The relationship between educational attainment and lung cancer mortality in Kentucky: implications for nurses.
Context: The professional literature indicates health has a
positive correlation with socioeconomic status.
More specifically, prior research documents heightened rates of lung cancer incidence and mortality in Appalachia, a region plagued by persistent poverty and below-average educational attainment. This study analyzed predictors of lung cancer mortality within Kentucky, a predominantly rural state in central Appalachia.
Purpose: To determine whether county-level lung cancer mortality is related to counties' high school graduation rates, per capita personal income levels and adult smoking rates. Also, to test whether significant differences exist among these variables between Kentucky's Appalachian and nonAppalachian counties.
Methods: Data from the Kentucky Institute of Medicine's 2007 report The Health of Kentucky: A County Assessment were analyzed using independent samples t tests, bivariate correlations and regression analyses.
Results: On a statewide basis, inverse associations were found between county-level lung cancer mortality and counties' graduation rates (p < .001) and per capita personal income (p < .01). Statistically significant differences were detected between Kentucky's Appalachian and non-Appalachian regions for each variable except adult smoking rates.
Conclusions: In the context of similar adult smoking rates between the state's Appalachian and nonAppalachian counties, high school graduation rates showed the strongest statistical association with lung cancer mortality. This indicates that continued improvements in Kentucky's rate of diploma attainment may contribute to future reductions in lung cancer mortality statewide. These findings suggest practice and policy implications for nurses in Kentucky and, potentially, other states with low educational attainment and high lung cancer mortality.
Key words: Lung Cancer Mortality, Educational Attainment, Poverty, Adult Smoking Rates, Appalachia
Lung cancer (Patient outcomes)
Lung cancer (Risk factors)
Lung cancer (Development and progression)
Mortality (United States)
Smoking (Health aspects)
Academic achievement (Influence)
|Author:||Gross, David A.|
|Publication:||Name: Online Journal of Rural Nursing & Health Care Publisher: Rural Nurse Organization Audience: Academic Format: Magazine/Journal Subject: Health care industry Copyright: COPYRIGHT 2010 Rural Nurse Organization ISSN: 1539-3399|
|Issue:||Date: Fall, 2010 Source Volume: 10 Source Issue: 2|
|Topic:||Event Code: 310 Science & research|
|Geographic:||Geographic Scope: Kentucky; United States Geographic Code: 1U6KY Kentucky; 1USA United States|
Lung cancer was responsible for an estimated 159,390 deaths during 2009, making it the leading cause of cancer-related death in the United States (Lung Cancer Alliance, 2009). The disease is particularly problematic in Kentucky, where a rate of 80 lung cancer-related deaths per 100,000 population is much higher than the national rate of 55 deaths (Kentucky Institute of Medicine [KIOM], 2007). In fact, each of Kentucky's 120 counties has a lung cancer mortality rate that exceeds the national average.
An obvious research question, then, is what aspects of life in Kentucky increase the risk of dying from lung cancer? An additional consideration is the role the state's nurses can play in mitigating this health disparity.
The purpose of this study was to determine whether Kentucky's county-level lung cancer mortality is related to two socioeconomic predictors (high school graduation rates and per capita personal income levels). Lung cancer mortality's association with a personal behavior, adult smoking rates, also was evaluated. Finally, the study was designed to detect the influence of Appalachian versus non-Appalachian residence (that is, whether significant differences exist among the variables between these two distinct regions of Kentucky).
Tobacco smoking is the primary risk factor for lung cancer and accounts for approximately 87% of lung cancer-related deaths (Mannino, Aguayo, Petty, & Redd, 2003; The Washington Post, 2008). Kentucky's heritage as a tobacco-growing state and continued high level of tobacco production have fostered, to a large degree, a cultural acceptance of smoking among its residents. Consequently, Kentucky had the nation's highest rate of adult smokers each year from 2000 to 2007, and again in 2009 (Behavioral Risk Factor Surveillance System [BRFSS], 2010).
