Abstrak: Badan Kesehatan Dunia (WHO) memprediksi adanya peningkatan jumlah penyandang diabetes melitus (DM) menjadi salah satu ancaman kesehatan global. Prediksi kenaikan jumlah penyandang DM di Indonesia dari 8,4 juta pada tahun 2000 menjadi sekitar 21,3 juta pada tahun 2030. Diabetes dan komplikasinya membawa kerugian ekonomi yang besar bagi penderita diabetes dan keluarga mereka, sistem kesehatan dan ekonomi nasional melalui biaya medis langsung, kehilangan pekerjaan dan penghasilan. Kepatuhan pengobatan yang rendah dapat mengakibatkan peningkatan resiko biaya perawatan, peningkatan penyakit komplikasi dan risiko rawat inap. Identifikasi faktorfaktor yang berhubungan dengan rendahnya kepatuhan pasien melakukan pengobatan DM merupakan tujuan dilakukannya penelitian ini sehingga penelitian ini diharapkan dapat memberikan solusi dan strategi untuk meningkatkan kepatuhan pengobatan. Penelitian ini merupakan penelitian analitik observasional dengan desain cross sectional. Data yang digunakan dalam penelitian ini adalah data sekunder Riskesdas 2018 se-Provinsi Bali dengan analisis regresi logistik dan besar hubungan dinyatakan dalam prevalance odds ratio (POR) dengan α= 0,05. Pada analisis multivariat didapatkan model akhir yang berhubungan dengan kepatuhan pengobatan DM di Bali tahun 2018 secara statistik yaitu pekerjaan (POR 1,164 95% CI 1,019-1,329 p-value 0,002), tempat tinggal (POR 0,864 95% CI 0,764-0,978 p-value 0,021), jenis kelamin (POR 0,816 95% CI 0,717-0,929 p-value 0,002), dan usia (POR 0,779 95% CI 0,6650,912 p-value 0,002) sementara tingkat pendidikan tidak bermakna secara statistik

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The World Health Organization (WHO) predicts an increase in the number of people with diabetes mellitus (DM) is one of the global health threats. The predicted increase in the number of people with diabetes in Indonesia from 8.4 million in 2000 to around 21.3 million in 2030.Diabetics and its complications bring huge economic losses to diabetics and their families, the national health system and economy through direct medical costs, lost work and income. Low adherence to medication can result in an increased risk of treatment costs, an increased risk of complications and the risk of hospitalization. Identification of the factors associated with low patient compliance with DM treatment is the aim of this study so that this study is expected to provide solutions and strategies to improve treatment adherence. This study was an observational analytic study with a cross sectional design. The data used in this study are secondary data from Riskesdas 2018 throughout Bali Province with logistic regression analysis and the size of the relationship is expressed in the prevalence odds ratio (POR) with α = 0.05. In the multivariate analysis, the final model that relates to adherence to DM treatment in Bali in 2018 is statistically namely work (POR 1.164 95% CI 1.019-1.329 p-value 0.002), residence (POR 0.864 95% CI 0.764-0.978 p-value 0.021 ), gender (POR 0.816 95% CI 0.717-0.929 p-value 0.002), and age (POR 0.779 95% CI 0.665-0.912 p-value 0.002) while the level of education was not statistically significant

Abstrak: Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling error in specification of the confounders can lead to substantial bias in the adjusted odds ratio for exposure. As a remedy, Tchetgen Tchetgen et al. (Biometrika. 2010;97(1):171-180) recently developed so-called doubly robust estimators of an adjusted odds ratio by carefully combining standard logistic regression with reverse regression analysis, in which exposure is the dependent variable and both the outcome and the confounders are the independent variables. Double robustness implies that only one of the 2 modeling strategies needs to be correct in order to make valid inferences about the odds ratio parameter. In this paper, I aim to introduce this recent methodology into the epidemiologic literature by presenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. A SAS macro (SAS Institute Inc., Cary, North Carolina) is given in an online appendix to facilitate use of the approach in routine epidemiologic practice, and a simulated data example is also provided for the purpose of illustration.