Home Current Issue Previous Issues Collections Podcasts Blogs CME For Authors Journal Info
Skip Navigation LinksHome > January 2013 - Volume 106 - Issue 1 > Long-Term Impact of Environmental Public Health Disaster on...
Southern Medical Journal:
doi: 10.1097/SMJ.0b013e31827c54fc
Healthcare System Preparedness

Long-Term Impact of Environmental Public Health Disaster on Health System Performance: Experiences from the Graniteville, South Carolina Chlorine Spill

Runkle, Jennifer R. PhD, MSPH; Zhang, Hongmei PhD; Karmaus, Wilfried MD, Dr med; Brock-Martin, Amy DrPH; Svendsen, Erik R. PhD

Free Access
Article Outline
Collapse Box

Author Information

From the Department of Epidemiology and Biostatistics, Arnold School of Public Health, and the Department of Health Services, Policy, and Management, University of South Carolina Columbia.

Reprint requests to Dr Jennifer R. Runkle, Nell Hodgson Woodruff School of Nursing, 1520 Clifton Rd NE, Rm 409, Emory University, Atlanta, GA 30322-4207. Email: jennifer.r.runkle@emory.edu

Funding was provided by the National Institutes of Health grant no. 7RO1 ES 015532-02 (E.R.S.) and 1R21 LM010833-01.

E.R.S. consults for the University of Maryland and University of South Carolina. The other authors have no financial relationships to disclose and no conflicts of interest to report.

Accepted July 24, 2012.

Collapse Box


Objectives: In the aftermath of an environmental public health disaster (EPHD) a healthcare system may be the least equipped entity to respond. Preventable visits for ambulatory care–sensitive conditions (ACSCs) may be used as a population-based indicator to monitor health system access postdisaster. The objective of this study was to examine whether ACSC rates among vulnerable subpopulations are sensitive to the impact of a disaster.

Methods: We conducted a retrospective analysis on the 2005 chlorine spill in Graniteville, South Carolina using a Medicaid claims database. Poisson regression was used to calculate change in monthly ACSC visits at the disaster site in the postdisaster period compared with the predisaster period after adjusting for parallel changes in a control group.

Results: The adjusted rate of a predisaster ACSC hospital visit for the direct group was 1.68 times the rate for the control group (95% confidence interval [CI] 1.47–1.93), whereas the adjusted ACSC hospital rate postdisaster for the direct group was 3.10 times the rate for the control group (95% CI 1.97–5.18). For ED ACSC visits, the adjusted rate among those directly affected predisaster were 1.82 times the rate for the control group (95% CI 1.61–2.08), whereas the adjusted ACSC rate postdisaster was 2.81 times the rate for the control group (95% CI 1.92–5.17).

Conclusions: Results revealed that an increased demand on the health system altered health services delivery for vulnerable populations directly affected by a disaster. Preventable visits for ACSCs may advance public health practice by identifying healthcare disparities during disaster recovery.

Key Points

* Reduced health system performance during disaster recovery creates a new vulnerable population that is likely to experience long-term disruptions in access to care.

* Hospital emergency department visits for ambulatory care–sensitive conditions may signal failures in public health and the medical system to adequately meet the medical care needs of vulnerable populations during recovery.

* Increased patient demand on the health system throughout response and recovery must be closely monitored by public health officials to prevent differential health effects.

In the aftermath of an environmental public health disaster1 (EPHD)—an accidental or intentional release of hazardous chemicals—a health system may be the least equipped entity to respond to the medical needs of the affected population. Research documenting the ability of a stressed health system to respond to the amplified and fluctuating demand for medical care during recovery is a current gap in hospital preparedness and response planning.2–6 Disasters are dynamic and unpredictable public health events that often overwhelm the initial surge capacity of the affected health system(s). Although disaster response plans frequently are designed to address the immediate impact of the disaster on the health and safety of the affected community, few plans fully anticipate and prepare for the secondary surge in medical care need during the recovery phase. This article describes secondary surge capacity as the ability of a healthcare system to respond to the surging and fluctuating volume in medical care needs throughout the extended response period. In some cases, these complementary health recovery issues precipitate a major crisis in chronic disease management and require more attention in national disaster recovery planning and associated research agendas.1 Limited evidence suggests that increased demand for ambulatory care services in the months following a disaster exacts strain on the health system throughout recovery, exacerbating health and healthcare access disparities for vulnerable disaster populations.7,8 Vulnerable subgroups have chronic health needs that may be aggravated, created, and in some cases overlooked during response and recovery efforts, resulting in a separate public health emergency.

