Administrative 62 R Diagnoses, diseases 70–99

26 National Medical Care Statistics 2014 Sampling æ eig ç è Sampling weight is the inverse of the probability of selecting a unit. 13 The sampling weight of each stratum calculated as follow 14 : �� � = � � � �.��� + � �.��� + � �.��� where M j is the total number of primary care clinics that can be sampled in the j th strata population, m éêë ì í is the number of primary care clinics responded for strata j, m j ê non is the number of primary care clinics who did not respond in the j th strata, and m é êì î c is the number of clinics excluded after being sampled for strata ïð ñ ò èó ô ó èõ æ eigh è The activity weight for each clinic was calculated to account for the different level of activities of each clinic. It was calculated as follows: �� �� = � �� � �� where N jk is the expected patients’ visits per day of the k th clinic in the j th strata while n jk is the number of encounters we received from the k th clinic in the j th strata. ñ d ö ÷ s è ø en è ù ú r non û response To account for less than 100 response rate, adjustment for the non-response is required. 12 The non- response adjustment weight was calculated as follows: � � = � �.��� + � �.��� � �.��� where m éêë ì í is the number of primary care clinics responded for strata j and m j ê non is the number of primary care clinics who did not respond in the j th strata. ü o è ý þ æ eigh è The final weight for each stratum was calculated as the multiplication of the sampling weight, activity weight and adjustment for non-response. �� = �� ×�� ×� The weighted estimates were generated using the survey package in R. 27 Chapter 2 : Methodology �� � = � � � �.��� + � �.��� + � �.��� �� �� = � �� � �� � � = � �.��� + � �.��� � �.��� �� = �� ×�� ×� Statistical analysis Analysis was done in R 15 with an R package called survey: analysis of complex survey samples. 16 Results are presented as number of unweighted counts, weighted counts, proportions and rate per 100 encounters along with 95 confidence interval CI. Rate per 100 diagnoses are reported for management that can occur at more than once per diagnosis.

2.6 ETHICS APPROVAL

The study was approved by the Medical Research and Ethics Committee MREC Approval Number: NMRR-09-842-4718. As per previous study, a public notice was placed at each participating clinic to inform patients that their prescription data would be collected for research purposes. Patients had the right to decline to participate at any point of time throughout the study period.

2.7 LIMITATIONS

1. The survey is self-administered and therefore precision of data depends largely on the completeness of recording by respondents, hence may not accurately reflect true practice. 2. The survey is encounter-based and reflects the morbidity pattern observed in the primary care setting rather than the prevalence of disease in the community. 3. The morbidity patterns reflect only those morbidities managed during the recorded encounters. There may be co-morbidity in the same patient which was not expected to be managed during the encounter and hence was not recorded. 4. This is a cross-sectional study. Therefore, no conclusions may be generated on the outcomes of management of acute and chronic diseases in the primary care setting. Prescriptions, procedures, imaging and referrals reported were those provided at the present point of encounter and did not necessarily indicate that the patient has not already received them in a previous encounter. 5. Maternal child health encounters in public clinics were mostly attended by trained nurses. NMCS 2014 might miss those cases as not all the trained nurses were involved in the study. 6. The sampling of public clinics can be improved by incorporating the classification of the type of clinics, which is based on the workload of the clinic. 7. ÿ erification of data received via audit process was not done. All data received were presumed to be accurate and precise. 8. Benchmarking the sample against population data cannot be performed as there is no readily available primary care population data, be it the providers or the patients. 9. Non-respondent details were not recorded; hence non-response analysis to compare the sample and the non-respondent cannot be performed. 28 National Medical Care Statistics 2014 REFERENCES 1. Cochran WG. Sampling techniques. 2nd ed. New York: John Wiley and Sons, Inc; 1963. 2. Meza RA, Angelis M, Britt H, Miles DA, Seneta E, Bridges-Webb C. Development of sample size models for national general practice surveys. Aust J Public Health. 1995 Feb;191:34-40. 3. Sivasampu S, Yvonne Lim, Norazida AR, Hwong WY, Goh PP, Hisham AN, editors. National Medical Care Statistics NMCS 2012. Kuala Lumpur Malaysia: National Clinical Research Centre MY, National Healthcare Statistics Initiative; 2014. 95 p. Report No.: NCRCHSU2013.3. Grant No.: NMRR-09-842-718. Supported by the Ministry of Health Malaysia. 4. Britt H, Miller GC, Henderson J, Charles J, alenti L, Harrison C, et al. General practice activity in Australia 2011–12. Sydney Australia: Sydney University Press; 2012. p. 184-5. General practice series; no. 31. 5. B ü chele G, Och B, Bolte G, Weiland SK. Single vs. double data entry. Epidemiology. 2005 Jan;161;130-1. 6. Goldberg SI, Niemierko A, Turchin A. Analysis of data errors in clinical research databases. AMIA Annu Symp Proc. 2008 Nov 6:242-6. 7. Fontaine P, Mendenhall TJ, Peterson K, Speedie SM. The Measuring Outcomes of Clinical Connectivity” MOCC trial: investigating data entry errors in the Electronic Primary Care Research Network ePCRN. J Am Board Fam Med. 2007 Mar-Apr;202:151-9. 8. Day S, Fayers P, Harvey D. Double data entry: what value, what price ? Control Clin Trials. 1998 Feb;191:15-24. 9. World Health Organization. World Health Organization family of international classifications [Internet ] . Geneva Switzerland: World Health Organization; 2004 June [cited 2014 Feb 8 ] Available from: http:www.who.intclassificationsenWHOFICFamily.pdf 10. ICPC-2 - International Classification for Primary Care [Interne t ] . Sydney Australia: University of Sydney, Family Medicine Research Centre; c2002-2015 [updated 2012 Nov 22, cited 2014 Jan 12 ] [about 1 screen ] . Available from: http:sydney.edu.aumedicinefmrcicpc-2index.php 11. WHO Collaborating Centre for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment 2012. Oslo Norway: WHO Collaborating Centre for Drug Statistics Methodology; 2011. 12. Lian LM, Kamarudin A, Siti Fauziah A, Nik Nor Aklima NO, Norazida AR, editors. Malaysian Statistics on Medicine 2008. Kuala Lumpur Malaysia: Ministry of Health Malaysia, Pharmaceutical Services Division and Clinical Research Centre; 2013. 166 p. 13. Hahs- aughn DL. A primer for using and understanding weights with national datasets. J Exp Educ. 2005;733:221-48. 14. Foy P. Calculation of sampling weights. In: Martin MO, Kelly DL, editors. Third International Mathematics and Science Study technical report. ol. 2, Implementation and analysis – primary and middle school years. Chestnut Hill MA: Boston College, Center for the Study of Testing, Evaluation, and Educational Policy; c1997. p. 71-9. 15. R Development Core Team. R: a language and environment for statistical computing. ienna Austria: R Foundation for Statistical Computing; 2015. Available from: https:www.R-project.org 16. Lumley T. Analysis of complex survey samples. J Stat Softw. 2004 Apr;98:1-19. CHAPTER three Response Rate