Bayesian Poisson Regression Model Research Articles

Janus Kinase Inhibitor Therapy and Risk of Age-Related Macular Degeneration in Autoimmune Disease

The involvement of chronic inflammation in the pathogenesis of age-related macular degeneration (AMD) opens therapeutic possibilities to AMD management. To determine whether Janus kinase inhibitors (JAKis) are associated with a reduced risk of AMD development in patients with autoimmune diseases. This retrospective observational cohort study used administrative claims data from Merative MarketScan research databases (Commercial and Medicare Supplemental) and Optum Clinformatics Data Mart databases between January 1, 2010, and January 31, 2022. Patients with autoimmune diseases satisfying study eligibility criteria and who received JAKi treatment (9126 in MarketScan and 5667 in Optum) were propensity score matched (1:1) to identical numbers of study-eligible patients who received non-JAKi-based immunotherapy. Treatment duration of 6 months or longer. Incidence rates of AMD (exudative and nonexudative) over the first 6 to 18 months of treatment were determined, and bayesian Poisson regression models were used to estimate incidence rate ratios, 95% CIs, and posterior probabilities of AMD. After matching, female sex represented the majority of the patient population in both MarketScan and Optum (14 019/18 252 [76.6%] and 8563/3364 [75.2%], respectively in the JAKi patient population). More than 60% of the patient population was older than 55 years of age in both cohorts. Over the specified treatment period, a 49% relative reduction in incidence of AMD was observed among patients who received JAKi therapy (10/9126 events; adjusted incidence rate ratio [AIRR], 0.51; 95% CI, 0.19-0.90) vs those who received non-JAKi therapy (43/9126 events; AIRR, 1 [reference]) in MarketScan, and a 73% relative reduction in incidence of AMD was observed among patients who received JAKi therapy (3/5667 events; AIRR, 0.27; 95% CI, 0.03-0.74) vs those who received non-JAKi therapy (21/5667 events; AIRR, 1 [reference]) in Optum. The absolute percentage reductions were 0.36% (MarketScan) and 0.32% (Optum), favoring patients who received JAKi therapy. Posterior probabilities of the adjusted risk being less than unity were 97.6% (MarketScan) and 98.9% (Optum) for those who received JAKi therapy vs those who received non-JAKi therapy in MarketScan and Optum, respectively. JAKi use may be associated with a reduced risk of incident AMD in US adults with major autoimmune diseases. The absolute percentage reduction is consistent with a potential role for JAKi in this population. Future studies with long-term follow-up are recommended to investigate the association between JAKi use and incident AMD in other disease indications. Investigation into the role of systemic inflammation and JAK-signal transducers and activators of transcription signaling in AMD may improve understanding of the pathophysiology of AMD and lead to new treatment options.

The Accompanying Effect in Responses to Auditory Perturbations: Unconscious Vocal Adjustments to Unperturbed Parameters.

The present study examined whether participants respond to unperturbed parameters while experiencing specific perturbations in auditory feedback. For instance, we aim to determine if speakers adjust voice loudness when only pitch is artificially altered in auditory feedback. This phenomenon is referred to as the "accompanying effect" in the present study. Thirty native Mandarin speakers were asked to sustain the vowel /ɛ/ for 3 s while their auditory feedback underwent single shifts in one of the three distinct ways: pitch shift (±100 cents; coded as PT), loudness shift (±6 dB; coded as LD), or first formant (F1) shift (±100 Hz; coded as FM). Participants were instructed to ignore the perturbations in their auditory feedback. Response types were categorized based on pitch, loudness, and F1 for each individual trial, such as Popp_Lopp_Fopp indicating opposing responses in all three domains. The accompanying effect appeared 93% of the time. Bayesian Poisson regression models indicate that opposing responses in all three domains (Popp_Lopp_Fopp) were the most prevalent response type across the conditions (PT, LD, and FM). The more frequently used response types exhibited opposing responses and significantly larger response curves than the less frequently used response types. Following responses became more prevalent only when the perturbed stimuli were perceived as voices from someone else (external references), particularly in the FM condition. In terms of isotropy, loudness and F1 tended to change in the same direction rather than loudness and pitch. The presence of the accompanying effect suggests that the motor systems responsible for regulating pitch, loudness, and formants are not entirely independent but rather interconnected to some degree.

Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study.

The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada. Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated. We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline. Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.

Organized abandonment under racial capitalism: Measuring accountable actors of structural racism for public health research and action

Understanding the shifting nature of structural racism historically and across institutions is vital for effective action towards racial health equity. While public health research on structural racism is rapidly increasing, most studies are missing the interdependence of policies and institutional practices over time that shape power imbalances and lead to entrenched health inequities. Here, we discuss Ruth Wilson Gilmore's concept of organized abandonment — the intentional disinvestment in communities which, in turn, creates opportunities for extraction, revenue generation, and carceral enforcement to fill the cracks of a compromised social infrastructure — to encourage action-oriented public health research that is grounded in history and an understanding of racial capitalism. We present a case example using publicly-available data on redlining, gentrification and policing in Seattle, Washington. We mapped the intersections of redlining and gentrification and estimated their neighborhood-level association with police activity using Bayesian spatial Poisson regression models. We found that histories of racist housing policies like redlining and processes of gentrification are interdependent and shape contemporary neighborhood racial and economic segregation and police activity. Compared to structurally advantaged neighborhoods, police stops were higher in neighborhoods that were 1) historically disinvested (i.e. redlined) and remain low-income and structurally disadvantaged and 2) formerly industrial and business districts that were not redlined and are now gentrified. Notably, we found that policing practices were significantly more intensive in neighborhoods that were both high redlined and gentrified. Together, these findings illustrate how the place-based racialized processes of dispossession, displacement and policing are deeply intertwined to maintain racial capitalism. Our findings also highlight the importance of examining multiple racialized processes simultaneously to fill critical gaps in the existing literature that are necessary for sustainable solutions to address structural racism.

Community-acquired Staphylococcus aureus skin and soft tissue infection risk assessment using hotspot analysis and risk maps: the case of California emergency departments

BackgroundCommunity-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk.MethodsUsing a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area.ResultsEmergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods.ConclusionsOur results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Daylight Saving Time Practice and the Rate of Adverse Cardiovascular Events in the United States: A Probabilistic Assessment in a Large Nationwide Study

We investigated the association of daylight saving time (DST) transitions with the rates of adverse cardiovascular events in a large, US-based nationwide study. The study cohort included 36,116,951 unique individuals from deidentified administrative claims data of the OptumLabs Data Warehouse. There were 74,722 total adverse cardiovascular events during DST transition and the control weeks (2 weeks before and after) in spring and autumn of 2015-2019. We used Bayesian hierarchical Poisson regression models to estimate event rate ratios representing the ratio of composite adverse cardiovascular event rates between DST transition and control weeks. There was an average increase of 3% (95% uncertainty interval, −3% to −10%) and 4% (95% uncertainty interval, −2% to −12%) in adverse cardiovascular event rates during Monday and Friday of the spring DST transition, respectively. The probability of this being associated with a moderate-to-large increase in the event rates (estimate event rate ratio, >1.10) was estimated to be less than 6% for Monday and Friday, and less than 1% for the remaining days. During autumn DST transition, the probability of any decrease in adverse cardiovascular event rates was estimated to be less than 46% and a moderate-to-large decrease in the event rates to be less than 4% across all days. Results were similar when adjusted by age. In conclusion, spring DST transition had a suggestive association with a minor increase in adverse cardiovascular event rates but with a very low estimated probability to be of clinical importance. Our findings suggest that DST transitions are unlikely to meaningfully impact the rate of cardiovascular events.

Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

IntroductionMozambique is a high-burden country for tuberculosis (TB). International studies show that TB is a disease that tends to cluster in specific regions, and different risk factors (HIV prevalence, migration, overcrowding, poverty, house condition, temperature, altitude, undernutrition, urbanization, and inadequate access to TB diagnosis and treatment) are reported in the literature to be associated with TB incidence. Although Mozambique has a higher burden of TB, the spatial distribution, and determinants of TB incidence at the sub-national level have not been studied yet for the whole country. Therefore, we aimed to analyze the spatial distribution and determinants of tuberculosis incidence across all 154 districts of Mozambique and identify the hotspot areas. MethodWe conducted an ecological study with the district as our unit of analysis, where we included all cases of tuberculosis diagnosed in Mozambique between 2016 and 2020. We obtained the data from the Mozambique Ministry of Health and other publicly available open sources. The predictor variables were selected based on the literature review and data availability at the district level in Mozambique. The parameters were estimated through Bayesian hierarchical Poisson regression models using Markov Chain Monte Carlo simulation. ResultsA total of 512 877 people were diagnosed with tuberculosis in Mozambique during our five-year study period. We found high variability in the spatial distribution of tuberculosis incidence across the country. Sixty-two districts out of 154 were identified as hotspot areas. The districts with the highest incidence rate were concentrated in the south and the country's central regions. In contrast, those with lower incidence rates were mainly in the north. In the multivariate analysis, we found that TB incidence was positively associated with the prevalence of HIV (RR: 1.23; 95 % CrI 1.13 to 1.34) and negatively associated with the annual average temperature (RR: 0.83; 95 % CrI 0.74 to 0.94). ConclusionThe incidence of tuberculosis is unevenly distributed across the country. Lower average temperature and high HIV prevalence seem to increase TB incidence. Targeting interventions in higher-risk areas and strengthening collaboration between HIV and TB programs is paramount to ending tuberculosis in Mozambique, as established by the WHO's End TB strategy and the Sustainable Development Goals.

Abstract 12827: Daylight Savings Time Practice and the Rate of Adverse Cardiovascular Events in the United States: A Probabilistic Assessment in a Large Nationwide Study

Introduction: Previous studies have shown inconsistent changes in the rates of adverse cardiovascular events after daylight savings time (DST) transitions. In this large-scale, U.S.-based nationwide study, we sought to investigate the association of DST transitions with the rates of adverse cardiovascular events. Methods: The study cohort included 36,116,951 unique individuals from de-identified administrative claims data of the OptumLabs Data Warehouse®. There were 74,722 total adverse cardiovascular events during the week of DST transition and the control weeks (two weeks prior and after) in spring and autumn of 2015-2019. We used Bayesian hierarchical Poisson regression models to estimate the event rate ratios (ERR) representing the ratio of composite adverse cardiovascular event rates (acute myocardial infarction, stroke, cardiogenic shock, cardiac arrest, and sudden death) between DST transition weeks and control weeks. Results: Our results showed an average increase of 3% [95% uncertainty interval; -3% to 10%] and 4% [-2% to 12%] in the rate of adverse cardiovascular events during Monday and Friday of the spring DST transition week, respectively. The probability of spring DST transition being associated with a moderate-to-large increase in the event rates (ERR>1.10) was estimated to be <6% for Monday and Friday, and <1% for the remaining days. During autumn DST transition, the probability of any decrease in adverse cardiovascular event rates was estimated to be <46% and a moderate-to-large decrease in the event rates to be <4% across all days of the week. Conclusions: Spring DST transition week had a suggestive association with a minor increase in adverse cardiovascular event rates on Monday and Friday. The probability of this association to be of clinically meaningful magnitude was estimated to be very low. We did not observe any evidence suggestive of a change in adverse cardiovascular event rates immediately following autumn DST transition. Taken together, our findings, based on a large, diverse cohort from across the United States, suggest that DST transitions are unlikely to significantly impact the rate of cardiovascular events.

