+1443 776-2705 panelessays@gmail.com
  

 

Sooyan wants to study perceptions of sports advertising on a college campus. The student body at this university is 60% female and 40% male. He advertises for his study and there is a strong interest among the student body. He obtains statistically significant results and is looking forward to publishing them. However, after he has finished his study and two days before his project is due, he realizes that his sample included 70% males and 30% females.

In reference to the above scenario, answer the following questions:

  • Does this represent proper quota or stratified sampling?
  • What do you think Sooyan should do?
  • List three research strategies each, you can use to follow both quota and stratified sampling.
  • Using the Online Library, find and summarize three articles. Focus on the methods they used to ensure representation.
  • Do you think any of these methods are also applicable in research involving animals?

RESEARCH ARTICLE Open Access

Area based stratified random sampling
using geospatial technology in a
community-based survey
Carrie R. Howell1* , Wei Su2, Ariann F. Nassel2, April A. Agne1 and Andrea L. Cherrington1

Abstract

Background: Most studies among Hispanics have focused on individual risk factors of obesity, with less attention
on interpersonal, community and environmental determinants. Conducting community based surveys to study
these determinants must ensure representativeness of disparate populations. We describe the use of a novel
Geographic Information System (GIS)-based population based sampling to minimize selection bias in a rural
community based study.

Methods: We conducted a community based survey to collect and examine social determinants of health and
their association with obesity prevalence among a sample of Hispanics and non-Hispanic whites living in a rural
community in the Southeastern United States. To ensure a balanced sample of both ethnic groups, we designed an
area stratified random sampling procedure involving three stages: (1) division of the sampling area into non-
overlapping strata based on Hispanic household proportion using GIS software; (2) random selection of the
designated number of Census blocks from each stratum; and (3) random selection of the designated number of
housing units (i.e., survey participants) from each Census block.

Results: The proposed sample included 109 Hispanic and 107 non-Hispanic participants to be recruited from 44
Census blocks. The final sample included 106 Hispanic and 111 non-Hispanic participants. The proportion of
Hispanic surveys completed per strata matched our proposed distribution: 7% for strata 1, 30% for strata 2, 58% for
strata 3 and 83% for strata 4.

Conclusion: Utilizing a standardized area based randomized sampling approach allowed us to successfully recruit
an ethnically balanced sample while conducting door to door surveys in a rural, community based study. The
integration of area based randomized sampling using tools such as GIS in future community-based research should
be considered, particularly when trying to reach disparate populations.

Keywords: Area based, Geographic information systems, Stratified random sampling, Hispanic population, Rural
population, Community based methods

© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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* Correspondence: [email protected]
1Department of Medicine, Division of Preventive Medicine, University of
Alabama at Birmingham, Medical Towers 62, 1717 11th Avenue South,
Birmingham, AL 35205, USA
Full list of author information is available at the end of the article

Howell et al. BMC Public Health (2020) 20:1678
https://doi.org/10.1186/s12889-020-09793-0

Background
Obesity is a leading risk factor for the development of
diabetes, cardiovascular illness, cancer and other chronic
conditions that cause significant morbidity and mortality
as well as increased health care costs [1]. Hispanics are
the largest and fastest growing racial/ethnic minority
group in the United States, comprising 17.3% of the
population in 2014 [2], with disproportionately high
obesity rates. Among adults living in the United States
in 2015, the prevalence of obesity was 47% among His-
panics compared to 38% among non-Hispanic whites
[3], highlighting the need to examine factors that con-
tribute to this increased risk. To date, most studies
among Hispanics have focused on individual risk factors
of obesity, with less attention on interpersonal, commu-
nity and environmental determinants. In order to con-
duct community level surveys to collect this type of data,
it is crucial to ensure representativeness of both His-
panic and non-Hispanic populations in the study sample.
Here we describe the use of a novel GIS-based popula-
tion based sampling approach to minimize selection bias
in a community based study.
Sampling for cross-sectional survey studies can be