An understudied but critically important public health concern is whether vulnerable populations (ie, chronically ill, disabled, or underresourced individuals who lack the capacity to predict, contend with, avoid, or recover from a disaster9) experience decreased access to care that is prompted by declines in health system performance in the months following a public health disaster.10–17 Research conducted after Hurricane Katrina brought the position of vulnerable populations to the forefront of disaster response and recovery, revealing large concentrations of the “invisible poor” and racial/ethnic groups with chronic health conditions in evacuee populations who lacked healthcare coverage and relied on the public hospital system that was destroyed by the storm.11,18,19 In addition, losses in medical infrastructure, including several hospital closures and the permanent relocation of health providers outside the disaster area, resulted in drastic reductions in hospital beds poststorm.18 A disaster exacerbates the existing social, political, historical, and economic circumstances of marginalized populations who are unaccounted for in disaster preparedness and mitigation efforts.13 Following the acute response, a growing body of research has shown that changes in demand for unmet primary care needs and chronic disease management and a health system’s ability to respond to these changes are an underappreciated priority in disaster recovery.10,11,17

Back to Top | Article Outline

Train Derailment and Chlorine Spill

Graniteville, a medically underserved area (MUA) in South Carolina, made national headlines on January 6, 2005 when a Norfolk Southern freight train derailed at 2:39 AM, resulting in a ruptured tank car discharging approximately 90 tons of chlorine into an unsuspecting community. Direct chlorine inhalation resulted in 9 fatalities and 550 acute care visits. Approximately 5400 residents within a 1-mile radius of the spill were subject to a 1- to 2-week mandatory evacuation. Governor Mark Sanford’s request for federal disaster relief was rejected, leaving this MUA without additional public assistance to aid in the recovery efforts. Two years into recovery, Graniteville residents repeatedly reported overbooked providers and insufficient medical resources. Residents are contextual experts and community concerns, whether real or misperceived, warrant investigation whenever issues of supposed healthcare disparities are in question. We were compelled by numerous accounts of access barriers from the Graniteville community to examine this event more closely.

Back to Top | Article Outline

Disparities in Healthcare System Access

Disparities in healthcare system access during recovery likely drive inequities in health outcomes in populations affected by disaster, further marginalizing vulnerable subgroups. Monitoring healthcare system access for medically vulnerable populations is not presently a directive for public health surveillance efforts during disaster recovery. Mokdad et al underscored the necessity for surveillance tools that track health services delivery to vulnerable populations, especially health services utilization among chronically ill people.6 Surveillance measures identifying and characterizing healthcare system performance during recovery and reconstruction have not yet been recommended. We propose public health tracking of ambulatory care–sensitive conditions (ACSC) as a proxy measure of primary care access that can be used to quantify healthcare system performance throughout the recovery period. ACSCs are a well-known group of diagnoses used as a surrogate to measure access to primary care in health policy research.20–23 The assumption is that timely and effective primary care has the potential to reduce the risk of preventable hospitalizations and emergency department (ED) ACSC visits. ACSCs have been used as an indicator of access to and adequacy of primary care24–27 and to evaluate healthcare system performance.28 In previous studies, ACSCs have been validated as conditions for which hospitalization and ED rates differed within population subgroups, indicating varied levels of access to health care.29,30 Preventable hospitalization rates for ACSCs are higher in areas of concentrated racial and ethnic minorities,31–33 with lower education levels,34,35 among poor and uninsured populations,33,34,36,37 rural residents,26,34,38 among Medicaid/Medicare beneficiaries,20,33,34,38 and MUAs/populations39–45 or health profession–shortage areas (HPSA).28,45–47 Elevated ACSC rates across geographic areas or population subgroups indicate disparities in access to health care, whereby decreased availability of primary care services has been linked to higher admission rates for ACSCs.48–51 Excess visits for ACSCs in the months following a disaster may indicate reduced healthcare system performance, revealing that certain members of the population are not receiving adequate and timely access to primary care.

