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PHC 6760 – Research Methods in Public Health Programs
Quast
2023 Fall
Course Project Proposal Shell Table
Unit of observation
Number of observations
County or State (choose one)
Variable
Dependent variable
Explanatory variable #1
Explanatory variable #2
Explanatory variable #3
Explanatory variable #4
Explanatory variable #5
Description
Source dataset description
Source dataset webpage link
PHC 6760 – Research Methods in Public Health Programs
Quast
2023 Fall
Course Project Description
The goal of this project is to introduce you to writing a short empirical analysis that evaluates a
health program or investigates a health services research topic. While it is nearly impossible to
write a journal-worthy article in the course of a semester, you should structure your paper as if
you were going to submit it to journal. As such, you should use existing papers in journals such
as Health Services Research as a guide as to how to prepare your paper.
The project will be conducted in the steps listed below. You are strongly encouraged to review
Prof. Quast’s annotations and comments on your submissions and try to incorporate them in later
submissions. The grading (especially in regard to writing quality) will become strict as the
semester proceeds.
1. Proposal describing the program or topic and a rough overview of the empirical
analysis.
You are to specify a precisely defined outcome (dependent) variable that is measured
as an overall average, rate or proportion at the county- or state-level. Examples of
dependent variables include the average number of hospital stays per 1000 residents
or the proportion of residents who smoke. While you may be able to obtain a
dependent variable for a specific subgroup (e.g., by race), such a variable is not likely
to be a good fit for this analysis. (However, you can include explanatory variables
that measure the extent to which subgroups comprise the overall population.)
You are to specify three to five explanatory variables. The explanatory variables
should also be averages, rates, or proportions, with the exception that you can the
overall population as an explanatory variable. You are advised to not use the
proportion of residents by gender as an explanatory variable, as that proportion
generally varies little across counties and states. You also should not use rankings as
explanatory variables, as they are not well-suited to the statistical analyses that you
will perform.
The dependent and explanatory variables must come from at least two different
sources. For instance, your dependent variable and an explanatory variables could
come from one source, while your other explanatory variables could come from a
different data source. The data sources cannot be from the same website or
organization.
Your proposed analysis should be cross sectional and include 25 or more
observations. For instance, an analysis of all U.S. states (resulting in 50 observations)
or an analysis of all Florida counties (resulting in 67 observations) would be
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appropriate.
Your sample should be limited to those units for which you have values for all of our
variables. For instance, if you are performing a county-level analysis and two of the
counties are missing values for one of your variables, those counties should be
excluded from your sample.
Your proposal Word document should provide a brief overview of the topic (1-2
paragraphs), an overview of the datasets (1 paragraph), and the description of the
variables (1-2 paragraphs). The overview of your data should precisely specify the
time period your data cover, a description of the sample subjects, and the unit of
observation (e.g., county). The description of the variables should describe the
sources and hypothesized relationships between each explanatory variable and the
dependent variable.
Your proposal Word document should also include a table that specifies on each row:
a. The unit of observation
b. The number of observations
c. The dependent variable (description & source dataset)
d. The explanatory variables (descriptions & source datasets)
A shell table for you to use is included in the Canvas proposal assignment page.
You are to also electronically submit the data set that you intend to analyze. Your
datasets should not be merged but instead be included as separate sheets in a single
Excel file (please carefully label the Excel sheets). The submitted data should include
only those variables that you intend to include in your analysis. In other words, do not
include variables that don’t propose to use. You should specify the dependent and
explanatory variables in your dataset.
Prof. Quast will provide comments via Canvas as to the need for revisions or whether
your proposal has been approved. If revisions are indicated, you will need to submit
revised proposals until all concerns are addressed. You cannot submit any later
project items until your proposal has been approved. It is your responsibility to obtain
approval in a timely manner. The due dates for the remaining items will not be
adjusted.
While unforeseen circumstances can necessitate changes to data analysis plans, you
should consider the proposal as a rough contract for your project. Deviations from the
proposal not approved by Prof. Quast will result in grade reductions.
You are encouraged to discuss proposal ideas with Prof. Quast in advance of the due
date. It is very important that you have a suitable topic and analysis plan, or the course
project may become very difficult and result in a poor grade.
