Consistent estimation of zero-inflated count models

Antenatal care service utilization disparities between urban and rural communities in Ethiopia: A negative binomial Poisson regression of 2019 Ethiopian Demography Health Survey

BACKGROUND

Although there have been consistent improvements in maternal mortality, it remains high in developing countries due to unequal access to healthcare services during pregnancy and childbirth. Thus, this study aimed to further analyze the variations in the number of antenatal care utilizations and associated factors among pregnant women in urban and rural Ethiopia.

METHODS

A total of 3962 pregnant women were included in the analysis of 2019 Ethiopian Demographic and Health Survey data. A negative binomial Poisson regression statistical model was used to analyze the data using STATA version 14.0. An incident rate ratio with a 95% confidence interval was used to show the significantly associated variables.

RESULTS

Of the 3962 (weighted 3916.67) pregnant women, about 155 (15.21%) lived in urban and 848 (29.29%) rural residences and did not use antenatal care services in 2019. Women age group 20-24 (IRR = 1.30, 95%CI:1.05-1.61), 25-29 (IRR = 1.56, 95%CI:1.27-1.92), 30-34 (IRR = 1.65, 95%CI:1.33-2.05), and 35-39 years old (IRR = 1.55, 95%CI:1.18-2.03), attending primary, secondary, and higher education (IRR = 1.18, 95%CI:1.07-1.30), (IRR = 1.26, 95%CI:1.13-1.42) and (IRR = 1.25, 95%CI:1.11-1.41) respectively, reside in middle household wealth (IRR = 1.31, 95%CI:1.13-1.52), richer (IRR = 1.45, 95%CI:1.26-1.66) and richest (IRR = 1.68, 95%CI:1.46-1.93) increases the number of antenatal care utilization among urban residences. While attending primary (IRR = 1.34, 95%CI:1.24-1.45), secondary (IRR = 1.54, 95%CI:1.34-1.76) and higher education (IRR = 1.58, 95%CI:1.28-1.95), following Protestant (IRR = 0.76, 95%CI:0.69-0.83), Muslim (IRR = 0.79, 95%CI:0.73-0.85) and Others (IRR = 0.56, 95%CI:0.43-0.71) religions, reside in poorer, middle, richer, and richest household wealth (IRR = 1.51, 95%CI:1.37-1.67), (IRR = 1.66, 95%CI:1.50-1.83), (IRR = 1.71, 95%CI:1.55-1.91) and (IRR = 1.89, 95%CI:1.72-2.09) respectively, being married and widowed/separated (IRR = 1.85, 95%CI:1.19-2.86), and (IRR = 1.95, 95%CI:1.24-3.07) respectively were significantly associated with the number of antenatal care utilization among rural residences.

CONCLUSION

The utilization of antenatal care is low among rural residents than among urban residents. To increase the frequency of antenatal care utilization, health extension workers and supporting actors should give special attention to pregnant women with low socioeconomic and educational levels through a safety-net lens.

Binary Classification with Imbalanced Data

When the binary response variable contains an excess of zero counts, the data are imbalanced. Imbalanced data cause trouble for binary classification. To simplify the numerical computation to obtain the maximum likelihood estimators of the zero-inflated Bernoulli (ZIBer) model parameters with imbalanced data, an expectation-maximization (EM) algorithm is proposed to derive the maximum likelihood estimates of the model parameters. The logistic regression model links the Bernoulli probabilities with the covariates in the ZIBer model, and the prediction performance among the ZIBer model, LightGBM, and artificial neural network (ANN) procedures is compared by Monte Carlo simulation. The results show that no method can dominate the other methods regarding predictive performance under the imbalanced data. The LightGBM and ZIBer models are more competitive than the ANN model for zero-inflated-imbalanced data sets.

Factors determining antenatal care utilization among mothers of deceased perinates in Ethiopia

Introduction

Receiving adequate antenatal care (ANC) had an integral role in improving maternal and child health outcomes. However, several factors influence the utilization of ANC from the individual level up to the community level factors. Thus, this study aims to investigate factors that determine ANC service utilization among mothers of deceased perinate using the proper count regression model.

