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Showing 1 to 15 of 26 data sets matching pregnancy
Data Set/Description Owner Last edited Size Views
North Carolina birth data
A Random Sample of 1000 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
jph422Sep 8, 200837KB5619
North Carolina premature births
A Random Sample of 1000 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
statcrunchhelpApr 10, 20144KB2272
nc2005birth300.xls
A Random Sample of 300 births from the state of North Carolina. Plurarility refers to the number of children associated with the birth. Gender 1=Male, 2=Female. fage is age of father (years), mage is age of mother (years), visits is number of pre-natal medical visits, marital is 1=married, 2=unmarried, racemom is Race of Mother (0=Other Non-white, 1=White, 2=Black 3=American Indian, 4=Chinese, 5=Japanese, 6=Hawaiian, 7=Filipino, 8=Other Asian or Pacific Islander), hispmom is whether mother is of Hispanic origin (C=Cuban, M=Mexican, N=Non-Hispanic, O=Other and Unknown Hispanic, P=Puerto Rican, S=Central/South American, U=Not Classifiable), gained is weight gain during pregnancy (pounds), lowbw is if birth weight is 2500 grams or lower, tpounds is birthweight in pounds, smoke is 0=no, 1=yes for mother admitted to smoking, mature is 0=no, 1-yes for mother is 35 or older, premie is 0=no, 1=yes to being born 36 weeks or sooner.
jph422Nov 5, 200711KB959
Low Birth Weight Study
SOURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986. DESCRIPTIVE ABSTRACT: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. LIST OF VARIABLES: Columns Variable Abbreviation ----------------------------------------------------------------------------- 2-4 Identification Code ID 10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW 1 = Birth Weight < 2500g) 17-18 Age of the Mother in Years AGE 23-25 Weight in Pounds at the Last Menstrual Period LWT 32 Race (1 = White, 2 = Black, 3 = Other) RACE 40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE 48 History of Premature Labor (0 = None 1 = One, etc.) PTL 55 History of Hypertension (1 = Yes, 0 = No) HT 61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI 67 Number of Physician Visits During the First Trimester FTV (0 = None, 1 = One, 2 = Two, etc.) 73-76 Birth Weight in Grams BWT ----------------------------------------------------------------------------- PEDAGOGICAL NOTES: These data have been used as an example of fitting a multiple logistic regression model. STORY BEHIND THE DATA: Low birth weight is an outcome that has been of concern to physicians for years. This is due to the fact that infant mortality rates and birth defect rates are very high for low birth weight babies. A woman's behavior during pregnancy (including diet, smoking habits, and receiving prenatal care) can greatly alter the chances of carrying the baby to term and, consequently, of delivering a baby of normal birth weight. The variables identified in the code sheet given in the table have been shown to be associated with low birth weight in the obstetrical literature. The goal of the current study was to ascertain if these variables were important in the population being served by the medical center where the data were collected. References: 1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
wikipetersonJul 23, 20126KB7818
Guatemalan Family Planning Data v2
BEST TO USE THIS VERSION BECAUSE SOME PROBLEMS HAVE BEEN FIXED. DO NOT CHOOSE age.cat - IT HAS TOO MANY CATEGORIES. This demographic and family planning data was collected by University of Maine at Farmington students in Comalapa, Guatemala in 2007 in partnership with Women’s International Network for Guatemalan Solutions (WINGS). WINGS is non-governmental organization whose mission is to create opportunities for Guatemalan families to improve their lives through family planning education and access to reproductive health services. Demographic data: gender, age.cat, civil.status, occ.place (occupation place), occ.type (occupation type), edu (education), religion, numchildren, pregtimes (pregnancy times – numeric), preg.times.cat (pregnancy times – categorical ),Amenities: car,television,phone,bicycle, Sexual Activity: firstsex (age of first sexual experience – numeric), firstsex.cat(age of first sexual experience – categorical) , everbeenpregnant, fagefirstpreg (age at first pregnancy, females only), mageatfirstchild (age at first child, males only), current.partner, Birth Control Method Awareness: awarebirthcontrol, awareCondoms, awareFemalesterilization, awareMalesterilization, awarePill, awareIUD, awareInjection, awareRhythm, agefirstlearn (age when first learned about birth control), Birth Control Information Obtained from: Healthpromoter, School, Media, Other.obtained (information obtained from another source), Birth Control Usage: everusedbirthcontrol, currusingbirthcontrol, Other Family Planning Data: comfortable (comfortable visiting family planning clinic) , delaying (opinion on delaying a first pregnancy) , spacing.pregnancies (opinion on spacing pregnancies), howlongwaitbetween (how long would they like to wait between pregnancies), limiting (opinion on limiting family size), howmanychildren (how many children would they like to have), pregplans12 (plan to become pregnant within the next 12 months).