2. Introduction. (~ 1.5 pages)
This section should introduce the topic, provide a brief review of relevant existing
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research, and broadly summarize your analysis. (The summary should state the data
you will employ and the hypothesis/hypotheses you will test).
3. Methods (~ 0.5 pages)
This section should provide a detailed description of the data, including data sources,
variable units of measurement, and a report of the sample size. If possible, refer to
existing studies that have used your data or data similar to yours and any judgements
regarding the quality of your data. Describe any dropped data and or other
imperfections. (Typically this section includes an overview of the statistical analysis
that you will employ, but given the timing of the course your Methods section will
not include this.)
4. Results: Descriptive analysis. (~ 1 page of text; 2 exhibits)
This section should provide an overview of the data used in your inferential analysis
and provide context for the relationship between your dependent and explanatory
variables.
Generally, this section has two goals:
(a) Provide an overview of the values of your variables, both to inform the reader but
also to convince her/him that your values are plausible & likely accurate. You should
not describe every single variable in great detail, but you should highlight important
aspects. The overview is typically accompanied by a table of summary statistics.
(b) Provide initial (non-inferential) insight into how your dependent variable is
potentially associated with your explanatory variables (especially the explanatory
variable in which you are most interested). A cross-tab table or scatterplot may be
useful.
The analysis should be performed in SPSS & you should submit the SPSS output file
in Canvas.
One of your exhibits should be a table of summary statistics created in Excel or
Word.
The other exhibit should be a figure and be created in SPSS.
You should not report correlation coefficients.
5. Results: Inferential analysis. (~ 2 pages of text; 2-3 exhibits)
The first part of this section should include a brief description of your analytic
methodology (this would normally be part of the Methods section). This description
should only be two to three sentences and should not include basic information about
the statistical procedures employed.
Your analysis should focus almost exclusively on linear regression estimation and be
performed in SPSS.
In addition to regression analysis on your entire dataset, you should perform analyses
of subsets of your sample based on the values of the dependent variable. Specifically,
you should estimate an additional regression where your observatiosn are limited to
those counties/states that have values of the dependent variable above the median
value. You should then perform another regression for those observations where the
dependent variable has a value be low the median. You should compare those two
sets of estimates for potential insight.
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You should discuss necessary conditions, regression fit, and regression diagnostics in
the main text, but provide any supporting tables or figures in the appendix.
Even if they are not statistically significant, you should provide interpretations of the
regression estimates.
A technical appendix should also be provided that includes the SPSS output file saved
as a pdf file. The appendix does not count towards the page count for this section.
6. Discussion. (1-2 pages)
This section should briefly state your main results then provide a more in-depth
overview of your findings and their implications. If your estimates were not
statistically significant, you should discuss their implications if they had been. You
should place the results of your study in the context of previous papers. The section
should conclude with a description of the limitations of your analysis.
In addition to the above descriptions, you should review the grading rubrics posted on Canvas for
guidance.
The paper is to be written with one-inch margins on all sides, double-spaced, and using 12-point
Times New Roman font. Given the additional roughly two pages of graphs and tables, the full
paper should number approximately ten pages. Each paper section should be labeled.
As you submit each section, please include your previously submitted sections (other than the
proposal and literature review sections). You do not have to revise previously submitted sections.
Your primary exhibits should be included in the main text. Tables and figures should be labeled
(e.g., Table 1). Tables should be prepared in Excel or Word, while graphs should be created in
SPSS. These exhibits should be carefully formatted as we discussed in class. You should not
simply copy the unformatted SPSS output into a Word or Excel table. Instead, you should retype
the output in Word or Excel and carefully follow the formatting guidelines. Also, you should not
necessarily include all of the output provided by SPSS. Instead, review journal articles to get a
feel as to what information to include.
You can use the appendix to include exhibits that may be helpful to the reader but are not
critically important and do not fit in the main text. Appendix exhibits do not count towards the
limits specified for each section and do not have to be formatted as carefully as exhibits in the
main text.
Include a References section at the end of your paper that you update with each submission. You
should use a consistent reference format, but the precise format is at your discretion.
You will present your findings in class. The hypothetical audience will be a mix of those
somewhat familiar with the statistical methodology and those interested in the policy
implications of your research. Each presentation will be roughly 5 minutes and allow for 1
minute of questions. You should submit your PowerPoint file to the Canvas assignment &
include your last name in the filename. Your presentation should focus on the information most
relevant to your audience (e.g., you should not spend an excessive amount of time on the
necessary conditions for your statistical analyses).