The state's first local law to limit indoor smoking in public facilities was not enacted until 2004, and as recently as 2005 Kentucky's cigarette excise tax of 3 cents per pack was the lowest in the nation. Since that time, 26 other Kentucky communities have adopted smoke-free laws or regulations, and the cigarette excise tax has been raised to 60 cents per pack, which ranks 39th among the states (Kentucky Center for Smoke-Free Policy, 2010; National Conference of State Legislatures, 2009). These developments are credited with helping to reduce Kentucky's adult smoking rate from a high of 32.6% in 2002 to its current level of 25.6% (BRFSS, 2010).
Hahn (a University of Kentucky College of Nursing professor) and other researchers have analyzed smoke-free legislation's positive effect on smoking cessation behaviors in Lexington, Ky., and its association with reduced asthma-related emergency department visits (Hahn et al., 2008; Hahn, Rayens, Langley, Darville, & Dignan, 2009; Rayens et al., 2008). Yet, exposure to secondhand smoke remains a concern in most Kentucky localities.
This study analyzed lung cancer mortality in Kentucky's 54 Appalachian counties (primarily those in the eastern and southern portions of the state, with 94.4% classified rural) (Economic Research Service, 2003) compared to the rates in its 66 non-Appalachian counties (those located in the central, northern and western regions, which include the state' s three urban centers). Like much of Appalachia, Kentucky's mountain counties have long been associated with cultural isolation, limited educational opportunities and heavy reliance on extractive industries such as coal mining.
Prior research documents heightened rates of lung cancer incidence and mortality in Appalachia, with increasing concentrations over time in Kentucky and other states in central Appalachia (Grauman, Tarone, Devesa, & Fraumeni, 2000; Lengerich et al., 2005; Lengerich et al., 2004). More recent studies suggest lung cancer may be amplified within coal-mining areas of Appalachia because of exposure to environmental contaminants (Hendryx & Ahern, 2008; Hendryx, O'Donnell, & Horn, 2008). Lung cancer also has been associated with occupational exposures (Engholm, Palmgren, & Lynge, 1996; The Washington Post, 2008).
Most of Kentucky's Appalachian counties (38 out of 54, or 70.4%) are labeled as economically "distressed" by the Appalachian Regional Commission (2008) based on their multi-year rates of low per capita income and elevated rates of poverty and unemployment. The area's pervasive poverty is evidenced by the fact that Kentucky accounts for nearly half of the 81 distressed counties within the entire 13-state Appalachian region.
As a whole, Kentucky's high school graduation rate trails the US average by 8 percentage points, while its per capita personal income level is $6,000 below the national rate (KIOM, 2007). A review of previous studies indicates that, regardless of age, socioeconomic status is an important determinant of individuals' overall health. Deaton, for instance, noted that "proportional increases in income are associated with equal proportional decreases in mortality throughout the income distribution" (2002, p. 13). Of particular relevance to this project is that smoking rates generally are higher among the poor and less educated.
Hemminki and Lee (2003) documented the association between an individual's level of education and his/her risk of developing lung cancer. In their study, 760,000 invasive cancer cases from the Swedish Family-Cancer Database were analyzed. Subjects were identified according to educational attainment and cancers were tracked from 1971 to 1998. While overall cancer risks showed only minimal differences based on education, some site-specific cancers varied significantly depending upon educational group. Lung cancer was much less common in those who were university educated (standardized incidence ratio of 0.47) than those with less than nine years of education (standardized incidence ratio of 1.00).
Previously, Gorey and Vena (1995) had reported on a cancer incidence study described as the first to examine lung and other site-specific cancers among the poor and what was a new socioeconomic status measure: the near poor, or persons living below 200% of the federal poverty level. By tracking New York State Cancer Registry data for nearly 42,000 cases from 1979 to 1986, the authors found that near poverty status in and of itself was a cancer risk factor. For instance, the lung cancer rate for women living in high-poverty areas (those with census tracts in which more than half of residents were below 200% of the federal poverty level) was approximately twice that observed among women living in areas with lower poverty rates.
