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Age-period-cohort analysis of hepatocellular carcinoma (HCC) incidence trends among Asians and non-Hispanic Whites in California, 1988-2017

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Age-period-cohort analysis of hepatocellular carcinoma (HCC) incidence trends among Asians and non-Hispanic Whites in California, 1988-2017

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Age-period-cohort analysis of Hepatocellular carcinoma (HCC) incidence trends among
Asians and non-Hispanic whites in California, 1988-2017.

by

Haoyue Shan

A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)

May 2021
ii

Acknowledgements

I would like to express my sincere gratitude to all who support me in my journey of completing
my master’s degree and this thesis.

Firstly, I would like to express my heartfelt thanks to my Advisor Professor Meredith Franklin,
as well as my committee chair, who has been generously sharing me the knowledge and
professional help for the last two years.
I am sincerely grateful for the support, guidance, delicate and infinite patient of Professor Lihua
Liu. The thesis would not be in this shape without her help.
I would like to convey my appreciation to Professor Ming Li for her valuable feedback as well as
precious opportunities.

I would also like to thank my family’s support, both mentally and financially. It would be
incredibly hard to start pursuing this higher education overseas just by myself. Thank you for
being my strongest backup.
I would like to thank my best friend as well as boyfriend Fan Yang for his dearest
companionship during the pandemic.

Finally, thank you to USC for the exceptional education platforms and resources.

iii
Table of Contents

Acknowledgements ii

List of Tables iv

List of Figures v

Abstract vi

Introduction 1

Data 3
Data Analysis 4

Results 5

Discussion 18

References 20

iv
List of Tables

Table 1 Age specific incidence rate (per 100,000) for different birth cohorts for
Non-Hispanic Whites 13

Table 2 Age specific incidence rate (per 100,000) for different time birth cohorts for
Asians 14

Table 3 Deviance table of Different fitted models of Hepatocellular Carcinoma case
number in California Non-Hispanic White patients in California, 1988-2017 15

Table 4 Deviance table of Different fitted models of Hepatocellular Carcinoma case
number in California Asian patients in California, 1988-2017 15

Table 5 Estimation of age-period-cohort model for Non-Hispanic Whites 17

Table 6 Estimation of age-period-cohort model for Asians 17

v

List of Figures

Figure 1 Counts of Hepatocellular Carcinoma by age, period and cohort in Non-Hispanic
Whites in California, 1988-2017 6

Figure 2 Counts of Hepatocellular Carcinoma by age, period, and cohort in Asians in
California, 1988-2017 6

Figure 3 Time series plots by age, period, or cohort against one of the other two
indices for Hepatocellular Carcinoma count of Non-Hispanic Whites in California,
1988-2017 8

Figure 4 Time series of plots by age, period, or cohort against one of the other two
indices for Hepatocellular Carcinoma count of Asians in California, 1988-2017 10

Figure 5 Incidence rate (per 100,000) of Hepatocellular Carcinoma in California
Non-Hispanic Whites presented by age and year of birth 12

Figure 6 Incidence rate (per 100,000) of Hepatocellular Carcinoma in California
Asians presented by age and year of birth 12

