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The respondent decides whether or not to participate, typically in one request without the chance for follow up.
Self Selection Sampling
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It asks respondents to recommend other respondents who might subsequently be invited to take the survey.
. Snowball Sampling
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The interview or study designer chooses sampled units who, by their judgment, will meet the specific purpose of the survey.
Purposive Sampling
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The goal is not to be representative of views on an issue but "to look at it from all angles".
Maximum Variance Sampling (Heterogeneous Sampling
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To deeply explore the views of a group of respondents with the same characteristics.
Homogenous Sampling
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It is interested in an in-depth assessment of the typical viewpoint.
Typical Case Sampling
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it is interested in understanding unusual cases such as successes or failures.
Extreme Case Sampling
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Studying those cases that have the most to offer in terms of understanding the population.
Critical Case Sampling
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Surveying experts on a particular topic, with their expertise left to the judgment of the interviewer or study designer.
Expert Sampling
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Surveying every single member of a qualifying subgroup.
Total Population Sampling
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he "proportional" in the name is because the population of interest is represented almost exactly by the percentage of each cell (major demographic group) in the final survey results. "Strata may be joint or interlocking" – so you might have quotas of younger women, older women, younger men, and older men. Sometimes known as "hard quotas".
Proportional Quota Sampling
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Also known as "soft quotas", non-proportional quota sampling captures a minimum number of respondents in a specific group.
Non-Proportional Quota Sampling
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should have both coverage or a non zero chance of selecting any member of target population and external selection which is the random selection of members of population to participate in the survey.
Probability Sampling
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refers to any sampling method that has the following
properties.
Simple Random Sampling
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Each of the N population members is assigned a unique number. The numbers are placed in a bowl and thoroughly mixed. Then, a blind-folded researcher selects n numbers. Population members having the selected numbers are included in the sample.
Lottery method
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population is divided into groups, based on some characteristic. Then, within each group, a probability sample (often a simple random sample) is selected. In stratified sampling, the groups are called strata.
Stratified Sampling
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Every member of the population is assigned to only one group. Each group is called a cluster. A sample of clusters is chosen, using a probability method (often simple random sampling)
Cluster Sampling
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list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kth element on the list.
Systematic Random Sampling
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Combination of two probability sampling.
Multistage Sampling
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Have not been summarized in any way
Ungrouped Data Also called "raw data"
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Data that has been organized into a frequency distribution table.
Grouped Data
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often called the average of a numerical set of data, is simply the sum of the data values divided by the number of values
mean
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number that falls in the middle position once the data has been organized from lowest to highest value.
median
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mode of a set of data is simply the value that appears most frequently in the set.
Mode
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Only one mode
Unimodal
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Two modes
Bimodal
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three modes
Trimodal
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four or more modes
Multimodal
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easy to compute and provides statisticians and the mathematician with a better understanding of the data set how varied it is. It is the simplest approach to calculate variance in statistics.
Range
Highest value - lowest value
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It looks at how spread out a group of numbers is from the mean.
It is only used to measure spread or dispersion around the mean of a data set.
It is never negative.
If all values of a data set are the same, the standard deviation is zero (because each value is equal to the mean).
Standard Deviation?
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the ratio of the standard deviation to the mean.
coefficient of variation (CV)
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quantity whose value changes.
variable
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variable whose value is obtained by counting.
discrete variable
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variable whose value is obtained by measuring.
continuous variable
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measured and can take any value over an interval. The data usually falls under continuum - have infinite values.
continuous variable
Examples are - Weight of a child, volume of blood extracted, age of a patient, blood pressure of a patient, pulse rate, or temperature of a room, or time elapsed for a clotting time test.
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counted and can only take certain values over an interval.
discrete variable
Examples are - Number of patients, number of medical technology books, number of doctors available.
102
from the Latin nomalis, which means “pertaining to names”. It’s another name for a category.
Nominal
Examples:
Gender: Male, Female, Other.
Hair Color: Brown, Black, Blonde, Red, Other.
Type of living accommodation: House, Apartment, Trailer, Other.
GenotypeLinks to an external site.: Bb, bb, BB, bB.
Religious preference: Buddhist, Mormon, Muslim, Jewish, Christian, Other.
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means in order. Includes “First,” “second” and “ninety ninth.”
Ordinal
Examples:
High school class ranking: 1st, 9th, 87th…
Socioeconomic status: poor, middle class, rich
The Likert ScaleLinks to an external site.: strongly disagree, disagree, neutral, agree, strongly agree
Level of Agreement: yes, maybe, no
Time of Day: dawn, morning, noon, afternoon, evening, night
Political Orientation: left, center, right
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has values of equal intervals that mean something.
Interval
Examples:
Celsius Temperature
Fahrenheit Temperature
IQ (intelligence scale)
SAT scores
Time on a clock with hands
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exactly the same as the interval scale except that the zero on the scale means: does not exist. Has a true zero.
