Studies of the Seasonal Pattern of Multiple Maternities

The seasonality of population data has been of great interest in demographic studies. When seasonality is analyzed, the population at risk plays a central role. In a study of the monthly number of births and deaths, the population at risk is the product of the size of the population and the length of the month. Usually, the population can be assumed to be constant, and consequently, the population at risk is proportional to the length of the month. Hence, the number of cases per day has to be analyzed. If one studies the seasonal variation in twin or multiple maternities, the population at risk is the total number of monthly confinements, and the study should be based on the rates of the multiple maternities. Consequently, if one considers monthly twinning rates, the monthly number of birth data is eliminated and one obtains an unaffected seasonality measure of the twin maternities. The strength of the seasonality is measured by a chi-squared test or by the standard deviation. When seasonal models are applied, one must pay special attention to how well the model fits the data. If the goodness of fit is poor, it can erroneously result in a statement that the seasonality is slight, although the observed seasonal fluctuations are marked.

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Demographic Seasonality

Twin Research and Human Genetics ◽

10.1017/thg.2019.18 ◽

2019 ◽

Vol 22 (03) ◽

pp. 187-194 ◽

Cited By ~ 1

Author(s):

Johan Fellman

Keyword(s):

At Risk ◽

Goodness Of Fit ◽

Extreme Values ◽

Demographic Data ◽

Climatic Conditions ◽

Data Sets ◽

Test Statistic ◽

Population At Risk ◽

Chi Squared ◽

Number Of Births

AbstractThe seasonality of demographic data has been of great interest. It depends mainly on the climatic conditions, and the findings may vary from study to study. Commonly, the studies are based on monthly data. The population at risk plays a central role. For births or deaths over short periods, the population at risk is proportional to the lengths of the months. Hence, one must analyze the number of births (and deaths) per day. If one studies the seasonality of multiple maternities, the population at risk is the total monthly number of confinements and the number of multiple maternities in a given month must be compared with the monthly number of all maternities. Consequently, when one considers the monthly rates of multiple maternities, the monthly number of births is eliminated and one obtains an unaffected seasonality measure of the rates. In general, comparisons between the seasonality of different data sets presuppose standardization of the data to indices with common means, mainly 100. If one assumes seasonality as ‘non-flatness’ throughout a year, a chi-squared test would be an option, but this test calculates only the heterogeneity and the same test statistic can be obtained for data sets with extreme values occurring in consecutive months or in separate months. Hence, chi-squared tests for seasonality are weak because of this arbitrariness and cannot be considered a model test. When seasonal models are applied, one must pay special attention to how well the applied model fits the data. If the goodness of fit is poor, nonsignificant models obtained can erroneously lead to statements that the seasonality is slight, although the observed seasonal fluctuations are marked. In this study, we investigate how the application of seasonal models can be applied to different demographic variables.

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Seasonal variation in births in rural East Pakistan

Journal of Biosocial Science ◽

10.1017/s0021932000008373 ◽

1972 ◽

Vol 4 (1) ◽

pp. 107-116 ◽

Cited By ~ 23

Author(s):

John Stoeckel ◽

A. K. M. Alauddin Choudhury

Keyword(s):

Seasonal Variation ◽

Social Factors ◽

Seasonal Pattern ◽

Seasonal Patterns ◽

First Order ◽

Monthly Temperature ◽

Monthly Distribution ◽

Number Of Births

SummaryAn analysis of the monthly distribution of births in two areas of Matlab Thana, East Pakistan, indicates that there is a seasonal variation in births different from what would be expected by chance. The highest proportion of births occur in the last three months of a year and the lowest proportion between May and July. Investigation into some of the environmental and social factors which might contribute to the seasonal pattern revealed the following: mean minimum monthly temperature 9 months before birth was inversely related to the number of births; all occupations had seasonal patterns different from what would be expected by chance and the business and mill-and-office occupations had distributions significantly different from each other; the distribution of births for all pregnancy orders was different from chance and the distribution for first order pregnancies was significantly different from those for third and fourth or higher orders.

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OP0146 TENOSYNOVITIS, SYNOVIAL HYPERTROPHY AND FEET BURSITIS ARE USEFUL ULTRASOUND BIOMARKERS FOR PREDICTING ARTHRITIS DEVELOPMENT IN A POPULATION AT-RISK FOR RHEUMATOID ARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1676 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 87.2-87

Author(s):