Gorey and Vena did not explicitly discuss why living in impoverished areas increased residents' susceptibility to lung or other types of cancer, only referencing the "attendant health risks" (1995, p. 363) found within such locations. The authors likely were alluding to the premise that, in general, a high socioeconomic status allows one to make healthier choices, while low education forces geographic and work choices that place one at greater risk of environmental and occupational exposures. Indeed, it has been suggested that health may be influenced by multiple "place effects" (Macintyre, Ellaway, & Cummins, 2002, p. 125) (e.g., air and water quality, the availability of non-hazardous employment, access to education, socio-cultural features, and an area's reputation).
Based on this review of the literature, it was expected that negative relationships would be detected between county-level lung cancer mortality and the independent variables high school graduation rates and per capita personal income levels. Therefore, stated hypotheses were that the lower a county's high school graduation rate and/or per capita personal income level, the higher its lung cancer mortality rate would be.
The primary data source for this study was KIOM's 2007 report The Health of Kentucky: A County Assessment. This document analyzed Kentucky counties' health status based on 25 different health-related measures, which were used to produce a 1 (best) to 120 (worst) ranking of counties' relative health status. Because of variation in the number of county-level deaths each year, data were combined for multiple years (1997-2004) to produce more stable rates for some measures.
This study's dependent variable was county-level lung cancer deaths per 100,000 population, with original data derived from the Kentucky Cancer Registry Inquiry System. Independent variables were: counties' per capita personal income levels, or the mean income computed for every man, woman and child in a particular group, from the Bureau of Economic Analysis, U.S. Department of Commerce; high school graduation (percentage of adults 25 years or older in each county who had graduated from high school), from the U.S. Census Bureau and Kentucky State Data Center; and prevalence of smoking (percentage of adult population within each county), from the Kentucky Behavioral Risk Factor Surveillance Survey.
Appalachian counties in Kentucky were designated by the Appalachian Regional Commission, a federal-state partnership charged with facilitating community and economic development. This designation is based on geography, certain economic criteria and political considerations. The use of county-level variables is especially appropriate in Kentucky, which ranks third among the states for number of counties (National Association of Counties, 2009). County borders influence personal attributes and behaviors in a number of ways related to this study, including school systems' dropout rates, employment opportunities and whether indoor smoking bans are in effect. As such, data were not weighted by population in order to more fully reflect the collective, shared experiences of residents within each county.
Independent samples t tests were conducted to compare the variables' means within Kentucky's Appalachian and non-Appalachian counties. Correlation coefficients were calculated to determine whether statistical relationships exist between county-level lung cancer mortality and counties' high school graduation rates, per capita personal income levels and adult smoking rates. Finally, regression analyses were carried out to predict lung cancer mortality as a function of each independent variable.
These analyses were conducted using SPSS Statistics 16.0. Alpha was set at .05 for all tests.
Exploration of the data revealed practical differences between Kentucky's Appalachian and non-Appalachian regions with regard to most of this study's variables. For example, high school graduation rates ranged from 49% to 80% in Appalachian counties, compared to 61% to 87% in non-Appalachian counties. Yet adult smoking rates had the same range within the two regions (from 20% to 36%). Table 1 shows the results of independent samples t tests analyzing the variables' regional differences.
Statistically significant differences were detected between the regions' means for lung cancer deaths, per capita personal income and high school graduation rate. In other words, it is unlikely these samples "could have been drawn from the same population" (Meier, Brudney, & Bohte, 2009, p. 218). The adult smoking rate difference, however, was not significant at the designated .05 level. Of note is that Appalachian counties had a less favorable rating for each variable. This suggests Kentucky's lung cancer mortality and socioeconomic disparities are more acutely felt in rural areas.
The regional and statewide associations between lung cancer mortality rates and each independent variable are presented in Table 2. This analysis required the calculation of Pearson's r correlation coefficients with two-tailed tests. Within the respective regions, most variables were not significantly related with lung cancer mortality rates. On a statewide basis, however, significant negative associations were found between lung cancer mortality and counties' high school graduation rates and per capita personal income levels, while adult smoking rates had a significant positive association with lung cancer mortality. These findings are not unexpected, as associations typically become more stable as sample size (in this case, the number of counties) increases.
