Segunda parte del Curso de Perfeccionamiento Profesional no Conducente a Grado Académico: Inglés Técnico para Profesionales de Ciencias de la Salud. DEPARTAMENTO ADMINISTRATIVO SOCIAL. Escuela de Enfermería. ULA. Mérida. Venezuela. Se oferta en la modalidad presencial de 3 ó 4 unidades crédito y los costos son solidarios y dependen de la zona del país que lo solicite. El inglés técnico se basa en el tipo de vocabulario que va a manejar y el objetivo para el que va a estudiar inglés. En general en inglés técnico se busca poder comprender textos, y principalmente, textos técnicos de las disciplinas de salud en este caso que esté buscando, por ejemplo, si estas estudiando algo que tenga que ver con Medicina o Enfermería, empezara a ver nombres de enfermedades, enfoques epidemiológicos, entre otros. A diferencia del inglés normal que es mayormente comunicación diaria y gramática. Durante las sesiones de aprendizaje se presentan las nociones generales acerca de la gramática de escritura inglesa y su transferencia en nuestra lengua española. En este módulo, se inicia la experiencia práctica eligiendo textos para observar los elementos facilitados. Seguidamente, los participantes las ideas que se encuentran alrededor de fuentes en línea para profundizar en el aprendizaje en materia de inglés técnico.

1. Statistic and Public Health
Dr. MPH José Ivo O. Contreras Briceño

2. Statistics
 What



are Statistics?
One single item or datum in a collection of items
A quantity that is computed from a sample
A branch of mathematics concerned with
collecting, organizing, summarizing and analyzing
data.

3. Origins of the Term
 Originally,
“statistics” referred to the collection
of information about and for the “State”

4. Reasons for Studying Statistics
 Decision


making
We must have some information
In healthcare we use statistics to:







Find out why patients come to the facility
Cost of taking care of patients
Quality of care given
Required by accrediting agencies
Required by third party payors
Prioritizing needed services
Maintain physician specialty mix

5. How do they get to the knowledge?
 Must

Unprocessed facts and figures
 Data

have the data
leads to information
Data that have been deliberately selected,
processed, and organized to be useful
 Information

A piece of information presented as the truth
 Facts

leads to the facts
lead to knowledge
What we know

6. Descriptive Statistics versus Inferential
Statistics
 Descriptive

Describe what the data show
 Inferential

Statistics
Statistics
Help us make inferences or draw conclusions
from a small group of data to a large group

7. Types of Statistics/Analysis

Descriptive Statistics


Frequencies
Basic measurements
Inferential Statistics





Hypothesis Testing
Correlation
Confidence Intervals
Significance Testing
Prediction

Describing a phenomena
How many? How much?
BP, HR, BMI, IQ, etc.
Inferences about a phenomena
Proving or disproving theories
Associations between
phenomena
If sample relates to the larger
population
E.g., Diet and health

8. Descriptive Statistics
Descriptive statistics can be used to summarize
and describe a single variable (aka, UNIvariate)
Frequencies (counts) & Percentages

Use with categorical (nominal) data

Levels, types, groupings, yes/no, Drug A vs. Drug B
Means

& Standard Deviations
Use with continuous (interval/ratio) data

Height, weight, cholesterol, scores on a test

9. Sample vs. Population
Population
Sample

10. Descriptive Statistics
An Illustration:
Which Group is Smarter?
Class A--IQs of 13 Students
102
115
128
109
131
89
98
106
140
119
93
97
110
Class B--IQs of 13 Students
127
162
131
103
96
111
80
109
93
87
120
105
109
Each individual may be different. If you try to understand a group by remembering the
qualities of each member, you become overwhelmed and fail to understand the group.

11. Descriptive Statistics
Which group is smarter now?
Class A--Average IQ
110.54
Class B--Average IQ
110.23
They’re roughly the same!
With a summary descriptive statistic, it is much easier
to answer our question.

