- Email: [email protected]
Association Between Social and Economic Needs With Future Healthcare Utilization
ARTICLE IN PRESS
RESEARCH BRIEF
Association Between Social and Economic Needs With Future Healthcare Utilization David M. Mosen, PhD, MPH,1 Matthew P. Banegas, PhD, MPH,1 Jose G. Benuzillo, MS,1 Weiming R. Hu, MS,1 Neon B. Brooks, PhD,1 Briar L. Ertz-Berger, MD, MPH2
Introduction: Unmet social and economic needs are associated with poor health outcomes, but little is known about how these needs are predictive of future healthcare utilization. This study examined the association of social and economic needs identified during medical visits with future hospitalizations and emergency department visits. Methods: Individuals with electronic health record−coded social and economic needs during a primary care, emergency department, or urgent care visit at Kaiser Permanente Northwest from October 1, 2016 to November 31, 2017 (case patients) were identified, as well as individuals who had visits during that time period but had no electronic health record−coded needs (control patients). The 2 groups were compared on sociodemographic characteristics, comorbidities, and healthcare utilization in the prior year. Finally, logistic regression assessed the relationship between documented needs and hospitalizations and emergency department visits in the 12 months following the index visit, controlling for sociodemographic characteristics, comorbidities, and prior healthcare utilization. Statistical analysis was completed in April 2019.
Results: Case patients differed significantly from control patients on sociodemographic characteristics and had higher rates of comorbidities and prior healthcare utilization. Social and economic needs documented during the index visit were associated with significantly higher rates of hospitalization and emergency department visits in the 12 months following the visit, controlling for sociodemographic characteristics, comorbidities, and prior utilization. Conclusions: These results demonstrate that documented social and economic needs are a powerful predictor of future hospitalization and emergency department use and suggest the need for research into whether interventions to address these needs can influence healthcare utilization. Am J Prev Med 2019;000(000):1−4. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
INTRODUCTION
R
esearch has found that unmet social and economic needs (SENs) account for a significant portion of health outcomes.1−5 Although these needs have traditionally been considered outside the domain of health care, using health system resources to address SENs could have broad positive health impacts and reduce cost burden on the system.6,7 To understand the economic effects of addressing SENs within the health system, more evidence is needed on the ways that SENs affect patterns of future healthcare utilization. To
address this knowledge gap, this study examines the association of electronic health record (EHR)-coded SENs during medical visits, using ICD-10-CM codes,8 with future hospitalization and emergency department (ED) utilization. From the 1Kaiser Permanente Center for Health Research, Portland, Oregon; and 2Northwest Permanente, Continuum of Care Department, Portland, Oregon Address correspondence to: David M. Mosen, PhD, MPH, 3800 N. Interstate Avenue, Portland OR 97227. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.10.004
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
Am J Prev Med 2019;000(000):1−4
1
ARTICLE IN PRESS Mosen et al / Am J Prev Med 2019;000(000):1−4
2
METHODS This study was a retrospective case/control analysis of Kaiser Permanente Northwest (KPNW) members with and without EHRcoded social or economic needs using ICD-10-CM z-codes from October 1, 2016 to November 31, 2017. Since approximately 2016, KPNW has implemented a healthcare approach to document SENs in the EHR using a taxonomy of ICD-10-CM z-codes as a mechanism to identify and refer patients to needed social services. This study compared rates of hospitalization and ED utilization in the 12 months following an index appointment between case and control patients, adjusting for sociodemographic and clinical covariates and prior healthcare utilization. All variables were identified using the KPNW EHR databases. This analysis was not considered human subjects’ research by the KPNW IRB. Individuals were eligible for inclusion if they were aged ≥18 years; had at least 1 primary care, ED, or urgent care visit from October 1, 2016 to November 30, 2017 (index visit); and had continuous healthcare coverage (identified from EHR databases) through KPNW for the 12 months before and 12 months following their index visit. Case patients included all individuals who met these criteria and had 1 or more select social or economic ICD-10-CM z-code documented in a primary care, ED, or urgent care setting during this time period (Table 1). The index visit for case patients was the date of the first qualifying visit within the identified time period in which an ICD-10-CM SEN z-code was documented. Control patients were a randomly selected sample of 10% of eligible individuals who had no documented SEN z-codes during the exposure period, despite having at least 1 qualifying visit. For control patients, the index visit was the date of the first visit to primary care, ED, or urgent care within the identified time period. Case and control patients were compared on the following 2 binary outcome measures: (1) presence or absence of any hospitalization (i.e., 1 or more hospital admission, yes versus no) and (2) presence or absence of any ED visit (i.e., 1 or more ED visit,
