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\begin{document}

\title{Spatial Outcomes of Myocardial Infarction (Heart Attack) from 2000 to
2018 in Southern West Virginia Using an Collective Analysis.}



\author[1]{Frank Annie}%
\author[2]{Mark C Bates}%
\author[2]{Aravinda Nanjundappa}%
\author[2]{Ali Farooq}%
\author[2]{Elise Anderson}%
\author[2]{Megan Wood}%
\affil[1]{Affiliation not available}%
\affil[2]{CAMC Vascular Center of Excellence, Area Medical Center, Charleston Area Medical Center, and Charleston Area Medical Center Research Institute and Center for Clinical Sciences Research, CAMC Health Education and Research Institute}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\textbf{Author Block}

Frank H Annie, PhD\textsuperscript{1}, Mark C Bates,
MD\textsuperscript{2}, Aravinda Nanjundappa,MD\textsuperscript{2}, Ali
Farooq, MD\textsuperscript{2}, Elise Anderson, MD\textsuperscript{2},
Megan Wood, DNP\textsuperscript{2}

\textsuperscript{1}CAMC Health Education and Research Institute

3200 MacCorkle Ave. SE, Charleston, WV 25304.

\textsuperscript{2}CAMC Vascular Center of Excellence, Charleston Area
Medical Center.

3200 MacCorkle Ave SE, Charleston, WV 25304

\emph{Study Locations:}

Charleston Area Medical Center, 3100 McCorkle Ave SE, Charleston, WV,
25302 and Charleston Area Medical Center Research Institute and Center
for Clinical Sciences Research, 3200 McCorkle Ave SE, Charleston, WV,
25302

\emph{Correspondence:}

Frank Annie M.A; MPA, PhD

Research Scientist~

CAMC Health Education and Research Institute

3200 MacCorkle Ave. SE,

Charleston, WV 25304

Phone 304-388-9921

Fax: 304-388-9921

Email: Frank.H.Annie@camc.org

\emph{Total word count:} 750

\emph{Author Disclosure Block:} None

\emph{Key words:} Spatial Outcomes, MI, West Virginia

\emph{Running Title:} Spatial Outcomes of Myocardial Infarction (Heart
Attack)

\emph{Total Number of Tables and Figures:} Figures 1

Data concerning the frequency and prevalence of heart disease within the
US is not uniform to the entire country. Some regions have experienced
increased progress relating to improving access and preventive measures
associated with heart disease. Other regions are lagging and continue to
have persistent challenges with heart disease. Most literature seeks to
understand regions from the county level (1-3). Researchers have used
county levels to understand the disease strands associated with
Myocardial Infarction and stroke, as well as other disease states. This
current work seeks to understand disease states at a zip code level to
understand sub-regional differences beyond the county level. Other
academic pieces, such as geographical clustering of incidence at a
localized level, further explored MI in Denmark at a city neighborhood
level to find differing patterns of MI in that country (2). A larger
level of analysis provides a snapshot of differing MI trends than at the
local level. Further work, such as that of Ersbool (2015) (4),
considered MI at an address level and found different patterns when
compared to a larger level, such as city, county, or state. The
understanding of different trends at a localized level promotes the
field of multi-spaced differences even from neighboring cities.

This is a retrospective analysis of patients admitted or transferred to
Charleston Area Medical Center in Charleston, West Virginia. We obtained
data from the CAMC data warehouse ranging between 2000 and 2018. We used
ICD-9 and ICD-10 codes to identify cases of MI between 2000 and 2018
treated at CAMC. When we identified these cases, we geocoded them using
Arch 10.6, with a 99\% accuracy rate. We identified all-cause MI from
2000 to 2018 (n = 37,600) and mortality from MI (n = 1,917). We used zip
codes to define boundaries of analysis within the neighborhoods
examined. We obtained zip code data from the West Virginia Geographic
Information Systems (GIS) database.

To understand mortality, we performed a collective analysis to
understand the differences in mortality based on differing zip codes in
the CAMC service area. This analysis considered the incidences of MI
divided by the recorded deaths from those zip codes and converted deaths
into a percentage and visually represented it in a nested map in Figure
1.To further analyze the incidence of MI and mortality within individual
zip codes in Southern West Virginia, we calculated mortality in each
individual zip code over the 18-year period of the study. These figures
are illustrated in Figure 1, which shows a very high mortality in
Southern West Virginia.

In conclusion, incidence and mortality appear to increase in Southern
West Virginia. The goal of this project was to define the geographical
landscape of MI within Southern West Virginia. The collective analysis
also showed that mortality appeared to be increasing outside the urban
area of Southern West Virginia. Cardiovascular mortality a leading cause
of death, contributing to 30\% of global mortality, and it is a public
health concern (5). Spatial analysis plays an important role in
identifying areas of increased incidence of CVS hospitalizations, which
can provide clues as to causal factors related to the increased burden
of disease. Previous studies have shown that CVS disease incidence
clusters around areas with markedly low socioeconomic status (2,). Our
study reinforces the findings of previous studies by showing that hot
spots of cardiovascular disease hospitalization have occurred around
rural areas and coal fields in Southern West Virginia.

Figure Legend: Figure 1: Buffer analysis of Hospitals and Rural Health
Facilities/ Mortality at the zip code level of MI 2000-2018.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

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\textbf{References} :

\begin{enumerate}
\tightlist
\item
  Pedigo, A., \& Aldrich, T. (2011). Neighborhood disparities in stroke
  and myocardial infarction mortality: a GIS and spatial scan statistics
  approach. \emph{BMC public health} , \emph{11} (1), 644.
\item
  Kj\selectlanguage{ngerman}ærulff, T. M., Ersbøll, A. K., Gislason, G., \& Schipperijn, J.
  (2016). Geographical clustering of incident acute myocardial
  infarction in Denmark: A spatial analysis approach. \emph{Spatial and
  spatio-temporal epidemiology} , \emph{19} , 46-59.
\item
  Loughnan, M. E., Nicholls, N., \& Tapper, N. J. (2008). Demographic,
  seasonal, and spatial differences in acute myocardial infarction
  admissions to hospital in Melbourne Australia. \emph{International
  journal of health geographics} , \emph{7} (1), 42.
\item
  Ersbøll, A. K., Kjærulff, T. M., Bihrmann, K., Schipperijn, J.,
  Gislason, G., \& Larsen, M. L. (2016). Geographical variation in a
  fatal outcome of acute myocardial infarction and association with
  contact to a general practitioner. \emph{Spatial and spatio-temporal
  epidemiology} , \emph{19} , 60-69.
\item
  Mendis S, Thygesen K, Kuulasmaa K, Giampaoli S, Mahonen M, Ngu
  Blackett K, et al. World Health Organization definition of myocardial
  infarction: 2008-09 revision. International journal of epidemiology.
  2011;40(1):139-46.
\end{enumerate}

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