Hubungan Faktor Demografis Populasi Dengan Kejadian Infeksi COVID-19 Di RS. Abdul Moeloek Provinsi Lampung = Correlation Between The Population Demographic Factor With COVID-19 Infection Event In Abdul Moeloek Hospital, Lampung Province

Siswaatmadja, Sandi Syaka Pratama (2021) Hubungan Faktor Demografis Populasi Dengan Kejadian Infeksi COVID-19 Di RS. Abdul Moeloek Provinsi Lampung = Correlation Between The Population Demographic Factor With COVID-19 Infection Event In Abdul Moeloek Hospital, Lampung Province. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Saat ini ada banyak penelitian dan artikel ilmiah yang meneliti dan mengulas berbagai segi dan sudut dari penyakit bernama COVID-19 beserta dengan virus penyebabnya, namun penelitian komprehensif yang menggabungkan demografi dan indikator kesehatan yang mendorong infeksi dan kematian akibat COVID-19 sangatlah jarang di Indonesia. Pandemi COVID-19 masih berlangsung sampai saat ini sehingga penelitian mendalam perlu dilakukan untuk mengidentifikasi apa saja yang mendorong jumlah kejadian infeksi COVID-19. Penelitian retrospektif ini bertujuan untuk mengidentifikasikan faktor demografis dan jenis faktor diskriminatif apa saja yang berkorelasi dengan kejadian infeksi COVID-19 serta menentukan hubungan jenis apa yang mendorong tingkat infeksi COVID-19. Penelitian akan berfokus ke RSUD Dr. H. Abdul Moeloek Provinsi Lampung sebagai lokasi pengumpulan data pasien yang dirawat dengan rentang tanggal data yang dikumpulkan dari Maret 2020 sampai Maret 2021. Data yang dikumpulkan dan disusun kemudian dimasukkan ke dalam perangkat lunak SPSS Statistics menggunakan metode analisa parametrik atau non-parametrik tergantung dengan distribusi datanya. Hasil analisa deskriptif awal menunjukkan jumlah pasien positif membentuk 91% dari keseluruhan set data yang dibentuk dengan 85.2% pasien sembuh, 9.4% pasien meninggal, dan diketahuinya distribusi data yang tidak normal yang mengarah ke penggunaan metode non-parametrik untuk hasil analisa yang sah khusus untuk data yang tidak normal distribusinya. Kemudian hasil analisa korelasi non-parametrik yakni Spearman dan Kendall menunjukkan korelasi yang signifikannya menonjol berulang kali dalam variabel usia dan durasi perawatan (p = < 0.001) kemudian dilakukan analisa lebih dalam berdasarkan jenis datanya yaitu uji Kruskall-Wallis dan Mood’s median untuk data kuantitatif dan analisa chi-kuadrat untuk data kategorikal. Dari sekian analisa korelasi, telah teridentifikasi faktor demografis dan faktor lainnya yang mendorong jumlah kejadian infeksi dan status terkini pasien COVID-19 yaitu usia dikarenakan oleh proses alami penuaan, durasi perawatan tergantung dengan seberapa parah kondisi penyakit, daerah tempat tinggal dikarenakan oleh kepadatan kota dan zona merah, dan penyakit penyerta berdasarkan jumlah jenis komorbiditas yang menonjol dan seberapa berpengaruhnya penyakit penyerta dengan hasil diagnosa dan status pasien COVID-19 dengan keempat variabel memiliki nilai signifikansi < 0.001. / Currently there are many studies and scientific articles that examine and review various aspects and angles of the disease called COVID-19 along with the virus that causes it, but comprehensive research that combines demographics and health indicators that drive infection and death from COVID-19 is very rare in Indonesia. The COVID-19 pandemic is still ongoing, so in-depth research needs to be done to identify what is driving the number of cases of COVID-19 infection. This retrospective study aims to identify demographic factors and what types of discrimination factors are correlated with the incidence of COVID-19 infection and determine what kind of relationship drives the rate of COVID-19 infection. The research will focus on RSUD Dr. H. Abdul Moeloek Lampung Province as the location for collecting data on patients who were treated there with a date range of data collected from March 2020 to March 2021. The data collected and compiled was then entered into the SPSS Statistics software using parametric or non- parametric analysis methods depending on the distribution the data. The results of the preliminary descriptive analysis show that positive patients makes up 91% of the whole dataset along with 85.2% patients recovered, 9.4% patients are dead, and the data distribution is not normal which leads to the use of non-parametric correlation analysis methods for valid analysis results specifically for data that are not normally distributed. Then the results of the non-parametric correlation analysis, namely Spearman and Kendall, showed multiple times of significant correlation in the variables of age and duration of treatment (p = < 0.001) then a deeper analysis was carried out based on the type of data, namely the Kruskall- Wallis test and Mood's median for quantitative data and chi-square for categorical data. From dozens of correlation analysis, the demographic factors and other factors that drive the number of cases of infection and the current status of COVID-19 patients have been identified, which are age due to the natural process of aging, duration of treatment depending on how severe the disease is, area of residence due to urban density and red zones, and comorbidities based on the number of prominent and common types of comorbidities experienced by patients with COVID-19 infection with the four variables having a significance value of < 0.001.

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