12. Descriptive Statistics
Types of descriptive statistics:
 Organize
Data
 Tables
 Graphs
 Summarize
Data
 Central Tendency
 Variation

13. Descriptive Statistics
Types of descriptive statistics:
 Organize Data

Tables



Frequency Distributions
Relative Frequency Distributions
Graphs



Bar Chart or Histogram
Stem and Leaf Plot
Frequency Polygon

14. Descriptive Statistics
Summarizing Data:

Central Tendency (or Groups’ “Middle Values”)
 Mean
 Median
 Mode

Variation (or Summary of Differences Within Groups)
 Range
 Interquartile Range
 Variance
 Standard Deviation

15. Mean
Most commonly called the “average.”
Add up the values for each case and divide by the total
number of cases.
Y-bar =
(Y1 + Y2 + . . . + Yn)
n
Y-bar = Σ Yi
n

16. Median
2.
3.
If the recorded values for a variable form a
symmetric distribution, the median and
mean are identical.
In skewed data, the mean lies further
toward the skew than the median.
Symmetric
Skewed
Mean
Mean
Median
Median

17. Mode
The most common data point is called the
mode.
The combined IQ scores for Classes A & B:
80 87 89 93 93 96 97 98 102 103 105 106 109 109 109 110 111 115 119 120
127 128 131 131 140 162
A la mode!!
BTW, It is possible to have more than one mode!

18. Descriptive Statistics
Summarizing Data:

Central Tendency (or Groups’ “Middle Values”)
 Mean
 Median
 Mode

Variation (or Summary of Differences Within Groups)
 Range
 Interquartile Range
 Variance
 Standard Deviation

19. Range
The spread, or the distance, between the lowest and
highest values of a variable.
To get the range for a variable, you subtract its lowest
value from its highest value.
Class A--IQs of 13 Students
102
115
128
109
131
89
98
106
140
119
93
97
110
Class A Range = 140 - 89 = 51
Class B--IQs of 13 Students
127
162
131
103
96
111
80
109
93
87
120
105
109
Class B Range = 162 - 80 = 82

20. Interquartile Range
A quartile is the value that marks one of the divisions that breaks a series of values into
four equal parts.
The median is a quartile and divides the cases in half.
25th percentile is a quartile that divides the first ¼ of cases from the latter ¾.
75th percentile is a quartile that divides the first ¾ of cases from the latter ¼.
The interquartile range is the distance or range between the 25th percentile and the 75th
percentile. Below, what is the interquartile range?
25%
of
cases
0
25%
250
25%
500
750
25%
of
cases
1000

21. Variance
A measure of the spread of the recorded values on a variable. A
measure of dispersion.
The larger the variance, the further the individual cases are from the
mean.
Mean
The smaller the variance, the closer the individual scores are to the
mean.
Mean

22. Standard Deviation
To convert variance into something of meaning, let’s create
standard deviation.
The square root of the variance reveals the average
deviation of the observations from the mean.
s.d. =
Σ(Yi – Y-bar)2
n-1

23. Standard Deviation
For Class A, the standard deviation is:
235.45
= 15.34
The average of persons’ deviation from the mean IQ of
110.54 is 15.34 IQ points.
Review:
1. Deviation
2. Deviation squared
3. Sum of squares
4. Variance
5. Standard deviation

24. Standard Deviation
1.
Larger s.d. = greater amounts of variation around the mean.
For example:
19
2.
3.
4.
25
31
13
25
37
Y = 25
Y = 25
s.d. = 3
s.d. = 6
s.d. = 0 only when all values are the same (only when you have a
constant and not a “variable”)
If you were to “rescale” a variable, the s.d. would change by the same
magnitude—if we changed units above so the mean equaled 250, the s.d.
on the left would be 30, and on the right, 60
Like the mean, the s.d. will be inflated by an outlier case value.