Ratio
Examples:
Age
Weight
Height
Sales Figures
Ruler measurements
Income earned in a week
Years of education
Number of children
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study of a population in its static and dynamic aspects. Static aspects include characteristics such as age, gender and race while dynamic aspect involve fertility and mortality.
Demography
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The study of components of variation and change in demographic variables and the relationships between them.
Demographic Analysis
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The study of the relationships between demographic variables and other variables such as social and economic variables.
Population Study
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structure based on gender, race or other factors that can be inserted to.
Population Composition
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absolute number of a population or any demographic event occurring in a specified area in a specified time period.
Count
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relation of one population subgroup to the total population or to another subgroup; that is, one subgroup divided by another.
Ratio
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frequency of demographic events in population during a specified time period divided by the population “at risk” of the event occurring during that time period.
Rate
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70 new cases of breast cancer per 1,000 women per year*
Incidence Rate
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20 or 130 persons developed diarrhea after attending a picnic.
Attack Rate/Incidence Proportion/Risk
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70 influenza case-patients in March 2019 reported in Taguig City.
Prevalence Rate
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2 deaths due to HIV in Pasay City.
Case-fatality Rate
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relation of a population subgroup to the entire population; that is, a population subgroup divided by the entire population.
Proportion
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unchanging, arbitrary number by which rates, ratios, or proportions can be multiplied to express these measures in a more understandable fashion.
Constant
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statistic that measures events occurring to a COHORT (a group of people sharing a common demographic experience) who are observed through time.
Cohort Measure
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statistic that measures events occurring to all or part of a population during one period of time.
Period Measure
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defined as branch of biometry which deals with data and the law of human mortality, morbidity and demography‖. These are collected, compiled and then analyse.
Vital Statistics
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total process of collecting, compiling and publishing demographic, economic and social data pertaining at a specified time or times, to all persons in a country or delimited territory. It has ten years interval
Census
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the method is to list all persons present in the household or other living quarters at midnight of the census day or all who passed the night there.
De facto census (in fact, whether by right or not)
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all persons who usually live in the household are listed on the form whether they are present or not. Visitors who have a usual residence elsewhere are excluded from the listing but are counted at their usual residence.
De jure census (by right)
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each individual is enumerated separately and characteristics of each person are recorded separately. The census covers a precisely defined territory and includes every person present or residing within its scope. defined reference period. q The census is taken at regular intervals.
Modern census
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used when these changes cannot be measured directly, as for example health or nutritional status
Health Indicators
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They should actually measure what they are supposed to measure
Valid
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The answers should be the same if measured by different people in similar circumstances.
Reliable
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They should be sensitive in the situation concerned
Sensitive
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They should reflect changes only in the situation concerned
Specific
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They should have the ability to obtain data needed
Feasible
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They should contribute to the understanding of the phenomenon
Relevant
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- Can be used for perinatal, neonatal, child, maternal and other more specific variables
- Crude death rate, cause-specific death rate, proportionate mortality, death-to-case ratio, neonatal mortality rate, post-neonatal mortality rate, infant mortality rate, maternal mortality rate
Mortality Indicators
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Incidence proportion/attack rate/ risk, secondary attack rate, incidence rate/person time rate, point prevalence, and period prevalence.
Morbidity Indicators
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- No of days of restricted activity, bed disability rate, work/days lost within a specific period
Event Type Indicators
Disability Rates
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Limitation of mobility
- Limitation of activity
Person Type Indicator
Disability Rates
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- Low birth weight, under weight (weight for age), stunting (height for age), total goiter age, iodized salt coverage, vitamin A deficiency, iron deficiency anemia (pregnant women), iron deficiency anemia (pre-school children)
Nutritional Status Indicators
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- Healthcare workers per 10,000 patients
- Physicians, nurses, midwives, dentists, pharmacists (and of course medical technologists/clinical laboratory scientists)
Healthcare Delivery Indicators
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- Actual coverage expressed as the proportion of people in need of a service who actually receive it in a given period, usually a year. Utilization rates give some indication of the care needed by a population, and therefore the health status of the population.
- A relationship exists between utilization of health care services and health needs and status.
Utilization Rates
140
- Fatigue, sleep impairment, GHQ score.
- General Health Questionnaire (GHQ-12) consists of 12 items, each assessing the severity of a mental problem over the past few weeks using a 4-point scale (from 0 to 3)
Social and Mental Health Indicators
141
These Indicators reflect the quality of physical and biological environment in which diseases occur and in which people live.
Environmental Indicators
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- These indicators do not directly measure health. These are of importance in interpretation of indicators of health care.
- Urban population (%), % living in formal dwelling, serious crime rate per 100,000, contribution to GDP (%), unemployment rate (%), gini index (for income inequality)
Socioeconomic Indicators
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Policy on: physical environment and ecology; socio-economic environment; lifestyle, behavior and risk factors; genetic endowment; and health system.
Health Policy Indicators
144
Income and job, housing conditions, health, education, environmental quality, personal security, civic engagement, work-life balance, infrastructure and services, mobility, and culture and leisure.
Quality of Life Indicators
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