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0712P – EPIDEMIOLOGICAL RESEARCH BASED ON LARGE DATA ANALYSIS: STUDY CHARACTERISTICS

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Paper by Omar Hasan Kasule Sr1
1Institute of Medicine, Universiti Brunei Darussalam


Abstract
Analysis of studies published in one volume of each of 3 major epidemiological studies revealed that the sources of data were as follows: routinely collected health data  %, data from defined population cohorts   %, and data from other studies   %. Only  % of the reports were based on data from a sample of the population. The analysis also revealed that the study subjects were many averaging. The paper concludes by highlighting that more thought should be given to the implications of the observed change in the paradigms and practices of epidemiological research.

Introduction
This study was motivated by the observation that recent epidemiological research is based on existing large data-bases or defined cohorts and that 100% sampling was the usual practice. This is a reversal of the traditional epidemiological practice of selecting a probability sample from a study population in order to reach conclusion about the target population.

Developments in information technology and mass access to the internet are opening up new fields of endeavor for the epidemiologist. For example data can be collected from  a large number of people using internet-based questionnaires[1]

The objective of the research was to survey epidemiological research published in 2006 in three high-impact journals to ascertain whether the trend of large-scale data-base studies had become the norm in epidemiological research. The three journals selected for study were the American Journal of Epidemiology 2006 Volume 169 No. 1-12, The International Journal of Epidemiology 2006 Volume 35 No. 1-4, and The European Journal of Epidemiology 2006 Volume 21 No. 1-9.

Methods
The study included only original papers that involved raw data. Reviews, meta-analyses, and analyses based on published data were excluded. A pre-tested data abstract form was used to abstract the following essential information from each original research article: title, authors, issue and volume number, date of publication, type of study (cross sectional, case control, cohort, randomized community control, randomized clinical), Study population (general population, defined population, ongoing study), type of data collection (routinely collected data, newly collected data, previously collected data or a combination among the above) and total number of study subjects (number recruited before any exclusions). Defined populations were hospitals, health insurance of health maintenance organizations, clinics, schools, factories etc were considered defined populations. Cohorts were considered as ongoing studies. For case control studies cases and controls were added up. For ongoing studies it was assumed that data collection was new unless a special mention was made of using previously collected data. The data was keyed into an SPSS data base for categorical analysis using the chi-square test statistic to test for association.

Results
A total of xxx studies were included in the research. Studies in defined groups were distributed as follows: 20 cross sectional, 9 case control, and 25 cohort studies. Studies in the general population were distributed as follows: xx cross sectional, xx case control, and xx cohort studies. Distribution of the studies by method of data collection was: xx newly collected data, xx routinely collected data, and xx previously collected data.


Results will be presented showing the increasing trend of doing epidemiological research based on large data sets of routinely collected data or data left over from previous research. The research trends will be described and characterized regarding size of study, methods of sampling, and implications on both internal and external validity

Discussion
The findings of the study indicate a major change in epidemiological research with serious practical and theoretical implications. The availability of large data bases and high speed computers has encouraged epidemiologists to analyze data without probability sampling. A large data set gives very stable parameters but the same degree of precision could have been obtained from a smaller sample. What is the lost is the ability of the epidemiologist to inspect a small manageable data set, internalize it, and let his intuition act before the data is analyzed. The more intimate contact of the epidemiologist with the data traditionally accounted for deep understanding and discussion which are missed in the new trend. Easy availability of large databases also encourages epidemiologists to plunge into data analysis before serious thought about the research questions. In some cases the research questions can be prompted by preliminary analysis which can lead to numerous biases. Use of large data sets has the advantage of external validity which had never been the primary objective of epidemiological research. Epidemiologists have traditionally aimed at carrying out a small study based on probability sampling so that they can easily identify and control confounding and other sources of bias with the ultimate aim of internal validity. They knew that external validity (generalization) would be attained inductively by consideration of several studies that are internally valid. Use of large sets of routinely collected data also raises the issue of the quality of the data which is collected with service and administrative and not research considerations in mind.

           
TABLE 1: CLASSIFICATION OF ARTICLES BY JOURNAL

AJE
IJE
EJE
TOTAL
p-value
Study design
Cross sectional






Case control






Follow up












Study population
Defined group






General population






On-going study












Data collection
New






Routine






Previous












Total








TABLE 2: MEAN NUMBER OF RESEARCH SUBJECTS BY STUDY CHARACTERISTICS AND JOURNAL

AJE
IJE
EJE
TOTAL
p-value
Study design
Cross sectional






Case control






Follow up












Study population
Defined group






General population












Data collection
New






Routine






Previous












Total








TABLE 3: MEAN NUMBER OF RESEARCH SUBJECTS BY STUDY DESIGNN AND STUDY POPULATION
Study design
Study population
p-value
Defined groups
General population

Cross sectional



Case control



Cohort









TABLE 4: MEAN NUMBER OF STUDY SUBJECTS BY STUDY DESIGN AND TYPE OF DATA COLLECTION
Study design
Tyoe of data collection
p-value
New
Routine
Previous

Cross sectional




Case control




Follow up





















TABLE 5: MEAN NUMBER OF STUDY SUBJECTS BY STUDY POPULATION AND METHOD OF DATA COLLECTION
Study Population



p-value
New
Routine
Previous
Defined group




General population











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[1] Alexandra Ekman, Paul W Dickman, Asa Klint, Elisabete Wederpass, and Jan-Eric Litton. Feasibility of using web-based questionnaires in large population-based epidemiological studies, European Journal of Epidemiology 2006; 21: (2): 103-111.