probability based or non-probability based. Probability
based (e.g. random sampling) requires a defined popula-
tion, where each possible unit has a known possibility of
being selected [4]. Non-probability sampling methods
(e.g. convenience sampling) have no known inclusion
probabilities [5], producing bias and unbalanced sample
representation [6–14]. Simple random sampling can also
pose a problem for studies conducting research in mi-
nority populations. This method targets the whole popu-
lation of interest and often results in minority under-
representation. Stratified random sampling increases
sample representativeness by dividing the study popula-
tion into strata based on characteristics that are of inter-
est to the researcher [15]. Random samples are then
drawn from each strata to ensure adequate sampling of
all groups. This approach reduces sampling bias; allows
researchers to estimate within and between strata out-
comes; and improves accuracy of results [15, 16].
Sampling design is important in large population stud-

ies with several national surveys utilizing stratified ap-
proaches to minimize bias. The US Census Bureau
conducts the American Community Survey (ACS) to
produce annually updated census data estimates based
on geographic units (e.g. census tract and block group).
The complex sampling design consists of first stratifying
the US population by census block, then calculating
population based sampling rates. Appropriate weights
are applied in the analytical phase so that estimates rep-
resent the full population [17]. Similarly, the National
Health and Nutrition Examination Survey (NHANES)
employs a stratified, multistage cluster design that

oversamples specific subgroups to increase precision in
health outcome estimates [18]. Smaller scale community
based population studies should draw upon and incorp-
orate aspects of these rigorous sampling designs to re-
duce sampling error and increase precision in estimates.
In recent years, technologies such as Geographic Infor-

mation System (GIS) have been used to facilitate the
sampling process in community-based research. Typic-
ally, GIS software have been used for data analysis and
visualization [19]; however, health researchers have
begun to realize its potential in facilitating the sampling
and recruitment process, particularly in rural, developing
countries [20–22]. To aid in sampling, GIS has been
used to define populations in areas without formal cen-
sus data [21, 22]; create clusters [22]; and stratify popu-
lations [20]. Area stratified random sampling methods
use area units as the strata, such as census blocks, and
produce samples comparable to random digit dialing re-
cruitment approaches [20, 23, 24]. This method provides
an innovative way to conduct community-based health
survey research, particularly when the study area is small
in population. Blending aspects of complex sampling de-
sign, such as those used in national surveys, with GIS
methods has the potential to strengthen community
based research. Here, we describe how geospatial data
and Geographic Information Systems (GIS) were used to
develop an area stratified random sampling protocol that
ensured demographic balance in conducting a
community-based, interviewer administered survey. The
study’s main aim was to examine social determinants of
health and their association with obesity prevalence
among a sample of Hispanics and non-Hispanic whites
living in a rural community in the Southeastern United
States.

Methods
Participants and setting
The population of interest resided in Albertville, Ala-
bama where researchers had previously conducted a cer-
vical screening study aimed at Hispanic women [25].
Located in Marshall County in the northeastern side of
the state, Albertville has a population of 21,160 with
64.7% non-Hispanic white and 30.2% Hispanic as of the
2010 Census [26]. The city has two zip codes and is 26
square miles with a population density of 817 per square
mile. The nearest metropolitan city with a population of

Table 1 Population and household characteristics in Albertville
city

Population Households

Total 20,883 7401

Hispanic 5861 (28%) 1229 (17%)

Non-Hispanic 15,022 (72%) 6172 (83%)

Howell et al. BMC Public Health (2020) 20:1678 Page 2 of 9

over 150,000 is located 38 miles away. The median yearly
income of Albertville is lower than Alabama as a whole
($35,878 vs. 40,489). The Hispanic population is concen-
trated to approximately 17% of the households in the
city (Table 1).
Data was collected from participants interviewed by

trained research interviewers in door-to-door canvas be-
tween June and December 2013. To be included, partici-
pants had to be at least 19 years of age, not pregnant,
speak English or Spanish fluently, and self-identify as
non-Hispanic white or as Hispanic/Latino. Participants
were compensated with a gift card for their time. All
study procedures were reviewed and approved by the
University of Alabama at Birmingham’s Institutional Re-
view Board.