Surveillance of ACSC visits may signal failures in public health and the medical system to adequately meet the primary care needs of vulnerable disaster populations. This study explores the use of ACSCs as a simple, practical, population-based indicator to track changes in healthcare system access for vulnerable populations affected by an EPHD. Our study examined whether the ACSC indicator for access increased or decreased over time and assessed changes in the ACSC rates before and after the chlorine spill. We hypothesized that declines in access to primary care would correspond to an increase in ACSC rates among medically vulnerable populations in the disaster area and that this increase would be greater than any concurrent increase in ACSC rates for a separate control population. This study expands disaster epidemiological research by examining the impact that health sector recovery has on access to care for vulnerable populations.

Back to Top | Article Outline


Study Population

Medicaid provides health care to nearly 25% of South Carolina’s most vulnerable residents, including low-income families, disabled people, and individuals in transition. Medicaid beneficiaries often experience persistent gaps in insurance coverage,29,52–56 reduced access to nonmedical services,57 chronic health problems,58 and medical service access difficulties.28,51,54,59–61 We chose South Carolina (SC) Medicaid beneficiaries as a target population most representative of medically vulnerable people.

Back to Top | Article Outline
Data Sources

Hospital and ED discharge data for SC Medicaid beneficiaries ages 18 to 64 years were obtained from the SC Medicaid paid UB-92 claims housed by the Office of Research and Statistics (ORS) of the South Carolina State Budget and Control Board for 2002–2007. We also requested data on primary care use for this population. Medicaid beneficiary enrollment dates were collected from the SC Medicaid recipient file and linked to the SC Medicaid paid claims by a unique Medicaid recipient number in a one-to-one linkage. All of the data were stripped of personal identifiers by ORS. ORS estimated that more than 99.5% of Medicaid hospitalization and ED records were successfully linked. Individuals older than 65 years were excluded from our analysis because of Medicare coverage, and research shows little variation in hospital admissions for ACSCs among this group.26,62 Pregnant mothers hospitalized for child birth were excluded in denominator counts.

Approval for this study was obtained from the institutional review board at the University of South Carolina. The project was determined to be exempt from human research subject regulations.

Back to Top | Article Outline
Healthcare System Capacity

To ensure comparable study of the healthcare system across communities we matched study groups on population demographics, healthcare system/capacity, and HPSA/MUA designations. The town of Graniteville, alongside two smaller communities in close proximity, was home to residents directly affected by the 2005 chlorine spill (ie, direct group). A geographically close control group was chosen to reflect broader local influences on health services use at the study site. Residents in the control group lived in the same county as the direct group but further south from the event (environmental data revealed that the chlorine gas traveled north). We included the controls because this group likely shared the same county-level healthcare resources and healthcare system profile as the direct group. Both the direct and control groups were similar in terms of health system capacity (focus of this study) as indicated by inpatient bed availability, presence of one federally qualified healthcare center, more than one rural health center (RHC), and similar HPSA/MUA designations.

Back to Top | Article Outline
Study Design

We conducted a retrospective analysis of preventable hospitalization and ED discharge rates for ACSCs proximal to Graniteville during the period 2002–2007, with 2005 representing the year of the chlorine accident. We examined hospital and ED rates for ACSCs separately. To test our primary hypothesis, we compared ACSC rates for the disaster-affected population before and after the chlorine spill. We also examined ACSC rates in a control group to account for secular trends within the study area. Before and after time periods (predisaster 36 months, postdisaster 36 months) were identical for treatment and control groups alike.

Back to Top | Article Outline

ACSC-related21–23 principal diagnoses were characterized as follows: tonic-clonic seizures, convulsions, severe ear-nose-throat infections, bacterial pneumonia, cellulitis, skin grafts, gastroenteritis, kidney and urinary infection, dehydration, dental conditions, pelvic inflammatory disease, hypoglycemia, tuberculosis, asthma, angina, diabetes, nutritional deficiencies, chronic obstructive pulmonary disease, congestive heart failure, and hypertension. Because of sample size limitations in the direct group, we analyzed all ACSCs combined.