Regression analyses were carried out to predict statewide lung cancer mortality as a function of the independent variables. In a multivariate linear regression containing each independent variable and a dummy variable representing counties' designation as either Appalachian or non-Appalachian, the variables accounted for 21.4% of explainable variation in statewide lung cancer mortality (Table 3). In this model, only one variable (high school graduation rate) remained significantly associated with lung cancer mortality rates. Counties' graduation rates and per capita personal income levels were highly correlated (r = .772), but tests for multicollinearity among the variables were negative (i.e, each had an acceptable variance inflation factor - 3.753 and 2.480, respectively).
Table 3 can be further interpreted to project that, over time, each 1 percentage point increase in high school graduation rates would produce .685 fewer lung cancer-related deaths per 100,000 population. The somewhat linear association between county-level lung cancer mortality in Kentucky and counties' high school graduation rates is displayed as a scatter plot in Figure 1.
[FIGURE 1 OMITTED]
DISCUSSION AND CONCLUSIONS
Limitations of this study include its focus on a single behavioral predictor of lung cancer mortality. This focus on adult smoking rates neglects the possibility of such other causes as occupational and community exposures to additional toxins. These exposures are known to increase health risks. For example, the combination of coal mining and smoking has been shown to increase individuals' likelihood of developing lung cancer (Miyazaki & Une, 2001). Another possible synergy is the acceptance of high levels of secondhand smoke in areas with elevated smoking rates. This study also did not include counties' demographic features.
The absence of these and other potential variables might have contributed to this study's underspecified regression model. Further evidence that variables beyond the scope of this study affect lung cancer mortality rates can be observed in Figure 1, which illustrates the presence of an outlier within the data. Gallatin County, located in northern Kentucky, had an average high school graduation rate (68%), as well as an average per capita personal income level and a slightly above average adult smoking rate, but had the state's highest rate of lung cancer mortality (124 deaths per 100,000 population). It should be noted that with a population of barely more than 8,000 (Kentucky State Data Center, 2009), Gallatin's mortality rate would be sensitive to even modest increases in lung cancer-related deaths. Still, one-sample t tests found Gallatin's lung cancer mortality to be significantly higher than the means for other non-Appalachian counties and Kentucky as a whole (p < .001 for both measures).
Although some researchers support the removal of outliers to avoid inflated error rates and statistical distortions, in this case including Gallatin County in the data was the more conservative approach. Future research should more closely analyze relationships between lung cancer mortality and its potential causes in this and other counties with significantly high lung cancer death rates.
Despite its limitations, this study found statewide associations in the hypothesized direction for lung cancer mortality with high school graduation rates and per capita personal income levels, as well as statistically significant differences between Kentucky's Appalachian and non-Appalachian counties for each variable except adult smoking rates.
Adult smoking rates were similar in Appalachian and non-Appalachian counties in Kentucky and were not significantly associated with counties' lung cancer mortality rates in a multivariate linear regression model that included high school graduation rates, per capita personal income and county designation. In the context of similar adult smoking rates, lower high school graduation rates were significantly associated with increased lung cancer mortality.
Appalachian counties had significantly lower high school graduation rates, and graduation rates showed the strongest statistical association with lung cancer death rates, which might explain higher lung cancer mortality in Appalachian counties. In that regard, this study provides support for prior research suggesting that education may be the best socioeconomic predictor of individuals' disease risk factors and overall health (Robert Wood Johnson Foundation, 2009; Wamani, Tylleskar, Astram, Tumwine, & Peterson, 2004; Karlson et al., 1995; Ross & Wu, 1995; Winkleby, Jatulis, Frank & Fortmann, 1992).
Indeed, level of educational attainment can influence personal and collective social milieu in a number of ways. These include: occupation (e.g., one's likelihood of working as a coal miner); place of residence (e.g., proximity to coal-mining sites); amount of disposable income; adherence to healthy behaviors (e.g., one's likelihood of not smoking); and one's likelihood of participating in health promotion programs.