Return to sender: Unraveling the role of structural and social network ties in patient sharing networks

Healthcare is increasingly delivered through networks of organizations. Well-structured patient sharing networks are known to have positive associations with the quality of delivered services. However, the drivers of patient sharing relations are rarely studied explicitly. In line with recent developments in network and integration theorizing, we hypothesize that structural and social network ties between organizations are uniquely associated with a higher number of shared patients. We test these hypotheses using a Bayesian zero-dispersed Poisson regression model within the Additive and Multiplicative Effects Framework based on administrative claims data from 732,122 dermatological patients from the Netherlands in 2017. Our results indicate that 2.6% of all dermatological patients are shared and that the amount of shared patients is significantly associated with structural (i.e. emergency contracts) and social (i.e. shared physicians) ties between organizations, confirming our hypotheses. We also find some evidence that patients are shared with more capable organizations. Our findings highlight the role of relational ties in the way health services are delivered. At the same time, they also raise some potential anti-trust concerns.

GOES GLM, biased bolides, and debiased distributions

The large combined field of view of the Geostationary Lightning Mapper (GLM) instruments onboard the GOES weather satellites makes them useful for studying the population of other atmospheric phenomena, such as bolides. Being a lightning mapper, GLM has many detection biases when applied to non-lightning and these systematics must be studied and properly accounted for before precise measurements of bolide flux can be ascertained. We developed a Bayesian Poisson regression model which simultaneously estimates instrumental biases and our statistic of principal interest: the latitudinal variation of bolide flux. We find that the estimated bias due to the angle of incident light upon the instrument corresponds roughly with the known sensitivity of the GLM instruments. We compare our latitudinal flux variation estimates to existing theoretical models and find our estimates consistent with GLM being strongly biased towards high-velocity bolides.

Spatio-temporal analysis of childhood vaccine uptake in Nigeria: a hierarchical Bayesian Zero-inflated Poisson approach

BackgroundGlobally, child mortality and morbidity remain a serious health challenge and infectious diseases are the leading causes. The use of count models together with spatial analysis of the number of doses of childhood vaccines taken is limited in the literature. We used a Bayesian zero-inflated Poisson regression model with spatio-temporal components to assess the number of doses of childhood vaccines taken among children aged 12–23 months and their associated factors.MethodsData of 19,564 children from 2003, 2008, 2013 and 2018 population-based cross-sectional Nigeria Demographic and Health Survey were used. The childhood vaccines include one dose of Bacillus-Calmette-Guérin; three doses of Diphtheria-Pertussis-Tetanus; three doses of Polio and one dose of Measles. Uptake of all nine vaccines was regarded as full vaccination. We examined the multilevel factors associated with the number of doses of childhood vaccines taken using descriptive, bivariable and multivariable Bayesian models. Analysis was conducted in Stata version 16 and R statistical packages, and visualization in ArcGIS.ResultsThe prevalence of full vaccination was 6.5% in 2003, 14.8% in 2008, 21.8% in 2013 and 23.3% in 2018. Full vaccination coverage ranged from 1.7% in Sokoto to 51.9% in Anambra. Factors associated with the number of doses of childhood vaccines taken include maternal age (adjusted Incidence “risk” Ratio (aIRR) = 1.05; 95% Credible Interval (CrI) = 1.03–1.07) for 25–34 years and (aIRR = 1.07; 95% CrI = 1.05–1.10) for 35–49 years and education: (aIRR = 1.11, 95% CrI = 1.09–1.14) for primary and (aIRR = 1.16; 95% CrI = 1.13–1.19) for secondary/tertiary education. Other significant factors are wealth status, antenatal care attendance, working status, use of skilled birth attendants, religion, mother’s desire for the child, community poverty rate, community illiteracy, and community unemployment.ConclusionAlthough full vaccination has remained low, there have been improvements over the years with wide disparities across the states. Improving the uptake of vaccines by educating women on the benefits of hospital delivery and vaccines through radio jingles and posters should be embraced, and state-specific efforts should be made to address inequality in access to routine vaccination in Nigeria.

Estimating the real burden of gestational syphilis in Brazil, 2007–2018: a Bayesian modeling study

Although several studies have estimated gestational syphilis (GS) incidence in several countries, underreporting correction is rarely considered. This study aimed to estimate the level of under-registration and correct the GS incidence rates in the 557 Brazilian microregions. Brazilian GS notifications between 2007 and 2018 were obtained from the SINAN-Syphilis system. A cluster analysis was performed to group microregions according to the quality of GS notification. A Bayesian hierarchical Poisson regression model was applied to estimate the reporting probabilities among the clusters and to correct the associated incidence rates. We estimate that 45,196 (90%-HPD: 13,299; 79,310) GS cases were underreported in Brazil from 2007 to 2018, representing a coverage of 87.12% (90%-HPD: 79.40%; 95.83%) of registered cases, where HPD stands for the Bayesian highest posterior density credible interval. Underreporting levels differ across the country, with microregions in North and Northeast regions presenting the highest percentage of missed cases. After underreporting correction, Brazil's estimated GS incidence rate increased from 8.74 to 10.02 per 1000 live births in the same period. Our findings highlight disparities in the registration level and incidence rate of GS in Brazil, reflecting regional heterogeneity in the quality of syphilis surveillance, access to prenatal care, and childbirth assistance services. This study provides robust evidence to enhance national surveillance systems, guide specific policies for GS detection disease control, and potentially mitigate the harmful consequences of mother-to-child transmission. The methodology might be applied in other regions to correct disease underreporting. National Council for Scientific and Technological Development; The Bill Melinda Gates Foundation and Wellcome Trust.

Increased incidence of congenital hypothyroidism in China: An analysis of 119 million screened newborns.

Despite the lack of nationwide epidemiological studies, congenital hypothyroidism (CH) incidence in China has increased. We aimed to evaluate the trends of CH and the possible reasons behind them. Data from screened newborns from the Chinese Newborn Screening Information System from 2012 to 2019 was collected. We applied the Bayesian hierarchical Poisson regression model and meta-analysis to estimate incidence or proportion over the years. The estimated CH incidence increased from 4.01 per 10,000 births in 2012 to 5.77 per 10,000 births in 2019. The average annual growth rate (ARG) of CH incidence for all provinces varied from 0.59 to 20.96%. The most rapid rise in incidence was observed in cases with an initial thyroid stimulating hormone (TSH) concentration of < 10 mIU/L. The meta-analysis results showed that the proportion of permanent CH increased by 0.024% (95%CI: 0.011%, 0.037%) annually. Each 1 mIU/L decrease in TSH cutoff value was associated with a 2.96% increase in CH incidence. In the same period, the proportion of premature CH cases increased from 6.60 to 9.10%, which was much higher than the increase in preterm births. A significant relationship was not found between provincial growth rates in screening coverage and provincial baseline incidences of CH. Conclusion:CH incidence has substantially increased in China. The slight adjustment of the TSH cutoff value and increasing preterm birth rate contribute to such a trend; however, the contribution is limited. What is Known: • An uptrend in congenital hypothyroidism (CH) incidence has been reported in many European and American countries in the last two decades; however, no studies have been conducted in China to explain the increased CH incidence. • We provide a detailed epidemiological report on the trends of CH during 2012-2019 in China, with an attempt to explore the reasons behind it. What is New: • This first-ever national-wide epidemiological report in China showed an uptrend in CH incidence with variations over regions and CH subtypes. The mild lowering of TSH cutoff values and the increasing preterm birth rate contributed to this uptrend.

An investigation of the effects of lockdowns and COVID-19 vaccinations in Ireland

Abstract. The COVID-19 pandemic resulted in many deaths and much upheaval worldwide. Public health responses to the pandemic differed greatly between countries. In 2023, as we emerge from the aftermath of the pandemic, it is now timely to assess the impact of specific public health response measures such as lockdowns and vaccinations. This assessment can help inform the development of evidence-based strategies for future public health responses in pandemic scenarios. We describe the implementation of a Bayesian Hierarchical Poisson Regression (BHPR) model to estimate the impact of pandemic response measures and vaccination on all-cause deaths, including COVID-19, in Ireland. We find that the implementation of lockdown measures and an appropriate vaccination timeline were effective in reducing the number of deaths in Ireland by, most likely, reducing the COVID- 19 mortality rate. We believe our approach could be used to assess the impact of pandemic response measures and vaccination in other countries as well where similar data is available.

Geographic Variation in Operative Management of Adhesive Small Bowel Obstruction

IntroductionVariation in surgical management exists nationally. We hypothesize that geographic variation exists in adhesive small bowel obstruction (aSBO) management. Materials and methodsA retrospective analysis of a national commercial insurance claims database (MarketScan) sample (2017-2019) was performed in adults with hospital admission due to aSBO. Geographic variation in rates of surgical intervention for aSBO was evaluated by state and compared to a risk-adjusted national baseline using a Bayesian spatial rates Poisson regression model. For individual-level analysis, patients were identified in 2018, with 365-d look back and follow-up periods. Logistic regression was performed for individual-level predictors of operative intervention for aSBO. ResultsTwo thousand one hundred forty-five patients were included. State-level analysis revealed rates of operative intervention for aSBO were significantly higher in Missouri and lower in Florida. On individual-level analysis, age (P < 0.01) and male sex (P < 0.03) but not comorbidity profile or prior aSBO, were negatively associated with undergoing operative management for aSBO. Patients presenting in 2018 with a history of admission for aSBO the year prior experienced a five-fold increase in odds of representation (odds ratio: 5.4, 95% confidence interval: 3.1–9.6) in 2019. Patients who received an operation for aSBO in 2018 reduced the odds of readmission in the next year by 77% (odds ratio: 0.23, 95% confidence interval: 0.1–0.5). The volume of operations performed within a state did not influence readmission. ConclusionsSurgical management of aSBO varies across the continental USA. Operative intervention is associated with decreased rates of representation in the following year. These data highlight a critical need for standardized guidelines for emergency general surgery patients.

Investigating the spatio-temporal variation of hepatitis A in Korea using a Bayesian model.

Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy in a sanitary environment are more susceptible to hepatitis A because they do not have the opportunity to acquire natural immunity. In Korea, hepatitis A is prevalent because of the distribution of uncooked seafood, especially during hot and humid summers. In general, the transmission of hepatitis A is known to be dynamically affected by socioeconomic, environmental, and weather-related factors and is heterogeneous in time and space. In this study, we aimed to investigate the spatio-temporal variation of hepatitis A and the effects of socioeconomic and weather-related factors in Korea using a flexible spatio-temporal model. We propose a Bayesian Poisson regression model coupled with spatio-temporal variability to estimate the effects of risk factors. We used weekly hepatitis A incidence data across 250 districts in Korea from 2016 to 2019. We found spatial and temporal autocorrelations of hepatitis A indicating that the spatial distribution of hepatitis A varied dynamically over time. From the estimation results, we noticed that the districts with large proportions of males and foreigners correspond to higher incidences. The average temperature was positively correlated with the incidence, which is in agreement with other studies showing that the incidences in Korea are noticeable in spring and summer due to the increased outdoor activity and intake of stale seafood. To the best of our knowledge, this study is the first to suggest a spatio-temporal model for hepatitis A across the entirety of Korean. The proposed model could be useful for predicting, preventing, and controlling the spread of hepatitis A.

Role of social determinants of health in differential respiratory exposure and health outcomes among children

BackgroundAttributes defining the Social Determinants of Health (SDoH) are associated with disproportionate exposures to environmental hazards and differential health outcomes among communities. The dynamics between SDoH, disproportionate environmental exposures, and differential health outcomes are often specific to micro-geographic areas.MethodsThis study focused on children less than 20 years of age who lived in Douglas County, Nebraska, during 2016–2019. To assess the role of SDoH in differential exposures, we evaluated the association between SDoH metrics and criteria pollutant concentrations and the association between SDoH and pediatric asthma exacerbations to quantify the role of SDoH in differential pediatric asthma outcomes. The Bayesian Poisson regression model with spatial random effects was used to evaluate associations.ResultsWe identified significant positive associations between the annual mean concentration of criteria pollutants (carbon monoxide, particulate matter2.5, nitrogen dioxide, sulfur dioxide) with race (Non-Hispanic Black and Hispanic/Latino), financial stability, and literacy. Additionally, there were significant positive associations between higher rates of pediatric asthma emergency department visits and neighborhoods with more Non-Hispanic Black children, children without health insurance coverage, and households without access to a vehicle.ConclusionsNon-Hispanic Black and Hispanic/Latino children living in Douglas County, NE experience disproportionately higher exposure to criteria pollutant concentrations. Additionally, higher rates of asthma exacerbations among Non-Hispanic Black children could be due to reduced access to respiratory care that is potentially the result of financial instability and vehicle access. These results could inform city planners and health care providers to mitigate respiratory risks among these higher at-risk populations.

