Incorporating Medical Supply and Demand into the Index of Physician Maldistribution Improves the Sensitivity to Healthcare Outcomes

Background: Since the association between disparity in physician distribution and specific healthcare outcomes is poorly documented, we aimed to clarify the association between physician maldistribution and cerebrovascular disease (CeVD), a high-priority health outcome in Japan. Methods: In this cross-sectional study, we conducted multivariable regression analysis with the Physician Uneven Distribution Index (PUDI), a recently developed and adopted policy index in Japan that uniquely incorporates the gap between medical supply and demand, as the independent variable and CeVD death rate as the dependent variable. Population density, mean annual income, and prevalence of hypertension were used as covariates. Results: The coefficient of the PUDI for the CeVD death rate was −0.34 (95%CI: −0.49–−0.19) before adjusting for covariates and was −0.19 (95%CI: −0.30–−0.07) after adjusting. The adjusted R squared of the analysis for the PUDI was 0.71 in the final model. However, the same multivariable regression model showed that the number of physicians per 100,000 people (NPPP) was not associated with the CeVD death rates before or after adjusting for the covariates. Conclusion: Incorporating the gap between the medical supply and demand in physician maldistribution indices could improve the responsiveness of the index for assessing the disparity in healthcare outcomes.

“It changed the atmosphere surrounding the baby I did have”: Making sense of reproduction during sustained uncertainty

Objective: This study examines the schemas that women employed during the COVID-19 pandemic to make sense of their reproductive experiences. Background: Existing research on reproduction during epidemics suggests that there are variable population responses to periods of long-term social uncertainty and that individuals and couples can respond to these circumstances in unexpected ways. However, less is known about how individuals make sense of their reproductive experiences during periods of social upheaval.Method: Twenty-nine women aged 25-35 from a mid-sized Midwestern county were recruited and were interviewed about their experiences during the first eight months of the 2021 COVID-19 pandemic. They were asked about their daily lived experiences and about their partnership and reproductive goals during in-depth interviews. These interviews were transcribed and analyzed using thematic coding of the three main schemas that participants used to describe their reproductive experiences.Results: Participants used three main schemas to describe their reproductive experiences during the COVID-19 pandemic. Heteronormative schemas were used by many participants to articulate their commitment to a heteronormative aged-staged progression of life events. Affective schemas were used by participants, primarily those who were currently or recently pregnant, to express grief and loss over the relational experience of having a new baby. Medical schemas were expressed by most participants to describe feelings of fear and risk at real or imagined encounters with medical institutions during the pandemic.Conclusion: The schema that participants use to make sense of their reproductive experiences have real and enduring consequences for their current and future reproduction.

Fast and flexible estimation of effective migration surfaces

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Poverty in Central Java using Multivariate Adaptive Regression Splines and Bootstrap Aggregating Multivariate Adaptive Regression Splines

Population poverty is one of the serious problems in Indonesia. The percentage of population poverty used as a means for a statistical instrument to be guidelines to create standard policies and evaluations to reduce poverty. The aims of the research are to determine model population poverty using MARS and Bagging MARS then to understand the most influence variable population poverty of Central Java Province in 2018. The result of this research is the Bagging MARS model showed better accuracy than the MARS model. Since, GCV value in the Bagging MARS model is 0,009798721 and GCV value in the MARS model is 6,985571. The most influential variable poverty population of Central Java Province in 2018 in the MARS model is the percentage of the old school expectation rate (X9). Then, the most influential variable in the Bagging MARS model is the number of diarrhea (X1).

DETERMINAN TINGKAT PENGANGGURAN ANTAR PROVINSI DI INDONESIA

Indonesia is one of the countries with the largest unemployment in ASEAN countries. This condition is because some provinces still have unemployment rates that exceed unemployment in Indonesia. Low public education, high poverty and population in several provinces of Indonesia are among the causes of the increase in the unemployment rate in Indonesia. This research will discuss how much influence the education sector has on the average years of schooling (RLS), population, poverty rate and government spending on housing in Indonesia. The research model uses data panel regression method with a scope of 33 provinces in Indonesia. The results explain the average length of school and spending negatively affect unemployment. Then variable population numbers, the poverty rate affects positively towards unemployment.

The population of Sclerotinia sclerotiorum in Brazil is structured by mycelial compatibility groups

The genetic structure of the population of Sclerotinia sclerotiorum was analyzed using 238 individuals collected from different hosts. Individuals were characterized for microsatellite genotypes and mycelial compatibility groups (MCGs). A total of 22 MCGs and 64 multilocus lineages (MLLs) were identified. There was a close relationship between the MCGs and MLLs, but there was no association between MLLs and hosts or regions. At least 39 MCGs are present in Brazil and 68.5% of the isolates were assigned to either MCG 1 or 2. Eight new MCGs were found. Seven genetic groups were identified and associated with MCGs. Most genetic variation (70.0%) was due to differences among MCGs. High values of estimates of linkage disequilibrium among loci were more frequent in the total population (all MCGs). In contrast, there was evidence of random mating in subpopulations defined by MCGs 1 and 2. Additionally, there was evidence of outcrossing in the population of S. sclerotiorum in Brazil. The population was structured by MCGs, lineages originated from asexual reproduction or selfing prevail and are widely distributed in space, are persistent in time and affect many hosts, but there is evidence of some degree of outcrossing which may lead to a more genetically variable population in the future.

Linguistic Complexity: Relationships Between Phoneme Inventory Size, Syllable Complexity, Word and Clause Length, and Population Size

Starting from a view on language as a complex, hierarchically organized system composed of many parts that have many interactions, this paper investigates statistical relationships between the linguistic variables “phoneme inventory size,” “syllable size,” “length of words,” “length of clauses,” and the nonlinguistic variable “population size.” By analyzing parallel textual material of 61 languages (18 language families) we found strong positive correlations between phoneme inventory size, mean number of phonemes per syllable, and mean number of monosyllables. We observed significant negative correlations between phoneme inventory size and the mean length of words and the mean length of clauses, measured as number of syllables. We then correlated the linguistic complexity data with estimated speaker population sizes and could reveal that languages with more speakers tend to have more phonemes per syllable, shorter words in number of syllables, a higher number of monosyllabic words, and a higher number of words per clause. Moreover, we reproduce the results of former studies that found a positive correlation between population size and phoneme inventory size for our language sample. The findings are discussed in light of previous research and within the framework of Systemic Typology. We propose that syllable complexity is a key factor in the correlations identified in this study, and that Zipf's law of Abbreviation explains the associations between “word length,” “syllable complexity,” “phoneme inventory size,” and the extralinguistic variable “population size.”

A theoretical model for optimal control of banana Moko (Musa AAB Simmonds)

A population simulation model with non-linear ordinary differential equations is presented, which interprets the dynamics of the banana Moko, with prevention of the disease and population of susceptible and infected plants over time. A crop with a variable population of plants and a logistic growth of replanting is assumed, taking into account the maximum capacity of plants in the delimited study area. Also, with the help of farmers, the costs of implementing prevention strategies and elimination of infected plants were calculated per week in order to determine the optimal conditions that control the disease and reduce production costs. We found that the implementation of prevention strategies (f) plays an important role, but the parameter that most influences the threshold value is the elimination of infected plants g. However, to reduce production costs due to the high implementation of prevention strategies and to maintain the disease in a controlled state, both controls u1 and u2 should be implemented between 40% and 60%, obtaining with this percentage an approximate reduction of 51.37% in production costs per week, where in 23 weeks following the same conditions it is expected to have a healthy plantation without infected plants.