Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

The Residential Stratification of Mixed-Race Couples

Thesis (Ph.D.)--University of Washington, 2016-06

Assessing the impact of business group diversification on the internationalization of their affiliates: The case of Latin American firms

This dissertation explored the capacity of business group diversification to generate value to their affiliates in an institutional environment characterized by the adoption of structural pro-market reforms. In particular, the three empirical essays explored the impact of business group diversification on the internationalization process of their affiliates. ^ The first essay examined the direct effect of business group diversification on firm performance and its moderating effect on the multinationality-performance relationship. It further explored whether such moderating effect varies depending upon whether the focal affiliate is a manufacturing or service firm. The findings suggested that the benefits of business group diversification on firm performance have a threshold, that those benefits are significant at earlier stages of internationalization and that these benefits are stronger for service firms. ^ The second essay studied the capacity of business group diversification to ameliorate the negative effects of the added complexity faced by its affiliates when they internationalized. The essay explored this capacity in different dimensions of international complexity. The results indicated that business group diversification effectively ameliorated the effects of the added international complexity. This positive effect is stronger in the institutional voids rather than the societal complexity dimension. In the former dimension, diversified business groups can use both their non-market resources and previous experience to ameliorate the effects of complexity on firm performance. ^ The last essay explored whether the benefits of business group diversification on the scope-performance relationship varies depending on the level of development of the network of subsidiaries and the region of operation of the focal firm. The results suggested that the benefits of business group diversification are location bound within the region but that they are not related to the level of development of the targeted countries. ^ The three essays use longitudinal analyses on a sample of Latin American firms to test the hypotheses. While the first essay used multilevel models and fix effects models, the last two essays used exclusively fix effects models to assess the impact of business group diversification. In conclusion, this dissertation aimed to explain the capacity of business group diversification to generate value under conditions of institutional change.^

Facteurs de risque pour les maladies diarrhéiques chez les enfants à Dakar : une analyse multi-niveaux avec variables latentes

Au Sénégal, les maladies diarrhéiques constituent un fardeau important, qui pèse encore lourdement sur la santé des enfants. Ces maladies sont influencées par un large éventail de facteurs, appartenant à différents niveaux et sphères d'analyse. Cet article analyse ces facteurs de risque et leur rôle relatif dans les maladies diarrhéiques de l'enfant à Dakar. Ce faisant, elle illustre une nouvelle approche pour synthétiser le réseau de ces déterminants. Une analyse en classes latentes (LCA) est d’abord menée, puis les variables latentes ainsi construites sont utilisées comme variables explicatives dans une régression logistique sur trois niveaux. Les résultats confirment que les déterminants des diarrhées chez l'enfant appartiennent aux trois niveaux d'analyse et que les facteurs comportementaux et l'assainissement du quartier jouent un rôle prépondérant. Les résultats illustrent aussi l'utilité des LCA pour synthétiser plusieurs indicateurs, afin de créer une image causale intégrée, tout en utilisant des modèles statistiques parcimonieux.

Job satisfaction among primary school personnel in Madagascar

This article analyzes the job satisfaction of primary school teachers in Madagascar. Based on the estimation of multilevel models, low wages and problems getting paid, job insecurity, lack of in-service training, high pupil-teacher ratios, and lack of basic infrastructure and teaching materials are identified as the main reasons for dissatisfaction. Principals’ control of teachers’ activities also adversely affects satisfaction, suggesting that, in Malagasy schools, neither school directors nor teachers have succeeded in adopting organizational cultures based on cooperation among their members. These results are likely to stimulate debates on educational policy, both in Madagascar and in many other developing countries.

Cow, farm, and management factors during the dry period that determine the rate of clinical mastitis after calving

The purpose of the research was to investigate cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on the rate of clinical mastitis after calving. Data were collected over a 2-yr period from 52 commercial dairy farms throughout England and Wales. Cows were separated for analysis into those housed for the dry period (8,710 cow-dry periods) and those at pasture (9,964 cow-dry periods). Multilevel models were used within a Bayesian framework with 2 response variables, the occurrence of a first case of clinical mastitis within the first 30 d of lactation and time to the first case of clinical mastitis during lactation. A variety of cow and herd management factors were identified as being associated with an increased rate of clinical mastitis and these were found to occur throughout the dry period. Significant cow factors were increased parity and at least one somatic cell count ≥200,000 cells/mL in the 90 d before drying off. A number of management factors related to hygiene were significantly associated with an increased rate of clinical mastitis. These included measures linked to the administration of dry-cow treatments and management of the early and late dry-period accommodation and calving areas. Other farm factors associated with a reduced rate of clinical mastitis were vaccination with a leptospirosis vaccine, selection of dry-cow treatments for individual cows within a herd rather than for the herd as a whole, routine body condition scoring of cows at drying off, and a pasture rotation policy of grazing dry cows for a maximum of 2 wk before allowing the pasture to remain nongrazed for a period of 4 wk. Models demonstrated a good ability to predict the farm incidence rate of clinical mastitis in a given year, with model predictions explaining over 85% of the variability in the observed data. The research indicates that specific dry-period management strategies have an important influence on the rate of clinical mastitis during the next lactation.

Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation

This study investigated cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on somatic cell counts (SCC) in early lactation. Data from 52 commercial dairy farms throughout England and Wales were collected over a 2-yr period. For the purpose of analysis, cows were separated into those housed for the dry period (6,419 cow-dry periods) and those at pasture (7,425 cow-dry periods). Bayesian multilevel models were specified with 2 response variables: ln SCC (continuous) and SCC >199,000 cells/mL (binary), both within 30 d of calving. Cow factors associated with an increased SCC after calving were parity, an SCC >199,000 cells/mL in the 60 d before drying off, increasing milk yield 0 to 30 d before drying off, and reduced DIM after calving at the time of SCC estimation. Herd management factors associated with an increased SCC after calving included procedures at drying off, aspects of bedding management, stocking density, and method of pasture grazing. Posterior predictions were used for model assessment, and these indicated that model fit was generally good. The research demonstrated that specific dry-period management strategies have an important influence on SCC in early lactation.

Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes : an application to the effects of stress on the cognitive function of nurses

Funded by Chief Scientist Office, Scotland. Grant Number: CZH/4/394 Economic and Social Research Council grant as part of the National Centre for Research Methods. Grant Number: RES-576-25-0032

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.