Loss to Follow-Up Risk among HIV Patients on ART in Zimbabwe, 2009-2016: Hierarchical Bayesian Spatio-Temporal Modeling.

Loss to follow-up (LTFU) is a risk factor for poor outcomes in HIV patients. The spatio-temporal risk of LTFU is useful to identify hotspots and guide policy. Secondary data on adult HIV patients attending a clinic in provinces of Zimbabwe between 2009 and 2016 were used to estimate the LTFU risk in each of the 10 provinces. A hierarchical Bayesian spatio-temporal Poisson regression model was fitted using the Integrated Nested Laplace Approximation (INLA) package with LTFU as counts adjusting for age, gender, WHO clinical stage, tuberculosis coinfection and duration on ART. The structured random effects were modelled using the conditional autoregression technique and the temporal random effects were modelled using first-order random walk Gaussian priors. The overall rate of LTFU was 22.7% (95%CI: 22.6/22.8) with Harare (50.28%) and Bulawayo (31.11%) having the highest rates. A one-year increase in the average number of years on ART reduced the risk of LTFU by 35% (relative risk (RR) = 0.651; 95%CI: 0.592–0.712). In general, the provinces with the highest exceedance LTFU risk were Matabeleland South and Matabeleland North. LTFU is one of the drawbacks of HIV prevention. Interventions targeting high-risk regions in the southern and northern regions of Zimbabwe are a priority. Community-based interventions and programmes which mitigate LTFU risk remain essential in the global HIV prevention campaign.

Weighted Bayesian Poisson Regression for The Number of Children Ever Born per Woman in Bangladesh

Number of children ever born to women of reproductive age forms a core component of fertility and is vital to the population dynamics in any country. Using Bangladesh Multiple Indicator Cluster Survey 2019 data, we fitted a novel weighted Bayesian Poisson regression model to identify multi-level individual, household, regional and societal factors of the number of children ever born among married women of reproductive age in Bangladesh. We explored the robustness of our results using multiple prior distributions, and presented the Metropolis algorithm for posterior realizations. The method is compared with regular Bayesian Poisson regression model using a Weighted Bayesian Information Criterion. Factors identified emphasize the need to revisit and strengthen the existing fertility-reduction programs and policies in Bangladesh.

Simulation Study of Bayesian Hurdle Poisson Regression on the Number of Deaths from Chronic Filariasis in Indonesia

One regression model to explain the relationship between predictor and response variable in the form of count is Poisson regression. In the case of certain Poisson with the presence of many zero values, causing overdispersion can be overcome with the Poisson Hurdle model. There is a good method for estimating the parameters on small sample sizes for all distributions, namely the Bayesian method. The response variable of the original data does not follow Poisson distribution, so parameter will be estimated by Bayesian method. The performance of the Bayesian Hurdle Poisson regression can be seen from simulation data on various sample sizes and overdispersion levels generated based on the parameters of original data showing that the Bayesian Hurdle Poisson regression model proposed in this study is suitable for large sample sizes or with varying levels of overdispersion due <img src=image/13426825_01.gif> or <img src=image/13426825_02.gif> because normal distribution is used as prior. Even though the response variable of the simulation data is generated with a Poisson distribution, it still does not follow a Poisson distribution because it's in accordance with the original data. The parameter estimated based on the simulation data is similar to the parameter estimated on the original data (both the estimator of the MLE Hurdle Poisson regression parameter and the parameter estimator of the Bayesian Hurdle Poisson regression). This indicates that the simulation scenario is appropriate.