Sinkronisasi Pasal 36 Ayat (2) Peraturan Presiden Nomor 12 Tahun 2013 Tentang Jaminan Kesehatan Terhadap Pasal 23 Ayat (1) Undang-undang Nomor 40 Tahun 2004 Tentang Sistem Jaminan Sosial Nasional Terkait Kerjasama Dengan Badan Penyelenggara Jaminan Sosial

Penelitian mengenai sinkronisasi antara Pasal 36 ayat (2) Peraturan Presiden Nomor 12 Tahun 2013 tentang Jaminan Kesehatan terhadap Pasal 23 ayat (1) Undang-undang Nomor 40 Tahun 2004 tentang Sistem Jaminan Sosial Nasional yang mengatur mengenai kerjasama fasilitas kesehatan milik Pemerintah dan Pemerintah Daerah ini dilatarbelakangi dengan adanya perbedaan dari segi bekerjanya aturan hukum di dalam kedua pasal tersebut, Pasal 36 ayat (2) Peraturan Presiden Nomor 12 Tahun 2013 tentang Jaminan Kesehatan bersifat memaksa karena fasilitas kesehatan milik pemerintah dan pemerintah daerah diwajibkan untuk bekerjasama dengan BPJS Kesehatan, sementara dalam Pasal 23 ayat (1) Undang-undang Nomor 40 Tahun 2004 tentang Sistem Jaminan Sosial Nasional, pasal tersebut bersifat mengatur karena fasilitas kesehatan milik pemerintah atau swasta dapat bekerjasama dengan BPJS atau dapat tidak bekerjasama dengan BPJS. Kemudian berdasarkan hierarki peraturan Perundang-undangan, kedudukan Peraturan Presiden dan Undang-undang tidaklah sama dan melahirkan sebuah pertentangan norma

The estimated DIC of the four models for datasets X4, X5 and X6 at various spatial scales.

<p>The most appropriate spatial scale for fitting dataset <b>X4</b> is at the grid 20×20 using the MATERN2D model or the grid 25×25 using the IGMRF model. For datasets <b>X5</b> and <b>X6</b>, the IID, IGMRF and MATERN2D models perform well at most spatial scales.</p

The estimated DIC of the four models for datasets X1, X2 and X3 at various spatial scales.

<p>The RW2D model fitted at small grid cell sizes appears to be a reasonable choice for dataset <b>X1</b>. For dataset <b>X2</b>, the RW2D model produces the smallest DIC at all spatial scales. The RW2D model also performs better than the three other models at grids 15×15 and above.</p

Summary of the number of events in the grid cells for all the non-zero cell counts at various spatial scales for the Humberside dataset.

<p>Summary of the number of events in the grid cells for all the non-zero cell counts at various spatial scales for the Humberside dataset.</p

The estimated precision parameters of the MATERN2D model at various spatial scales for dataset X4.

<p>The estimated precision parameters of the MATERN2D model at various spatial scales for dataset X4.</p

Summary of results for fitting the four models at various spatial scales for various spatial patterns.

<p>Summary of results for fitting the four models at various spatial scales for various spatial patterns.</p

The estimated DIC and LS of the four models for the Humberside dataset at various spatial scales.

<p>The dataset should be fitted at the grid 30×30 using the MATERN2D prior for spatial smoothing.</p

(a) Six patterns of simulated point-based data (top).

<p>Various spatial patterns are considered, including inhomogeneous point patterns, patterns with local repulsion, patterns with local clustering, and patterns with local clustering in the presence of a larger-scale inhomogeneity. <b>(b) The Humberside data on childhood leukaemia and lymphoma</b> (bottom). The dataset portrays a sparse spatial pattern with a cluster.</p

Additional file 1: of Spatio-temporal mapping of Madagascar’s Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

Figure S1. Sample screening during the three MIS events in Madagascar, showing the progressive delay in the sampling time window. a Overall bar plots of sampling months. b–d Maps by sampling month by cluster location. (PNG 1353 kb