vi

Abstract
Background: The incidence rates of Hepatocellular carcinoma (HCC) cancer in the United
States have increased since the 1980s. HCC incidence rates vary immensely by age, biological
sex, and race/ethnicity. It has been shown that hepatitis B virus infection is the most common
potential cause of HCC among Asians, while the risk factors of HCC for non-Asian races are
hepatitis C virus infection, fatty liver disease, diabetes, alcohol consumption, and metabolic
disorders. This study examines the case number and incidence patterns of HCC cancer in non-
Hispanic Whites (NHW) and Asians in California from 1988 to 2017. It also evaluates the effect
of age, time period in years, and birth cohort as determinants of the observed time trend of the
disease for both races.
Methods: Data were obtained from the California Cancer Registry (CCR). The secular trends
for both NHW and Asian HCC cancer were evaluated using sum of absolute counts of case
numbers by age, period, and cohort index. The age-specific incidence rates (per 100,000) were
calculated using CCR absolute count and population. Plots and tables were preduced to present
the effects of birth cohorts on age-specific rates. We also used age–period–cohort (APC) models
to disentangle period and cohort effects as well as identify dominant determinants of HCC
cancer.
Results: All three time factors(age, period, and birth cohort) together are crucial for the observed
increased incidence of HCC cancer in California over the study period. The birth cohort analysis
as well as the APC model show that people born before 1963 and over 60 years old were affected
most for both NHW and Asian groups.
vii
Conclusion: Increased HCC incidence rates can in part be explained by age and birth cohort for
both NHW and Asians. People over 60 years of age and born before 1963 were affected most for
both race groups. Differences in risk factors as well as time led to disparate outcome distribution
of Non-Hispanic White and Asians. Specifically, HCV infection was found to be the most
prominent risk factor for HCC in NHWs. This pattern corresponds to the increase new infections
that due to abusing with injection drugs among young adults in the 1970s and 1980s. The
abnormal high incidence rate among Asian group can be explain as the consequence of Asian
immigration from HBV-endemic areas due to the golden rush around 1850s. Overall, the patterns
we found is reasonable and coherent.

1
Introduction
Cancer is the second leading cause of death globally, results in approximately one of sixth
of total mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer and
a dominate cause for cancer-related deaths across the world. According to the World Health
Organization (WHO), HCC is the fifth most common cancer worldwide. Incidence rates of HCC
vary widely between geographic regions with the top two highest-incidence regions being
Eastern Asia and sub-Saharan Africa. The incidence rates of HCC in the United States (US) have
risen since the 1980s (Tang, Hallouch, Chernyak, Kamaya, & Sirlin, 2018) (Altekruse,
McGlynn, & Reichman, 2009). Similar to other cancers, HCC incidence rates vary immensely by
age, biological sex, and race/ethnicity. HCC is uncommon among people below age 40, but the
incidence rate increases linearly with age before reaching a plateau at approximately 70 years of
age. While the reason remains unclear (Wands, 2007), HCC occurs in males more frequently
than females, with the male-to-female ratio up to 4:1 worldwide. HCC is 2 to 4 times more
common in Hispanics and Asians/Pacific Islanders then non-Hispanic Whites (NHW) in the US.
Asians and Hispanics currently have the highest incidence rates of HCC in the US, and NHW
has the lowest rate compared to all other racial/ethnic groups (El-Serag & Kanwal, 2014); (El-
Serag, Lau, Eschbach, Davila, & Goodwin, 2007).
At present, most HCC cases are connected with chronic infection from the Hepatitis B
virus (HBV) or Hepatitis C virus (HCV) (Mak et al., 2018). HBV is the major pathogenic factor
in the areas with high incidence, while HCV is the main cause in low incidence HCC areas such
as North America and Western Europe (Petruzziello, 2018) . Almost half of the increase in HCC
has been seen in older individuals with chronic HCV infection (Mittal & El-Serag, 2013).It is
also clear that HBV infection is the most common potential cause of HCC among Asians, while
2
the risk factors of HCC for non-Asian races are HCV infection, fatty liver disease and diabetes,
alcohol consumption, as well as metabolic disorders (McGlynn, Petrick, & London, 2015).
The best strategy to prevent HCC is to thoroughly eliminate viral infection. The HBV
vaccine, which is typically administered at birth or in early childhood, has reduced persistent
HBV in many countries since 1982, and has also brought profound reductions in HBV-related
liver cancer (Aleksic et al., 2011). Despite encouraging experimental results, currently no
vaccine can prevent HCV infection (Feinstone, Hu, & Major, 2012). Fortunately, a treatment has
come out and it is effective in 50–80% of patients with persistent HCV infection. Even more
effective treatments are in progress (Dore, 2012). It has been shown that with implementation of
surveillance and curative therapy, HCC early-stage detection significantly improved the 5-year
survival of the patient (Singal, Pillai, & Tiro, 2014). Since there are populations at particularly
high risk for developing HCC, the medical field encourages systematic screening of these target
populations to facilitate early-stage detection (Tang et al., 2018).
California has the largest population in the U.S. (United States Census Bureau, 2019) with
36.6% of its total population being NHW and 14.7% Asian. Almost one-third of American
Asians reside in California (United States Census Bureau, 2019). While it is true that the cancer
incidence and mortality rates are reducing, it is forecasted that most likely one out of four people
who has cancer will die of it (Registry, 2021). Notably, the HCC incidence and mortality rates in
California ranked only after Texas, Hawaii, and New Mexico in 2017 (White, Thrift, Kanwal,
Davila, & El-Serag, 2017), and had the largest number of incident cases due to its large
population (White et al., 2017). Significant racial/ethnic and nativity differences have been found
in terms of HCC incidence rates (IR) and trends (Sangaramoorthy et al., 2020). However, the
racial/ethnic-specific IR patterns of HCC over time remain unclear in California. The cross-
3
sectional study design, which has been widely used for assessing the prevalence of disease
utilizing cancer registry data(Kesmodel, 2018), cannot capture trends in an outcome to be
monitored over time. Therefore, the purpose of this study is to inspect effects of age, period time
in years, and birth cohort on HCC incidence in California during 1988-2017, using age-period-
cohort (APC) analysis. This research is important in terms of better understanding HCC
incidence patterns among Asians and NHWs in California.
Period effects usually reflect the population exposures, for example, the treatment of risk
factors and diagnostic measures. Cohort effects can be used to identify at-risk birth cohorts, such
as HBV infection and vaccination among Asian immigrants and first generations, and growing
obesity in younger generations. Another example is people born between 1945 and 1965 in
United Sates were affected by the HCV epidemic (Zhang, El-Serag, & Thrift, 2020). It could be
confusing to distinguish these effects conceptually and it is not the easy way to justify the
nuances by just state formal definitions. The advantage of APC analysis is that it can disentangle
factors that equally affect all age groups at a specific calendar times (period effects) and those
differences by birth cohort effects, hence better identifying the population groups that are at
higher risk for developing HCC and risk factors are responsible for increasing incidence.
Data
Both the age-specific incidence rates (per 100,000 population) and case counts of liver
cancer (HCC only) from 1988 to 2017 were obtained from the California Cancer Registry
(CCR). The CCR is a program of the California Department of Public Health’s Chronic Disease
Surveillance and Research Branch (CDSRB). It is a statewide population-based cancer registry
that collects information about almost all cancers diagnosed in California. The CCR is a
collection of data reported by local health care facilities, which are aggregated and checked for
4
accuracy. The CCR data are continually updated and meet all National Program of Cancer
Registries and Surveillance, Epidemiology, and End Results (SEER) standards for quality
timeliness and completeness. It is a reliable data source for researchers to conduct cancer
epidemiology investigations. The University of Southern California collaborates with the
ongoing work of CCR (Registry, 2021).

