Information about Statistics and Public Health. Curso de Inglés Técnico para profesionales...

Published on October 30, 2013

Author: ivoulavenezuela

Source: slideshare.net

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.

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.

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.

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

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

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

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

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

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

Sample vs. Population Population Sample

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.

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.

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

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

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

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

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

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!

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

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

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

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

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

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

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.

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.

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

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

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)

Measures of Population Health

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

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.

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) .

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) .

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.

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).

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.

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

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

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

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