Area stratified random sampling for recruitment
The goal to recruit an equal number of Hispanic and
non-Hispanic participants would have been difficult to
achieve by employing a completely random sampling
procedure across the entire city. Therefore, a stratified
random sampling procedure was created based on the
Center for Disease Control and Prevention’s (CDC)
Community Assessment for Public Health Emergency
Responses (CASPER) sampling methodology [27]. The
CASPER approach was developed using cross-sectional
epidemiological principles and is a form of a community
needs assessment that provides a systematic approach to
collecting household information on community public
health status. The cluster sampling design involves two
stages: selecting clusters based on household proportions
and then interviewing a set random number of house-
holds in each cluster. The CDC recommends using GIS
software in the selection of the sampling frame to allow
users to select portions (clusters) of geographically de-
fined areas, such as counties or cities. In addition, GIS
software provides the ability to easily develop maps for
community interviewers based on the selected clusters.
For this reason, CASPER provides a toolbox for use in
ArcGIS software to facilitate this methodology. Using
this approach in our study involved three stages: (1) div-
ision of the sampling area into non-overlapping strata
based on Hispanic household proportion; (2) random se-
lection of the designated number of Census blocks from
each stratum; and (3) random selection of the designated
number of housing units (i.e., survey participants) from
each Census block.

Stage 1: Divide the sampling area into non-overlapping
strata based on Hispanic household proportion
To ensure that the interviewers would be able to reach
sufficient Hispanic households, all Census blocks within
Albertville were divided into four strata based on per-
centage of Hispanic households using GIS software.

Since Albertville city boundaries and Census block
boundaries do not perfectly align with each other, a cen-
troid criterion was used to determine whether or not a
Census block belonged to Albertville city. As a result,
647 Census blocks were assigned to Albertville city. Of
those, only 455 blocks contained households and the
other 192 blocks were non-residential. Since the His-
panic population was concentrated in a relatively small
geographic area, the 455 blocks were further divided into
four unbalanced strata identified by Hispanic household
proportion: < 10% Hispanic households, 10–30% His-
panic, 30–50% Hispanic, and = 50% Hispanic. Roughly
60% of the blocks were assigned to the =10% of Hispanic
households stratum, with 7% (N = 32) of the blocks
assigned to the > 50% of Hispanic households stratum
(see Table 2 and Fig. 1).

Stage 2: Randomly select the designated number of Census
blocks from each stratum
Our goal was to recruit a total of 200 participants, with
a distribution of 50% Hispanic and 50% non-Hispanic
white (1:1 ratio). Maps denoted that the Hispanic popu-
lation was largely concentrated in small area blocks
(Fig. 1). Although smaller blocks suggest higher popula-
tion density, they also contain fewer individuals and
households compared with larger blocks. Since His-
panics comprised a smaller proportion of total house-
holds (17%), we needed to oversample blocks with
higher concentrations of Hispanic households in order
to reach an equal number of Hispanic and Non-
Hispanic surveys. For these reasons we took the follow-
ing approach to determine the number of Census blocks
to select from each group, and the number of housing
units to select from each Census block.

1. Considering the varying population size across
blocks, it was determined to be more feasible to
plan fewer surveys per block in more Hispanic
population concentrated areas (i.e., strata 3 & 4 in
Table 2), and more surveys per block in more non-
Hispanic population concentrated areas (i.e., strata
1 & 2 in Table 2). As a result, we selected 10 blocks
with 6 surveys per block from strata 1 and 2 and 12
blocks with 4 surveys per block from strata 3 and 4.
These numbers were somewhat arbitrary, balancing
the concern that selecting too many blocks which
would increase cost, while taking care to not plan
for an unrealistic quota of surveys per block when
not feasible (e.g. the smallest block in the study area
contained only 8 households).

2. For strata 1 and 2, distribution of Hispanic versus
non-Hispanic surveys within each block roughly
reflected the proportions of Hispanic and non-
Hispanic households in the corresponding group.

Howell et al. BMC Public Health (2020) 20:1678 Page 3 of 9

Since oversampling of the Hispanic population was
needed to achieve the recruitment goal, proportions
of Hispanic surveys in strata 3 and 4 were set higher
than the actual proportions of Hispanic households.
Table 2 shows the proposed number of blocks to
select from each group and numbers of Hispanic
versus non-Hispanic surveys projected within each
block. In total, we proposed 109 Hispanic surveys
and 107 non-Hispanic surveys from 44 blocks.