Data Analysis
The overall trends for both NHW and Asian HCC were evaluated using sum of case counts by
age, period, and cohort as index (e.g. Figures 1 and 2). The time-series of HCC by age, period,
and cohort were plotted to identify trends within any combination of two time-related effects
(e.g. Figure 3 and 4). All trend plots were created using the R “apc” package.
Age-specific HCC incidence rates (per 100,000) were calculated using CCR absolute
counts and population. The IR of each 10-year age interval within every 5-year time period was
averaged and multiplied by 100,000 (e.g. Figures 5 and 6). The birth cohort was calculated by
age and corresponding time period (1988-1992, 1993-1997, 1998-2002, 2003-2007, 2008-2012
and 2013-2017). Plots were constructed to show the effect of birth cohort based on the age-
specific rates by time period.
The identification problem in regards to the independent effects of age, period and cohort
has long been an issue since all three factors are time-related and are perfectly collinear;
knowing two of them can easily get another: age = period - cohort. This model addresses the
identification problem by using canonical parameterization based on the accelerations of the
trends in the three time-related factors. These development based on the second difference of
age, period and cohort factors as well as a three parameter (level and two slopes) for a linear
5
plane. The age, period and cohort factors themselves are not identifiable, but they could be
identified ad hoc by relating the levels and two slopes to these three factors in a particular way
(Zoe Fannon, 2020). Therefore, for us to use this package, it is enough to be aware of that
predictor can be found from the parsimonious parameter by a formula of the form: μage,cohort = a
linear plane + ∑ ∑ ∆
2
𝛼 𝑠 + ∑ ∑ ∆
2
𝛽 𝑠 + ∑ ∑ ∆
2
𝛾 𝑠 𝑐𝑜 ℎ 𝑜𝑟𝑡 𝑝 𝑒 𝑟𝑖𝑜𝑑 𝑎 𝑔 𝑒 , which is same as a generalized
linear model (GLM) with the form: μage,cohort = αage + βperiod + γcohort + δ. The regression
parameters describe the trends of the three factors. One of the advantages of canonical
parameterization is that it is easy to interpret. It is also freely varying and invariant to extensions
of the data matrix (Kuang, Nielsen, & Nielsen, 2008). The data were aggregated into 5-year age
intervals, which beginning at age 0, and the time period in year was divided as above. The case
count of HCC cancer was seen as the “response” in this model. The APC analysis was conducted
using R statistical package “apc”.
Results
Figures 1 and 2 gives the overall trend of NHW and Asian HCC case-counts with respect to
age interval, time period and birth cohort. We observe that NHW HCC cases were very few
before age 35, and at age 60 it reached a peak then rapidly declined until the last recorded age
interval (age 85) (Figure 1a). In Figure 1b we see that the number of cases is positively
correlated with time period. The number of cases was constantly increasing for people who were
born before 1950, then dramatically dropped for people born after that. Finally, individuals born
after 1975 show a much smaller number of cases (Figure 1c), which corresponds with Figure 1a.
Figure 2a shows that the HCC cases in Asians were few until age 25 and peaked at age 65
before starting to drop. Similar to NHW, the number of cases in Asians had a steady growth
6
along with time period (Figure 2b). There was a minor drop in the cohort, which happened after
the first peak in 1935. It increased again in 1945 before decreasing consistently.
Figure 1. Counts of Hepatocellular Carcinoma by age, period and cohort in Non-Hispanic Whites
in California, 1988-2017

