Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health

<p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalen...
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Autor*in:	Cossman Ronald E [verfasserIn] Cossman Jeralynn S [verfasserIn] James Wesley L [verfasserIn] Blanchard Troy [verfasserIn] Thomas Richard [verfasserIn] Pol Louis G [verfasserIn] Cosby Arthur G [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Übergeordnetes Werk:	In: Population Health Metrics - BMC, 2003, 8(2010), 1, p 25
Übergeordnetes Werk:	volume:8 ; year:2010 ; number:1, p 25

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1478-7954-8-25

Katalog-ID:	DOAJ007517432

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520			\|a <p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p<
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spelling	10.1186/1478-7954-8-25 doi (DE-627)DOAJ007517432 (DE-599)DOAJba4d683cafdc4ca88ab4db4ab8d6b3ce DE-627 ger DE-627 rakwb eng R858-859.7 RA1-1270 Cossman Ronald E verfasserin aut Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p< Computer applications to medicine. Medical informatics Public aspects of medicine Cossman Jeralynn S verfasserin aut James Wesley L verfasserin aut Blanchard Troy verfasserin aut Thomas Richard verfasserin aut Pol Louis G verfasserin aut Cosby Arthur G verfasserin aut In Population Health Metrics BMC, 2003 8(2010), 1, p 25 (DE-627)374139911 (DE-600)2127230-X 14787954 nnns volume:8 year:2010 number:1, p 25 https://doi.org/10.1186/1478-7954-8-25 kostenfrei https://doaj.org/article/ba4d683cafdc4ca88ab4db4ab8d6b3ce kostenfrei http://www.pophealthmetrics.com/content/8/1/25 kostenfrei https://doaj.org/toc/1478-7954 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2010 1, p 25
allfields_unstemmed	10.1186/1478-7954-8-25 doi (DE-627)DOAJ007517432 (DE-599)DOAJba4d683cafdc4ca88ab4db4ab8d6b3ce DE-627 ger DE-627 rakwb eng R858-859.7 RA1-1270 Cossman Ronald E verfasserin aut Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p< Computer applications to medicine. Medical informatics Public aspects of medicine Cossman Jeralynn S verfasserin aut James Wesley L verfasserin aut Blanchard Troy verfasserin aut Thomas Richard verfasserin aut Pol Louis G verfasserin aut Cosby Arthur G verfasserin aut In Population Health Metrics BMC, 2003 8(2010), 1, p 25 (DE-627)374139911 (DE-600)2127230-X 14787954 nnns volume:8 year:2010 number:1, p 25 https://doi.org/10.1186/1478-7954-8-25 kostenfrei https://doaj.org/article/ba4d683cafdc4ca88ab4db4ab8d6b3ce kostenfrei http://www.pophealthmetrics.com/content/8/1/25 kostenfrei https://doaj.org/toc/1478-7954 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2010 1, p 25
allfieldsGer	10.1186/1478-7954-8-25 doi (DE-627)DOAJ007517432 (DE-599)DOAJba4d683cafdc4ca88ab4db4ab8d6b3ce DE-627 ger DE-627 rakwb eng R858-859.7 RA1-1270 Cossman Ronald E verfasserin aut Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p< Computer applications to medicine. Medical informatics Public aspects of medicine Cossman Jeralynn S verfasserin aut James Wesley L verfasserin aut Blanchard Troy verfasserin aut Thomas Richard verfasserin aut Pol Louis G verfasserin aut Cosby Arthur G verfasserin aut In Population Health Metrics BMC, 2003 8(2010), 1, p 25 (DE-627)374139911 (DE-600)2127230-X 14787954 nnns volume:8 year:2010 number:1, p 25 https://doi.org/10.1186/1478-7954-8-25 kostenfrei https://doaj.org/article/ba4d683cafdc4ca88ab4db4ab8d6b3ce kostenfrei http://www.pophealthmetrics.com/content/8/1/25 kostenfrei https://doaj.org/toc/1478-7954 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2010 1, p 25
allfieldsSound	10.1186/1478-7954-8-25 doi (DE-627)DOAJ007517432 (DE-599)DOAJba4d683cafdc4ca88ab4db4ab8d6b3ce DE-627 ger DE-627 rakwb eng R858-859.7 RA1-1270 Cossman Ronald E verfasserin aut Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p< Computer applications to medicine. Medical informatics Public aspects of medicine Cossman Jeralynn S verfasserin aut James Wesley L verfasserin aut Blanchard Troy verfasserin aut Thomas Richard verfasserin aut Pol Louis G verfasserin aut Cosby Arthur G verfasserin aut In Population Health Metrics BMC, 2003 8(2010), 1, p 25 (DE-627)374139911 (DE-600)2127230-X 14787954 nnns volume:8 year:2010 number:1, p 25 https://doi.org/10.1186/1478-7954-8-25 kostenfrei https://doaj.org/article/ba4d683cafdc4ca88ab4db4ab8d6b3ce kostenfrei http://www.pophealthmetrics.com/content/8/1/25 kostenfrei https://doaj.org/toc/1478-7954 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 8 2010 1, p 25
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This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p<</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer applications to medicine. 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callnumber-first	R - Medicine
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topic_title	R858-859.7 RA1-1270 Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health
topic	misc R858-859.7 misc RA1-1270 misc Computer applications to medicine. Medical informatics misc Public aspects of medicine
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title	Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health
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title_sort	correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health
callnumber	R858-859.7
title_auth	Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health
abstract	<p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need.</p< <p<Methods</p< <p<We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns.</p< <p<Results</p< <p<Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses.</p< <p<Conclusions</p< <p<County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.</p<
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title_short	Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health
url	https://doi.org/10.1186/1478-7954-8-25 https://doaj.org/article/ba4d683cafdc4ca88ab4db4ab8d6b3ce http://www.pophealthmetrics.com/content/8/1/25 https://doaj.org/toc/1478-7954
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Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health

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