Once the number of blocks from each group were de-
termined, the CASPER toolkit developed by the CDC
was utilized to generate random samples [27]. We used
an add-on program developed for ArcGIS by the CDC
to generate random samples using a polygon layer that
represents the sampling area and non-overlapping clus-
ters within the sampling area. In our study, the four
strata were our sampling areas with Census blocks the
non-overlapping clusters, accounting for the number of

Table 2 Proposed stratified sampling based on Hispanic household proportion

Strata % Hispanic
Households

Eligible
Blocks

Selected
Blocks

Surveys
per
Block

Surveys
per
stratum
a

Surveys per Stratum by Hispanic Origin b

Hispanic Non-Hispanic

Stratum 1 =10% 268 10 6 60 5 55

Stratum 2 10–30% 101 10 6 60 20 40

Stratum 3 30–50% 54 12 4 48 36 12

Stratum 4 > 50% 32 12 4 48 48 0

Total 455c 44 216 109 107
aDerived by multiplying selected blocks and surveys per block
bFor strata 1 and 2, distribution of Hispanic and non-Hispanic surveys within each block roughly equate to household proportions. Oversampling of Hispanics was
planned a priori to reach recruitment goals thus proportions of Hispanic surveys in strata 3 and 4 were set higher than actual Hispanic household proportions
cTotal of 647 blocks in Albertvile, AL with 192 blocks with zero household, leaving n = 455 eligible blocks for sampling

Fig. 1 Census blocks in Albertville, AL by Hispanic household proportion. Map of census block groups in Albertville, AL. Darker shading indicates
higher Hispanic household proportions. Map developed using licensed ArcGIS software

Howell et al. BMC Public Health (2020) 20:1678 Page 4 of 9

housing units within each cluster. The random sampling
procedure was repeated four times, once for each
stratum. Figure 2 shows the 44 random blocks selected
from the entire study area using this approach.

Stage 3: Randomly select the designated number of
housing units from each Census block
Interviewers were provided with satellite maps (Fig. 3)
for each block randomized with detailed instructions re-
garding how to randomly select the designated number
of housing units within each block. The systematic ran-
dom sampling method described in the CASPER toolkit
[27] was adapted and modified to develop the study’s
survey protocol:

1. A starting point (address) for each sampling block
was provided. This was the first house for the
interviewers to survey.

2. After completing the first survey, interviewers
would walk or drive in either direction to the next
Nth house. This would be the next household for
the interviewers to survey.

3. If no one answers the door, continue to the next
Nth house.

4. Continue traveling through the sampling block,
selecting every Nth house until they have completed the
designated number of surveys for that sampling block.

5. If the interviewers circled back to the starting point
and had not completed the designated number of
surveys, they would then proceed through the block
again and select every (N + 1)th house. For example,
if Block A had an N of 8, in the next pass the
interviewer would approach every 9th house.

The N used in the protocol was determined by dividing
the total number of housing units by the designated number
of surveys to complete in each block, and thus could vary
from block to block. For example, if a block contained 50
housing units and the designated number of surveys was 6
for that block, the N would be 8. Values of N for each indi-
vidual block were provided in the instructions to the inter-
viewers. Additional instructions with regards to abandoned
homes, businesses, duplexes and apartment complexes, mul-
tiple family homes, and trailer parks were also provided.

Results
The proposed sample included 109 Hispanic and 107
non-Hispanic participants to be recruited from 44

Fig. 2 Census blocks selected for recruitment. Map of the 44 census block groups randomly selected in Albertville, AL using an area stratified
random sampling approach. Blue outline indicates block group selected. Map developed using licensed ArcGIS software

Howell et al. BMC Public Health (2020) 20:1678 Page 5 of 9

Fig. 3 Field interviewer block map. An example of the satellite image map provided to interviewers to conduct field surveys. Map data image
provided by© 2013 Google; Imagery© 2013 MaxarTechnologies

Table 3 Summary of actual sample

Strata Proposed Sample Actual Sample P a

Selected
Blocks

Hispanic Non-
Hispanic

Group
N

Selected
Blocks

Hispanic Non-
Hispanic

Group
N

% Hispanic
Recruited

Stratum 1 (=10%) b 10 5 55 60 12 3 41 44 7% 1.0d

Stratum 2 (10–30%) 10 20 40 60 12 15 35 50 30% 0.71c

Stratum 3 (30–50%) 12 36 12 48 18 33 24 57 58% 0.07c

Stratum 4 (> 50%) 12 48 0 48 22 55 11 66 83% 0.002d

Total 44 109 107 216 64 106 111 217
aComparing the proposed distribution of surveys by ethnicity status to the proportions of surveys completed. P-values > 0.05 indicate that actual proportions did
not differ from proposed population based proportions
bProportion of Hispanic households in each individual block
cChi-square test
dFishers exact test

