Obesity and Time to Conception

2008 ◽  
Vol 1 ◽  
pp. CMRH.S869 ◽  
Author(s):  
Susan Richman
Keyword(s):  
Body Mass Index ◽  
Data Analysis ◽  
Body Mass ◽  
Prospective Cohort ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Significant Difference ◽  
In Pregnancy

Objective To examine the association between body mass index and time to conception. Methods Secondary data analysis of The Asthma in Pregnancy Study, a Prospective cohort of 2205 women assembled from April 1997-June 2000. Results Time to conception in women not on contraception, by BMI category, did not show a significant difference among the groups, p = 0.81 for test of equality over the strata. Conclusions No association between time to conception and BMI was demonstrated in these data.

The Predictive Relationship of Pediatric Body Mass Indexes and Comorbid Diagnoses

2012 ◽  
Vol 4 (5) ◽  
pp. 283-288
Author(s):  
Jessica Parrott ◽  
Carolyn Rutledge ◽  
Kathie Zimbro ◽  
Laurel Shepherd
Keyword(s):  
Data Analysis ◽  
Children And Adolescents ◽  
Body Mass ◽  
Pediatric Obesity ◽  
Secondary Data ◽  
Original Data ◽  
Secondary Data Analysis ◽  
Health Care Environment ◽  
Health Services Use ◽  
Significant Difference

Pediatric obesity is a leading threat to the long-term health of the population. The rise in pediatric obesity has resulted in increased comorbidities and decreased health status. A secondary data analysis was completed to evaluate the relationship between body mass index (BMI), health care environment, and health services use of pediatric patients. This research used a retrospective secondary data analysis of 942 charts of which 526 contained critical data elements. The information was gathered by means of an original data collection tool. Statistical analysis was achieved through χ2 and ANOVA techniques. The project was congruent with the then current Healthy People 2010 goals of improving health, fitness, and quality of life through daily physical activity for children and adolescents and also aimed to reduce the proportion of children and adolescents who are overweight or obese. There was a statistically significant difference between BMI levels and comorbid diagnoses ( χ2, P = .035) with an increase among obese levels, particularly with abscesses (ANOVA, P = .003). It was found that pediatric obesity results in increased comorbidities, most significantly abscesses.

scholarly journals Factors influencing COVID-19 case burden and fatality rates findings from secondary data analysis of major urban agglomerations in India

2020 ◽  
Vol 7 (8) ◽  
pp. 3284
Author(s):  
Deodatt M. Suryawanshi ◽  
Raghuram Venugopal ◽  
Ramchandra Goyal
Keyword(s):  
Data Analysis ◽  
Negative Correlation ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Secondary Data ◽  
Health Financing ◽  
Secondary Data Analysis ◽  
Fatality Rate ◽  
Health Index ◽  
Significant Difference

In December 2019, SARS COV-2 which originated in the Chinese city of Wuhan achieved pandemic proportions and spread rapidly to countries through International air traffic causing acute respiratory infection and deaths. Presence of International airports, demography, health financing and human developments factors were assumed to influence COVID-19 cases burden and case fatality rate (CFR). So, this study was undertaken to find a association between these factors and COVID-19 cases and deaths. The study used 48 districts using purposive sampling as proxy for cities and used secondary data analysis. Data was obtained for various variables like demographic, Health Financing, Indices and Testing infrastructure, COVID cases burden and case fatality from trusted sources. Descriptive statistics correlational statistics using Pearsons coefficient students T was used to describe, correlate and find significant difference in the data. The analysis found a significant difference between COVID cases burden in districts with International Airports (p<0.039) and those without it. Positive correlation of population density (r=0.65) with COVID-19 case burden and negative correlation of case fatality rate with NITI Aayogs health index (r=-0.12), human development index (HDI) (r=-0.18), per-capita expenditure on health (r=-0.072) and a correlation of r=0.16 was observed for gross state domestic product. Decongestion of cities through perspective urban planning is the need of the hour. Stricter quarantine measures in those districts with international airports can help reduce the transmission. Negative correlation of HDI and NITI Aayogs health index with CFR emphasizes the importance of improvements in social determinants of health.

scholarly journals Vitamin D Status and Long-Term Mortality in Community-Acquired Pneumonia: Secondary Data Analysis from a Prospective Cohort

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0158536 ◽  
Author(s):  
Jan C. Holter ◽  
Thor Ueland ◽  
Jon Norseth ◽  
Cathrine Brunborg ◽  
Stig S. Frøland ◽  
...  
Keyword(s):  
Vitamin D ◽  
Data Analysis ◽  
Prospective Cohort ◽  
Secondary Data ◽  
Secondary Data Analysis ◽  
Community Acquired Pneumonia ◽  
Vitamin D Status ◽  
Long Term Mortality

scholarly journals Concentrations of prealbumin and some appetite-controlling hormones in pregnancies associated with hyperemesis gravidarium

2016 ◽  
Vol 54 (2) ◽  
pp. 258-263 ◽  
Author(s):  
Gulfer Ozturk ◽  
A Seval Ozgu-Erdinc ◽  
Fatma Ucar ◽  
Zeynep Ginis ◽  
Gonul Erden ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Hyperemesis Gravidarum ◽  
Serum Concentrations ◽  
Pregnant Females ◽  
Significant Difference ◽  
Gestational Week ◽  
Serum Ghrelin ◽  
The Relationship ◽  
In Pregnancy

Background Hyperemesis gravidarum, which affects 0.3–2.3% of pregnancies, is defined as excessive vomiting during pregnancy and usually starts in week 4 or 5 of gestation. Symptoms include weight loss, dehydration, ketonaemia, ketonuria, fasting acidosis, alkalosis due to hydrochloric acid loss and hypokalaemia and its exact cause is unknown. The present study was undertaken to investigate the relationship between prealbumin, ghrelin, nesfatin-1 and obestatin concentrations in pregnancies associated with hyperemesis gravidarum. Methods A total of 40 pregnant females with hyperemesis gravidarum and 38 pregnant females without hyperemesis gravidarum as controls were included in this study. Serum concentrations of prealbumin, ghrelin, obestatin and nesfatin-1 were measured. Results There were no significant differences in age, gestational week, gravidity and parity between the two groups. Body mass index was significantly lower in cases than in controls. Serum ghrelin and prealbumin concentrations were significantly lower in cases than in controls ( P  <0.05 and P < 0.001, respectively). There was no significant difference in serum concentrations of obestatin and nesfatin-1 between the two groups. There was no significant association between body mass index and serum ghrelin, nesfatin-1, obestatin or prealbumin concentrations in patients with hyperemesis gravidarum. Conclusions Decreased serum concentrations of ghrelin and prealbumin in patients with hyperemesis gravidarum are independent of body mass index. Based on our results, we believe that ghrelin may be considered to play a role in the aetiopathogenesis of hyperemesis gravidarum and that hyperemesis gravidarum may result in disruption of the relationship between nesfatin-1 and ghrelin. In addition, we believe that the measurement of serum prealbumin may be used for assessing nutritional status in pregnancy.

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