swhardyMar 31, 201687KB446
Guatemala Family Planning Data
This demographic and family planning data was collected by University of Maine at Farmington students in Comalapa, Guatemala in 2007 in partnership with Women’s International Network for Guatemalan Solutions (WINGS). WINGS is non-governmental organization whose mission is to create opportunities for Guatemalan families to improve their lives through family planning education and access to reproductive health services. Demographic data: gender, age.cat, civil.status, occ.place (occupation place), occ.type (occupation type), edu (education), religion, numchildren, pregtimes (pregnancy times – numeric), preg.times.cat (pregnancy times – categorical ),Amenities: car,television,phone,bicycle, Sexual Activity: firstsex (age of first sexual experience – numeric), firstsex.cat(age of first sexual experience – categorical) , everbeenpregnant, fagefirstpreg (age at first pregnancy, females only), mageatfirstchild (age at first child, males only), current.partner, Birth Control Method Awareness: awarebirthcontrol, awareCondoms, awareFemalesterilization, awareMalesterilization, awarePill, awareIUD, awareInjection, awareRhythm, agefirstlearn (age when first learned about birth control), Birth Control Information Obtained from: Healthpromoter, School, Media, Other.obtained (information obtained from another source), Birth Control Usage: everusedbirthcontrol, currusingbirthcontrol, Other Family Planning Data: comfortable (comfortable visiting family planning clinic) , delaying (opinion on delaying a first pregnancy) , spacing.pregnancies (opinion on spacing pregnancies), howlongwaitbetween (how long would they like to wait between pregnancies), limiting (opinion on limiting family size), howmanychildren (how many children would they like to have), pregplans12 (plan to become pregnant within the next 12 months).
swhardyMar 9, 201692KB299
Low Birth Weight Study
OURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986. DESCRIPTIVE ABSTRACT: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. LIST OF VARIABLES: Columns Variable Abbreviation ----------------------------------------------------------------------------- 2-4 Identification Code ID 10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW 1 = Birth Weight < 2500g) 17-18 Age of the Mother in Years AGE 23-25 Weight in Pounds at the Last Menstrual Period LWT 32 Race (1 = White, 2 = Black, 3 = Other) RACE 40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE 48 History of Premature Labor (0 = None 1 = One, etc.) PTL 55 History of Hypertension (1 = Yes, 0 = No) HT 61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI 67 Number of Physician Visits During the First Trimester FTV (0 = None, 1 = One, 2 = Two, etc.) 73-76 Birth Weight in Grams BWT ----------------------------------------------------------------------------- PEDAGOGICAL NOTES: These data have been used as an example of fitting a multiple logistic regression model. STORY BEHIND THE DATA: Low birth weight is an outcome that has been of concern to physicians for years. This is due to the fact that infant mortality rates and birth defect rates are very high for low birth weight babies. A woman's behavior during pregnancy (including diet, smoking habits, and receiving prenatal care) can greatly alter the chances of carrying the baby to term and, consequently, of delivering a baby of normal birth weight. The variables identified in the code sheet given in the table have been shown to be associated with low birth weight in the obstetrical literature. The goal of the current study was to ascertain if these variables were important in the population being served by the medical center where the data were collected. References: 1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
lauren.bartschMar 24, 20156KB949
Low Birth Weight Study
SOURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986. DESCRIPTIVE ABSTRACT: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. LIST OF VARIABLES: Columns Variable Abbreviation ----------------------------------------------------------------------------- 2-4 Identification Code ID 10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW 1 = Birth Weight < 2500g) 17-18 Age of the Mother in Years AGE 23-25 Weight in Pounds at the Last Menstrual Period LWT 32 Race (1 = White, 2 = Black, 3 = Other) RACE 40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE 48 History of Premature Labor (0 = None 1 = One, etc.) PTL 55 History of Hypertension (1 = Yes, 0 = No) HT 61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI 67 Number of Physician Visits During the First Trimester FTV (0 = None, 1 = One, 2 = Two, etc.) 73-76 Birth Weight in Grams BWT ----------------------------------------------------------------------------- PEDAGOGICAL NOTES: These data have been used as an example of fitting a multiple logistic regression model. STORY BEHIND THE DATA: Low birth weight is an outcome that has been of concern to physicians for years. This is due to the fact that infant mortality rates and birth defect rates are very high for low birth weight babies. A woman's behavior during pregnancy (including diet, smoking habits, and receiving prenatal care) can greatly alter the chances of carrying the baby to term and, consequently, of delivering a baby of normal birth weight. The variables identified in the code sheet given in the table have been shown to be associated with low birth weight in the obstetrical literature. The goal of the current study was to ascertain if these variables were important in the population being served by the medical center where the data were collected. References: 1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