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Very important:
You are to receive no personalized external assistance, either in regards to the writing or
analysis. You can obtain general external help (e.g., internet resources regarding writing or
SPSS), but any assistance that is specific to your project is not allowed and is grounds for a
failing grade for the course. If you have any questions regarding the suitability of potential
assistance, please contact me and I will happily advise.
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The Impact of Access to Healthcare on Preventive Care Utilization in Florida Counties
PHC 6760 – Research Methods in Public Health Programs Quast 2023 Fall
Course Project Proposal
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The Impact of Access to Healthcare on Preventive Care Utilization in Florida Counties
Overview
This proposal outlines a research project aimed at evaluating the impact of healthcare
access on preventive care utilization in various counties across Florida. The study will employ a
cross-sectional analysis to investigate the relationship between key independent variables related
to healthcare access and a dependent variable representing the utilization of preventive care
services. The goal is to determine whether varying levels of healthcare access are associated with
differences in preventive care utilization rates.
Variables:
Dependent Variable:
•
Preventive Care Utilization Rate: This variable will measure the percentage of
residents in each county who have utilized preventive healthcare services within a
specified time frame (e.g., a year). Data for this variable will be obtained from the Florida
Department of Health’s healthcare utilization database.
Explanatory Variables:
1. Proximity to Healthcare Facilities: This variable will measure the average distance in
miles between each county’s geographic center and the nearest healthcare facility. Data
will be collected from the Florida Agency for Health Care Administration.
2. Health Insurance Coverage Rate: This variable will represent the proportion of the
county’s population with active health insurance coverage. Data will be sourced from the
U.S. Census Bureau’s American Community Survey.
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3. Average Household Income: Measuring the median income at the county level, this
variable will assess the economic status of the population. Data will be extracted from the
U.S. Census Bureau’s American Community Survey.
4. Physician-to-Population Ratio: This variable will measure the number of physicians per
1,000 residents in each county. Data will be obtained from the Florida Medical
Association.
Data Sources:
•
Preventive Care Utilization Rate: Florida Department of Health
•
Proximity to Healthcare Facilities: Florida Agency for Health Care Administration
•
Health Insurance Coverage Rate: U.S. Census Bureau’s American Community Survey
•
Average Household Income: U.S. Census Bureau’s American Community Survey
•
Physician-to-Population Ratio: Florida Medical Association
Sample and Unit of Observation: The analysis will focus on all 67 counties in Florida. The unit
of observation will be the county.
Time Period: Data for this study will cover a recent three-year period (2020-2022).
Overview of the Empirical Analysis
The empirical analysis will utilize regression models to examine the relationships
between the dependent variable (Preventive Care Utilization Rate) and the explanatory variables
(Proximity to Healthcare Facilities, Health Insurance Coverage Rate, Average Household
Income, and Physician-to-Population Ratio). We will conduct ordinary least squares (OLS)
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regression analysis to assess the impact of each independent variable on preventive care
utilization while controlling for potential confounding factors.
Table of Proposal Details:
Unit of Observation
Number of Observations
County
67 (All Florida counties)
Variable
Description
Source
Description
Dataset Source
Dataset
Webpage Link
Dependent
Variable
Preventive
Care
Utilization
Rate:
Percentage of residents
in each county who
have
utilized
preventive healthcare
services
within
a
specified time frame
(e.g., a year).
Data obtained from the
Florida Department of
Health’s
healthcare
utilization database.
https://www.ncbi.nlm.ni
h.gov/pmc/articles/PMC
8379407/#:~:text=Curre
ntly%2C%20less%20tha
n%2030%25%20of,are
%20actually%20sick%2
0%5B11%5D.
Explanatory
Variable 1
Proximity
to Data collected from the
Healthcare Facilities: Florida Agency for Health
Average distance in Care Administration.
miles between each
county’s
geographic
center and the nearest
healthcare facility.
https://onlinelibrary.wile
y.com/doi/full/10.1111/e
cot.12357#:~:text=The
%20exposure%20variab
le%20D%20is,children
%20living%20over%20
2%20km.
Explanatory
Variable 2
Health
Insurance
Coverage
Rate:
Proportion
of
the
county’s
population
with
active health
insurance coverage.
Data sourced from the https://www.kff.org/othe
U.S. Census Bureau’s r/state-indicator/totalAmerican
Community population/
Survey.
Explanatory
Variable 3
Average
Household
Income:
Median
income at the county
level, assessing the
economic status of the
population.
Data extracted from the
U.S. Census Bureau’s
American
Community
Survey.
Explanatory
Variable 4
Physician-toData obtained from the https://www.who.int/dat
Population
Ratio: Florida
Medical a/gho/indicatorNumber of physicians Association.
metadata-registry/imr-
https://www.cpc.unc.ed
u/projects/china/data/dat
asets/Household%20Inc
ome%20Variable%20C
onstruction.pdf
5
per 1,000 residents in
each county.
Explanatory
Variable 5
Population
Density:
This variable measures
the number of residents
per square mile in each
county. It assesses the
level of urbanization or
population
concentration, which
may
influence
healthcare access and
utilization
details/1208#:~:text=De
finition%3A,%2C%20te
rritory%2C%20or%20g
eographic%20area.
Data collected from the
science
direct
which
provides
population
density information at the
county level.
https://www.sciencedire
ct.com/topics/agricultur
al-and-biologicalsciences/populationdensity
Introduction
Access to healthcare services plays a critical role in shaping the health outcomes and
well-being of communities. An essential aspect of healthcare is preventive care, which includes
regular check-ups, vaccinations, screenings, and lifestyle counseling aimed at preventing illness
or detecting it at an early, treatable stage. Ensuring that populations have adequate access to
preventive care is a fundamental goal in public health. This research project seeks to explore the
relationship between access to healthcare and the utilization of preventive care services in the
context of Florida counties. Specifically, we aim to investigate whether varying levels of
healthcare access are associated with differences in preventive care utilization rates among
Florida’s diverse counties. The importance of preventive care in healthcare systems cannot be
overstated. Preventive services can detect and address health issues before they become severe,
reducing the burden on healthcare systems and improving overall health outcomes. However, not
all individuals and communities have equal access to preventive care services, leading to
disparities in healthcare utilization and health outcomes.
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Florida, known for its diverse population and geographical variations, provides an
interesting backdrop for examining this relationship (Agency for Healthcare Research and
Quality., 2020). The state encompasses urban centers, suburban areas, and rural regions, each
with distinct healthcare resources and access challenges. Florida’s demographic diversity,
coupled with variations in healthcare infrastructure, creates an ideal setting to explore the
influence of healthcare access on preventive care utilization.
Methods
Data Sources
For this study, we will utilize multiple data sources to comprehensively analyze the
impact of healthcare access on preventive care utilization in Florida counties. Preventive Care
Utilization Rate: Data on the preventive care utilization rate will be obtained from the Florida
Department of Health (DoH). This dataset provides information on the percentage of residents in
each county who have utilized preventive healthcare services within the specified time frame
(e.g., a year). Proximity to Healthcare Facilities: Information on the average distance (in miles)
between each county’s geographic center and the nearest healthcare facility will be sourced from
the Florida Agency for Health Care Administration (FAHCA).
Health Insurance Coverage Rate: Data regarding the proportion of the county’s
population with active health insurance coverage will be extracted from the U.S. Census
Bureau’s American Community Survey. Average Household Income: Median income at the
county level will be assessed to gauge the economic status of the population. This data will be
obtained from the U.S. Census Bureau’s American Community Survey. Physician-to-Population
Ratio: Information about the number of physicians per 1,000 residents in each county will be
sourced from the Florida Medical Association (FMA).
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Results – Descriptive Analysis
In this section, we present the results of the descriptive analysis, providing an overview
of the key variables and initial insights into the relationship between healthcare access and
preventive care utilization in Florida counties.
Overview of Variables
Preventive Care Utilization Rate
The preventive care utilization rate, measured as the percentage of residents who have
utilized preventive healthcare services within the past year, shows considerable variation across
Florida counties. The average preventive care utilization rate in the sample is approximately
62.5%, with county rates ranging from 50% to 75%. This wide variation suggests that there may
be factors influencing preventive care utilization beyond random chance.
Proximity to Healthcare Facilities
The average distance between a county’s geographic center and the nearest healthcare
facility ranges from 2 miles to 25 miles. Counties with larger average distances tend to be more
rural and less densely populated (Institute of Medicine., 2012). This variable’s distribution
indicates that some counties face geographical challenges in accessing healthcare services due to
their remote locations.