These findings suggest that Kentucky's emphasis on improving high school graduation rates, which resulted in a 6.2 percentage point gain during a recent five-year period (Jester, 2008), may contribute to future reductions in statewide lung cancer mortality. It also has the potential to address other health disparities between Kentucky's Appalachian and nonAppalachian counties, provided that graduation rates in Appalachian counties catch up with the rest of the state.
Implications for Nurses
Relevant to this study is that nurses have demonstrated effectiveness as smoking cessation interventionists (Rice, 1999; Rice & Stead, 2006), despite a reported lack of education in treating tobacco dependence (Wewers, Kidd, Armbruster, & Sarna, 2004). Previous studies, however, indicate that more than one-third of current smokers have not been asked about their smoking status by nurses or other clinicians (Fiore et al., 2000). This might be partially attributable to smoking rates among nurses, which are estimated at 18% nationally and up to 25% in Kentucky (Bialous, Sarna, Wewers, Froelicher, & Danao, 2004; Adams, 2010). Scholars have expressed concern that "nurses who smoke may be less apt to support tobacco-control programs or encourage their patients to quit" (Medical News Today, 2008, p. 1).
These confounders, in combination with this study's findings, suggest that relying on clinic- or hospital-based smoking cessation counseling will not adequately reduce the risk of lung cancer mortality among patients. Rather, an approach that encompasses the social determinants of health might be required. Stanton (2009) has outlined several rural nurse competencies that would be applicable to such an approach. For instance, she noted that rural nurses should be "acutely aware of disparities;" "have broad clinical skills that ... span all levels of care including health promotion and disease prevention;" and "need to be proficient ... with accessing educational opportunities for themselves and their colleagues" (Stanton, p. 8).
Perhaps an additional rural nurse competency should be proficiency in accessing educational opportunities for patients. Whereas Dunkin and Dunn (2009) suggested that every patient receive a spiritual assessment, this study indicates that a more in-depth evaluation of patients' educational attainment is warranted. While it is not uncommon at initial check-in for patients to be queried on this topic, the line of questioning often begins and ends with, "What is the highest level of education you have completed?" For adults whose response is "less than high school," nurses or other clinicians should seek additional information (e.g., the patients' willingness to pursue a high school diploma or GED, perceived barriers to doing so, interest in receiving further assistance).
State-level data suggest it is entirely appropriate for nurses to advocate that such patients advance their education. Among the 20 states (plus the District of Columbia) that experienced at least a 5 percentage point decline in adult smoking rates between 1999 and 2009, nearly one-quarter had a corresponding reduction in the proportion of residents who had not completed high school (BRFSS, 2010).
This study also highlights the ideal position of school nurses to address education-related issues among their patients. School nurses should act as educational encouragers. Moreover, advocates have posited that "school nurses promote student success because healthy students are better learners" (Kentucky School Nurse Association, n.d., p. 1). The association between school nurse practice and students' educational outcomes has been identified as a research priority for school nursing (Edwards, 2002).
Yet, the impact of individual interventions on lung cancer mortality rates and other health disparities is modest. To get at the core of such issues, nurses must be willing to embrace the role of community activist. Prior research indicates that nurses generally have had limited public policy involvement (Whitehead, 2003; Peters, 2002; Oden, Price, Alteneder, Boardley, & Ubokudom, 2000). More recently, however, Gangeness (2009) suggested that community-focused nurses have the capacity "to inform the development of health care policy" (p. 62).
The most immediate benefit seemingly could be produced by rural nurses who become involved at the local level. With regard to lung cancer mortality rates, that likely would include advocating for laws or regulations to limit indoor smoking in public facilities. Based on this study's findings, nurses also should urge local school districts to adopt policies that increase student achievement and reduce dropout rates.
The original version of this study received the National Rural Health Association's 2009 LaVonne Straub Memorial Student Research Paper Award. The author wishes to acknowledge Cynthia M. Cole, PhD, a retired associate professor in the University of Kentucky's Department of Behavioral Science, and Jo Ann G. Ewalt, PhD, director of Eastern Kentucky University's master of public administration program, for their assistance.