Denominator data for rates were obtained from ORS Medicaid eligibility files. We calculated ACSC rates by dividing the number of hospital or ED discharges for previously validated ACSCs (numerator) by SC Medicaid enrollee population counts (denominator) for each study group in a given year.

Back to Top | Article Outline
Statistical Analysis

Descriptive statistics were used to summarize patient populations before and after the 2005 chlorine spill. Chi square tests were conducted for categorical data and t tests were conducted for interval variables. Poisson regression with generalized estimating equations was used to calculate the change in the number of ACSC visits at the disaster site in the postdisaster period compared with the predisaster period after adjusting for parallel changes in the control group. The quasi-likelihood information criterion (QIC) was used to choose the best correlation structure, and goodness-of-fit was determined by using the QICu.63 Data analysis was conducted using PROC GENMOD in SAS version 9.2 (SAS Institute, Cary, NC); an α level of 0.05 was used throughout.

We used the following model:

Equation (Uncited)
Equation (Uncited)
Image Tools

where i represents population subgroups defined by distinct sets of covariates (study group, age, marital status, qualifying category, and disaster status); Y represents number of monthly ACSC hospital or ED counts; study group represents direct, primary control, and secondary control; age group represents the groups 18 to 24, 25 to 34, 35 to 44, 45 to 54, and 55 to 64; marital status represents divorced, married, separated, single, or widowed; qualifying category represents Aid to Families with Dependent Children, disabled, and other; and time period represents predisaster (2002–2004) versus postdisaster (2005–2007). We were interested primarily in the coefficient γ, which quantified the extent to which ACSC rates for the direct group changed after the chlorine spill above and beyond the prior adverse status of some study groups and the simple effect of the 2005 accident. To estimate the combined effect of study group and disaster status through Poisson regression we calculated

Equation (Uncited)
Equation (Uncited)
Image Tools

This expression assesses the impact of prior adverse status, disaster, and the interaction term of disaster × prior disadvantage.

Back to Top | Article Outline


Healthcare Services Use

Table 1 displays category of service use by study group and disaster period. We detected a significant increase in postdisaster hospital ACSC visits among the direct group (pre 42.6% vs post 57.4%; P = 0.03). Results showed no difference in ACSC hospital discharges before and after the chlorine spill for the control group, although both the direct (pre 54.8% vs post 45.2%; P < 0.01) and primary control (pre 52.0% vs post 48.0%; P < 0.05) groups revealed a significant decline in ACSC ED discharges compared with the predisaster period.

Table 1
Table 1
Image Tools

Primary care visits declined in both study areas post-2005. For the direct group, fewer beneficiaries sought care from a clinic (pre 71.9% vs post 28.1%; P < 0.0001) and RHC (pre 60.9% vs post 39.2%; P < 0.001) in the postdisaster period. After 2005, disaster-affected Medicaid beneficiaries increasingly sought primary care from the local federally qualified healthcare center (pre 35.5% vs post 64.5%; P < 0.0001). Conversely, primary care visits to an RHC surged in the postdisaster period for the control group, whereas the direct group showed statistically significant declines in RHC visits.

Back to Top | Article Outline
Trends in ACSC Rates

Crude ACSC hospital rates among the direct group were declining in the years pre-event (Fig.). After the 2005 chlorine spill, trends in ACSC hospital rates reversed and established a new trend among individuals directly affected by the disaster. Trends in the control group remained stable across years. Unadjusted ACSC ED rates for the direct group were higher before the chlorine spill and steadily declined in the years following the disaster (Fig.). Annual ED ACSC rates for the control group exhibited a slow decline. Elevated pre-2005 hospital ACSC rates in the direct group suggest that baseline disparities in access to primary care were present before the chlorine spill.

Fig. Unadjusted hosp...
Fig. Unadjusted hosp...
Image Tools
Back to Top | Article Outline
Postdisaster Changes in Healthcare System Access

Because annual trend data could mask real disparities in access, we examined pre- and postmean ACSC visits per subject using an independent group t test (Satterthwaite method for unequal variances; Table 2). Although the total number of ED visits were lower post-EPHD for the direct group (N = 401 vs 487), mean visit per subject was higher (mean pre 1.46 vs post 2.33; P < 0.0001) and the range in repeat visits doubled (9 to 18) during the post-EPHD period. During the postdisaster period, the direct group exhibited 1.3 more ACSC hospital visits per subject, and the range in repeat visits increased from 5 in the pre-event period to 20 postchlorine spill. Results showed that individual-level access to primary care declined for beneficiaries in the direct group post-EPHD.