Howell et al. BMC Public Health (2020) 20:1678 Page 6 of 9

Census blocks. After exhausting all 44 blocks, inter-
viewers were unable to meet recruitment goals for the
proposed number of surveys in each block. Twenty add-
itional blocks were selected using the same random sam-
pling procedure described above, including two from
strata 1 (=10% of Hispanic households), two from strata
2 (10–30% of Hispanic households), six from strata 3
(30–50% of Hispanic households), and ten from strata 4
(> 50% of Hispanic households). More blocks with
higher Hispanic population density were selected be-
cause field interviewers found that recruitment of His-
panic participants was particularly challenging. The final
sample included 106 Hispanic and 111 non-Hispanic
participants. The number of surveys completed from
each block ranged from 0 to 11, with an average of 3.4
surveys per block (Table 3 and Fig. 4).
Post-hoc chi-square and Fishers exact tests were used

to test the proposed distribution of surveys by ethnicity
status to the proportions of surveys completed. P-values
> 0.05 indicate that actual proportions did not differ
from proposed population based proportions. The pro-
portion of Hispanic surveys completed per strata were
similar to our proposed distribution for strata 1–3: 7%
for strata 1 (p = 1.0), 30% for strata 2 (p = 0.71), and 58%

for strata 3 (p = 0.07). Although Strata 4 (83% Hispanic
surveys, p = 0.002) had statistically different proportions,
this was expected due to the need to oversample His-
panic surveys from this strata.

Discussion
Here we demonstrate the successful use of a novel area
stratified random sampling technique utilizing GIS that
ensured ethnic balance in the recruitment of our com-
munity canvased study sample. Field recruitment in
community studies presents challenges in minimizing
selection bias and ensuring demographic representation.
Here, integrating GIS based technology with census data
provided a standardized and objective approach to re-
cruitment to address these issues. Specifically, we uti-
lized GIS to create and visualize non-overlapping strata
to determine individual stratums and to randomly select
Census blocks within those strata. Our approach en-
sured the 1:1 ratio of Hispanics to non-Hispanics in our
study, minimized selection bias, and provided an ap-
proach that was easy for the ‘boots on the ground’ inter-
viewers to implement. Moreover, the distribution of
completed Hispanic surveys by stratum closely matched
our original proposed proportions (defined based on

Fig. 4 Number of participants by Census block. Map of census block groups in Albertville, AL with the number of participants who completed a
survey. Darker shading indicates more participants. Map developed using licensed ArcGIS software

Howell et al. BMC Public Health (2020) 20:1678 Page 7 of 9

percentage of Hispanic households in block), giving our
sample geographic representation by Albertville block.
Utilizing GIS to facilitate community-based research,

such as targeting areas for program planning or ensuring
random sampling of survey respondents [28], has been
implemented in recent population based studies. This
method has been particularly useful in rural, developing
countries [20–22, 29]. Defar et al. used GIS methods to
conduct a cross-sectional survey in Ethiopia on maternal
and child health care utilization in a similar two-stage
process as the current study [29] while Wampler et al.
used GIS to facilitate the random selection of house-
holds in specific areas in Haiti for water quality research
[22]. Akin to the results here, a study that compared
simple random sampling to stratified sampling by zip
code and census tract found that area based stratified
sampling ensured a higher representativeness of His-
panic residents in audits of tobacco retailers in an urban
area [30]. In the public health realm, Lafontaine et al.
developed a spatial random sampling method to conduct
neighborhood built environment audits and concluded
that this approach was more cost and time effective [31].
Likewise, using the approach herein resulted in recruit-
ing our Hispanic sample in a more efficient manner.
It is important to note that we selected the number of