Table 1. Social Determinants of Health Z-Codes ICD-10 z-code Social codes Z63.8 Z59.7 Z75.4 Z63.4 Z62.5 Economic codes Z59.9 Z59.4 Z59.0 Z91.120 Z56.0 Z59.6
Definition Caregiver stress/family stress Insufficient social insurance or welfare support Ineligibility for community resources Disappearance and/or death of family member Problems related to needed legal resources and/or release from prison Problems related to housing or economic circumstances Lack of adequate food and/or safe drinking water Homelessness Patient’s intentional underdosing of medication because of financial hardship Unemployment Low income
yes versus no) in the 12 months following the index visit. All utilization data was obtained from KPNW’s EHR system. Within the study population, select sociodemographic measures available from the EHR, medical comorbidities, and prior utilization were used to compare case and control patients and to adjust for these measures in the regression analysis. Select sociodemographic measures included age at index visit, sex, race (white or nonwhite), and area deprivation index (ADI), a measure of socioeconomic deprivation within given Census block−level data.9 The ADI is reported on a 10-point scale where higher values indicate more disadvantage. ADI values were grouped into the following 3 categories: 1−3 (least disadvantaged neighborhoods), 4−6 (moderately disadvantaged neighborhoods), and 7−10 (most disadvantaged neighborhoods). Use of the ADI allowed the authors to separate out the effects of living in a socially deprived environment from those of personally facing specific social or economic hardships (as indicated by the z-codes). Medical comorbidities were measured using the Charlson comorbidity index (CCI), identifying the number of qualifying conditions that appeared in the EHR as diagnosis codes in the 12 months before the index visit. CCI was categorized as 0, 1, or 2 or more conditions.10,11 Healthcare utilization before the index visit was assessed as presence or absence of hospital admissions, ED visits, and primary care visits in the 12 months before the index visit. Statistical analysis was completed in April 2019. Descriptive statistical analyses were conducted comparing case and control patients. Comparisons were made across sociodemographic measures, CCI, and prior healthcare utilization measures. To assess the independent effect of SENs on subsequent healthcare utilization, multiple logistic regression models were fit for each utilization outcome variable (hospitalizations and ED visits) with case versus control patients (i.e., presence or absence of SENs at the index visit) as the independent variable of interest. All aforementioned covariate measures were included.
RESULTS This study identified 12,054 case patients who had documented SENs, along with 31,175 randomly selected control patients who had at least 1 primary care, ED, or urgent care visit but did not have any documented SENs during the study period. Characteristics of both samples are shown in Table 2. Case patients were significantly older than control patients and were significantly more likely to be female, white, and live in disadvantaged neighborhoods. Case patients were also more likely to have comorbidities and had higher baseline rates of healthcare utilization than control patients. About 79.0% (n=9,517) of case patients had 1 or more social need, whereas 30.5% (n=3,641) had at least 1 economic need and 9.2% (n=1,104) had both needs (results not shown). In logistic regression analyses, case patients were significantly more likely to have a hospitalization and an ED visit in the 12 months following the index date, adjusting for sociodemographic measures, CCI, and prior healthcare utilization. As shown in Table 3, the www.ajpmonline.org
ARTICLE IN PRESS Mosen et al / Am J Prev Med 2019;000(000):1−4
3
Table 2. Population Characteristics
Characteristic Sociodemographic measures Age, mean §SD Sex, n (%) Female Race, n (%) Nonwhite ADI, n (%) 1−3 (least disadvantaged neighborhoods) 4−6 7−10 (most disadvantaged neighborhoods) CCI, n (%) 0 1 ≥2 Utilization measures 12 months before index date, n (%) ≥1 hospital admissions (versus none) ≥1 ED visits (versus none) ≥1 PC visits (versus none)
Had social or economic needs (n=12,054)
Did not have social or economic needs (n=31,175)
48.2 §26.0
43.8 §23.5
8,155 (67.7)
3,705 (54.2)
1,291 (10.7)
3,705 (11.9)
3,469 (28.8)
11,439 (36.7)
4,123 (34.2) 4,298 (35.7)
10,559 (33.9) 8,754 (28.1)
7,274 (60.4) 2,090 (17.3) 2,690 (22.3)
2,453 (78.7) 3,733 (12.0) 2,903 (9.3)
1,243 (10.3) 3,258 (27.0) 9,121 (75.7)
1,261 (4.0) 3,432 (11.0) 17,609 (56.5)
p-value <0.001 <0.001 0.001 <0.001
<0.001
<0.001 <0.001 <0.001
Note: Boldface indicates statistical significance (p<0.05). ADI, area deprivation index; CCI, Charlson comorbidity index; ED, emergency department; PC, primary care.