Fig 1a Fig 1b Fig 1c

Figure 2. Counts of Hepatocellular Carcinoma by age, period and cohort in Asians in California,
1988-2017

7

Fig 2a Fig 2b Fig 2c

Figure 3 shows time series within age, period, and cohort by one of the other two indices
for NHW case counts. Figure 3a shows that the age distribution of cases was similar by time
period with the later period (2013-2017) having higher caseload than earlier ones that reached a
peak around age 60. Across time periods the peaks occurred between ages 55-75. Figure 3b
shows that the 1943 and 1923 birth cohorts had obvious changes difference in HCC counts than
the other cohorts. Figure 3c shows clearly the increase in HCC by birth cohort over time,
supporting the observation shown in Figure 3a. Figure 3d shows age 60+ and age 50-59 has the
8
largest number of cases for the 1950 birth cohort. In Figure 3e, we see most age groups have a
positive correlation with time period, except age 40-49. In Figure 3f, we see that the case number
for the 1943 birth cohort has a stable growth over time.
Figure 3. Time series plots by age, period, and cohort against one of the other two indices for
Hepatocellular Carcinoma count of Non-Hispanic Whites in California, 1988-2017

Fig3a Fig3b

Fig 3c Fig 3d

9

Fig 3e Fig 3f

Figure 4 gives the same combinations of three indices for Asians HCC case counts. In
Figure 4a only 2008-2012 time period have a concave approximately on age 65. In Figure 4b, the
growth of age affected 1943 and 1923 birth cohort most. The 2008-2012 time period also shows
a sink in 1940 birth cohort in Figure 4c. For Figure 4d, age 20-29 reached its peak first around
1930 birth cohort compare with other age group. The overall trend for every age group is
positive related with time period except age 40-49 in Figure 4e. The trends of 1923 and 1943
cohort are identical before 2004, they run in the opposite direction after.

