Socio-environmental predictors of diabetes incidence disparities in Tanzania mainland: a comparison of regression models for count data

BACKGROUND

Diabetes is one of the top four non-communicable diseases that cause death and illness to many people around the world. This study aims to use an efficient count data model to estimate socio-environmental factors associated with diabetes incidences in Tanzania mainland, addressing lack of evidence on the efficient count data model for estimating factors associated with disease incidences disparities.

METHODS

This study analyzed diabetes counts in 184 Tanzania mainland councils collected in 2020. The study applied generalized Poisson, negative binomial, and Poisson count data models and evaluated their adequacy using information criteria and Pearson chi-square values.

RESULTS

The data were over-dispersed, as evidenced by the mean and variance values and the positively skewed histograms. The results revealed uneven distribution of diabetes incidence across geographical locations, with northern and urban councils having more cases. Factors like population, GDP, and hospital numbers were associated with diabetes counts. The GP model performed better than NB and Poisson models.

CONCLUSION

The occurrence of diabetes can be attributed to geographical locations. To address this public health issue, environmental interventions can be implemented. Additionally, the generalized Poisson model is an effective tool for analyzing health information system count data across different population subgroups.

Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking framework

INTRODUCTION

This study aims to increase the prediction accuracy of crash frequency on roadway segments that can forecast future safety on roadway facilities. A variety of statistical and machine learning (ML) methods are used to model crash frequency with ML methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including "stacking," have emerged as more accurate and robust intelligent techniques providing more reliable and accurate predictions.

METHODS

This study applies "Stacking" to model crash frequency on five-lane undivided (5 T) segments of urban and suburban arterials. The prediction performance of "Stacking" is compared with parametric statistical models (Poisson and negative binomial) and three state-of-the-art ML techniques (Decision tree, random forest, and gradient boosting), each of which is termed as the base-learner. By employing an optimal weight scheme to combine individual base-learners through stacking, the problem of biased predictions in individual base-learners due to differences in specifications and prediction accuracies is avoided. Data including crash, traffic, and roadway inventory were collected and integrated from 2013 to 2017. The data are split into training (2013-2015), validation (2016), and testing (2017) datasets. After training five individual base-learners using training data, prediction outcomes are obtained for the five base-learners using validation data that are then used to train a meta-learner.

RESULTS

Results of statistical models reveal that crashes increase with the density (number per mile) of commercial driveways whereas decrease with average offset distance to fixed objects. Individual ML methods show similar results - in terms of variable importance. A comparison of out-of-sample predictions of various models or methods confirms the superiority of "Stacking" over the alternative methods considered.

CONCLUSIONS AND PRACTICAL APPLICATIONS

From a practical standpoint, "stacking" can enhance prediction accuracy (compared to only one base-learner with a particular specification). When applied systemically, stacking can help identify more appropriate countermeasures.

Penalized likelihood methods for modeling count data

The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a moderate sample size per variable. The data were collected during the assessment of oral reading fluency (ORF) in school-aged children. A sample of fourth-grade students were given one of ten available passages to read with these differing in length and difficulty. The observed number of words read incorrectly (WRI) is used to measure ORF. Three models are considered for WRI scores, namely the binomial, the zero-inflated binomial, and the beta-binomial. We aim to efficiently estimate passage difficulty, a quantity expressed as a function of the underlying model parameters. Two types of penalty functions are considered for penalized likelihood with respective goals of shrinking parameter estimates closer to zero or closer to one another. A simulation study evaluates the efficacy of the shrinkage estimates using Mean Square Error (MSE) as metric. Big reductions in MSE relative to unpenalized maximum likelihood are observed. The paper concludes with an analysis of the motivating ORF data.

How do air pollution and meteorological parameters contribute to the spread of COVID-19 in Saudi Arabia?

The current global health crisis is unprecedented in modern times. It has killed numerous people, caused great suffering, and turned many people's lives upside down. This study seeks to investigate the role of some pollutants and the meteorological parameters in the transmission of the virus (SARS-CoV-2). The number of infections identified in Saudi Arabia, a country with a hot climate, was studied for a period between March 9, 2020 and November 19, 2020, which was characterized by a single wave with a peak of 4,919 cases on June 17, 2020. Based on count data models, we observed that air pollution and meteorological parameters considerably influenced the daily evolution of infections in most affected cities of Saudi Arabia (Riyadh, Jeddah, and Makkah) where the prevalence of the disease was relatively high during summer 2020. Our study suggests that air pollution could be a significant risk factor for respiratory infections and virus transmission. On the other hand, meteorological factors and high concentration of air pollutants should be taken into account by public decision-makers in Saudi Arabia when seeking to limit COVID-19 transmission.

The value of blue-space recreation and perceived water quality across Europe: A contingent behaviour study

This study estimates the value of recreational visits to blue-space sites across 14 EU Member States, representing 78% of the Union's population. Across all countries surveyed, respondents made an average of 47 blue-space visits per person per year. Employing travel cost and contingent behaviour methods, the value of a visit is estimated at €41.32 which adds up to a recreational value of €631bn per year for the total adult population surveyed. Using the Bathing Water Directive's water quality designation, the analysis shows that a one-level improvement in water quality leads to 3.13 more visits (+6.67%), whereas a one-level deterioration leads to 9.77 fewer annual visits (-20.83%). This study provides valuations of benefits of recreation and changes of recreational values due to changes in surface water quality, which can be compared to the implementation and monitoring costs of efforts under the EU's Bathing Water, Water Framework and Marine Strategy Framework Directives.