25. Standard Deviation
 Note




about computational formulas:
Your book provides a useful short-cut formula for
computing the variance and standard deviation.
This is intended to make hand calculations as
quick as possible.
They obscure the conceptual understanding of
our statistics.
SPSS and the computer are “computational
formulas” now.

26. INFERENTIAL STATISTICS
Inferential statistics can be used to prove or
disprove theories, determine associations between
variables, and determine if findings are significant
and whether or not we can generalize from our
sample to the entire population
The types of inferential statistics we will go over:
Correlation
T-tests/ANOVA
Chi-square
Logistic Regression

27. Type of Data & Analysis
 Analysis


Correlation T-tests
T-tests
 Analysis


of Categorical/Nominal Data
of Continuous Data
Chi-square
Logistic Regression

28. Chi-square
 When

When you want to know if there is an association between
two categorical (nominal) variables (i.e., between an
exposure and outcome)


Ex) Smoking (yes/no) and lung cancer (yes/no)
Ex) Obesity (yes/no) and diabetes (yes/no)
 What

to use it?
does a chi-square test tell you?
If the observed frequencies of occurrence in each group
are significantly different from expected frequencies (i.e., a
difference of proportions)

29. Measures of
Population Health

30. Mortality rate
Is a measure of the number of deaths (in general,
or due to a specific cause) in a population, scaled
to the size of that population, per unit of time.
Mortality rate is typically expressed in units of
deaths per 1000 individuals per year; thus, a
mortality rate of 9.5 (out of 1000) in a population
of 1,000 would mean 9.5 deaths per year in that
entire population

31. The crude death rate
The total number of deaths per year per 1000
people
The perinatal mortality rate
The sum of neonatal deaths and fetal deaths
(stillbirths) per 1000 births.

32. The maternal mortality ratio
The number of maternal deaths per 100,000 live
births in same time period.
The maternal mortality rate
The number of maternal deaths per 1,000 women
of reproductive age in the population (generally
defined as 15–44 years of age) .

33. The maternal mortality ratio
The number of maternal deaths per 100,000 live
births in same time period.
The maternal mortality rate
The number of maternal deaths per 1,000 women
of reproductive age in the population (generally
defined as 15–44 years of age) .

34. The infant mortality rate
The number of deaths of children less than 1 year
old per 1000 live births.
The child mortality rate
The number of deaths of children less than 5
years old per 1000 live births.

35. The standardised mortality ratio (SMR)
This represents a proportional comparison to the
numbers of deaths that would have been expected
if the population had been of a standard
composition in terms of age, gender, etc
The age-specific mortality rate (ASMR)
This refers to the total number of deaths per year
per 1000 people of a given age (e.g. age 62 last
birthday).

36. Morbidity Rates
Morbidity rates refer to the number of people
within a certain unit of the general population who
have a certain disease or condition. The unit of
population is generally 100,000, although this may
vary depending on location and the condition in
question. Morbidity rates are used to help
determine the overall prevalence of a specific
illness, as well as where the most instances of the
condition occur when compared to the population
as a whole.

37. Life Expectancy

Summarizes all age-specific mortality rates

Estimates hypothetical length of life of a cohort
born in a particular year

This assumes that current mortality rates will
continue

38. Potential Years of Life Lost (PYLL)

PYLL =  ( “normal age at death” – actual age
at death). Doesn’t much matter what age is
chosen as reference; typically 75

Attempts to represent impact of a disease on
the population: death at a young age is a
greater loss than death of an elderly person

Focuses attention on conditions that kill
younger people (accidents; cancers)

All-causes or cause-specific PYLL

39. Measures of Impact of Interventions to
Reduce Inequalities

Population attributable risk: The reduction in
mortality that would occur if everyone
experienced the rates in the highest
socioeconomic group

Population attributable life lost index: The
absolute or proportional increase in life
expectancy if everyone experienced the life
expectancy of the highest SES group

40. ivojosebrice@gmail.com