Pemodelan Jumlah Penduduk Miskin Di Provinsi Sumatera Barat Menggunakan Analisis Regresi Spasial

Lestari, Ami; Yozza, Hazmira; Hg, Izzati Rahmi

doi:10.25077/jmu.9.3.222-230.2020

Cited by 2 publications

(2 citation statements)

References 4 publications

(5 reference statements)

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“…Sedangkan, periode Januari-Mei 2023 kasus tuberkulosis yang terkonfirmasi sudah mencapai 44 kasus. Menurut data dari Kementerian Kesehatan menunjukkan, bahwa kota Depok masuk dalam 10 besar kota di Jawa Barat dengan prevalensi angka tuberkulosis resistan obat tertinggi (Rosyanti & Kusumaningtiar, 2020) (Lestari et al, 2023).…”

Section: Pendahuluan *unclassified

Risk Factor Analysis of Drug-Resistant Tuberculosis in Tuberculosis Patients at Level II Health Facilities in Depok City

Rachmita Arianti,

Syamsudin,

Sumarny

et al. 2024

daengku j. hum. soc. sci. innov.

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Based on Global Tuberculosis Report In 2022, Indonesia will be among the 10 countries contributing to the highest incidence of drug-resistant tuberculosis each year. There are 12,794 cases of drug-resistant tuberculosis in Indonesia with 7,884 cases starting treatment. The number of drug-resistant tuberculosis cases involves a variety of risk factors. This study aims to analyze the risk factors associated with drug-resistant tuberculosis. The research method is observational analytic design case control study. The research samples were patients with drug-resistant tuberculosis and drug-sensitive tuberculosis in Depok City Health Facilities for the period March 2022–May 2023. Data were collected using questionnaires and SITB (Tuberculosis Information System). Bivariate analysis using Chi Square, while multiple logistic regression multivariate analysis. The results of the study of 73 cases and 73 controls showed that the majority were aged 18 - 65 years, 89% of cases and 62% of controls, male gender was the most dominant, 64.4% of cases and 52% of controls, most of the respondents did not work 68.5 % of cases and 53.4% of controls, most of them had low income, 82.8% of cases and 62% of controls. Most of the case group had a high level of education, namely 58.5%. Meanwhile, most of the control group had low education, 50.7%. Bivariately, risk factors associated with drug-resistant tuberculosis are HIV, history of previous treatment, adherence to taking medication and the role of drug swallowing supervisor (PMO). Based on multivariate analysis, the dominant variable associated with the incidence of drug-resistant tuberculosis is history of previous treatment (OR6.021 with CI 2.055-18.721) meaning that patients with a history of previous treatment have a 6 times risk of developing drug-resistant tuberculosis compared to patients without a history of previous treatment.

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Section: Pendahuluan *unclassified

Risk Factor Analysis of Drug-Resistant Tuberculosis in Tuberculosis Patients at Level II Health Facilities in Depok City

Rachmita Arianti,

Syamsudin,

Sumarny

et al. 2024

daengku j. hum. soc. sci. innov.

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“…Terkait dengan autokorelasi spasial, (LESTARI et al, 2020) menemukan bahwa penduduk miskin itu saling berkelompok dan jumlah kelompok penduduk miskin di suatu lokasi juga diikuti dengan peningkatan jumlah kelompok penduduk miskin di lokasi lain yang saling berdekatan. Hal itu ditunjukkan dengan nilai Getis-Ord Gi* yang sangat tinggi mendekati 1 yaitu mencapai 0,723.…”

Section: Pendahuluanunclassified

Analisis Autokorelasi Spasial Lisa menggunakan Sistem Informasi Geografis terhadap Angka Tumpatan Gigi di Kabupaten Ciamis

Wibowo

2022

lageografia

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Currently the spatial pattern information of dental and oral health is still limited. One method to study dental and oral health patterns is using spatial approach assisted by a Geographic Information System. Tooth filling itself is one of the treatments to overcome dental and oral health. So this study aims to determine the pattern of grouping and the number of dental fillings spatially. The grouping pattern or called spatial autocorrelation was analyzed using several spatial autocorrelation methods. The methods include analysis of Moran I, Getis-Ord Gi*, and LISA. While LISA stands for Local Indicator of Spatial Association. The research sample is 27 subdistricts. The number of dental fillings in 27 subdistricts ranges from 0 to 163. From the results of the study, it is known that the Moran I index ranges from -0.625 to 0.763. It is known that 12 subdistricts have a Moran I value > 0. While the Getis-Ord Gi* index range is from -1,438 to 5,175 with 12 subdistricts having a Getis-Ord Gi* value > 0. Based on LISA analysis, it is known that there is a spatial autocorrelation and grouping with LISA classification goes into in the HH class covers 2 subdistricts. This means that in the 2 subdistricts the number of dental fillings is known to be the highest, clustered, and has spatial autocorrelation compared to other subdistricts. AbstrakSaat ini informasi pola spasial kesehatan gigi dan mulut masih terbatas. Sedangkan salah satu metode untuk mempelajari pola kesehatan gigi dan mulut adalah dengan menggunakan pendekatan spasial yang dibantu dengan Sistem Informasi Geografis. Tumpatan gigi sendiri adalah salah satu perawatan untuk mengatasi kesehatan gigi dan mulut. Maka penelitian ini bertujuan untuk mengetahui pola pengelompokkan dan jumlah angka tumpatan gigi secara spasial. Pola pengelompokkan atau disebut autokorelasi spasial dianalisa dengan menggunakan beberapa metode autokorelasi spasial. Adapun metode itu mencakup analisis Moran I, Getis-Ord Gi*, dan LISA. Sedangkan LISA merupakan singkatan dari Local Indicator of Spatial Association. Sampel penelitian adalah 27 kecamatan. Angka tumpatan gigi sendiri pada 27 kecamatan berkisar dari 0 sampai 163. Dari hasil penelitian diketahui kisaran indeks Moran I adalah dari -0,625 sampai 0,763. Diketahui bahwa 12 kecamatan memiliki nilai Moran I > 0. Sedangkan kisaran indeks Getis-Ord Gi* adalah dari -1,438 sampai 5,175 dengan 12 kecamatan memiliki nilai Getis-Ord Gi* > 0. Berdasarkan analisa LISA, diketahui adanya autokorelasi spasial dan pengelompokkan dengan klasifikasi LISA masuk ke dalam kelas HH mencakup 2 kecamatan. Artinya di 2 kecamatan itu angka tumpatan gigi diketahui tertinggi, mengelompok, dan memiliki autokorelasi spasial dibandingkan kecamatan lainnya.

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Pemodelan Jumlah Penduduk Miskin Di Provinsi Sumatera Barat Menggunakan Analisis Regresi Spasial

Cited by 2 publications

References 4 publications

Risk Factor Analysis of Drug-Resistant Tuberculosis in Tuberculosis Patients at Level II Health Facilities in Depok City

Risk Factor Analysis of Drug-Resistant Tuberculosis in Tuberculosis Patients at Level II Health Facilities in Depok City

Analisis Autokorelasi Spasial Lisa menggunakan Sistem Informasi Geografis terhadap Angka Tumpatan Gigi di Kabupaten Ciamis

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