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- Member of: ASU Electronic Theses and Dissertations
Description
Developing new markets in tourism is vital for the prosperity of Small Island Tourism Economies like Bermuda (McElroy). Countries must continuously improve and reinvent themselves in order to maintain growth. SCUBA diving in Bermuda is a market that could be improved. Most SCUBA divers are of higher than average household income and can make an attractive tourist base. This thesis analyzes SCUBA tourists in Bermuda to ascertain their characteristics, economics impacts, and participation in island activities in order to help guide future endeavors involving SCUBA tourism in Bermuda and provide an outline of how to analyze other Niche markets. Comparisons are made between SCUBA and Non-SCUBA tourists (those who participated in Scuba against those that did not). The results show that spending, activities/events participated, and SCUBA tourists characteristics are not all significantly different from one another at the 5% level. Meaning that some variables were significant and some weren't , with in their respective groups. Within Trip Expenditures it was shown that, of the 9 variables tested, 3 were significant. In Activities, 8 of the 11 tested were significant, attractions there were 8 of the 18 variables that were significant and in Evening Entertainment, there was 2 out the 6 variables being significant at the 5% level.
ContributorsJack, Alastair (Author) / Tyrrell, Timothy (Thesis advisor) / Knopf, Richard (Committee member) / Herbst, Chris (Committee member) / Arizona State University (Publisher)
Created2012
Description
Research shows that the subject of mathematics, although revered, remains a source of trepidation for many individuals, as they find it difficult to form a connection between the work they do on paper and their work's practical applications. This research study describes the impact of teaching a challenging introductive applied mathematics course on high school students' skills and attitudes towards mathematics in a college Summer Program. In the analysis of my research data, I identified several emerging changes in skills and attitudes towards mathematics, skills that high-school students needed or developed when taking the mathematical modeling course. Results indicated that the applied mathematics course had a positive impact on several students' attitudes, in general, such as, self-confidence, meanings of what mathematics is, and their perceptions of what solutions are. It also had a positive impact on several skills, such as translating real-life situations to mathematics via flow diagrams, translating the models' solutions back from mathematics to the real world, and interpreting graphs. Students showed positive results when the context of their problems was applied or graphical, and fewer improvement on problems that were not. Research also indicated some negatives outcomes, a decrease in confidence for certain students, and persistent negative ways of thinking about graphs. Based on these findings, I make recommendations for teaching similar mathematical modeling at the pre-university level, to encourage the development of young students through educational, research and similar mentorship activities, to increase their inspiration and interest in mathematics, and possibly consider a variety of sciences, technology, engineering and mathematics-related (STEM) fields and careers.
Contributorsagoune, linda (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Castillo-Garsow, Carlos W (Thesis advisor) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2020
Description
Understanding the consequences of changes in social networks is an important an-
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
ContributorsNesse, Hans P (Author) / Hurtado, Ana Magdalena (Thesis advisor) / Castillo-Chavez, Carlos (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2019
Description
Combination therapy has shown to improve success for cancer treatment. Oncolytic virotherapy is cancer treatment that uses engineered viruses to specifically infect and kill cancer cells, without harming healthy cells. Immunotherapy boosts the body's natural defenses towards cancer. The combination of oncolytic virotherapy and immunotherapy is explored through deterministic systems of nonlinear differential equations, constructed to match experimental data for murine melanoma. Mathematical analysis was done in order to gain insight on the relationship between cancer, viruses and immune response. One extension of the model focuses on clinical needs, with the underlying goal to seek optimal treatment regimens; for both frequency and dose quantity. The models in this work were first used to estimate parameters from preclinical experimental data, to identify biologically realistic parameter values. Insight gained from the mathematical analysis in the first model, allowed for numerical analysis to explore optimal treatment regimens of combination oncolytic virotherapy and dendritic vaccinations. Permutations accounting for treatment scheduled were done to find regimens that reduce tumor size. Observations from the produced data lead to in silico exploration of immune-viral interactions. Results suggest under optimal settings, combination treatment works better than monotherapy of either type. The most optimal result suggests treatment over a longer period of time, with fractioned doses, while reducing the total dendritic vaccination quantity, and maintaining the maximum virotherapy used in the experimental work.
ContributorsSummer, Ilyssa Aimee (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Nagy, John (Thesis advisor) / Mubayi, Anuj (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2016
Description
The Mathematical and Theoretical Biology Institute (MTBI) is a summer research program for undergraduate students, largely from underrepresented minority groups. Founded in 1996, it serves as a 'life-long' mentorship program, providing continuous support for its students and alumni. This study investigates how MTBI supports student development in applied mathematical research. This includes identifying of motivational factors to pursue and develop capacity to complete higher education.