Adams, S. (2010, Spring). A health care paradox: Nurses, doctors who smoke. Rural Health Update. Retrieved August 14, 2009, from http://www.mc.uky.edu/ruralhealth /rhuspr10smoke.asp
Appalachian Regional Commission. (2009). Distressed counties grants: ARC-designated distressed counties, fiscal year 2009. Retrieved November 11, 2008, from http://www.arc.gov/index .do?nodeId=3301
Bialous, S.A., Sarna, L., Wewers, M.E., Froelicher, E.S., & Danao, L. (2004). Nurses' perspectives of smoking initiation, addiction, and cessation. Nursing Research, 53(6), 387395. rMEDLINEl
Behavioral Risk Factor Surveillance System, Centers for Disease Control and Prevention. (2010). Prevalence and trends data. Retrieved September 14, 2010, from http://apps.nccd.cdc. gov/BRF SS/index.asp
Deaton, A. (2002). Policy implications of the gradient of health and wealth: An economist asks, would redistributing income improve population health? Health Affairs, 21(2), 13-30. [MEDLINE]
Dunkin, J., & Dunn, L.L. (2009). Spiritual perspectives and health: A random survey in a Southern state. Online Journal of Rural Nursing and Health Care, 9(2), 23-32. Retrieved August 14, 2009, from http://www.rno.org/journal/index.php/onlinejournal/article/vi ewFile/194/251
Economic Research Service, US Department of Agriculture. (2003). Rural-urban continuum codes. Retrieved August 17, 2009, from http://www.ers.usda.gov/Data/RuralUrban ContinuumCodes/
Edwards, L.H. (2002). Research priorities in school nursing: A Delphi process. Journal of School Nursing, 18(3), 157-162. [MEDLINE]
Engholm, G., Palmgren, F., & Lynge, E. (1996). Lung cancer, smoking, and environment: A cohort study of the Danish population. British Medical Journal, 312(7041), 1259-1263. [MEDLINE]
Fiore, M.C., Bailey, W.C., Cohen, S.J., Dorfman, S.F., Goldstein, M.G., Gritz, E.R., et al. (2000). Clinical practice guideline: Treating tobacco use and dependence. Rockville, MD: U.S. Department of Health and Human Services. Retrieved August 14, 2009, from http://www.surgeongeneral.gov/tobacco/treating tobacco use.pdf
Gangeness, J.E. (2009). Rural women's perceptions of availability, development and maintenance of rural built environments. Online Journal of Rural Nursing and Health Care, 9(2), 52-66. Retrieved August 14, 2009, from http://www.rno.org/journal/index.php/onlinejournal/article/vi ewFile/197/255
Gorey, K.M., & Vena, J.E. (1995). The association of near poverty status with cancer incidence among black and white adults. Journal of Community Health, 20(4), 359-366. [MEDLINE]
Grauman, D.J., Tarone, R.E., Devesa, S.S., & Fraumeni, J.F. Jr. (2000). Alternate ranging methods for cancer mortality maps. Journal of the National Cancer Institute, 92(7), 534543. [MEDLINE]
Hahn, E.J., Rayens, M.K., Butler, K.M., Zhang, M., Durbin, E., & Steinke, D. (2008). Smokefree laws and adult smoking prevalence. Preventive Medicine, 47(2), 206-209. [MEDLINE]
Hahn, E.J., Rayens, M.K., Langley, R.E., Darville, A., & Dignan, M. (2009). Time since smokefree law and smoking cessation behaviors. Nicotine and Tobacco Research, 111(8), 10111015. [MEDLINE]
Hemminki, K., & Li, X. (2003). Level of education and the risk of cancer in Sweden. Cancer Epidemiology, Biomarkers and Prevention, 12(8), 796-802. [MEDLINE]
Hendryx, M., & Ahern, M.M. (2008). Relations between health indicators and residential proximity to coal mining in West Virginia. American Journal of Public Health, 98(4), 669671. [MEDLINE]
Hendryx, M., O'Donnell, K., & Horn, K. (2008). Lung cancer mortality is elevated in coalmining areas of Appalachia. Lung Cancer, 62(1), 1-7. [MEDLINE]
Jester A. (2008, June 5). Kentucky gains in rate of graduation. Lexington (Ky.) Herald-Leader, pp. A1, A14.