Table 2
Table 2
Image Tools

We performed a retrospective analysis calculating change in monthly ACSC visits at the disaster site in the postdisaster period compared with the predisaster period after adjusting for parallel changes in a control group. The significant interaction effect (ie, estimated differences in slopes 0.4220) for study group by time period suggested that the increase in monthly ACSC visits post-EPHD was greater in the direct group than in the control group (study area × disaster status interaction term; P < 0.05; Table 3). The adjusted rate of an ACSC hospital visit pre-EPHD for the direct group was 1.68 times the rate for the control group (95% confidence interval [CI] 1.47–1.93), whereas the adjusted ACSC hospital rate post-EPHD for the direct group was 3.10 times the rate for the control group (95% CI 1.97–5.18). For ACSC ED visits, the adjusted rate among individuals directly affected predisaster was 1.82 times the rate for the control group (95% CI 1.61–2.08). The adjusted ACSC ED rate postdisaster was 2.81 times the rate for the control group (95% CI 1.92–5.17).

Table 3
Table 3
Image Tools
Back to Top | Article Outline


Reduced healthcare system performance during disaster recovery creates a new vulnerable population that is likely to experience long-term disruptions in access to care. Findings from this study showed that ACSC-related visits among Medicaid beneficiaries were sensitive to the impact of an EPHD after controlling for secular trends and demographics of the at-risk population. Our results indicated excess postdisaster ACSC rates for Medicaid beneficiaries directly affected by the chlorine spill. These results were supported by similar postdisaster declines in primary care visits, signifying that the 2005 chlorine spill resulted in decreased access to primary care for this Medicaid population. To our knowledge, this is the first study to examine healthcare system performance and subsequent primary care utilization for vulnerable Medicaid beneficiaries in the months and years following an EPHD. Our findings corroborate mounting evidence that excess patient demand on the healthcare system promotes discontinuity and disruptions in access to primary care, altering health services delivery for vulnerable subgroups during recovery.64–66 Access to healthcare services must be closely monitored throughout recovery to avert differential health outcomes in vulnerable populations.

Tracking health services encounters in the disaster area proved challenging because a large portion of SC residents sought medical care across state lines in Georgia. Although Medicaid claims data are primarily used for billing purposes, one advantage is that patient encounters are captured irrespective of provider location, including services received in other states. Medicaid beneficiaries are a relatively homogenous population with a higher percentage of individuals who are disabled, low income, and chronically ill. We would, therefore, expect higher rates of ACSCs for this population, limiting generalizability to other populations. Alternatively, patterns of ACSC rates across subpopulations are likely to provide evidence for healthcare system overload strengthening our results and should be explored in other subpopulations, including privately insured, uninsured, and Medicare beneficiaries. Although we were unable to measure changes in physician practice and referral patterns, patient satisfaction, and disease severity trends, the inclusion of control groups adequately accounted for secular changes in ACSC visits over time.

Although we cannot infer causation because of inherent design limitations, after ruling out major threats to validity we can assert with confidence that ACSC rates among directly affected Medicaid beneficiaries were affected by the 2005 chlorine spill. Other explanations for the association may include changes in Medicaid eligibility and enrollment patterns. Data showed that enrollment rates remained stable in all groups throughout the study period.

Back to Top | Article Outline


Hospital surge plans are in place to care for large numbers of trauma patients during the acute response; consequently, there are no plans in place that direct care for large volumes of incident or exacerbated chronically ill patients during recovery efforts. Surveillance of ACSCs may better position public health and medical professionals to characterize and respond to disaster-related exacerbation of chronic health conditions and subsequent changes in system access with improved sensitivity. We recommend ACSC surveillance to investigate long-term trends in health service utilization, enabling real-time measure of specific ACSCs, including asthma, congestive heart failure, diabetes, and hypertension rates throughout the disaster response cycle. By leveraging existing public health data, ACSC surveillance pre-event, following the acute response, and throughout recovery efforts can be used as a scalable health metric to enhance public health situational awareness at the local, state, regional, and national levels. Public health surveillance of ambulatory care data should be used to inform evidence-based hospital recovery plans that expand primary care services. Anticipating increased primary care needs will better equip healthcare systems to effectively manage patient load and improve access to care for all members of the population in disaster recovery.

