scholarly journals STUDI TINGKAT KEPATUHAN MASYARAKAT PROVINSI KEPULAUAN RIAU UNTUK MELAKSANAKAN SOCIAL/PHYSICAL DISTANCING DALAM UPAYA MENCEGAH PENYEBARAN VIRUS COVID-19

2020 ◽  
Vol 20 (2) ◽  
pp. 222
Author(s):  
Indra Martias ◽  
Luh Pitriyanti ◽  
Novian Aldo
Keyword(s):  
Population Proportion ◽  
Confident Interval

Pemerintah Indonesia sudah melakukan intervensi untuk menekan penyebaran virus Covid-19 yang semakin masif. Namun, bila setengah dari masyarakat tidak melakukan social/physical distancing maka jumlah kasus dan kematian akan terus bertambah. Propinsi Kepulauan Riau terdiri atas 7 kabupaten/kota kepulauan yang berbatasan langsung dengan negara tetangga Singapura, Malaysia dan Vietnam. Berdasarkan hal tersebut perlu dilakukan studi tentang kepatuhan masyarakat Propinsi Kepulauan Riau untuk melaksanakan social/physical distancing dalam upaya mencegah penyebaran virus Covid-19 sebagai Pintu Gerbang Negara Republik Indonesia. Jenis penelitian yang dilakukan adalah penelitian kuantitatif dengan metode survei. Instrumen dalam penelitian ini adalah kuesioner online dengan menggunakan google form. Perhitungan besar sampel dilakukan menggunakan rumus survei Lemeshow dengan jumlah populasi 970.132 jiwa sesuai dengan data jumlah usia produktif, anticipated population proportion 50% dan confident interval 95%. Besar sampel yang diperlukan adalah sebanyak 384 jiwa. Perhitungan besar sampel untuk masing-masing kabupaten/kota dihitung dengan proportional to size (PPS). Hasil penelitian menunjukkan bahwa ada 60% masyarakat yang tidak patuh dan 40% masyarakat yang patuh untuk melaksanakan social/physical distancing di Propinsi Kepulauan Riau. Responden didominasi oleh masyarakat yang tinggal di Kota Tanjungpinang (40,9%) dan Kota Batam (26,7%). Pendidikan responden paling banyak berasal dari perguruan tinggi (51,7%). Adapun akses informasi tentang covid-19 diperoleh paling banyak berasal dari media sosial (93,5%). Masih banyak masyarakat yang tidak patuh terhadap himbauan pemerintah untuk melaksanakan social/physical distancing. Dibutuhkan langkah tegas dari pemerintah khususnya pemerintah Propinsi Kepulauan Riau. Hal ini dimaknai bukan himbauan lagi tapi perintah yang harus dilaksanakan oleh segenap masyarakat Propinsi kepulauan Riau.

scholarly journals COVID-19-related state-wise racial and ethnic disparities across the USA: an observational study based on publicly available data from The COVID Tracking Project

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e048006
Author(s):  
Zhaoying Xian ◽  
Anshul Saxena ◽  
Zulqarnain Javed ◽  
John E Jordan ◽  
Safa Alkarawi ◽  
...  
Keyword(s):  
Asian American ◽  
Alaska Native ◽  
Ethnic Disparities ◽  
Relative Difference ◽  
Ethnic Composition ◽  
Regional Patterns ◽  
Population Proportion ◽  
Asian Populations ◽  
The Usa ◽  
Racial Ethnic

ObjectiveTo evaluate COVID-19 infection and mortality disparities in ethnic and racial subgroups in a state-wise manner across the USA.MethodsPublicly available data from The COVID Tracking Project at The Atlantic were accessed between 9 September 2020 and 14 September 2020. For each state and the District of Columbia, % infection, % death, and % population proportion for subgroups of race (African American/black (AA/black), Asian, American Indian or Alaska Native (AI/AN), and white) and ethnicity (Hispanic/Latino, non-Hispanic) were recorded. Crude and normalised disparity estimates were generated for COVID-19 infection (CDI and NDI) and mortality (CDM and NDM), computed as absolute and relative difference between % infection or % mortality and % population proportion per state. Choropleth map display was created as thematic representation proportionate to CDI, NDI, CDM and NDM.ResultsThe Hispanic population had a median of 158% higher COVID-19 infection relative to their % population proportion (median 158%, IQR 100%–200%). This was followed by AA, with 50% higher COVID-19 infection relative to their % population proportion (median 50%, IQR 25%–100%). The AA population had the most disproportionate mortality, with a median of 46% higher mortality than the % population proportion (median 46%, IQR 18%–66%). Disproportionate impact of COVID-19 was also seen in AI/AN and Asian populations, with 100% excess infections than the % population proportion seen in nine states for AI/AN and seven states for Asian populations. There was no disproportionate impact in the white population in any state.ConclusionsThere are racial/ethnic disparities in COVID-19 infection/mortality, with distinct state-wise patterns across the USA based on racial/ethnic composition. There were missing and inconsistently reported racial/ethnic data in many states. This underscores the need for standardised reporting, attention to specific regional patterns, adequate resource allocation and addressing the underlying social determinants of health adversely affecting chronically marginalised groups.

scholarly journals Tamaño óptimo de la muestra

2017 ◽  
Vol 5 (9) ◽  
Author(s):  
M. H. Badii ◽  
J. Castillo ◽  
A. Guillen
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Key Words ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Bias Estimation ◽  
Population Variance ◽  
Population Proportion ◽  
Estimation Process ◽  
The Mean

Key words: Bias, estimation, population, sampleAbstract. The basics of sample size estimation process are described. Assuming the normal distribution, the procedures for estimation of sample size for the mean; with and without knowledge of the population variance, and population proportion are noted. Sample size for more than one population feature is also given.Palabras clave: Estimación, muestra, población, sesgoResumen. Se describen los fundamentos del proceso de la estimación del tamaño óptimo de la muestra. Suponiendo una distribución normal para una población, se notan los procedimientos de la estimación del tamaño óptimo de la muestra para la media muestral con y sin el conocimiento de la varianza poblacional. Se presenta el tamaño óptimo de la muestra con más de una característica poblacional.

scholarly journals Coverage versus Confidence

2021 ◽  
Vol 23 ◽  
Author(s):  
Peyton Cook
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Coverage Probability ◽  
Negative Binomial ◽  
Asymptotic Confidence Interval ◽  
Population Proportion ◽  
Coverage Probabilities ◽  
The Mean

This article is intended to help students understand the concept of a coverage probability involving confidence intervals. Mathematica is used as a language for describing an algorithm to compute the coverage probability for a simple confidence interval based on the binomial distribution. Then, higher-level functions are used to compute probabilities of expressions in order to obtain coverage probabilities. Several examples are presented: two confidence intervals for a population proportion based on the binomial distribution, an asymptotic confidence interval for the mean of the Poisson distribution, and an asymptotic confidence interval for a population proportion based on the negative binomial distribution.

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