Table 3. Logistic Regression Results: Association of SENs With Utilization Outcomes Characteristic SENs (versus none) Sociodemographic measures Age (continuous) Female (versus male) White (versus nonwhite) ADI 1−3 (ref) 4−6 7−10 CCI 0 (ref) 1 ≥2 Utilization measures 12 months before index date ≥1 hospital admissions (versus none) ≥1 ED visits (versus none) ≥1 PC visits (versus none)
Hospital admissionsa OR (95% CI)
p-value
ED visitsb OR (95% CI)
p-value
2.33 (2.16, 2.51)
<0.001
2.32 (2.21, 2.45)
<0.001
1.02 (1.02, 1.02) 0.90 (0.83, 0.97) 1.12 (1.01, 1.24)
<0.001 0.008 0.039
1.00 (1.00, 1.01) 0.96 (0.91, 1.01) 0.99 (0.93, 1.06)
<0.001 0.120 0.841
1.00 (—) 1.10 (1.01, 1.21) 1.19 (1.09, 1.30)
— 0.030 <0.001
1.00 (—) 1.06 (1.00, 1.13) 1.17 (1.10, 1.24)
— 0.049 <0.001
1.00 (—) 1.48 (1.33, 1.64) 2.41 (2.17, 2.67)
— <0.001 <0.001
1.00 (—) 1.37 (1.28, 1.47) 2.15 (1.99, 2.32)
— <0.001 <0.001
1.97 (1.75, 2.21) 1.87 (1.71, 2.05) 0.96 (0.88, 1.06)
<0.001 <0.001 0.442
1.24 (1.12, 1.36) 3.10 (2.91, 3.31) 1.16 (1.10, 1.23)
<0.001 <0.001 <0.001
Note: Boldface indicates statistical significance (p<0.05). a Area under the ROC curve=0.79. b Area under the ROC curve=0.72. ADI, area deprivation index; CCI, Charlson comorbidity index; ED, emergency department; PC, primary care; ROC, receiver operating characteristic; SEN, social or economic need.
& 2019
ARTICLE IN PRESS 4
Mosen et al / Am J Prev Med 2019;000(000):1−4
adjusted odds of having at least 1 hospitalization in the year after the index date were 2.33 times higher for case versus control patients (95% CI=2.16, 2.51), and the adjusted odds of an ED visit in the year after index were 2.32 times higher (95% CI=2.21, 2.45). Other variables that significantly predicted hospitalization included older age, male sex, white race, ADI, CCI, and prior hospitalizations and ED visits. Other variables that significantly predicted ED visits included older age; ADI; CCI; and prior hospitalizations, ED visits, and primary care visits.
DISCUSSION Patients with EHR-coded SENs were significantly more likely to use the ED or be admitted to the hospital in the year following their index visit than patients who did not have EHR-coded needs, even when controlling for covariate measures. The effects of having social or economic need were larger than the effects of most demographic and clinical factors, except for multiple comorbidities (for hospitalizations) and prior ED visits (for ED visits).
Limitations A limitation of this work is that the documentation of ICD-10-CM codes is likely underutilized. Nevertheless, this may suggest an even greater effect of SENs on future healthcare utilization than that observed in this study.
CONCLUSIONS These findings suggest that SENs are an important indicator of future hospitalization and ED use. More research is warranted to determine whether addressing SENs in the healthcare setting can reduce future healthcare utilization. These data also demonstrate the value of documenting SENs within the EHR. Future work should provide validation of EHR-coded SENs and determine if ICD-10-CM social and economic z-codes are undercounted. More research is needed to better understand the extent to which patients identified with SENs receive the resources to address their needs and the timing in which these SENs are addressed.
ACKNOWLEDGMENTS No financial disclosures were reported by the authors of this paper.
