Analytical Epidemiology: Cross sectional study, Case control study and Cohort study | PSM MBBS

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Analytical Epidemiology: Cross sectional study, Case control study and Cohort study | PSM MBBS
Cross-Sectional Study
A cross-sectional study provides a snapshot of a population at a single point in time, capturing both the exposure and the outcome variables simultaneously. Commonly used in public health surveys, these studies are useful for determining the prevalence of a disease or condition. However, they fall short in establishing causality because they don't offer insights into the temporal relationship between exposure and outcome. In medical contexts, they are often employed to assess the prevalence of health behaviors, risk factors, or diseases in a specific population.
Case-Control Study
Case-control studies are retrospective and start with the identification of individuals with the disease outcome (cases) and those without it (controls). These groups are then assessed for past exposure to a certain risk factor. This study design is particularly useful for studying rare diseases or outcomes and can generate hypotheses about causality. However, they are susceptible to various biases like selection bias and recall bias. In medical research, these studies are often used for identifying potential risk factors for specific diseases.
Cohort Study
Cohort studies are prospective in nature and start by identifying a group of individuals who are exposed to a certain factor and another group that is not exposed. These cohorts are then followed over time to observe the occurrence of the disease outcome. This design can effectively establish temporal relationships and is instrumental in deriving causal inferences. They are, however, time-consuming and can be expensive to conduct. In medicine, these studies are frequently used to study the long-term effects of medical treatments or lifestyle choices on health outcomes.
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[@jessen1462]

One of the best explanation. Thank you very much Sir.

[@doctorbhanuprakash]

Thanks and welcome

[@Nope6e07]

The best epidemiologist Big up

[@mariazaka4701]

First time I feel like that a lengthy video can also be interesting and just easy...🤩 Jazakallah Sir for clearing my concepts...✨ I was struggling with this topic...🙃

[@doctorbhanuprakash]

Thanks for liking

[@zenaoby4450]

Thanks for this teaching

[@doctorbhanuprakash]

You are welcome

[@GuruAmirthraj]

thank you so much . first i thought 1 hr video but now understood the concept sir

[@doctorbhanuprakash]

You are most welcome

[@MedicalTreasure77]

Thank you very much ❤. Easily understood

[@loganthirantaigharaja4942]

really love this

[@amanyirebigabwa918]

Wow nice lecture

[@doctorbhanuprakash]

Thanks and welcome

[@dataman6744]

Awesome explanations

[@doctorbhanuprakash]

Tysm

[@hidijahassen]

thanks for the best and easy understandable video but dr please add questions based on cases ,we have exit exam in ethiopian student please help

[@hassansesay1131]

Sir, give us calculation work examples to apply into the formula for better understanding

[@LoveMusicByPriyankaDutta]

Excellent lecture 😊... How can I get the video of descriptive epidemiology sir ?

[@ReemaLatheef]

Thank you so much sir

[@doctorbhanuprakash]

So nice of you

[@jameschuoljohn1400]

Nice presentation and beneficial.

[@doctorbhanuprakash]

Glad it was helpful!

[@onuohachinenye6936]

How do I have access to the previous lecture

[@stonetales_]

Where can i find descriptive epidemiology lecture

[@mathsfun9899]

I can't get the playlists 😢

[@tapeepawar9041]

Also not get

[@kulbhushan2668]

I better understood your lecture than taneja