Method

Secondary data analysis was performed on perinatal death surveillance data. A total of 3,814 mothers of deceased perinates were included in this study. Hurdle Poisson regression with a random intercept at both count-and zero-part (MHPR.ERE) model was selected as a best-fitted model. The result of the model was presented in two ways, the first part of the count segment of the model was presented using the incidence rate ratio (IRR), while the zero parts of the model utilized the adjusted odds ratio (AOR).

Result

This study revealed that 33.0% of mothers of deceased perinates had four ANC visits. Being in advanced maternal age [IRR = 1.03; 95CI: (1.01-1.09)], attending primary level education [IRR = 1.08; 95 CI: (1.02-1.15)], having an advanced education (secondary and above) [IRR = 1.14; 95 CI: (1.07-1.21)] and being resident of a city administration [IRR = 1.17; 95 CI: (1.05-1.31)] were associated with a significantly higher frequency of ANC visits. On the other hand, women with secondary and above education [AOR = 0.37; 95CI: (0.26-0.53)] and women who live in urban areas [AOR = 0.42; 95 CI: (0.33-0.54)] were less likely to have unbooked ANC visit, while women who resided in pastoralist regions [AOR = 2.63; 95 CI: (1.02-6.81)] were more likely to have no ANC visit.

Conclusion

The uptake of ANC service among mothers having a deceased perinate was determined by both individual (maternal age and educational status) and community (residence and type of region) level factors. Thus, a concerted effort is needed to improve community awareness through various means of communication by targeting younger women. Furthermore, efforts should be intensified to narrow down inequalities observed in ANC service provision due to the residence of the mothers by availing necessary personnel and improving the accessibility of service in rural areas.

The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study

BACKGROUND

Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral.

OBJECTIVE

This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators' and policy makers' decisions on how to promote reliable sharing of crucial pandemic science on social media.

METHODS

We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate.

RESULTS

Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists' participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications.

CONCLUSIONS

Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public.

Latin American Agri-Food Exports, 1994–2019: A Gravity Model Approach

This study analyses the causes of the strong growth in the agri-food exports of Latin America between 1994 and 2019. To do this, a series of gravity models are estimated, using as a dependent variable the agri-food exports of 15 Latin American countries to their 185 principal trading partners. The empirical specification is based on the gravity theory of trade, according to which, trade between two countries is determined by the size of both of their markets and their transport costs. Other variables have also been included, considering the theoretical foundations proposed for the gravity model. We initially used the PPML estimator since it is the method that provides estimates with the best properties. We later compared these results with those obtained through OLS and the Heckman selection model. Our findings show that the growth in agri-food exports is explained by factors of both supply and demand, but that the latter plays a more important role since we have obtained evidence of a reverse home-market effect. Furthermore, we can conclude that the creation of regional trade agreements, such as NAFTA, MERCOSUR, CACM, APEC, and TPP, has significantly favoured agri-food exports in the region.

R&D and market size: Who benefits from orphan drug legislation?

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered.

Impact of the Federal Conservation Program Participation on Conservation Practice Adoption Intensity in Louisiana, USA

Conservation practices focusing on improving the soil and water quality of working lands are implemented across the United States, supported partially through the United States Department of Agriculture Natural Resources Conservation Service cost-share or incentive payment programs. We assess whether participation in federal conservation support programs induces a change in the number of conservation practices adopted by farmers. We also identify the factors that affect the adoption intensity of different best management practices. We use survey data collected from Louisiana farmers and estimate models using the matching method and Poisson quasi-likelihood model. We find that participation in the cost-share or incentive program leads to an increase in the number of conservation practices on the farms. Similarly, the use of precision technology application and farm being integrated are likely to have a higher number of on-farm conservation practices. Results have implications for federal working lands conservation support programs in the United States.

