Bayesian Estimation ofWarner’s Randomized Response Technique with Transmuted Kumaraswamy Prior

In recent time, the Bayesian approach to randomized response technique has been used for estimating the population proportion especially of respondents possessing sensitive attributes such as induced abortion, tax evasion and shoplifting. This is done by combining suitable prior information about an unknown parameter of the population with the sample information for the estimation of the unknown parameter. In this study, possibility of using a transmuted Kumaraswamy prior is raised, yielding a new Bayes estimator for estimating population proportion of sensitive attribute for Warner’s randomized response technique. Consequently, the proposed Bayes estimator with transmuted Kumaraswamy prior is compared with existing Bayes estimators developed with a simple beta and Kumaraswamy priors in terms of their mean square error. The proposed estimator competes well with the existing estimators for some values of population proportion. The performances of Bayes estimators were also compared using some benchmark data.

Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China

The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development.

Change in cardiovascular risk assessment tool and updated Norwegian guidelines for cardiovascular disease in primary prevention increase the population proportion at risk: the Tromsø Study 2015–2016

AimsTo compare the population proportion at high risk of cardiovascular disease (CVD) using the Norwegian NORRISK 1 that predicts 10-year risk of CVD mortality and the Norwegian national guidelines from 2009, with the updated NORRISK 2 that predicts 10-year risk of both fatal and non-fatal risk of CVD and the Norwegian national guidelines from 2017.MethodsWe included participants from the Norwegian population-based Tromsø Study (2015–2016) aged 40–69 years without a history of CVD (n=16 566). The total proportion eligible for intervention was identified by NORRISK 1 and the 2009 guidelines (serum total cholesterol ≥8 mmol/L, systolic blood pressure ≥160 mm Hg or diastolic blood pressure ≥100 mm Hg) and NORRISK 2 and the 2017 guidelines (serum total cholesterol ≥7 mmol/L, low density lipoprotein (LDL) cholesterol ≥5 mmol/L, systolic blood pressure ≥160 mm Hg or diastolic blood pressure ≥100 mm Hg).ResultsThe total proportion at high risk as defined by a risk score was 12.0% using NORRISK 1 and 9.8% using NORRISK 2. When including single risk factors specified by the guidelines, the total proportion eligible for intervention was 15.5% using NORRISK 1 and the 2009 guidelines and 18.9% using NORRISK 2 and the 2017 guidelines. The lowered threshold for total cholesterol and specified cut-off for LDL cholesterol stand for a large proportion of the increase in population at risk.ConclusionThe population proportion eligible for intervention increased by 3.4 percentage points from 2009 to 2017 using the revised NORRISK 2 score and guidelines.

Geographical variation and factors associated with gastric cancer in Manitoba

Objectives We investigated the spatial disparities and factors associated with gastric cancer (GC) Incidence in Manitoba. Methods We combined information from Manitoba Cancer registry and Census data to obtain an age-sex adjusted relative risk (IRR) of GC incidence. We geocoded the IRR to the 96 regional health authority districts (RHADs) using the postal code conversion file (PCCF). Bayesian spatial and spatio-temporal Poisson regression models were used for the analysis. Results Adjusting for the effect of socio-economic score index (SESI), Indigenous, and immigrant population, 25 districts with high overall GC risk were identified. One unit increase in SESI was associated with reduced risk of cardia GC (CGC) by 14% (IRR = 0.859; 95% CI: 0.780–0.947) and the risk of non-cardia GC (NCGC) by approximately 10% (IRR = 0.898; 95% CI: 0.812–0.995); 1% increase in regional Indigenous population proportion reduced the risk of CGC by 1.4% (IRR = 0.986; 95% CI: 0.978–0.994). In the analysis stratified by sex, one unit increase in SESI reduced the risk of CGC among women by 26.2% (IRR = 0.738; 95% CI: 0.618–0.879), and a 1% increase in Indigenous population proportion reduced the risk of CGC among women by 1.9% (IRR = 0.981; 95% CI: 0.966–0.996). Conclusion Our results support a significant association between SESI and NCGC. We report regional variation of GC IRR and a varying temporal pattern across the RHADs. These results could be used to prioritize interventions for regions with high and progressive risk of GC.

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

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.

Coverage versus Confidence

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.

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

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.