Back to Top | Article Outline

We acknowledge the Graniteville Community Coalition, which helped bring into focus the real issues facing medically vulnerable people during disaster recovery.

Back to Top | Article Outline


1. de Ville de Goyet C, Marti RZ, Osorio C. Natural disaster mitigation and relief, in Jamison DT, Breman JG, Measham AR, et al. eds: Disease Control Priorities in Developing Countries. 2nd ed. Washington, DC, World Bank, 2006.

2. Aronoff M, Gunter V. Defining disaster: local constructions for recovery in the aftermath of chemical contamination. Soc Probl 1992; 39: 345–365.

3. Baum A, Fleming I. Implications of psychological research on stress and technological accidents. Am Psychol 1993; 48: 665–672.

4. Edwards MLK. An interdisciplinary perspective on disasters and stress: the promise of an ecological framework. Sociol Forum 1998; 13: 115–132.

5. Kirk MA, Deaton ML. Bringing order out of chaos: effective strategies for medical response to mass chemical exposure. Emerg Med Clin North Am 2007; 25: 527–548.

6. Mokdad AH, Mensah GA, Posner SF, et al.. When chronic conditions become acute: prevention and control of chronic diseases and adverse health outcomes during natural disasters. Prev Chronic Dis 2005; 2 (Spec no. A04).

7. Franco C, Toner E, Waldhorn R, et al.. The national disaster medical system: past, present, and suggestions for the future. Biosecur Bioterror 2007; 5: 319–325.

8. Kleinpeter MA. Rebuilding New Orleans and the Gulf Coast—lessons learned to strengthen nation’s healthcare. Response to Ferdinand’s “public health and Hurricane Katrina.” J Natl Med Assoc 2006; 98: 814–816.

9. Blaikie PT, Cannon ID, Wisner B. At Risk: Natural Hazards, Peoples’ Vulnerability and Disasters. London, Routledge, 1984.

10. Bayard D. Haiti earthquake relief, phase two—long-term needs and local resources. N Engl J Med 2010; 362: 1858–1861.

11. Brodie M, Weltzien E, Altman D, et al.. Experiences of hurricane Katrina evacuees in Houston shelters: implications for future planning. Am J Public Health 2006; 96: 1402–1408.

12. Dorn T, Yzermans CJ, Kerssens JJ, et al.. Disaster and subsequent healthcare utilization: a longitudinal study among victims, their family members, and control subjects. Med Care 2006; 44: 581–589.

13. Kates RW, Colten CE, Laska S, et al.. Reconstruction of New Orleans after Hurricane Katrina: a research perspective. Proc Natl Acad Sci U S A 2006; 103: 14653–14660.

14. Kim H, Han SB, Kim JH, et al.. Post-Nargis medical care: experience of a Korean disaster relief team in Myanmar after the cyclone. Eur J Emerg Med 2010; 17: 37–41.

15. Noji EK. The public health consequences of disasters. Prehosp Disaster Med 2000; 15: 147–157.

16. Shover H. The disaster response system: unanswered questions. Perspect Psychiatr Care 2007; 43: 3.

17. Axelrod C, Killam PP, Gaston MH, et al.. Primary health care and the Midwest flood disaster. Public Health Rep 1994; 109: 601–605.

18. Berggren RE, Curiel TJ. After the storm—health care infrastructure in post-Katrina New Orleans. N Engl J Med 2006; 354: 1549–1552.

19. Jhung MA, Shehab N, Rohr-Allegrini C, et al.. Chronic disease and disasters medication demands of Hurricane Katrina evacuees. Am J Prev Med 2007; 33: 207–210.

20. Billings J. Using administrative data to monitor access, identify disparities, and assess performance of the safety net.http://archive.ahrq.gov/data/safetynet/billings.htm. Published 2003. Accessed March 10, 2011.

21. Billings J, Anderson GM, Newman LS. Recent finding on preventable hospitalizations. Health Aff (Millwood) 1996; 15: 239–249.

