Gambaran Demografi COVID-19 di Kabupaten Cirebon
The Demographic Factor of COVID-19 in Cirebon
Abstract
Latar belakang: Sebuah penyakit infeksi yang disebabkan oleh virus SARS CoV2 ditetapkan sebagai pandemi pada 11 Maret 2020 setelah menginfeksi 118.000 orang di 114 negara hanya dalam waktu 3 bulan. Penyakit ini menyerang semua lapisan masyarakat sehingga menjadi tantangan global yang sulit dikendalikan.
Tujuan: Berdasarkan hal tersebut peneliti tertarik untuk melihat gambaran demografi berdasarkan usia dan jenis kelamin di Kabupaten Cirebon dimana Kabupaten Cirebon adalah Kabupaten perbatasan Jawa Barat dan Jawa Tengah
Metode: Desain penelitian ini adalah observasional analitik potong lintang dengan sampel sebanyak 36.700 yang diambil dari laboratorium COVID-19 FK UGJ dalam rentang Januari 2022 - Juni 2022.
Hasil: Hasil penelitian menunjukan bahwa penderita COVID-19 mayoritas adalah perempuan (56,7%) prevalensi usia tertinggi adalah pada kelompok usia 26-35 tahun (23,7%).
Kesimpulan: Berdasarkan kesimpulan tersebut maka disarankan kepada Dinas Kesehatan Kota dan Kabupaten Cirebon agar lebih meningkatkan lagi kegiatan promosi kesehatan dan mengajak masyarakat agar berperan aktif dalam upaya pencegahan penularan COVID-19.
References
WHO. WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020 [Internet]. WHO Director General’s speeches. 2020 [cited 2022 May 6]. p. 4. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med [Internet]. 2020 Feb 20 [cited 2023 May 13];382(8):727–33. Available from: https://www.nejm.org/doi/10.1056/NEJMoa2001017
Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA, et al. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020 54 [Internet]. 2020 Mar 2 [cited 2022 Jun 19];5(4):536–44. Available from: https://www.nature.com/articles/s41564-020-0695-z
CDC. SARS-CoV-2 Variant Classifications and Definitions [Internet]. Cdc. 2021 [cited 2022 Jun 3]. p. 1–12. Available from: https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html
World Health Organization (WHO). Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern [Internet]. 2021 [cited 2023 May 14]. Available from: https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern
Mahase E. Covid-19: What do we know about omicron sublineages? BMJ [Internet]. 2022 Feb 11 [cited 2023 Dec 11];376. Available from: https://pubmed.ncbi.nlm.nih.gov/35149516/
Direktorat Surveilans dan Karantina Kesehatanm KR. Bagaimana Tingkat Keparahan Penyakit Saat Terinfeksi COVID-19 Varian Omicron? [Internet]. [cited 2023 Jul 8]. Available from: https://faq.kemkes.go.id/faq/bagaimana-tingkat-keparahan-penyakit-saat-terinfeksi-covid-19-varian-omicron
Direktorat Surveilans dan Karantina Kesehatan KR. Apakah Vaksinasi yang Sudah Dilakukan Efektif Cegah Penularan COVID-19 Varian Omicron? [Internet]. [cited 2023 Jul 8]. Available from: https://faq.kemkes.go.id/faq/apakah-vaksinasi-yang-sudah-dilakukan-efektif-cegah-penularan-covid-19-varian-omicron
Widyawati. Antisipasi Gelombang Ketiga, Kenali Ciri dan Cara Mencegah Penularan Omicron – Sehat Negeriku [Internet]. KEMENKES. 2022 [cited 2023 Jul 8]. Available from: https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20220127/0839222/antisipasi-gelombang-ketiga-kenali-ciri-dan-cara-mencegah-penularan-omicron/
Surveilans COVID-19 Kabupaten Cirebon. Evaluasi Penanganan Kasus COVID-19 Kabupaten Cirebon. Cirebon; 2022.
Cortis D. On Determining the Age Distribution of COVID-19 Pandemic. Front Public Heal. 2020 May 15;8:548691.
Davies NG, Klepac P, Liu Y, Prem K, Jit M, Pearson CAB, et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med [Internet]. 2020 Jun 16 [cited 2023 Dec 10];26(8):1205–11. Available from: https://www.nature.com/articles/s41591-020-0962-9
Pennington AF, Kompaniyets L, Summers AD, Danielson ML, Goodman AB, Chevinsky JR, et al. Risk of Clinical Severity by Age and Race/Ethnicity among Adults Hospitalized for COVID-19 - United States, March-September 2020. Open Forum Infect Dis. 2021 Feb 1;8(2).
Czeisler MÉ, Tynan MA, Howard ME, Honeycutt S, Fulmer EB, Daniel ;, et al. MMWR - Public Attitudes, Behaviors, and Beliefs Related to COVID-19, Stay-at-Home Orders, Nonessential Business Closures, and Public Health Guidance — United States, New York City, and Los Angeles, May 5–12, 2020. [cited 2023 Dec 11]; Available from: https://www.hrsa.gov/rural-health/about-us/
Furuse Y, Sando E, Tsuchiya N, Miyahara R, Yasuda I, K.Ko Y, et al. Clusters of Coronavirus Disease in Communities, Japan, January–April 2020. Emerg Infect Dis [Internet]. 2020 Sep 1 [cited 2023 Dec 11];26(9):2176. Available from: /pmc/articles/PMC7454082/
Boehmer TK, Devies J, Caruso E, Van Santen KL, Tang S, Black CL, et al. Morbidity and Mortality Weekly Report Changing Age Distribution of the COVID-19 Pandemic-United States, May-August 2020. [cited 2023 Dec 11]; Available from: https://www.cdc.gov/covid-data-tracker/index.html#trends.
Wu C, Qian Y. The gender peak effect: Women are most vulnerable to infections during COVID-19 peaks. Front Public Heal [Internet]. 2022 Aug 9 [cited 2023 Dec 10];10. Available from: /pmc/articles/PMC9395988/
Kabeer N, Razavi S, van der Meulen Rodgers Y. Feminist Economic Perspectives on the COVID-19 Pandemic. Fem Econ [Internet]. 2021 Apr 3 [cited 2023 Dec 12];27(1–2):1–29. Available from: https://www.tandfonline.com/doi/abs/10.1080/13545701.2021.1876906
Doerre Id A, Doblhammer G. The influence of gender on COVID-19 infections and mortality in Germany: Insights from age- and gender-specific modeling of contact rates, infections, and deaths in the early phase of the pandemic. 2022; Available from: https://doi.org/10.1371/journal.pone.0268119.g001
Sobotka T, Brzozowska Z, Muttarak R, Zeman K, Lego V di. Age, gender and COVID-19 infections. medRxiv [Internet]. 2020 May 26 [cited 2023 Dec 12];2020.05.24.20111765. Available from: https://www.medrxiv.org/content/10.1101/2020.05.24.20111765v1
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