blocks for randomization and recruitment based on
feasibility but nonetheless in an arbitrary fashion. While
this resulted in a balanced sample for our study, this will
likely not translate into other scenarios. Since stratifica-
tion by design results in subgroups that are over or
under represented compared to the overall population
[15], taking the actual population weights of each census
tract into account when selecting blocks would have
been more appropriate. Since the ultimate goal in sam-
pling is to select a study sample that is representative of
the population, applying population sampling weights
and using model-based approaches such as raking prior
to analysis are essential. Raking adjusts the sampling
weights by forcing the survey totals to match propor-
tions in the known population [32].
Our approach was not without challenges or limita-

tions. When conducting the door to door surveys, inter-
viewers were provided with detailed protocol and
satellite maps. However, multiple issues arose. First,
there was a significant number of houses that provided
“no answer” and we had to implement the N + 1 sam-
pling multiple times to reach recruitment targets. Time
constraints also impacted interviewers. Some blocks
sampled had a count number that was large (N > 14),
which decreased sampling efficiency as driving from one
house to the next could exceed 10 min. Another limita-
tion of the study is that we used the population and
household counts from the 2010 Decennial Census data,
which may have underestimated the number of

Hispanics in Albertville at the time of data collection
(2013). Further, the criterion used to divide the study
area was Census block group and 2010 Census estimates
were likely different than the true distribution of His-
panic households by block in 2013. Lastly, it is important
to note that CASPER was designed for use in the United
States and associated territories and uses data collected
from the census bureau to create population based sam-
pling areas and clusters. However, since CASPER was
developed based on an epidemiological two-stage cluster
sampling approach, it is possible to conduct this type of
sampling in other countries where census type data are
available using the CASPER protocol as a guide.

Conclusion
Overall, we developed a standardized area based ran-
domized sampling protocol that allowed us to successful
recruit an ethnically balanced sample while conducting
door to door community surveys. Minimizing selection
bias in community-based surveys can be difficult; how-
ever, advancement in technological tools such as GIS
provides novel approaches to address these biases. Based
on our results here, we advocate the integration of area
based randomized sampling in future community-based
research, particularly when trying to reach disparate
populations.

Abbreviations
GIS: Geographic information systems; CASPER: Community Assessment for
Public Health Emergency Response; CDC: Centers for Disease Control and
Prevention; ACS: American Community Survey; NHANES: National Health and
Nutrition Examination Study

Acknowledgements
We especially thank Matthew Carle, Morgan Griesemer Lepard, Ynhi Thai,
Meghan Meehan, Amancia Carrera, Sylvia Alavarez Mancinas, Susan Henry
Barber, and Chris Caudill for their tireless efforts to canvas neighborhoods
and interviews participants. We would also like to thank all our participants,
the office of the Mayor of Albertville, the Albertville Police Department,
support staff, and others who helped make this study possible.

Authors’ contributions
WS, AC made substantial contributions to the design of the work. CH, WS,
AC made substantial contributions to the analysis, interpretation of data and
drafted the work. AN and AA made substantial contributions to the
acquisition and interpretation of data. All authors read and approved the
final manuscript and are accountable for the accuracy and integrity of the
work presented.

Funding
This work was supported by grants from the University of Alabama with
funding from the National Institute of Minority Health and Health Disparities
(U54MD008176) and support from the National Institutes of Health, National
Institute of Diabetes and Digestive and Kidney Diseases, UAB Diabetes
Research Center [1P60DK079626–01]. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institute of Minority Health and Health or National Institute of
Diabetes and Digestive and Kidney Diseases or the National Institutes of
Health or others supporting this work. All sources of funding had no role in
study design; collection, analysis, and interpretation of data; writing the
report; or the decision to submit the report for publication.

Howell et al. BMC Public Health (2020) 20:1678 Page 8 of 9

Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated
or analyzed during the current study.

Ethics approval and consent to participate
This study was approved by the University of Alabama at Birmingham
Institutional Review board and documented written informed consent was
obtained from all participants prior to participation.

Consent for publication
Not applicable.

Competing interests
The authors declare they have no competing interests or financial
relationships relevant to this article to disclose.

Author details
1Department of Medicine, Division of Preventive Medicine, University of
Alabama at Birmingham, Medical Towers 62, 1717 11th Avenue South,
Birmingham, AL 35205, USA. 2School of Public Health, University of Alabama
at Birmingham, 1665 University Blvd, Birmingham, AL 35233, USA.

Received: 22 May 2020 Accepted: 29 October 2020