The theoretical lens of developmental psychologists Lev Vygotsky (1978, 1987) and Lois Holzman (2010) that sees learning and development as a social process is used. From this view student development in MTBI is attributed to the collaborative and creative way students co-create the process of becoming scientists. This results in building a continuing network of academic and professional relationships among peers and mentors, in which around three quarters of MTBI PhD graduates come from underrepresented groups.
The extent to which MTBI creates a Vygotskian learning environment is explored from the perspectives of participants who earned doctoral degrees. Previously hypothesized factors (Castillo-Garsow, Castillo-Chavez and Woodley, 2013) that affect participants’ educational and professional development are expanded on.
Factors identified by participants are a passion for the mathematical sciences; desire to grow; enriching collaborative and peer-like interactions; and discovering career options. The self-recognition that they had the ability to be successful, key element of the Vygotskian-Holzman theoretical framework, was a commonly identified theme for their educational development and professional growth.
Participants characterize the collaborative and creative aspects of MTBI. They reported that collaborative dynamics with peers were strengthened as they co-created a learning environment that facilitated and accelerated their understanding of the mathematics needed to address their research. The dynamics of collaboration allowed them to complete complex homework assignments, and helped them formulate and complete their projects. Participants identified the creative environments of their research projects as where creativity emerged in the dynamics of the program.
These data-driven findings characterize for the first time a summer program in the mathematical sciences as a Vygotskian-Holzman environment, that is, a `place’ where participants are seen as capable applied mathematicians, where the dynamics of collaboration and creativity are fundamental components.
The theoretical lens of developmental psychologists Lev Vygotsky (1978, 1987) and Lois Holzman (2010) that sees learning and development as a social process is used. From this view student development in MTBI is attributed to the collaborative and creative way students co-create the process of becoming scientists. This results in building a continuing network of academic and professional relationships among peers and mentors, in which around three quarters of MTBI PhD graduates come from underrepresented groups.
The extent to which MTBI creates a Vygotskian learning environment is explored from the perspectives of participants who earned doctoral degrees. Previously hypothesized factors (Castillo-Garsow, Castillo-Chavez and Woodley, 2013) that affect participants’ educational and professional development are expanded on.
Factors identified by participants are a passion for the mathematical sciences; desire to grow; enriching collaborative and peer-like interactions; and discovering career options. The self-recognition that they had the ability to be successful, key element of the Vygotskian-Holzman theoretical framework, was a commonly identified theme for their educational development and professional growth.
Participants characterize the collaborative and creative aspects of MTBI. They reported that collaborative dynamics with peers were strengthened as they co-created a learning environment that facilitated and accelerated their understanding of the mathematics needed to address their research. The dynamics of collaboration allowed them to complete complex homework assignments, and helped them formulate and complete their projects. Participants identified the creative environments of their research projects as where creativity emerged in the dynamics of the program.
These data-driven findings characterize for the first time a summer program in the mathematical sciences as a Vygotskian-Holzman environment, that is, a `place’ where participants are seen as capable applied mathematicians, where the dynamics of collaboration and creativity are fundamental components.
ContributorsEvangelista, Arlene Morales (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Holmes, Raquell M. (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2015
Description
The 2009-10 influenza and the 2014-15 Ebola pandemics brought once again urgency to an old question: What are the limits on prediction and what can be proposed that is useful in the face of an epidemic outbreak?
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
ContributorsMorales, Romarie (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Towers, Sherry (Committee member) / Arizona State University (Publisher)
Created2016
Description
Statistical Methods have been widely used in understanding factors for clinical and public health data. Statistical hypotheses are procedures for testing pre-stated hypotheses. The development and properties of these procedures as well as their performance are based upon certain assumptions. Desirable properties of statistical tests are to maintain validity and to perform well even if these assumptions are not met. A statistical test that maintains such desirable properties is called robust. Mathematical models are typically mechanistic framework, used to study dynamic interactions between components (mechanisms) of a system, and how these interactions give rise to the changes in behavior (patterns) of the system as a whole over time.
In this thesis, I have developed a study that uses novel techniques to link robust statistical tests and mathematical modeling methods guided by limited data from developed and developing regions in order to address pressing clinical and epidemiological questions of interest. The procedure in this study consists of three primary steps, namely, data collection, uncertainty quantification in data, and linking dynamic model to collected data.
The first part of the study focuses on designing, collecting, and summarizing empirical data from the only national survey of hospitals ever conducted regarding patient controlled analgesia (PCA) practices among 168 hospitals across 40 states, in order to assess risks before putting patients on PCA. I used statistical relational models and exploratory data analysis to address the question. Risk factors assessed indicate a great concern for the safety of patients from one healthcare institution to other.