10
Figure 4. Time series plots by age, period, or cohort against one of the other two indices for
Hepatocellular Carcinoma count of Asians in California, 1988-2017

Fig 4a Fig 4b

Fig 4c Fig 4d

11

Fig 4e Fig 4f

Table 1 and Table 2 list the age-specific IR of HCC cancer for NHWs and Asians for
different birth cohorts. The data points in Figure 5 and Figure 6 are derived from Table 1 and
Table 2. Figure 5 and Figure 6 describe the age-specific incidence rate (IR) trends of HCC
cancer among NHWs and Asians by year of birth. Both Figure 5 and Figure 6 were plotted to
present the effect of birth cohort on the age-specific rates. For NHW at each group greater than
49 years, the earlier birth cohorts generally have higher incidence rates than more recent ones. It
is worth noting that among age between 60-69, the 1953 birth cohort have the highest IR of all
age groups. Also, for the age between 50-59, there is an inflection point for the 1958 cohort.
Things are quite different for the Asian group where the IR for 80+ cases is more than twice time
of all other age groups. The birth cohort before 1958 generally have higher age-specific IR.

12
Figure 5. Incidence rate (per 100,000) of Hepatocellular Carcinoma in California for Non-
Hispanic Whites presented by age and year of birth

Figure 6. Incidence rate (per 100,000) of Hepatocellular Carcinoma in California Asians
presented by age and year of birth
13

Table 1. Age specific incidence rate (per 100,000) for different birth cohorts for Non-Hispanic
Whites

Table 2. Age specific incidence rate (per 100,000) for different birth cohorts for Asians
14

Table 3 and Table 4 both give an overview of relative performances of different models for
NHWs and Asians. Table 5 and 6 present 15 rows for all possible design combinations. "A" is
age model, "P" is period model, and "C" is cohort model. "APC" stands for age-period-cohort
model. "AP" is age-period model, "AC" is age-cohort model and "PC" is period-cohort model.
All three of them are nested in "APC". "Ad" is age-trend model, "Pd" is period-trend model,
"Cd" is cohort-trend model. All three of them including themselves effects and two linear trends.
"t" is trend model with two linear trends. "tA" is single trend model in age index, "tP" is single
trend model in period index, and "tC" is single trend model in cohort index. "1" is constant
model. The columns are as follows: “deviance” is the difference in -2 log likelihood value
between estimated and whole model; "df.residual" is degrees of freedom of residual, which is
nrow x ncol - dim(parameter) and since our model family is Poisson, the degrees of freedom is one
lower; "prob(>chi_sq)" is the p-value of the deviance; "LR vs APC" is the likelihood ratio
statistic compared to the "APC" model; “df vs APC” is "df" Degrees of freedom compared to the
"APC" model; and the second "prob(>chi_sq)" is the p-value of log likelihood ratio(Nielsen,
2015). We specifically look into the AIC for model selection, which is the Akaike’s Information
Criterion (AIC) minus twice the maximized log-likelihood plus twice the number of parameters
up to a constant. AIC compares the quality of a set of statistical models to each other (Zoe
15
Fannon, 2020) where the less information a model loss, the higher quality of the model
(H.Akaike, 1974). For both race groups, the age-cohort and age-period-cohort models (denoted
as “AC” and “APC”, respectively) have the smallest AICs. Compared to the other models, there
is a significant AIC reduction when age and cohort are in the model, which is reduced even
further when period is included. This is observed for both NHWs (Table 3) and Asians (Table 4).

Table 3. Deviance table of Different fitted models of Hepatocellular Carcinoma case number in
California Non-Hispanic White patients in California, 1988-2017

Table 4. Deviance table of Different fitted models of Hepatocellular Carcinoma case number in
California Asian patients in California, 1988-2017

16

Tables 5 and 6 show the parameter estimates for the “APC” model for each race group. The
number of canonical parameters is 39, the first 8 estimates are presented. “level” is the estimate
of the predictor 45,1958,1913, it is the predictor of the middle age group in the oldest time period.
The age (cohort) slope presents how much the predictor would change when increasing age
(cohort) by one unit, while keeping the other one unchanged.
In the estimation of age-period-cohort model for NHWs, both “age” and “cohort” slopes have
positive effect in the APC model (0.89,0.52), and both coefficients are statistically significant (p
< 0.05). The APC parameter estimates for Asians is similar. While the coefficient of “age” and
“cohort” are both statistically significant (p < 0.05), the positive effect of “age” and “cohort”
(0.62,0.41) are less than the other race group (NHW).

