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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Cross-Sectional Research01:50

Cross-Sectional Research

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Mind the Gap: Hospitalizations from Multiple Sources in a Longitudinal Study.

Samuel T Savitz1, Sally C Stearns1, Jennifer S Groves2

  • 1Department of Health Policy and Management, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 5, 2017
PubMed
Summary

Medicare claims and study data show good agreement for hospitalization records, but completeness varies. Electronic health records may offer a more efficient future data source for elderly populations.

Keywords:
Medicaredata linkagedata sourceshospitalizations

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Area of Science:

  • Epidemiology
  • Health Services Research
  • Gerontology

Background:

  • Medicare claims and prospective studies are key sources for hospitalization data in epidemiologic and outcomes research.
  • Accurate hospitalization data is crucial for understanding disease patterns and treatment effectiveness.

Purpose of the Study:

  • To evaluate the agreement between Medicare claims and interview-based surveillance data.
  • To identify factors influencing the completeness of hospitalization data from different sources.

Main Methods:

  • The Atherosclerosis Risk in Communities (ARIC) study matched hospitalization records (2006-2011) from cohort reports and hospital abstractions to Medicare inpatient records (MedPAR).
  • Statistical analyses, including graphical assessment and multinomial logit regression, were used to determine factors associated with data concordance.
  • The study included 15,792 participants aged 45-64 years at recruitment (1987-1989).

Main Results:

  • Medicare inpatient records (MedPAR) and ARIC hospitalizations matched for approximately 67% of fee-for-service enrollees.
  • Data completeness improved for Medicare Advantage enrollees after hospitals received financial incentives to submit shadow bills starting in 2008.
  • Concordance was influenced by factors such as geographic location, participant age, veteran status, proximity to death, study attrition, and hospitalization within ARIC catchment areas.

Conclusions:

  • While Medicare claims and ARIC records showed good concordance for fee-for-service enrollees, many hospitalizations were recorded in only one dataset.
  • Potential gaps exist in Medicare inpatient records for veterans and observation stays.
  • Sustaining participant engagement enhances data completeness, but emerging sources like electronic health records may prove more efficient for tracking mobile elderly populations.