22. Billings J, Parikh N, Miljanovich T. Emergency department use: the New York story. Issue brief. Commonw Fund 2000; 434: 1–12.

23. Billings J, Zeitel L, Lukomnik J, et al.. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood) 1993; 12: 162–173.

24. Basu J, Friedman B, Burstin H. Primary care, HMO enrollment, and hospitalization for ambulatory care sensitive conditions: a new approach. Med Care 2002; 40: 1260–1269.

25. Gill JM, Mainous AG 3rd, Nsereko M. Does having an outpatient visit after hospital discharge reduce the likelihood of readmission? Del Med J 2003; 75: 291–298.

26. Shi L, Samuels ME, Pease M, et al.. Patient characteristics associated with hospitalizations for ambulatory care sensitive conditions in South Carolina. South Med J 1999; 92: 989–998.

27. Basu J, Friedman B, Burstin H. Managed care and preventable hospitalization among Medicaid adults. Health Serv Res 2004; 39: 489–510.

28. Garg A, Probst JC, Sease T, et al.. Potentially preventable care: ambulatory care-sensitive pediatric hospitalizations in South Carolina in 1998. South Med J 2003; 96: 850–858.

29. Saha S, Solotaroff R, Oster A, et al.. Are preventable hospitalizations sensitive to changes in access to primary care? The case of the Oregon Health Plan. Med Care 2007; 45: 712–719.

30. Brown AD, Goldacre MJ, Hicks N, et al.. Hospitalization for ambulatory care-sensitive conditions: a method for comparative access and quality studies using routinely collected statistics. Can J Public Health 2001; 92: 155–159.

31. Laditka JN, Laditka SB. Race, ethnicity and hospitalization for six chronic ambulatory care sensitive conditions in the USA. Ethn Health 2006; 11: 247–263.

32. Pappas G, Hadden WC, Kozak LJ, et al.. Potentially avoidable hospitalizations: inequalities in rates between US socioeconomic groups. Am J Public Health 1997; 87: 811–816.

33. Trivedi AN, Zaslavsky AM, Schneider EC, et al.. Trends in the quality of care and racial disparities in Medicare managed care. N Engl J Med 2005; 353: 692–700.

34. Agabiti N, Pirani M, Schifano P, et al.. Income level and chronic ambulatory care sensitive conditions in adults: a multicity population-based study in Italy. BMC Public Health 2009; 9: 457.

35. Roos LL, Walld R, Uhanova J, et al.. Physician visits, hospitalizations, and socioeconomic status: ambulatory care sensitive conditions in a Canadian setting. Health Serv Res 2005; 40: 1167–1185.

36. Booth GL, Hux JE. Relationship between avoidable hospitalizations for diabetes mellitus and income level. Arch Intern Med 2003; 163: 101–106.

37. Zhang W, Mueller KJ, Chen LW. Uninsured hospitalizations: rural and urban differences. J Rural Health 2008; 24: 194–202.

38. Culler SD, Parchman ML, Przybylski M. Factors related to potentially preventable hospitalisations among elderly. Med Care 1998; 36: 804–817.

39. Basu J, Friedman B. Preventable illness and out-of-area travel of children in New York counties. Health Econ 2001; 10: 67–78.

40. Chen LW, Zhang W, Sun J, et al.. Regional variation in rural hospital charges due to ambulatory care sensitive conditions. Rural Policy Brief 2007; (PB2007-5): 1–4.

41. Chen LW, Zhang W, Sun J, et al.. National rural hospital charges due to ambulatory care sensitive conditions. Rural Policy Brief 2007; (PB2007-4): 1–4.

42. Laditka JN, Laditka SB, Probst JC. More may be better: evidence of a negative relationship between physician supply and hospitalization for ambulatory care sensitive conditions. Health Serv Res 2005; 40: 1148–1166.

43. Northridge ME, Meyer IH, Dunn L. Overlooked and underserved in Harlem: a population-based survey of adults with asthma. Environ Health Perspect 2002; 110 (Suppl 2): 217–220.

44. Tang N, Stein J, Hsia RY, et al.. Trends and characteristics of US emergency department visits, 1997–2007. JAMA 2010; 304: 664–670.