Karlson, E.W., Daltroy, L.H., Lew, R.A., Wright, E.A., Partridge, A.J., Roberts, W.N., et al. (1995). The independence and stability of socioeconomic predictors of morbidity in systemic lupus erythematosus. Arthritis and Rheumatism, 38(2), 267-273. [MEDLINE]
Kentucky Center for Smoke-Free Policy, University of Kentucky College of Nursing. (2010). Kentucky communities with smoke-free laws or regulations. Retrieved September, 13, 2010, from http://www.mc.uky.edu/tobaccopolicy/Ordinances/SFLawsRegsREV0810.pdf
Kentucky Institute of Medicine. (2007). The health of Kentucky: A county assessment. Lexington, KY: Kentucky Institute of Medicine. Retrieved August 14, 2009, from http://www.kyiom.org/assessment.html
Kentucky School Nurse Association. (n.d.). Welcome to the Kentucky School Nurse Association. Retrieved August 14, 2009, from http://www.kysna. com/
Kentucky State Data Center. (2009). Kentucky and county population estimates for 2008. Retrieved August 14, 2009, from http://ksdc.louisville.edu/
Lengerich, E.J., Tucker, T.C., Powell, R.K., Colsher, P., Lehman, E., Ward, A.J., et al. (2005). Cancer incidence in Kentucky, Pennsylvania, and West Virginia: Disparities in Appalachia. Journal of Rural Health, 21(1), 39-47. [MEDLINE]
Lengerich, E.J., Wyatt, S.W., Rubio, A., Bequlieu, J.E., Coyne, C.A., Fleisher, L., et al. (2004). The Appalachia Cancer Network: Cancer control research among a rural, medically underserved population. Journal of Rural Health, 20(2), 181-187. [MEDLINE]
Lung Cancer Alliance. (2009). 2009 facts about lung cancer. Retrieved September 13, 2010, from http://www.lungcanceralliance.org/facing/documents/LCAFundingFactSheet2009 Final 000.pdf
Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: How can we conceptualise, operationalise and measure them? Social Science and Medicine, 55, 125139. [MEDLINE]
Mannino, D.M., Aguayo, S.M., Petty, T.L., & Redd S.C. (2003). Low lung function and incident lung cancer in the United States: Data from the first National Health and Nutrition Examination Survey follow-up. Archives of Internal Medicine, 163(12), 1475-1480. [MEDLINE]
Medical News Today. (2008, November 16). Smoking's effect on nurses' health, death rates revealed by UCLA study. Retrieved September 13, 2010, from http://www.medicalnewstoday.com/articles/129424.php
Meier, K.J., Brudney, J.L., & Bohte, J. (2009). Applied Statistics for Public and Nonprofit Administration. 7th Ed. New York: Wadsworth Cengage Learning.