In the second part, I quantify uncertainty associated with data obtained from James A Lovell Federal Healthcare Center to primarily study the effect of Benign Prostatic Hypertrophy (BPH) on sleep architecture in patients with Obstructive Sleep Apnea (OSA). Patients with OSA and BPH demonstrated significant difference in their sleep architecture in comparison to patients without BPH. One of the ways to validate these differences in sleep architecture between the two groups may be to carry out a similar study that evaluates the effect of some other chronic disease on sleep architecture in patients with OSA.
Additionally, I also address theoretical statistical questions such as (1) how to estimate the distribution of a variable in order to retest null hypothesis when the sample size is limited, and (2) how changes on assumptions (like monotonicity and nonlinearity) translate into the effect of the independent variable on the outcome variable. To address these questions we use multiple techniques such as Partial Rank Correlation Coefficients (PRCC) based sensitivity analysis, Fractional Polynomials, and statistical relational models.
In the third part, my goal was to identify socio-economic-environment-related risk factors for Visceral Leishmaniasis (VL) and use the identified critical factors to develop a mathematical model to understand VL transmission dynamics when data is highly underreported. I primarily studied the role of age-specific- susceptibility and epidemiological quantities on the dynamics of VL in the Indian state of Bihar. Statistical results provided ideas on the choice of the modeling framework and estimates of model parameters.
In the conclusion, this study addressed three primary theoretical modeling-related questions (1) how to analyze collected data when sample size limited, and how modeling assumptions varies results of data analysis? (2) Is it possible to identify hidden associations and nonlinearity of these associations using such underpowered data and (3) how statistical models provide more reasonable structure to mathematical modeling framework that can be used in turn to understand dynamics of the system.
In this thesis, I have developed a study that uses novel techniques to link robust statistical tests and mathematical modeling methods guided by limited data from developed and developing regions in order to address pressing clinical and epidemiological questions of interest. The procedure in this study consists of three primary steps, namely, data collection, uncertainty quantification in data, and linking dynamic model to collected data.
The first part of the study focuses on designing, collecting, and summarizing empirical data from the only national survey of hospitals ever conducted regarding patient controlled analgesia (PCA) practices among 168 hospitals across 40 states, in order to assess risks before putting patients on PCA. I used statistical relational models and exploratory data analysis to address the question. Risk factors assessed indicate a great concern for the safety of patients from one healthcare institution to other.
In the second part, I quantify uncertainty associated with data obtained from James A Lovell Federal Healthcare Center to primarily study the effect of Benign Prostatic Hypertrophy (BPH) on sleep architecture in patients with Obstructive Sleep Apnea (OSA). Patients with OSA and BPH demonstrated significant difference in their sleep architecture in comparison to patients without BPH. One of the ways to validate these differences in sleep architecture between the two groups may be to carry out a similar study that evaluates the effect of some other chronic disease on sleep architecture in patients with OSA.
Additionally, I also address theoretical statistical questions such as (1) how to estimate the distribution of a variable in order to retest null hypothesis when the sample size is limited, and (2) how changes on assumptions (like monotonicity and nonlinearity) translate into the effect of the independent variable on the outcome variable. To address these questions we use multiple techniques such as Partial Rank Correlation Coefficients (PRCC) based sensitivity analysis, Fractional Polynomials, and statistical relational models.
In the third part, my goal was to identify socio-economic-environment-related risk factors for Visceral Leishmaniasis (VL) and use the identified critical factors to develop a mathematical model to understand VL transmission dynamics when data is highly underreported. I primarily studied the role of age-specific- susceptibility and epidemiological quantities on the dynamics of VL in the Indian state of Bihar. Statistical results provided ideas on the choice of the modeling framework and estimates of model parameters.
In the conclusion, this study addressed three primary theoretical modeling-related questions (1) how to analyze collected data when sample size limited, and how modeling assumptions varies results of data analysis? (2) Is it possible to identify hidden associations and nonlinearity of these associations using such underpowered data and (3) how statistical models provide more reasonable structure to mathematical modeling framework that can be used in turn to understand dynamics of the system.