45. Zhang W, Mueller KJ, Chen LW, et al.. The role of rural health clinics in hospitalization due to ambulatory care sensitive conditions: a study in Nebraska. J Rural Health 2006; 22: 220–223.

46. Wang F, Luo W. Assessing spatial and nonspatial factors for healthcare access: towards an integrated approach to defining health professional shortage areas. Health Place 2005; 11: 131–146.

47. Schreiber S, Zielinski T. The meaning of ambulatory care sensitive admissions: urban and rural perspectives. J Rural Health 1997; 13: 276–284.

48. Agency for Healthcare Research andQuality. AHRQ Quality Indicators—Guide to Prevention Quality Indicators: Hospital Admission for Ambulatory Care Sensitive Conditions. Revision 4. Rockville, MD, AHRQ, 2004.

49. Ansari Z, Laditka JN, Laditka SB. Access to health care and hospitalization for ambulatory care sensitive conditions. Med Care Res Rev 2006; 63: 719–741.

50. Caminal J, Starfield B, Sanchez E, et al.. The role of primary care in preventing ambulatory care sensitive conditions. Eur J Public Health 2004; 14: 246–251.

51. Falik M, Needleman J, Wells BL, et al.. Ambulatory care sensitive hospitalizations and emergency visits: experiences of Medicaid patients using federally qualified health centers. Med Care 2001; 39: 551–561.

52. Bindman AB, Chattopadhyay A, Auerback GM. Medicaid re-enrollment policies and children’s risk of hospitalizations for ambulatory care sensitive conditions. Med Care 2008; 46: 1049–1054.

53. Bindman AB, Chattopadhyay A, Auerback GM. Interruptions in Medicaid coverage and risk for hospitalization for ambulatory care-sensitive conditions. Ann Intern Med 2008; 149: 854–860.

54. Gill JM. Can hospitalizations be avoided by having a regular source of care? Fam Med 1997; 29: 166–171.

55. Gill JM, Mainous AG 3rd. The role of provider continuity in preventing hospitalizations. Arch Fam Med 1998; 7: 352–357.

56. Steiner JF, Braun PA, Melinkovich P, et al.. Primary-care visits and hospitalizations for ambulatory-care-sensitive conditions in an inner-city health care system. Ambul Pediatr 2003; 3: 324–328.

57. Kim J, Norton EC, Stearns SC. Transportation brokerage services and Medicaid beneficiaries’ access to care. Health Serv Res 2009; 44: 145–161.

58. Powe NR, Weiner JP, Starfield B, et al.. Systemwide provider performance in a Medicaid program. Profiling the care of patients with chronic illnesses. Med Care 1996; 34: 798–810.

59. Friedman B, Basu J. Health insurance, primary care, and preventable hospitalization of children in a large state. Am J Manag Care 2001; 7: 473–481.

60. Hoffman C, Sered SS. Threadbare: Holes in America’s Health Care Safety Net. Menlo Park, CA, Kaiser Commission on Medicaid and the Uninsured, 2005.

61. Laditka JN, Laditka SB. Insurance status and access to primary health care:disparate outcomes for potentially preventable hospitalization. J Health Soc Policy 2004; 19: 81–100.

62. Oster A, Bindman AB. Emergency department visits for ambulatory care sensitive conditions: insights into preventable hospitalizations. Med Care 2003; 41: 198–207.

63. Pan W. Akaike’s information criterion in generalized estimating equations. Biometrics 2001; 57: 120–125.

64. Manley WG, Furbee PM, Coben JH, et al.. Realities of disaster preparedness in rural hospitals. Disaster Manag Response 2006; 4: 80–87.

65. Gursky EA. Hometown Hospitals: The Weakest Link? Bioterrorism Readiness in America’s Rural Hospitals. Washington, DC, Center for Technology and National Security Policy, 2004.

66. Schur CL. Understanding the Role of the Rural Hospital Emergency Department in Responding to Bioterrorist Attacks and Other Emergencies: A Review of the Literature and Guide to the Issues. Bethesda, MD, National Opinion Research Center of the University of Chicago Walsh Center for Rural Health Analysis, 2004.


disaster recovery; environmental public health disaster; health system performance; medically vulnerable populations; primary care access

© 2013 Southern Medical Association


Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.