kappa statistics an important health statistical tool in clinical observation n.
Skip this Video
Loading SlideShow in 5 Seconds..
KAPPA STATISTICS: An important health statistical tool in clinical observation PowerPoint Presentation
Download Presentation
KAPPA STATISTICS: An important health statistical tool in clinical observation

KAPPA STATISTICS: An important health statistical tool in clinical observation

290 Vues Download Presentation
Télécharger la présentation

KAPPA STATISTICS: An important health statistical tool in clinical observation

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. KAPPA STATISTICS: An important health statistical tool in clinical observation A.P.TRIPATHI Sr. Process Associate Tata Consultancy Services Noida Uttar Prasesh

  2. INTRODUCTION • The number of sophistication of statistical procedures reported in medical research is increasing. • Due to the collaboration of medical science with other modern science and the advance research in medical science the new diagnostic procedures are invented rapidly.

  3. Medical practitioners are often confused to choose appropriate diagnostic procedures or tools where many are available for a single disease. Theoretically sensitivity, specificity, and productivity of the test are different in different methods. In many situations having good predectivity the method is not operationally feasible.

  4. The problem become more tedious when two different diagnostic procedures having no good agreement in their out come. • The present study is design to assess the agreement between two different diagnostic procedures for a single disease.

  5. The disease of interest is – Acute Respiratory Infection • And the method of diagnosis is • Symptom based diagnosis • Respiratory Rate Count based diagnosis. • In the symptom based diagnosis mainly four symptoms like running nose, cough & could, and fever are considered.

  6. The IMNCI criteria for diagnosis of ARI In respiratory rate count based diagnosis the IMNCHI criteria has been followed.

  7. Data • The present study carried out with a cross-sectional study design in Sunderpur an urban slum of Varanasi district Uttar Pradesh. • The study consist a sample of 129 only under five children. • All the children were clinically diagnosed by both diagnostic procedures as respiratory rate count based & symptom based for ARI.

  8. Diagnosis of the children • Symptom based:- the presence and absence of symptoms (running nose, cough & cold, fever) were recorded. • Respiratory rate count based:- the respiratory rate count were recorded in sleeping position. • According to IMNCHI guideline the children were classified either a case of ARI or normal.

  9. Results of the analysis • The diagnosis based on symptoms of ARI shows that the prevalence of ARI is approximately 23.3 percent. • Which is higher than the national figure (16.2% NFHS 1998-99). • But shows the near about similarity with Kolkata Metropolitan area (21.1% Parthe De, B.N. Bhattacharya, & K.L. Mukharjee “status of ARI & diarrhea among children & their health seeking behavior”).

  10. The diagnosis based on respiratory rate count shows that the prevalence of ARI is 41.1 percent, which is considerably high.

  11. The diagonal agreement (agreement observed) between two diagnostic procedures is 62.015 percent. • While agreement by chance is 54.75 percent. • The value of K is 0.16002, this is a poor agreement

  12. The agreement classificationbased on value of K • 0.93-1.00-excellent agreement 0.81-0.92-very good agreement 0.61-0.80-good agreement 0.41-0.60-fair agreement 0.21-0.40-slight agreement 0.01-0.20-poor agreement <0.00- no agreement Basic & clinical Biostatistics -Beth Dawson

  13. Grater than 0.75 excellent agreement 0.04<K<0.75 intermediate to good agreementbelow 0.40 poor agreementLandis & Koch.

  14. Technical problem • Kappa may be mislead when • There is low prevalence of the event of interest. • There may be biased in diagnostic procedures.

  15. Prevalence adjusted biased adjusted kappa • To avoid these problems the PABK is calculated & PABAK=0.2403 • This shows a slight agreement but not good.

  16. Conclusion • The diagnosis of ARI based on symptoms does not agree with the diagnosis based on respiratory rate count. • For the diagnosis of ARI one should follow Respiratory rate count based diagnosis which is recommended by IMNCHI, UNICHEF, & WHO etc.

  17. There is a need to provide expert committee guideline which is operationally feasible for the diagnosis when many procedures and tools are available for a single disease. The medical practitioners should have no doubt in their mind about the diagnostic procedures and should follow expert committee guideline as it is possible.

  18. Thank you