Y. Kisten ◽

A. Circiumaru ◽

M. Loberg ◽

N. Vivar-Pomiano ◽

A. Antovic ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

At Risk ◽

Musculoskeletal Complaints ◽

Population At Risk ◽

Synovial Hypertrophy ◽

Extensor Carpi Ulnaris ◽

Chi Squared ◽

Good Potential ◽

Study Participants ◽

Mtp Joints

Background:Musculoskeletal ultrasound (MSUS) evaluation of individuals at risk for developing rheumatoid arthritis (RA) having Anti-Citrullinated Protein Antibody (ACPA) positivity and musculoskeletal complaints, may play an important role in the very early detection of RA.Objectives:We aimed to identify which ultrasound markers could predict arthritis development.Methods:Individuals with musculoskeletal complaints with a positive anti-CCP2 test were referred to the rheumatology department for a detailed clinical (68 joint count) and MSUS examination of the hands, feet and any symptomatic joints. Only those without clinical and/or MSUS detected arthritis were included in the RISK RA prospective cohort and followed-up over 3 years/ or until arthritis onset. Using EULAR-OMERACT guidelines1, MSUS markers for synovial hypertrophy (SH) and hyperemia (Doppler activity) were documented for each visit. Finger and wrist tendons were screened for any signs of tenosynovitis (TS), and between metatarsal joints for bursitis. Association of MSUS biomarkers with arthritis development was tested (comparing proportions) using Chi-Squared or Fisher’s exact tests.Results:288 individuals were included from January 2014 to October 2019 (79% female, 35% RF positive, median age 48 years: IQR: 36-58). Within a median of 38 months (IQR: 1-72) since recruitment, 84 individuals (28%) developed an arthritis diagnosis.Prior to obtaining any diagnosis (at inclusion and/or follow-up visit), 95 of the 288 individuals (33%) had at least one type of MSUS anatomical modification present (around the tendons, joint synovium and/or within bursal cavities), and 56% (53/95) of these individuals eventually developed arthritis. Of the remaining 193 that did not present with any obvious MSUS changes, 16% progressed towards arthritis development.The presence of tenosynovitis was detected in 64 of 288 individuals scanned prior to diagnosis and were more frequent in those developing arthritis (44%, 37/84) as compared to those with TS not developing arthritis (13%, 27/204), p<0.0001. The extensor carpi ulnaris wrist tendons were mostly involved. Sonographic changes within the synovium were noted in 11% (32/288) of all individuals, mostly affecting the metacarpophalangeal (MCP) and metatarsophalangeal (MTP) joints. There was a higher incidence of synovial hypertrophy detected in those developing arthritis (22%, 18/24), as compared to those that remained arthritis free (7%, 14/204), p<0.0001. The MCP joints with synovial hypertrophy were more prone to arthritis development as compared to the MTP’s. Furthermore, we observed a higher frequency of bursitis between the MTP joints in individuals developing arthritis, as compared to individuals having a bursitis who did not develop arthritis (13%, 11/84 versus 7%, 14/204, p=0.009).Conclusion:Ultrasound biomarkers such as tenosynovitis of the extensor carpi ulnaris, synovial hypertrophy of the MCP joints and feet bursitis have good potential to predict arthritis development in a population at-risk for rheumatoid arthritis.References:[1]Maria-Antonietta D’Agostino et al. RMD Open 2017;3:e 000428Acknowledgements:All study participants and patients, including researchers that are part of the multidisciplinary laboratory, clinical and academic teams of the RISK RA study group, as well as all assisting this research in one form or the other are greatly acknowledged.Disclosure of Interests:None declared

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Faculty Opinions recommendation of The international limits and population at risk of Plasmodium vivax transmission in 2009.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.5133959.14726297 ◽

2011 ◽

Author(s):

Patrick Duffy

Keyword(s):

At Risk ◽

Plasmodium Vivax ◽

Population At Risk

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Penyelidikan KLB keracunan makanan acara ruwahan di desa Mulo, Gunung Kidul provinsi DIY

Berita Kedokteran Masyarakat ◽

10.22146/bkm.37616 ◽

2018 ◽

pp. 1

Author(s):

Mur Prasetyaningrum ◽

Z. Chomariyah ◽

Trisno Agung Wibowo

Keyword(s):