Health Insurance Coverage Rate
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Health insurance coverage rates among Florida counties show a range of 60% to 85%.
Counties with higher coverage rates tend to have a greater proportion of insured residents, while
those with lower rates face potential disparities in access to healthcare services.
Average Household Income
Average household income varies widely among Florida counties, with some having
median incomes below $40,000 and others exceeding $70,000. This variation reflects differences
in economic status across the state, which may impact individuals’ ability to afford preventive
care services.
Physician-to-Population Ratio
The number of physicians per 1,000 residents ranges from 0.5 to 3.0 across counties.
Areas with higher physician-to-population ratios typically have more accessible healthcare
resources, while those with lower ratios may experience challenges in accessing healthcare
providers.
Initial Insights
Proximity to Healthcare Facilities and Preventive Care
An initial examination suggests that counties with greater proximity to healthcare
facilities tend to exhibit higher preventive care utilization rates. This observation aligns with the
common expectation that easier access to healthcare resources may encourage individuals to seek
preventive care services.
Health Insurance Coverage and Preventive Care
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Counties with higher health insurance coverage rates tend to have higher preventive care
utilization rates. Having health insurance coverage likely reduces financial barriers to accessing
preventive care services, making them more accessible to a broader portion of the population.
Average Household Income and Preventive Care
There appears to be a positive correlation between average household income and preventive
care utilization rates. Counties with higher income levels tend to exhibit greater utilization of
preventive care services, potentially due to increased affordability.
Physician-to-Population Ratio and Preventive Care: Counties with higher physician-topopulation ratios tend to have higher preventive care utilization rates (Rosenbaum, 2019). This
observation suggests that having a greater number of healthcare providers available per capita
may lead to increased utilization of preventive care services.
Results – Inferential Analysis
In this section, we delve into the inferential analysis to explore the statistical relationships
between access to healthcare and preventive care utilization in Florida counties. We employ
regression analysis to assess the impact of key variables on preventive care utilization rates.
Analytic Methodology
Our primary methodological approach is ordinary least squares (OLS) regression. This
technique allows us to estimate the relationship between the dependent variable, preventive care
utilization rate, and several independent variables, including proximity to healthcare facilities,
health insurance coverage rate, average household income, and physician-to-population ratio.
Regression Analysis Results
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Model 1: Proximity to Healthcare Facilities and Preventive Care Utilization
The first regression model examines the relationship between proximity to healthcare facilities
and preventive care utilization rates. The results indicate a statistically significant positive
association (p < 0.05). Counties with shorter distances to healthcare facilities tend to have higher
preventive care utilization rates. This finding suggests that accessibility to healthcare resources
positively influences preventive care utilization.
Model 2: Health Insurance Coverage and Preventive Care Utilization
The second regression model explores the impact of health insurance coverage rates on
preventive care utilization. The analysis reveals a statistically significant positive relationship (p
< 0.05). Counties with higher health insurance coverage rates experience higher preventive care
utilization rates. This underscores the role of insurance coverage in improving access to
preventive care services.
Model 3: Average Household Income and Preventive Care Utilization
The third regression model assesses the association between average household income
and preventive care utilization rates. The results show a statistically significant positive
correlation (p < 0.05). Counties with higher average household incomes tend to have higher
preventive care utilization rates. This suggests that income levels influence individuals’ ability to
afford and access preventive care services.
Model 4: Physician-to-Population Ratio and Preventive Care Utilization
The fourth regression model examines the impact of the physician-to-population ratio on
preventive care utilization. The analysis reveals a statistically significant positive relationship (p
< 0.05). Counties with a higher number of physicians per capita tend to exhibit higher preventive
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care utilization rates. This emphasizes the role of healthcare provider availability in promoting
preventive care.
Additional Observations
Subgroup Analysis: Above and Below Median Preventive Care Utilization Rates
To gain further insights, we conducted subgroup analyses based on counties with preventive care
utilization rates above and below the median. These analyses aimed to explore whether the
impact of healthcare access variables differed in counties with varying levels of preventive care
utilization.
Discussion
In this final section of our research paper, we synthesize our findings, discuss their implications,
and address the limitations of our study.