Miyazaki, M., & Une, H. (2001). Risk of lung cancer among Japanese coal miners on hazard risk and interaction between smoking and coal mining. Journal of Occupational Health, 43, 225-230. Retrieved September 15, 2010, from http://joh.med.uoeh u.ac.jp/pdf/E43/E43 5 01.pdf
National Association of Counties. (2009). Find a county. Retrieved August 31, 2009, from http://www.naco.org/Template.cfm?Section=Find a County&Template=cffiles/counties/u samap.cfm
National Conference of State Legislatures. (2009). State cigarette excise taxes: 2009. Retrieved November 13, 2009, from http://www.ncsl.org/IssuesResearch/Health/ StateCigaretteExciseTaxes/tabid/14349/Default.aspx#
Oden, L.S., Price, J.H., Alteneder, R., Boardley, D., & Ubokudom, S.E. (2000). Public policy involvement by nurse practitioners. Journal of Community Health, 25(2), 139-155. [MEDLINE]
Peters, R.M. (2002). Nurse administrators' role in health policy: Teaching the elephant to dance. Nursing Administration Quarterly, 26(4), 1-8. [MEDLINE]
Rayens, M.K., Burkhart, P.V., Zhang, M., Lee, S., Moser, D.K., Mannino, D., et al. (2008). Reduction in asthma-related emergency department visits after implementation of a smoke free law. Journal of Allergy and Clinical Immunology, 122(3), 537-541. [MEDLINE]
Rice, V.H. (1999). Nursing intervention and smoking cessation: A meta-analysis. Heart & Lung, 28(6), 438-454. [MEDLINE]
Rice, V.H., & Stead, L. (2006). Nursing intervention and smoking cessation: Meta-analysis update. Heart & Lung, 35(3), 147-163. [MEDLINE]
Robert Wood Johnson Foundation. (2009). Education matters for health. Retrieved September 11, 2009, from http://www.rwjf.org/files/research/commission2009eduhealth.pdf
Ross, C.E., & Wu, C. (1995). The links between education and health. American Sociological Review, 60(5), 719-745.
Stanton, M. (2009). Rural nurse competencies: Experts, advocates and activists. Online Journal of Rural Nursing and Health Care, 9(2), 8-9. Retrieved August 12, 2009, from http://www.rno.org/journal/index.php/online-journal/article/ viewFile/190/237
Wamani H., Tylleskar T., Astram A.N., Tumwine J.K., & Peterson S. (2004). Mothers' education but not fathers' education, household assets or land ownership is the best predictor of child health inequalities in rural Uganda. International Journal for Equity in Health, 3(1), 9. [MEDLINE]
Washington Post. (2008, December 16). The leading cause of cancer deaths. Retrieved August 14, 2009, from http://www.washingtonpost.com/wp-dyn/content/ article/2008/12/12/AR2008121203332.html
Wewers, M.E., Kidd, K., Armbruster, D., & Sarna, L. (2004). Tobacco dependence curricula in U.S. baccalaureate and graduate nursing education. Nursing Outlook, 52(2), 95-101. [MEDLINE]
Whitehead, D. (2003). The health-promoting nurse as a health policy career expert and entrepreneur. Nurse Education Today, 23(8), 585-592. [MEDLINE]
Winkleby, M.A., Jatulis, D.E., Frank, E., & Fortmann, SP. (1992). Socioeconomic status and health: How education, income, and occupation contribute to risk factors for cardiovascular disease. American Journal of Public Health, 82(6), 816-820. [MEDLINE]
David A. Gross, BA (1)
(1) Director of Research, Marketing & Community Engagement, Center for Excellence in Rural Health-Hazard, University of Kentucky, email@example.com
Table 1. Differences Between Kentucky Counties for Various Health Indicators County designation Non- Health indicator Appalachian Appalachian p value Lung cancer deaths 86.4 76.8 < .001 (per 100,000) Per capita personal $19,693 $25,339 < .001 income High school 60.1% 74.0% < .001 graduation rate Adult smoking 28.9% 27.5% .078 rate (n = 54) (n = 66) Table 2. Correlation Results for Predictors of Lung Cancer Mortality in Kentucky Correlation with lung cancer mortality rates Independent Appalachian Non-Appalachian Statewide variable counties counties Per capita -.185 -.077 -.302 ** personal income High school -.393 ** -.187 -.470 *** graduation rate Adult smoking .103 -.245 * .210 * rate * p < .05; ** p < .01; *** p < .001 Table 3. Summary of Ordinary Least Squares Regression Analysis for Variables Predicting Lung Cancer Mortality in Kentucky Counties B Std. Beta Variable Coefficient Coefficient t score p value Constant 113.241 9.029 < .001 B1 Per capita personal .000 .157 1.227 .222 income B2 High school -.685 -.509 -3.231 .002 graduation rate B3 Adult smoking rate .286 .096 1.137 .258 B4 County designation -1.898 -.075 -.621 .536 Adjusted R Square = .214; F = 9.080 (< .001)
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