At Risk ◽

Total Population ◽

Total Coliform ◽

Case Control ◽

P Value ◽

Common Source ◽

Population At Risk ◽

E Coli

Tujuan: Studi ini untuk mengetahui gambaran KLB keracunan pangan yang terjadi di desa Mulo menurut deskripsi epidemiologi, faktor risiko dan penyebab KLB keracunan makanan. Metode: Studi ini menggunakan studi analitik case control, dimana kasus adalah orang yang mengalami sakit pada tanggal 7 - 8 Mei 2017, tinggal di desa Mulo dan mengkonsumsi makanan olahan dari bapak S dan K. Instrument menggunakan kuesioner. Hasil: KLB terjadi di Desa Mulo RT 5 dan 6 dengan jumlah kasus sebanyak 18 orang dari total population at risk 112 orang dengan gejala utama diare (100%), mual (72,2%), demam (66,6%), pusing (66,6%) dan muntah (50%). Dari diagnosa banding menurut gejala, masa inkubasi dan agent penyebab keracunan, kecurigaan kontaminasi bakteri mengarah pada E. Coli (ETEC). Masa inkubasi 1-16 jam (rata-rata 9 jam) dan common source curve. Penyaji makanan ada dua (pak K dan pak S). Dari perhitungan AR, berdasarkan sumber makanan mengarah pada makanan dari pak S (AR=42,8%). Bedasarkan menu, perhitungan OR dan CI 95 % jenis makanan yang dicurigai sebagai penyebab KLB adalah urap/gudangan (OR=4,33; p value0,0071) dan sayur lombok (OR=6,31; p value 0,0071). Sampel yang didapatkan adalah sampel air bersih, feses, dan muntahan penderita, sampel makanan tidak didapatkan karena keterlambatan informasi dari masyarakat. Hasil laboratorium, Total Coliform sampel air bersih melebihi ambang batas, sampel feses dan muntahan mengandung bakteri Klebsiella pneumonia.Simpulan: Terdapat 3 (tiga) faktor yang diduga sebagai penyebab keracunan pada warga Desa Mulo yaitu air bersih untuk mengolah makanan tercemar bakteri patogen, pengolahan makanan tidak hygienis dan penyajian makanan pada suhu ruang lebih dari 1 jam.

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Missing the Point in Point Prevalence: Harnessing EMR Data to Identify Epi-Linked Patients in an Outbreak Investigation

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.913 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s318-s318

Author(s):

Lisa Stancill ◽

Lauren DiBiase ◽

Emily Sickbert-Bennett

Keyword(s):

At Risk ◽

Disease Transmission ◽

Healthcare Setting ◽

Point Prevalence ◽

Positive Patient ◽

Outbreak Investigation ◽

Population At Risk ◽

Outbreak Investigations ◽

Overlap Method ◽

Prevalence Screen

Background: A critical step during outbreak investigations is actively screening for additional cases to assess ongoing transmission. In the healthcare setting, one widely used method is point-prevalence screening on the whole unit where a positive patient is housed. Although this point-prevalence approach captures the “place,” it can miss the “person” and “time” elements that define the population-at-risk. Methods: At University of North Carolina (UNC) Hospitals, we used business intelligence tools to build a query that harnesses the admission, discharge, and transfer (ADT) data from the electronic medical record (EMR). Using this data identifies every patient who overlapped in time and space with a positive patient. An additional query identifies currently admitted overlap patients and their current location. During an outbreak investigation, an analyst executes these queries in the mornings when surveillance screens are scheduled. The queries generate a list of patients to screen that are prioritized on the number of days they were in the same unit with the positive patient. This overlap methodology successfully captures the person, place, and time associated with possible disease transmission. We implemented the overlap method for the last 3 months following 1 year of point-prevalence approach screening during a novel disease outbreak at UNC Hospitals. Results: In total, 4,385 unique patients overlapped with previously identified infected or colonized patients, of which 781 (17.8%) from 40 departments were screened over 15 months. During a subsequent, currently ongoing, outbreak, we are utilizing the overlap method and in 6 weeks have already screened 161 of the 1,234 overlapping patients (13%). After 3 rounds of overlap screening, we have already been able to identify 1 additional positive patient. This patient was on the same unit as patient zero 4 months prior but was readmitted to a unit that would not have received a point-prevalence screen using the standard approach. Conclusions: Surveillance screening is a time-consuming, resource-intensive effort that requires collaboration between infection prevention, clinical staff, patients, and the laboratory. By harnessing EMR ADT data, we can better target the population at risk and more efficiently utilize resources during outbreak investigations. In addition, the overlap method fills a gap in the current CDC guidelines by focusing on patients who were on the same unit with any positive patient, including those who discharged and readmitted. Most importantly, we identified an additional positive patient that would not have been detected through a point-prevalence screen, helping us prevent further disease transmission.Funding: NoneDisclosures: None

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Effects of Criminal Sanctions on Drunk Drivers: Beyond Incarceration

Crime & Delinquency ◽

10.1177/0011128788034001002 ◽

1988 ◽

Vol 34 (1) ◽

pp. 29-42 ◽

Cited By ~ 18

Author(s):

Gerald R. Wheeler ◽

Rodney V. Hissong

Keyword(s):

At Risk ◽

Statistical Analysis ◽

Survival Time ◽

Drunk Driving ◽

Population At Risk ◽

Criminal Sanctions ◽

Drunk Drivers ◽

First Offenders ◽

Alternative Sanctions

Proponents of mandatory jail laws contend that alternative sanctions such as probation and fines have failed to modify behavior of those convicted of drunk driving (DWI). In order to test this proposition, we evaluated the effects of probation, fines, and jail sentences on DWI recidivism of a randomly selected DWI population at risk for 36 months. Utilizing survival time statistical analysis, the findings showed no significant differences in outcome among sanctions. As predicted, persons with a DWI history recidivated significantly sooner than first offenders. We conclude by advocating a policy of alternative sanctions to incarceration for drunk drivers.

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