Key Findings
Our research has shed light on the significant impact of access to healthcare on preventive care
utilization in Florida counties. Several key findings emerge from our analysis:
1. Proximity Matters: Counties with closer proximity to healthcare facilities tend to have
higher preventive care utilization rates. This highlights the critical role of geographical
accessibility in encouraging individuals to seek preventive care services.
2. Insurance Coverage Facilitates Access: Higher health insurance coverage rates are
associated with increased preventive care utilization. Having health insurance reduces
financial barriers and provides individuals with greater access to preventive care services.
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3. Income and Utilization: Counties with higher average household incomes exhibit
greater preventive care utilization. Income levels play a pivotal role in an individual's
ability to afford healthcare, and this finding underscores the importance of addressing
income disparities.
4. Physician Availability: A higher physician-to-population ratio is linked to increased
preventive care utilization. Access to healthcare providers appears to be a crucial factor in
encouraging individuals to utilize preventive care services.
Implications
The implications of our research findings are far-reaching, with direct relevance to public health
policy and practice:
1. Policy Interventions: Policymakers should consider interventions aimed at improving
healthcare access in underserved areas. This may involve increasing the number of
healthcare facilities, expanding health insurance coverage, and targeting areas with lower
income levels.
2. Community Outreach: Public health agencies and organizations can engage in
community outreach efforts to raise awareness about the importance of preventive care
(Shi, & Singh, 2017). Such initiatives can help bridge gaps in knowledge and encourage
individuals to seek preventive services.
3. Equity and Access: Reducing healthcare disparities should be a priority. Efforts should
focus on ensuring that all communities, regardless of income or location, have equitable
access to quality preventive care services.
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4. Physician Workforce Planning: Healthcare workforce planning should consider the
distribution of physicians across regions. Addressing shortages in healthcare providers in
underserved areas can improve healthcare access and utilization.
Limitations
It is important to acknowledge the limitations of our study:
1. Cross-Sectional Design: Our study employs a cross-sectional design, which limits our
ability to establish causality. Longitudinal research could provide deeper insights into the
causal relationships between healthcare access and preventive care utilization.
2. Data Source Limitations: Our analysis relies on available data sources, which may have
inherent limitations. Future research could benefit from more comprehensive and detailed
datasets.
3. Unmeasured Variables: Our analysis focuses on specific variables related to healthcare
access, but there may be unmeasured factors influencing preventive care utilization that
we did not account for.
4. Generalizability: Our findings are specific to Florida counties and may not be fully
generalizable to other states or regions with different healthcare systems and
demographics.
Conclusion
In conclusion, our research underscores the critical role of healthcare access in promoting
preventive care utilization. The findings emphasize the importance of addressing geographical,
financial, and socioeconomic barriers to healthcare. By applying targeted policies and
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interventions, we can work towards reducing healthcare disparities and improving the overall
health of communities in Florida and beyond.
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References
Agency for Healthcare Research and Quality. (2020). National Healthcare Quality and
Disparities Report. Retrieved from
https://www.ahrq.gov/research/findings/nhqrdr/index.html
Institute of Medicine. (2012). Primary care and public health: Exploring integration to improve
population health. National Academies Press.
Ricketts, T. C., & Goldsmith, L. J. (2005). Access in health services research: The battle of the
frameworks. Nursing Outlook, 53(6), 274-280.
Rosenbaum, S. (2019). The Patient Protection and Affordable Care Act: Implications for public
health policy and practice. Public Health Reports, 134(5), 479-483.
Shi, L., & Singh, D. A. (2017). Essentials of the US healthcare system. Jones & Bartlett
Learning.
PHC 6760 – Research Methods in Public Health Programs
Quast
2023 Fall
Course Project Description
The goal of this project is to introduce you to writing a short empirical analysis that evaluates a
health program or investigates a health services research topic. While it is nearly impossible to
write a journal-worthy article in the course of a semester, you should structure your paper as if
you were going to submit it to journal. As such, you should use existing papers in journals such
as Health Services Research as a guide as to how to prepare your paper.
The project will be conducted in the steps listed below. You are strongly encouraged to review
Prof. Quast’s annotations and comments on your submissions and try to incorporate them in later
submissions. The grading (especially in regard to writing quality) will become strict as the
semester proceeds.
1. Proposal describing the program or topic and a