ContributorsGonzalez, Beverly, 1980- (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Nuno, Miriam (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Visceral Leishmaniasis (VL) is primarily endemic in five countries, with India and Sudan having the highest burden. The risk factors associated with VL are either unknown in some regions or vary drastically among empirical studies. Here, a dynamical model, motivated and informed by field data from the literature, is analyzed and employed to identify and quantify the impact of region dependent risks on the VL transmission dynamics. Parameter estimation procedures were developed using model-derived quantities and empirical data from multiple resources. The dynamics of VL depend on the estimates of the control reproductive number, RC, interpreted as the average number of secondary infections generated by a single infectious individual during the infectious period. The distribution of RC was estimated for both India (with mean 2.1 ± 1.1) and Sudan (with mean 1.45 ± 0.57). This suggests that VL can be established in naive regions of India more easily than in naive regions of Sudan. The parameter sensitivity analysis on RC suggests that the average biting rate and transmission probabilities between host and vector are among the most sensitive parameters for both countries. The comparative assessment of VL transmission dynamics in both India and Sudan was carried out by parameter sensitivity analysis on VL-related prevalences (such as prevalences of asymptomatic hosts, symptomatic hosts, and infected vectors). The results identify that the treatment and symptoms’ developmental rates are parameters that are highly sensitive to VL symptomatic and asymptomatic host prevalence, respectively, for both countries. It is found that the estimates of transmission probability are significantly different between India (from human to sandflies with mean of 0.39 ± 0.12; from sandflies to human with mean 0.0005 ± 0.0002) and Sudan (from human to sandflies with mean 0.26 ± 0.07; from sandflies to human with mean 0.0002 ± 0.0001). The results have significant implications for elimination. An increasing focus on elimination requires a review of priorities within the VL control agenda. The development of systematic implementation of control programs based on identified risk factors (such as monitoring of asymptomatically infected individuals) has a high transmission-blocking potential.
ContributorsBarley, Kamal K (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Safan, Muntaser (Committee member) / Arizona State University (Publisher)
Created2016
Description
Soft errors are considered as a key reliability challenge for sub-nano scale transistors. An ideal solution for such a challenge should ultimately eliminate the effect of soft errors from the microprocessor. While forward recovery techniques achieve fast recovery from errors by simply voting out the wrong values, they incur the overhead of three copies execution. Backward recovery techniques only need two copies of execution, but suffer from check-pointing overhead.
In this work I explored the efficiency of integrating check-pointing into the application and the effectiveness of recovery that can be performed upon it. After evaluating the available fine-grained approaches to perform recovery, I am introducing InCheck, an in-application recovery scheme that can be integrated into instruction-duplication based techniques, thus providing a fast error recovery. The proposed technique makes light-weight checkpoints at the basic-block granularity, and uses them for recovery purposes.
To evaluate the effectiveness of the proposed technique, 10,000 fault injection experiments were performed on different hardware components of a modern ARM in-order simulated processor. InCheck was able to recover from all detected errors by replaying about 20 instructions, however, the state of the art recovery scheme failed more than 200 times.
In this work I explored the efficiency of integrating check-pointing into the application and the effectiveness of recovery that can be performed upon it. After evaluating the available fine-grained approaches to perform recovery, I am introducing InCheck, an in-application recovery scheme that can be integrated into instruction-duplication based techniques, thus providing a fast error recovery. The proposed technique makes light-weight checkpoints at the basic-block granularity, and uses them for recovery purposes.
To evaluate the effectiveness of the proposed technique, 10,000 fault injection experiments were performed on different hardware components of a modern ARM in-order simulated processor. InCheck was able to recover from all detected errors by replaying about 20 instructions, however, the state of the art recovery scheme failed more than 200 times.
ContributorsLokam, Sai Ram Dheeraj (Author) / Shrivastava, Aviral (Thesis advisor) / Clark, Lawrence T (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2016
Description
Social determinants of health present significant barriers to utilization of maternal health services in transitional countries. This dissertation study examined associations between household autonomy and utilization of prenatal services among women of reproductive age in Armenia and Azerbaijan. Using nationally representative survey data, this study explored if household autonomy of women positively influenced the timing of the first prenatal visit, the number of prenatal care visits, and the content of care during visits. Results showed that household autonomy was positively associated with the timing of the first visit for prenatal care and the number of prenatal care visits. The content of care was negatively associated with the autonomy of women. Findings also pointed to an endogenous influence of a woman's position in the household structure. Additionally, this study analyzed associations between women's reproductive history and utilization, and economic disparities in utilization of prenatal care. The findings demonstrated that a history of complications during pregnancy and stillbirths were positively associated with utilization of prenatal care. Economic disparities in utilization of care were identified. Future interventions to increase utilization of maternal health services should account for traditional household structures in transitional countries. Women from poor families should receive support from social assistance and the health sector in accessing services pertaining to their health and well-being.
ContributorsUrbaeva, Zhyldyz (Author) / Anderson, Steven (Thesis advisor) / Krysik, Judy (Thesis advisor) / Herbst, Chris (Committee member) / Arizona State University (Publisher)
Created2015