Determinants of CO2 emissions generated by air travel vary across reasons for the trip

This study estimates factors of importance for the carbon dioxide equivalent (CO2e) emissions generated by travellers flying for different reasons based on representative Austrian micro data for the period 2014-2016. The annual average number of flights taken by adults vary between 0.1 (visiting friends) and 0.8 (going on holiday), and the amount of CO2e emissions generated by each return flight is approximately 1100 kg. This leads to a total of 6 million tonnes CO2e emissions per year. Results of the Pseudo Poisson Maximum Likelihood estimations reveal that the amount of CO2e emissions created is related to socio-demographic, locational and seasonal factors, although mainly for the largest group of travellers: the holiday makers. In this group, individuals with university degrees, young persons (16-24 years) and capital city residents generate the largest amounts of emissions, as opposed to persons with children and large households. Residents of the capital region each quarter cause 64 kg more CO2e emissions than inhabitants of rural areas, persons with university degrees create 74 kg larger emissions than those without degrees and young adults instigate 90 kg more emissions than middle-aged persons. CO2e emissions of holiday flights are highest in the first quarter of the year. The importance of education is also pronounced for CO2e emissions related to business travel, as is gender.

Respiratory illness, hospital visits, and health costs: Is it air pollution or pollen?

BACKGROUND

Both air pollution and airborne pollen can cause respiratory health problems. Since both are often jointly present in ambient air, it is important to control for one while estimating the effect of the other when considering pollution-abating policies. To date only a limited number of studies have considered the health effects of both irritants jointly for a general population, and for a sufficiently long time period to allow for variation in seasonal concentrations of both components. The primary goal of this study is to determine the causal impact of fine particulate matter (PM_2.5) on hospital visits and related treatment costs, while controlling for potentially confounding pollen effects. Our study area is the metropolitan hub of Reno/Sparks in Northern Nevada.

METHODS

Taking advantage of a rare sample of daily pollen counts over a prolonged period of time (2009-2015), we model the effects of PM_2.5 and pollen on respiratory-related hospital admissions for the population at large, plus specific age groups. Pollen data are provided by a local allergy clinic. Data on PM_2.5 and other air pollutants are obtained from the U.S. Environmental Protection Agency's air quality data web site. We collect daily meteorological data from the National Centers for Environmental Information's data repository. Data on hospital admissions are given by the Nevada Center for Surveys, Evaluations, and Statistics. Our econometric approach centers on a fully robust count data (Poisson) model, estimated via Quasi-Maximum Likelihood.

RESULTS

We find that for our sample PM_2.5 effects are largely robust to the inclusion of both pollen counts and temporal indicators. In contrast, pollen effects vanish when time fixed effects are added, pointing at their correlation with unobserved temporal confounders. At the same time, model fit improves with the inclusion of temporal indicators. Based on our preferred specification, we find a significant PM_2.5 effect of approximately 0.5% additional hospital visits per day due to a one μg/m³ increase in PM_2.5. This translates into expected augmented treatment costs of $2700 per day for the same unit-change in PM_2.5. These figures can mount quickly when more pronounced and/or longer episodes of particulate matter pollution are considered, perhaps due to wildfire smoke. For instance, the expected increase in patients and costs due to a month-long 10-unit-jump of PM_2.5 over the long-run annual average would amount to an extra 70 patients and approximately $680,000 in additional treatment costs.

Modelling adoption intensity of improved soybean production technologies in Ghana - a Generalized Poisson approach

Soybean is an important cash crop especially for farmers in the north of Ghana. However, cultivation of the commodity is dominated by smallholders equipped with traditional tools, coupled with low or no adoption of improved soybean production technologies. Using primary data collected from 300 soybean farmers across northern Ghana, the study employed count data modelling to estimate the determinants of adoption intensity of sustainable soybean production technologies. The study accounted for potential estimation errors due to under-dispersion and over-dispersion, by using a model based on the generalized Poisson distribution. On the average, a farmer adopted 50% of the identified sustainable soybean production technologies. Age, education, extension visits, mass media through radio, and the perception of adoption of soybean production technologies being risky are significant with positive influence on the adoption intensity of sustainable soybean production technologies. The study therefore recommends among others, that various extension programmes should intensify education on the benefits of adopting sustainable soybean production practices. There is the need to set up many technology demonstration farms to give farmers hands-on training during field days.

Assessing risk factors in the organic control system: evidence from inspection data in Italy

Certification is an essential feature in organic farming, and it is based on inspections to verify compliance with respect to European Council Regulation-EC Reg. No 834/2007. A risk-based approach to noncompliance that alerts the control bodies to activate planning inspections would contribute to a more efficient and cost-effective certification system. An analysis of factors that can affect the probability of noncompliance in organic farming has thus been developed. This article examines the application of zero-inflated count data models to farm-level panel data from inspection results and sanctions obtained from the Ethical and Environmental Certification Institute, one of the main control bodies in Italy. We tested many a priori hypotheses related to the risk of noncompliance. We find evidence of an important role for past noncompliant behavior in predicting future noncompliance, while farm size and the occurrence of livestock also have roles in an increased probability of noncompliance. We conclude the article proposing that an efficient risk-based inspection system should be designed, weighting up the known probability of occurrence of a given noncompliance according to the severity of its impact.