17
Table 5. Estimation of age-period-cohort model for Non-Hispanic Whites

Table 6. Estimation of age-period-cohort model for Asians

18

Discussion

Our results suggest that age, period as well as birth cohort together be a key factor in the
observed increasing incidence of HCC cancer in California over the past three decades. The birth
cohort analysis as well as the APC model shows that people born before 1958 and who were
over 60 years old were affected most for both NHW and Asian groups. As we mentioned above,
the most important risk factors for HCC differ for Non-Hispanic White and Asians. Therefore,
we split the discussion in regards of more specific risk factors. For Non-Hispanic White, HCV is
the major risk factor for HCC. A latest analysis of birth cohort HCV prevalence in the U.S.
showed that the highest rates in persons born during 1945-1965 (Smith et al., 2012), which is
consistent with the patterns we observed in California. This pattern also corresponds to the
increase in new infections associated with abusing with injection drugs among young adults in
the 1970s and 1980s (Armstrong et al., 2006). The incidence of HCC under age 40 is rare,
evidently, the era peak of HCC infection around 1980s did not contribute much to the HCC
incidence rate at that time. It may have impacts in latter time period. The abnormally high
incidence rates among Asians can be explain as the consequence of Asian immigration from
HBV-endemic areas due to the golden rush around 1850s. The relative low incidence rates in
recent birth cohort can be seen as the concurrent effect of increasing HBV vaccination and the
age effect (Kulik & El-Serag, 2019). Overall, the patterns we found are reasonable and coherent.
The increase of HCC incidence rates can be explained by the age and birth cohort effects for
both race groups. The differences between Non-Hispanic Whites and Asians are being examined
19
and explained. Identifying those patterns could help to implement more targeted public health
programs. It could also be utilized for other phase of medical interventions. For secondary and
tertiary preventions, researchers could utilize our results to save money by recruiting participants
more precisely. However, there are still some limitations. While APC analysis is an ideal method
for aggregated data, it is unable to manage large datasets. For the plots generated by “apc”
package (Figure 3 and 4), the period is 5-year intervals while the age is 10 years per group and
cohort is 20 years per group. The incoherent time range could cause inaccuracy when
interpretation overall trends. Another approach using both case-counts and population in the
model has not been evaluated. We only fit the Age-Period-Cohort model in a conservative way
with cases number as “response” for now. A comparison between two models should be made in
order to better understand the trends. Factors leading to HCC incidence rates differing
significantly between males and females should also be examined in future studies.

20

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Abstract (if available)

Abstract Increased HCC incidence rates can in part be explained by age and birth cohort for both NHW and Asians. People over 60 years of age and born before 1963 were affected most for both race groups. Differences in risk factors as well as time led to disparate outcome distribution of Non-Hispanic White and Asians. Specifically, HCV infection was found to be the most prominent risk factor for HCC in NHWs. This pattern corresponds to the increase new infections that due to abusing with injection drugs among young adults in the 1970s and 1980s. The abnormal high incidence rate among Asian group can be explain as the consequence of Asian immigration from HBV-endemic areas due to the golden rush around 1850s. Overall, the patterns we found is reasonable and coherent.

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Asset Metadata

Creator Shan, Haoyue (author)

Core Title Age-period-cohort analysis of hepatocellular carcinoma (HCC) incidence trends among Asians and non-Hispanic Whites in California, 1988-2017

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 04/15/2021

Defense Date 04/13/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag age-period-cohort analysis,Asians,hepatocellular carcinoma,incidence trends,non-Hispanic Whites,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Franklin, Meredith (committee chair), Li, Ming (committee member), Liu, Lihua (committee member)

Creator Email haoyuesh@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-443968

Unique identifier UC11668781

Identifier etd-ShanHaoyue-9461.pdf (filename),usctheses-c89-443968 (legacy record id)

Legacy Identifier etd-ShanHaoyue-9461.pdf

Dmrecord 443968

Document Type Thesis

Rights Shan, Haoyue

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA