The effect of rurality and gender on stroke awareness of adults in west Virginia.
Abstract: urality, characterized with limited access to emergency stroke care, adds another risk factor to victims of stroke. Although there is a growing literature on gender differences, very little is known about how these gender differences intersect with rurality. A random sample of 2,000 adults were invited to participate (n=1,114) in a mail survey in West Virginia. The population is split into four groups: urban men, urban women, rural men and rural women. The findings suggest that rural older women face higher risks of stroke than other groups. The study recommends that this group be targeted by campaigns to raise stroke awareness.
Subject: Health education (Usage)
Health education (Demographic aspects)
Stroke (Disease) (Risk factors)
Stroke (Disease) (Diagnosis)
Stroke (Disease) (Prevention)
Authors: Alkadry, Mohamad G.
Tower, Leslie E.
Pub Date: 06/22/2010
Publication: Name: Journal of Health and Human Services Administration Publisher: Southern Public Administration Education Foundation, Inc. Audience: Academic Format: Magazine/Journal Subject: Government; Health Copyright: COPYRIGHT 2010 Southern Public Administration Education Foundation, Inc. ISSN: 1079-3739
Issue: Date: Summer, 2010 Source Volume: 33 Source Issue: 1
Geographic: Geographic Scope: West Virginia Geographic Code: 1U5WV West Virginia
Accession Number: 250033492
Full Text: Stroke, a cardiovascular incident that occurs in the brain, is the leading cause of adult disability and the third leading cause of death in the United States (AHA, 2004; Alkadry, 2005; Alkadry, Wilson, & Nicholas, 2005; Eaves, 2000; Hux, Rogers, & Mongar, 2000; Yoon, Heller, Levi, Wiggers, & Fitzgerald, 2001; York, 2003). The disease has devastating effects on individuals and their families. The aftermath of a stroke tends to be extremely difficult with an abundance of adjustments and changes in patients' physical abilities and family roles (Eaves, 2000; Hux et al., 2000). Stroke can be prevented through proper management of known health risks. Once a stroke is imminent, early detection of stroke symptoms and timely medical attention can substantially improve survival rates and can reduce the likelihood of disability as a result of a stroke. Therefore, the ability to recognize stroke symptoms and proper management of stroke risk factors are the cornerstones of any stroke awareness research.

Most studies that report limited or poor knowledge of stroke risks and signs focus on urban populations. However, the few studies of rural populations document similar findings (Alkadry et al., 2005; Eaves, 2000; Hux et al., 2000; Record et al., 2000; Schneider et al., 2003; Yoon et al., 2001). The realities of rural communities make stroke awareness and risk management more critical for rural residents who have less access to acute medical care, or whose access to such facilities is limited by geography or unsophisticated road systems. Given the limited access to acute care hospitals in rural areas, rural populations face an even greater danger if they fail to manage their risks, recognize the signs of a stroke, or seek immediate emergency care at a hospital that could be far away from the patient's rural residence.

It is also important to understand gender differences within urban and rural populations. In general, stroke outcomes appear to be more unfavorable for women than men, particularly in the following areas: pre-stroke physical functioning, management of risk by patients and their physicians, medical treatment of women who have had a stroke, and outcomes of stroke treatment. This article focuses on the differences in stroke awareness and risk management among four groups: Urban Men, Urban Women, Rural Men and Rural Women.


Before focusing on the problem--consequences of lack of stroke awareness and appropriate risk management-it is important to define the terms and give a background of stroke prevention and treatment. Stroke awareness includes knowledge of risk factors, management of risk factors, ability to identify stroke signs, and responsiveness once a stroke occurs. There are modifiable and nonmodifiable risk factors associated with stroke. Nonmodifiable risk factors include age, gender, heredity, prior history of stroke or heart attack, and race/ethnicity. Modifiable risk factors include hypertension, diabetes, obesity, high cholesterol, smoking, consumption of illegal drugs, and excessive alcohol consumption (AHA, 2004). Many studies with large sample sizes, ranging from 920 to 2,512 have focused on knowledge of risk factors. Participants' ability to report at least one stroke risk factor ranged from 68% to 80% and their ability to report three ranged from 10% to 32% (Pancoli, Broderick, & Kothari, 1998; Reeves, Hogan, & Rafferty, 2002; Evci, Memis, Ergin, & Beser, 2007). Respondents with the highest risk of stroke had the poorest stroke knowledge (Reeves et al., 2002). Knowledge of modifiable and non-modifiable risk factors does not by itself reduce the risk of stroke (Kothari et al., 1997; Pancioli et al., 1998; Yoon et al., 2001; Travis et al., 2003). Proper management of modifiable risk factors, however, can reduce the likelihood of a stroke (Kothari et al., 1997; Pancioli et al., 1998; Yoon et al., 2001; Travis et al., 2003).

If knowledge and proper management of stroke risk factors are the first lines of defense against stroke, the ability to identify stroke signs is certainly the second line of defense. Early recognition of stroke symptoms and immediate intervention are critical to patient survival and recovery. The CDC (2004) reports that only 17% of the public recognize enough of the major stroke warning signs of stroke to call 911. Other researchers report that about a third of respondents could not identify any early sign of stroke (Kothari et al., 1997; Reeves et al., 2002). Studies have also shown that fewer than half of the respondents knew more than one warning sign of stroke (Alkadry, 2005; Hux et al., 2000; Yoon & Boyle, 2002; Yoon et al., 2001). Rowe, Frankel, and Sanders (2001) found that of 602 respondents, none could name all five stroke warning signs.

Race, age, income and education were found to affect people's awareness of stroke risks and warning signs. Socioeconomic conditions are also related to the prevalence of risk factors and the incidence of stroke. Bone et al. (2000) found that high unemployment, lower education and lower income are all positively correlated with prevalence of stroke risk factors. A study by Hux et al. (2000) showed that people with a high school diploma or less had less knowledge about stroke (Hux et al., 2000; Schneider et al., 2003; Yoon et al., 2001).

Once an individual has identified that she or he is having a stroke, s/he should call an ambulance, an emergency service, or visit a hospital's emergency department. It is difficult to respond appropriately to a stroke, if one does not recognize that s/he is having one. Yoon and Boyle (2002) explain that many stroke patients did not identify their warning signs because what they felt was different from the signs and symptoms presented to them in information on stroke.

Proper management of stroke risk factors, recognition of stroke signs, and promptness in seeking medical intervention decrease morbidity and mortality associated with stroke (Alkadry et al., 2005; Hux et al., 2000; Record et al., 2000; Yoon et al., 2001). One of the most popular classes of medications, thrombolytics, used to acutely reduce the impact of stroke is effective if given intravenously within 3-6 hours of the onset of stroke symptoms (AHA, 2004; Quality Standards Subcommittee of the American Academy of Neurology, 1996; Morgenstern, 1997). Rapid action, therefore, is critical.

Now that the key terms have been defined and issues of prevention and treatment were explored, the next two sections will document why lack of stroke awareness and inappropriate risk management may be exacerbated by one's demographics.


High levels of stroke awareness and appropriate risk management may be more critical for rural residents given the unique barriers they encounter. Rural communities have cultural values and beliefs that may adversely impact their health-seeking behavior. This situation is compounded by low socioeconomic status and associated stressors, which increase predisposition to risk factors such as hypertension, diabetes, obesity and smoking, and the poor management of their chronic diseases, making rural residents particularly vulnerable to stroke (Alkadry et al., 2005; York, 2003). It is thus not surprising that West Virginia, which is the second most rural state in the USA, has the fourth highest hospitalization rate from stroke (CDC, 2004).

In addition to the prevalence of risk factors, rural communities have special challenges with access to quality health care in terms of geographic location, availability of qualified health care providers, facilities, and adequate insurance coverage (Eaves, 2000; Record et al., 2000; York, 2003). Yang, Howard, Coffey, and Roseman (2004) showed that 2-15% of excess stroke mortality attributed to race was actually due to geographic location and not race.

Access to quality health care was found to be a top priority among all decision makers on health related issues in rural areas and stroke was among the priority areas for many rural practitioners and policy makers (Gumm & Hutchison, 2003). These have translated into little action, since the development of health infrastructure in these areas is slow.

The peculiar needs and characteristics of rural areas make improved risk factor management and prompt identification of danger signs paramount, and these issues need to be addressed as a matter of urgency, even though people's limited knowledge about stroke is comparable in both urban and rural areas (Yoon et al., 2001). Blades et al. (2005) reported that rural women compared to rural men were more likely to be aware of two or more stroke warning signs. While both rural men and women had limited knowledge about stroke risk factors, women were more likely to identify two or more risk factors.

Rural residents are also affected by lower quality of health care in general and cardiovascular health in particular. Compared to urban settings, lower quality of care in cardiovascular disease management has been found to occur in rural settings (Havranek et al., 2004; Sheikh & Bullock, 2001). Sheikh and Bullock (2001) found that Medicare patients with acute myocardial infarction (AMI) in rural Kansas received inferior care on six quality indicators (e.g., use of aspirin).

Additional variables that may be related to rurality may also be associated with lower quality of care and outcomes. Such variables include lower income, nonteaching hospital status, and availability of a cardiologist (Allison et al., 2000; Jollis et al., 1996; Rathore et al., 2000). Poor patients compared to affluent patients were less likely to receive any of the four appropriate AMI therapies (Rathore et al., 2000). For Medicare patients presenting with AMI, Allison et al. (2000) found that appropriate receipt of therapy at non-teaching hospitals was lower and mortality was higher compared to teaching hospitals. Jollis et al. (1996) compared AMI patients admitted to a hospital by cardiologists and primary care providers, finding that patients cared for by the former were 12% less likely to die within one year. Rurality and associated variables appear to adversely impact stroke management and outcomes.


In addition to rurality, being female impacts one's experience with stroke. There are important gender differences between men and women in pre-stroke physical functioning, management of risk by patients and their physicians, medical treatment of women who have had a stroke, and outcomes of stroke treatment. Redberg (2005) notes that although less care may not be equivalent to inferior care, and more care is not always superior care, differences in care due to bias--whether conscious or unconscious--are troubling.

Women's pre-stroke physical functioning is poorer than men's. Compared to male stroke patients, female stroke patients tend to be older, live alone or are institutionalized, and have higher levels of functional impairment prior to a stroke incidence (Di Carlo et al., 2003; Lai, Duncan, Dew, & Keighley, 2005). Women also tend to have higher frequency of hypertension, atrial fibrillation, and antihypertensive treatment; whereas men, tend to have a history of myocardial infarction, smoking, alcohol consumption, and antiplatelet therapy (Di Carlo et al., 2003; Holroyd-Leduc, Kapral, Austin, & Tu, 2000; Lai et al., 2005). Although medical co-morbidities differ between men and women, co-morbidity may not impact stroke recovery (Lai et al., 2005; Studenski, Lai, Duncan, & Riger, 2004), perhaps making the management of risk and treatment of the disease more important in stroke outcomes.

For women to manage their risk of stroke, they first must be aware that coronary artery disease is a serious health threat to women. This fact is not widely known by women or their providers. Therefore, a partnership between women and providers is important for risk management. Legato, Padus and Slaughter (1997) suggest that despite educational campaigns targeting providers and the public, both women and doctors do not perceive coronary artery disease to be a significant problem for women, with 59% of women reporting that their doctor had never talked about it (47% of those 45-59 years old and 44% of women > 60). Forty-four percent of women also did not perceive that they were at risk. This perception may reduce women's likelihood of minimizing disease risk factors.

Furthermore, women must be informed about the risk factors and signs. From national data, Mosca et al. (2000) reported that the majority of women in their study could not identify the major risk factors of cardiovascular disease and were not well informed about stroke. While, 90% of women reported an interest in discussing risk reduction with their physicians, more than 70% of women did not in fact have this discussion. Important opportunities to manage risk among women are missed, potentially increasing the number of women who have a stroke.

There is evidence of some differences in the medical treatment of women for stroke. DiCarlo et al. (2003) studied 4,499 patients at 22 hospitals located in 7 European countries. They reported that women received fewer investigative resources (i.e., brain imaging, Doppler examination, echocardiogram, and angiography). Holroyd-Leduc et al. (2000) studied 44,832 patients discharged from Ontario hospitals. After controlling for age, they found that women received fewer surgical interventions (i.e., carotid surgery). Sex differences, however, were not found among recipients of in-patient rehabilitation services--physiotherapy, speech therapy, or occupational therapy (Holroyd-Leduc et al., 2000). Some differences were also found in the use of secondary preventive therapies (i.e., aspirin & ticlopidine). Men 85 and older were more likely to receive these therapies than women (Holroyd-Leduc et al., 2000). No differences were found for men and women between 65 and 84 years of age; furthermore, no significant differences were found for the use of warfarin. Noteworthy differences in the management of stroke exist between men and women. These differences in stroke management, likely influence outcomes.

Outcomes after a stroke tend to differ by gender, with women often having more functional impairment than men. Lofgren, Nyberg, Osterlind, and Gustafson (1998) reported that being male was a significant factor in predicting improvements in ADLs after a stroke. Wyller, Sodring, Sveen, Ljunggren, and Baultz-Holter (1997) found that after controlling for age, women had poorer motor, cognitive, and activities of daily living functioning than men after a stroke. More recently, Di Carlo et al. (2003) reported that, after adjusting for age and country, three months after a stroke, women were more disabled and more severely handicapped than men, but survival did not differ. Given the increased likelihood of disability for women, it is not surprising that upon hospital discharge, women were more likely than men to be referred to an institution (e.g., nursing home or rehabilitation hospital) (Di Carlo et al., 2003; Holroyd-Leduc et al., 2000; Lai et al., 2005; Wyller et al., 1997). Stroke outcomes appear to be considerably more unfavorable for women than men.

Although there is a growing literature on gender differences in the stroke literature, there is limited research on the impact of rurality on stroke awareness and risk management. Moreover, very little is known about how these two important sociodemographic characteristics may intersect with each other. The interest in stroke awareness and risk management is to reap the benefits of primary and secondary prevention, which impact stroke outcomes. Stroke outcomes appear to be influenced by a complex set of variables including sociodemographic factors (e.g., age, gender, and socioeconomic status), pre-stroke physical functioning, disease severity of stroke (Di Carlo et al., 2003; Lai et al., 2005) and treatment. Hence, this study seeks to describe differences in stroke awareness and risk management between and among men and women living in rural and urban communities.



A simple random sample of 2,000 West Virginians was invited to participate in a mail survey. The sampling frame was obtained from a marketing prospects' vendor with no strata or clusters specified. The response rate was 56% (1,114 respondents). To eliminate non-response bias, the demographic characteristics of respondents were compared to those of the West Virginia population in terms of age, income and racial and ethnic distributions (Table 1).

The average age of respondents is 57 years. Almost half of all respondents have completed twelve years of education or less. Over 51% of respondents do not work. Approximately 56% of respondents earn less than $35,000. Sixty percent of respondents indicated that they "never" discussed stroke with their doctors, while 18% indicated that they "rarely" discuss stroke with their doctors. Nearly 8% of respondents reported having a stroke in the past. More than 52% of respondents have a family history of stroke, and 12% are obese.


The Stroke Awareness Survey is a 40-item survey. It measures access to medical care, risk management, awareness of stroke signs, and demographic items. Risk management includes questions about frequency of visiting a doctor, whether or not respondents had discussed stroke with a doctor, and frequency of testing respondents' blood pressure, blood sugar, and cholesterol. To measure risks, we computed the number of health risks known to cause stroke: hypertension, diabetes, high cholesterol, heart disease, prior stroke, family history of stroke, alcoholism, lack of physical activity, improper diet, obesity, and smoking.

Awareness of stroke signs was measured by whether respondents could accurately differentiate stroke symptoms (e.g., sudden numbness or weakness of face, arm, or leg; slurred speech; blurred vision; and sudden severe headache) from symptoms unrelated to stroke (i.e., high fever, loss of appetite, and excessive sweating). Demographic items include, for example, gender, age, education, employment status, zip code, and income.

Respondents were divided into 4 groups: urban men, urban women, rural women and rural men. To determine the rurality of respondents, we cross-referenced respondents' reported zip codes with the Rural-Urban Continuum Codes for 2003. The Rural-Urban Continuum Codes are published by the Economic Research Service of the United States Department of Agriculture (2008). To further delineate rural from urban residents, we used the standard Office of Management and Budget metro and nonmetro categories that divide the continuum into three metro and six nonmetro categories. Urban men constituted 14% of respondents; urban women constituted 40%; rural men constituted 11%, and rural women constituted 35% of all respondents.


The current study identifies similarities and differences between the four study groups: urban men, urban women, rural men, and rural women. To understand how members of the four sample groups differ in terms of their awareness of stroke signs and proper management of stroke risks, we analyzed different relationships within each of the four groups. Somer's d, a directional measure of the strength, sign and significance of the relationships among ordinal level variables, was used to analyze the relationship among variables. These correlations were reported for each of the four groups. Somer's d values for correlations between age, education and income on one hand and prevalence of stroke risks, awareness of stroke environmental risks and awareness of signs on the other. The table also presents correlations between actions taken to manage these risk factors and the prevalence of these risks.

Prevalence of Risks

We computed the number of health risks known to cause stroke: hypertension, diabetes, high cholesterol, heart disease, prior stroke, family history of stroke, alcoholism, lack of physical activity, improper diet, obesity, and smoking. The result was a score ranging from 0 to 11, where a score of 0 indicates no prevalent stroke risks, and a score of 11 indicates the prevalence of all known stroke risks. Among respondents, the average score of stroke risks ranged from 3.49 among urban women to 4.08 among rural men. Urban men and rural women had mean scores of 3.70 and 3.75, respectively.

Of all respondents, 25.5% had less than three risk factors. Respondents in different groups did not have the same risk factors. For instance, while 30.5% of urban women had fewer than three risk factors, 15.1% of rural men were in that risk category (Table 3). Approximately 22% and 25% of urban men and rural women respectively had fewer than three risk factors. This means that urban women were the least prone to stroke risk factors while rural men were the most prone to stroke risk factors.

The effect of known socioeconomic and age factors on the prevalence of stroke risks was studied next using Somer's d (Table 2). Age had a weak, positive, and statistically significant effect on prevalence of stroke risk factors for all four study groups. Income had a negative, weak, and statistically significant correlation with prevalence of stroke risks for urban and rural women. However, income had no statistically significant effect on stroke risk prevalence, among urban and rural men. Education had a negative, weak correlation with stroke risk prevalence among respondents in all four groups.

Management of Risks

To test the extent to which respondents are managing their risks, we used five sets of correlations. First, we examined the correlation between the frequency of visiting a doctor and the prevalence of stroke risk factors. Rural men had the highest Somer's d correlation between these two variables. The Somer's d value was 0.248, 0.206, 0.408, and 0.176 for urban men, urban women, rural men, and rural women, respectively.

Second, we tested the relationship between prevalence of risk factors and whether or not respondents had discussed stroke with a doctor. The Somer's d value was 0.198, 0.136, 0.266, and 0.248 for urban men, urban women, rural men, and rural women, respectively.

Third, we tested the prevalence of hypertension and the frequency of testing respondents' blood pressure. The Somer's d value was 0.422, 0.387, 0.528 and 0.413 for urban men, urban women, rural men, and rural women, respectively. Fourth, we tested the prevalence of diabetes and the frequency of checking respondent's blood sugar levels. The Somer's d value was 0.617, 0.671, 0.830, and 0.706 for urban men, urban women, rural men, and rural women, respectively.

Finally, we tested the relationship between the two variables measuring the prevalence of high cholesterol and the frequency of checking one's cholesterol levels. The relationship was not as strong for urban men and rural women. The Somer's d value was 0.476, 0.574, 0.537 and 0.432 for urban men, urban women, rural men, and rural women, respectively.


Respondents were asked whether seven symptoms were stroke symptoms. Of these seven symptoms, only four were correct stroke symptoms. A score was then computed for each of the respondents, and an average score was computed for all respondents within each study group. The average score (0-4) is 2.83, 3.07, 3.14, and 3.06 for urban men, urban women, rural men, and rural women respectively. Of all respondents, 9.7% could recognize fewer than two signs; 16%, 9%, 7% and 8% of urban men, urban women, rural men, and rural women, respectively, could recognize fewer than two stroke warning signs (see Table 3).


The next step was to find out the relationship between known socioeconomic and age factors and awareness of stroke signs. Age had no effect on awareness for urban and rural men, while there was a negative, weak, but statistically significant effect on awareness of stroke signs in the urban and rural women study groups. Income had no statistically significant effect on awareness of stroke signs by rural men, but it had a weak, positive effect on awareness of signs (0.210, 0.131, and 0.189 respectively) for urban men, urban women, and rural women.

One would imagine that those who are at greater risk of stroke would be more able to recognize the signs of stroke. However, the data showed something different. For urban men, urban women, and rural women there was no statistically significant correlation between risk factors and awareness of signs. There was a weak, negative, and statistically significant correlation for rural men.


Although we found that women had fewer risk factors than men, women tend to have more strokes than men and are more likely to die from it (Rosamond et al., 2008). Women, moreover, are not a homogeneous group; they tend to have different numbers of risk factors based on their geographic location. We found that rural women had more risk factors than urban women. This is an important finding, as we were not able to find a study from a sample of U.S. residents that made comparisons of these four groups--rural men, urban men, rural men and rural women. Nishi et al. (2007), studying Japanese residents, examined three levels of municipality population size: large, medium, and small. It appears that means of individuals from small and large municipalities have about the same number of risk factors. More research is needed to confirm our findings.

As age increased, risk factors increased for women in our study. Also, as income and education decreased, stroke risks increased for women. This is consistent with the research, with Mosca et al. (2000) reporting that older age and low income tend to increase risk of having a stroke. These variables are also associated with higher mortality (Zhang, Guan, Mao, & Liu, 2007).

Knowledge of stroke warning signs continues to be problematic. We found that 16% of urban men, 9% of urban women, 7% of rural men, and 8% of rural women could recognize one stroke warning sign. This is much lower than other findings, which show between 25% and 55% participants knowing one stroke warning sign (Evci et al., 2007; Pandian et al., 2005; Reeves et al., 2002; Yoon et al., 2001).

Knowledge of stroke warning signs is particularly low for rural women (and rural men). Mosca et al. (2000) reported that the majority of women in their study were not well informed about stroke. Older age and low income tend to increase the risk of having a stroke. Yet, younger age and higher income are related to awareness of more warning signs and awareness of risk factors (Evci et al., 2007). For urban and rural women, as age increases, awareness of stroke signs decreases. But, as income increases, awareness of signs increases for women.

Despite these findings, women's doctors do not appear to be properly managing women's risks for stroke. Doctors may not perceive it to be a problem for women (Legato et al., 1997). Out of the five risk management activities studied, women fared worse on four out of five of these. In particular, rural women had the lowest correlations of risk and seeing a doctor or having cholesterol checks; while, urban women had the lowest correlations of risk and discussing stroke with doctors or having blood pressure checks.

These findings suggest that poor, older women should be the target of campaigns to raise stroke awareness. These women are at risk and at the same time are unaware of signs and risks. Encouragingly, women may be more likely to be reached by educational campaigns. From interviews of 500 German residents after a multimedia campaign on stroke, Marx, Nedelmann, Haertle, Dieterich, and Eicke (2008) found that women tended to notice the campaign more than men, in nearly all of the media outlets (e.g., newspaper, TV/radio, posters, and buses).

Recent research suggests that stroke mortality is now higher in rural areas (Zhang et al., 2007), particularly in women (Nishi, et al., 2007). This higher mortality may be explained by age (Zhang et al., 2007), diet, and lack of medical resources (Nishi et al., 2007). It is promising that in the United States, Wright, Ranmuthugala, Jones, Maydom and Disler (2008) demonstrate from a project consisting of six rural hospitals, that educating providers on evidence-based guidelines to stroke management can improve adherence. Therefore, rural hospitals can attain best practices consistent with urban hospitals.

There are limitations to this study that should be noted. First, data were exclusively from West Virginia. Although West Virginia is primarily a rural state, there are cities in the state that are urban, particularly when it comes to medical services. These cities, however, do not necessarily compare to major urban centers like New York, Washington, or Chicago. Second, the study is based on self-reported mail surveys. Issues of social desirability and recall bias may have affected how respondents answered the questions. Third, the use of closed-ended and multiple-choice formatted questions may result in overestimating awareness, because such questions could cue respondents about risks, signs, or actions that they would not have identified on their own. The fourth limitation stems from the possibility that survey recipients with less knowledge of risks and signs might have been less likely to consider participating in the survey. However, this limitation is undermined by the fact that respondents reported a wide range of social, economic, and demographic backgrounds (see Table 1).


This study is not simply a comparison of stroke awareness between men and women. It recognizes that not all men and not all women have the same awareness levels. The study is conducted on four groups: urban men, urban women, rural men and rural women. While men and women are at the same risk when confronted with an impending stroke, urban and rural populations face different risks. Men and women in rural communities do not have the same access to emergency stroke care as those living in urban communities. While it could take a Morgantown or a Charleston, WV resident 5-10 minutes to get to first class emergency medical facilities, it could take an individual in Pocahontas County an hour or two to get to less than desirable emergency care. This is not an oversimplification of the reality of living in rural areas versus urban centers. The reality is that faced with the same levels of risks and awareness of signs, rural residents are at a much higher disadvantage when it comes to survival due either to quality or distance of healthcare facilities (and sometimes due to both quality and distance together).

Therefore, decision makers in rural communities have three choices. First, they can increase awareness of stroke risks and improve the management of these risks for rural residents. Risk management campaigns can save lives through preventing a stroke in the first place. Second, policymakers could also focus on improving awareness of stroke warning signs and knowledge of what to do in case of stroke. Obviously, such skills are important once a stroke becomes imminent. For a rural resident who is having a stroke, early recognition could mean life or death. Early recognition of signs gives individuals the time to get to a distant healthcare facility, provided that the individual knows that this is what she or he should do. Third, policymakers could focus their efforts on improving stroke care in rural communities so that rural and urban stroke victims have the same chance of survival. This third choice may not be an option for several reasons including the lack of financial resources, the lack of neurologists and neurosurgeons who are willing to work and live in rural communities, and in some cases, the lack of the medical infrastructure to accommodate stroke teams. Dealing with medical infrastructure could be much more complicated than adding stroke treatment capacity to an existing, well-equipped hospital. Indeed, policymakers will have to build the well-equipped hospital before adding such a team.

The complications involved in improving stroke care in rural communities make expenditures associated with improving the following interventions less costly and more feasible: knowledge of risk factors, management of risk factors, ability to identify stroke signs, and responsiveness once a stroke occurs. It is clear from this study that rural residents do not have fewer stroke risk factors, are not managing their risks more effectively, and do not have better knowledge of stroke signs than urban residents with easy access to quality emergency care. It is also clear that awareness campaigns are the most feasible and effective tools to save lives and money in the short and long runs.

Finally, women in this study did not manage their stroke risks as well as men. This is an alarming finding that is consistent with previous studies that showed that doctors do not perceive stroke to be as big of a problem for women and therefore ignore some of the risk factors (Legato et al., 1997). The reality is that stroke can be deadly or devastating for both men and women. Not gender, but risk factors determine whether one is more likely to have a stroke. Therefore, it is important in any awareness campaign to target doctors and women with the message that anyone who has a risk factor is indeed at risk of stroke and needs to reduce these risks.


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Old Dominion University


West Virginia University
Table 1 Comparison of Respondents to WV Residents

                                           West Virginia
Description                 N     Survey   (Census 2000)

Age                       1,088
  Under 35                         12%         28.5%
  35-49 Years                     25.2%        29.5%
  50-64 Years                     26.9%        22.3%
  65-74 Years                     19.6%        10.6%
  75-84 Years                     12.9%        6.8%
  85 and Older                     3.4%        2.3%
Gender                    1,091
  Male                            26.2%         49%
  Female                          73.8%         51%
Annual Household Income    992
  < $15,000                       21.6%        25.4%
  $15,000-$34,999                 34.2%        32.0%
  $35,000-$49,999                 18.4%        16.4%
  $50,000-$99,999                 20.6%        21.2%
  More than $100,000               5.2%         5%
Race/Ethnicity            1,093
  White Non-Hispanic              93.1%        94.5%
  Black Non-Hispanic               5.2%        3.1%
  Hispanic                         0.3%        0.7%
  Other                            1.4%        1.7%

Table 2
Somer's d of Gender and Rurality of Stroke Risk Factors, Risk
Management, and Awareness

                      Urban Men    Urban        Rural Men    Rural
                                   Women                     Women

                      Somer's d    Somer's d    Somer's d    Somer's d

Prevalence of Risk       3.70         3.49         4.08         3.75
Factors (Mean)

Age on Risk Factors   0.215 ***    0.209 ***     0.198 *      0.132 **
Income on Risk          -0.136     -0.263 ***     -0.162     -0.209 ***
Education on Risk      -195 **     -0.181 ***   -0.223 **    -0.159 ***

Risk Management

See MD on Risk        0.248 ***    0.206 ***    0.408 ***    0.176 ***
Discuss stroke        0.198 ***    0.136 ***    0.266 ***    0.248 ***
  with MD on risk
Blood pressure (BP)   0.422 ***    0.387 ***    0.528 ***    0.413 ***
  checked with
  prevalence of BP
Sugar checked with    0.617 ***    0.671 ***    0.830 ***    0.706 ***
Cholesterol checked   0.476 ***    0 574 ***    0.537 ***    0.432 ***
  with cholesterol

Awareness of             2.76         2.68         2.71         2.53
Environmental Risks

Age                    -0.161 *    -0.113 ***   -0.262 ***    0.119 **
Income                -0.218 ***     0.081       0.207 **    0.152 ***
Education             0.259 ***    0.252 ***      0.141      0.204 ***
Awareness of Signs       2.83         3.07         3.14         3.06
0-4 (Mean)

Age                    -0.148 *      -0.079      -0.173 *     -0.096 *
Income                 0.210 **     0.131 **      0.136      0.189 ***
Education             0.225 ***    0.217 ***     0.199 **     0.134 **
Risk Factors            -0.091       -0.010      -0.160 *      -0.035

* significant at .05 level or better

** significant at <.01 level or better

*** significant at <0.001 level or better

Table 3 Risk Factors and Awareness of Signs

Number of Risks      Urban    Urban    Rural    Rural    Total
                      Men     Women     Men     Women

N                     132      364      106      311      913
0-2 Risk Factors     22.0%    30.5%    15.1%    24.8%    25.5%
0-5 Risk Factors     87.9%    88.2%    78.3%    85.9%    86.2%
0-8 Risk Factors     99.2%    100.0%   99.1%    100.0%   99.8%
Awareness of Signs   Urban    Urban    Rural    Rural    Total
                      Men     Women     Men      Women

N                     140      389      110      341      980
1 Sign               16.4%     9.3%     7.3%     8.2%     9.7%
2 or less Signs      35.0%    26.5%    24.5%    28.2%    28.1%
3 or less Signs      58.6%    54.2%    52.7%    55.7%    55.2%
4 or less Signs      100.0%   100.0%   100.0%   100.0%   100.0%

FIGURE 1. Socio-Economic Factors & Prevalence of Risk

                      Rural women  Rural Men  Urban Women  Urban Men

Education of
  Risk factors          -0.159      -0.223       -0.181      -0.195
Income on
  Risk factors          -0.209                               -0.263
Age on Risk Factors      0.132       0.198        0.209       0.215

Note: Table made a bar graph.

Figure 2. Risk Management

                            Rural women    Rural Men

Cholesterol checked with
cholesterol prevalence         0.432         0.537

Sugar checked with
Diabetes prevalence            0.706         0.83

BP checked with BP
prevalence                     0.413         0.528

Discuss stroke with MD on
risk factors                   0.248         0.528

See MD on Risk Factors         0.176         0.408

                             Urban Women    Urban Men
Cholesterol checked with
cholesterol prevalence          0.574        0.476

Sugar checked with
Diabetes prevalence             0.671        0.617

BP checked with BP
prevalence                      0.387        0.422

Discuss stroke with MD on
risk factors                    0.136        0.198

See MD on Risk Factors          0.206        0.248

Note: Table made a bar graph.
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