shleeApr 11, 20166KB763
Teen Birth Rates and Pregnancy Rates (U.S. 2014)riestradaOct 10, 20162KB613
Low Birth Weight Study
SOURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition Data were collected at Baystate Medical Center, Springfield, Massachusetts during 1986. DESCRIPTIVE ABSTRACT: The goal of this study was to identify risk factors associated with giving birth to a low birth weight baby (weighing less than 2500 grams). Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy. LIST OF VARIABLES: Columns Variable Abbreviation ----------------------------------------------------------------------------- 2-4 Identification Code ID 10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW 1 = Birth Weight < 2500g) 17-18 Age of the Mother in Years AGE 23-25 Weight in Pounds at the Last Menstrual Period LWT 32 Race (1 = White, 2 = Black, 3 = Other) RACE 40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE 48 History of Premature Labor (0 = None 1 = One, etc.) PTL 55 History of Hypertension (1 = Yes, 0 = No) HT 61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI 67 Number of Physician Visits During the First Trimester FTV (0 = None, 1 = One, 2 = Two, etc.) 73-76 Birth Weight in Grams BWT ----------------------------------------------------------------------------- References: 1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
cetrohaNov 14, 20166KB255
Sosha Thaxton: Find Data
This data provides numerical data as global infant mortality rates per 1000 births. The data provides categorical data as sets of weeks after birth between 22-23 weeks, 24-27 weeks, 28-31,32-36 weeks, and 37+ weeks and by documenting the results by individual country. I was hopeful that I would find useful and intriguing information on either the website for the Centers for Disease Control and Prevention (CDC) or the website for the World Health Organization (WHO). I have been interested in global health for many years and figured these to be the best places to start. Although I was able to locate an ample amount of studies, this particular study provided the only set of data I found that stat crunch was able to analyze. Without access to health care and a health care professional to diagnose any potential complications and monitor the baby at the embryo, neonate, and infant stages, the infants represented in the data often do not survive due to lack of medical care for the mother before and during pregnancy as well as during and after giving birth.
soshatFeb 20, 2018470B42
Guatemalan Family Planning Data v2
BEST TO USE THIS VERSION BECAUSE SOME PROBLEMS HAVE BEEN FIXED. DO NOT CHOOSE age.cat - IT HAS TOO MANY CATEGORIES. This demographic and family planning data was collected by University of Maine at Farmington students in Comalapa, Guatemala in 2007 in partnership with Women’s International Network for Guatemalan Solutions (WINGS). WINGS is non-governmental organization whose mission is to create opportunities for Guatemalan families to improve their lives through family planning education and access to reproductive health services. Demographic data: gender, age.cat, civil.status, occ.place (occupation place), occ.type (occupation type), edu (education), religion, numchildren, pregtimes (pregnancy times – numeric), preg.times.cat (pregnancy times – categorical ),Amenities: car,television,phone,bicycle, Sexual Activity: firstsex (age of first sexual experience – numeric), firstsex.cat(age of first sexual experience – categorical) , everbeenpregnant, fagefirstpreg (age at first pregnancy, females only), mageatfirstchild (age at first child, males only), current.partner, Birth Control Method Awareness: awarebirthcontrol, awareCondoms, awareFemalesterilization, awareMalesterilization, awarePill, awareIUD, awareInjection, awareRhythm, agefirstlearn (age when first learned about birth control), Birth Control Information Obtained from: Healthpromoter, School, Media, Other.obtained (information obtained from another source), Birth Control Usage: everusedbirthcontrol, currusingbirthcontrol, Other Family Planning Data: comfortable (comfortable visiting family planning clinic) , delaying (opinion on delaying a first pregnancy) , spacing.pregnancies (opinion on spacing pregnancies), howlongwaitbetween (how long would they like to wait between pregnancies), limiting (opinion on limiting family size), howmanychildren (how many children would they like to have), pregplans12 (plan to become pregnant within the next 12 months).
lmcmath34Jun 27, 2016103KB6
teen pregnancy 1970-2016.xlsxadamsruthieJun 6, 2018692B21
teen pregnancy 1970-2016.xlsxadamsruthieJun 6, 2018536B11
Rev02_Smoking_and_pregnancy.xlsturnerkvDec 8, 2008168B138

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