Modelling the number of antenatal care visits in Bangladesh to determine the risk factors for reduced antenatal care attendance

The existence of excess zeros in the distribution of antenatal care (ANC) visits in Bangladesh raises the research question of whether there are two separate generating processes in taking ANC and the frequency of ANC. Thus the main objective of this study is to identify a proper count regression model for the number of ANC visits by pregnant women in Bangladesh covering the issues of overdispersion, zero-inflation, and intra-cluster correlation with an additional objective of determining risk factors for ANC use and its frequency. The data have been extracted from the nationally representative 2014 Bangladesh Demographic and Health Survey, where 22% of the total 4493 women did not take any ANC during pregnancy. Since these zero ANC visits can be either structural or sampling zeros, two-part zero-inflated and hurdle regression models are investigated along with the standard one-part count regression models. Correlation among response values has been accounted for by incorporating cluster-specific random effects in the models. The hurdle negative binomial regression model with cluster-specific random intercepts in both the zero and the count part is found to be the best model according to various diagnostic tools including likelihood ratio and uniformity tests. The results show that women who have poor education, live in poor households, have less access to mass media, or belong to the Sylhet and Chittagong regions are less likely to use ANC and also have fewer ANC visits. Additionally, women who live in rural areas, depend on family members’ decisions to take health care, and have unintended pregnancies had fewer ANC visits. The findings recommend taking both cluster-specific random effects and overdispersion and zero-inflation into account in modelling the ANC data of Bangladesh. Moreover, safe motherhood programmes still need to pay particular attention to disadvantaged and vulnerable subgroups of women.

Urban-Rural Differences in Aerobic Physical Activity, Muscle Strengthening Exercise, and Screen-Time Sedentary Behavior

PURPOSE

Compared to their urban counterparts, US residents in rural settings face an increased risk of premature mortality and health problems that have been linked to insufficient physical activity (PA) levels. There is limited literature regarding urban-rural differences in adherence to national guidelines for all 3 PA-related behaviors.

METHODS

We investigated urban-rural differences in aerobic PA, leisure-time muscle strengthening PA, and leisure screen-time sedentary behavior in a combined data set of the 2011-2014 waves (N = 14,188) of the nationally representative National Cancer Institute's Health Information National Trends Survey.

FINDINGS

We found no evidence of a difference between large urban and rural residents' aerobic PA levels. The typical number of weekly bouts of leisure-time muscle strengthening PA was 25% lower for rural residents (incidence rate ratio [IRR] = 0.751, P < .001); this relationship was no longer statistically significant after controlling for potentially confounding covariates. In adjusted models, we found rural residents to engage in 6.6% less daily leisure screen-time sedentary behavior than their large urban counterparts (IRR = 0.934, P = .031).

CONCLUSIONS

Taken together with previous literature, these results suggest that rural residents may engage in comparable levels of total PA, but less leisure-time PA, than their urban counterparts.

Distribution-free estimation of zero-inflated models with unobserved heterogeneity

This paper presents a quasi-conditional likelihood method for the consistent estimation of both continuous and count data models with excess zeros and unobserved individual heterogeneity when the true data generating process is unknown. Monte Carlo simulation studies show that our zero-inflated quasi-conditional maximum likelihood (ZI-QCML) estimator outperforms other methods and is robust to distributional misspecifications. We apply the ZI-QCML estimator to analyze the frequency of doctor visits.

Immigration status and property crime: an application of estimators for underreported outcomes

This paper studies the individual-level relationship between immigration and property crime in England and Wales using crime self-reports from the Crime and Justice Survey. Models that account for underreporting are used, since this is a major concern in crime self-reports. The results indicate that the reported crime is substantially underreported, but if anything, immigrants are less likely to underreport than natives. More importantly, controlling for underreporting and basic demographics, the estimates across all model specifications, although imprecise, indicate that immigration status and property crime are negatively associated. We finally find that the estimated relationship between immigration status and property crime differs across regions and ethnic groups.

JEL Codes

K42, J15, J22, C25, C51

Modeling zero-inflated count data when exposure varies: With an application to tumor counts

This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with thiotepa on the number of new bladder tumors.