REFERENCES 1. Booske B, Athens J, Kindig D, Park H, Remington P. County Health Rankings Working paper. Different Perspectives for Assigning Weights to Determinants of Health. Madison, WI: University of Wisconsin, Population Health Institute. www.countyhealthrankings.org/sites/ default/files/differentPerspectivesForAssigningWeightsToDeterminantsOfHealth.pdf. Published 2010. Accessed November 8, 2019. 2. Woolf SH, Braveman P. Where health disparities begin: the role of social and economic determinants—and why current policies may make matters worse. Health Aff (Millwood). 2011;30(10):1852–1859. https://doi.org/10.1377/hlthaff.2011.0685. 3. Krieger J, Higgins DL. Housing and health: time again for public health action. Am J Public Health. 2002;92(5):758–768. https://doi. org/10.2105/ajph.92.5.758. 4. Mansfield C, Novick LF. Poverty and health: focus on North Carolina. N C Med J. 2012;73(5):366–373. 5. Seligman HK, Laraia BA, Kushel MB. Food insecurity is associated with chronic disease among low-income NHANES participants. J Nutr. 2010;140(2):304–310. https://doi.org/10.3945/jn.109.112573. 6. Artiga S, Hinton E. Beyond health care: the role of social determinants in promoting health and health equity. https://www.kff.org/disparities-policy/issue-brief/beyond-health-care-the-role-of-social-determinants-in-promoting-health-and-health-equity/. Accessed on November 8, 2019. 7. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood). 2016;35(11):2116–2123. https://doi. org/10.1377/hlthaff.2016.0723. 8. Friedman NL, Banegas MP. Toward addressing social determinants of health: a health care system strategy. Perm J. 2018;22:18–095. https:// doi.org/10.7812/TPP/18-095. 9. Tuliani TA, Shenoy M, Parikh M, Jutzy K, Hilliard A. Impact of area deprivation index on coronary stent utilization in a medicare nationwide cohort. Popul Health Manag. 2017;20(4):329–334. https://doi. org/10.1089/pop.2016.0086. 10. Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. J Clin Epidemiol. 1999;52 (2):137–142. https://doi.org/10.1016/s0895-4356(98)00154-1. 11. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. https://doi.org/10.1016/0895-4356(92) 90133-8.
www.ajpmonline.org
RESEARCH BRIEF
Association Between Social and Economic Needs With Future Healthcare Utilization David M. Mosen, PhD, MPH,1 Matthew P. Banegas, PhD, MPH,1 Jose G. Benuzillo, MS,1 Weiming R. Hu, MS,1 Neon B. Brooks, PhD,1 Briar L. Ertz-Berger, MD, MPH2
Introduction: Unmet social and economic needs are associated with poor health outcomes, but little is known about how these needs are predictive of future healthcare utilization. This study examined the association of social and economic needs identified during medical visits with future hospitalizations and emergency department visits. Methods: Individuals with electronic health record−coded social and economic needs during a primary care, emergency department, or urgent care visit at Kaiser Permanente Northwest from October 1, 2016 to November 31, 2017 (case patients) were identified, as well as individuals who had visits during that time period but had no electronic health record−coded needs (control patients). The 2 groups were compared on sociodemographic characteristics, comorbidities, and healthcare utilization in the prior year. Finally, logistic regression assessed the relationship between documented needs and hospitalizations and emergency department visits in the 12 months following the index visit, controlling for sociodemographic characteristics, comorbidities, and prior healthcare utilization. Statistical analysis was completed in April 2019.
Results: Case patients differed significantly from control patients on sociodemographic characteristics and had higher rates of comorbidities and prior healthcare utilization. Social and economic needs documented during the index visit were associated with significantly higher rates of hospitalization and emergency department visits in the 12 months following the visit, controlling for sociodemographic characteristics, comorbidities, and prior utilization. Conclusions: These results demonstrate that documented social and economic needs are a powerful predictor of future hospitalization and emergency department use and suggest the need for research into whether interventions to address these needs can influence healthcare utilization. Am J Prev Med 2019;000(000):1−4. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
INTRODUCTION
R
esearch has found that unmet social and economic needs (SENs) account for a significant portion of health outcomes.1−5 Although these needs have traditionally been considered outside the domain of health care, using health system resources to address SENs could have broad positive health impacts and reduce cost burden on the system.6,7 To understand the economic effects of addressing SENs within the health system, more evidence is needed on the ways that SENs affect patterns of future healthcare utilization. To
address this knowledge gap, this study examines the association of electronic health record (EHR)-coded SENs during medical visits, using ICD-10-CM codes,8 with future hospitalization and emergency department (ED) utilization. From the 1Kaiser Permanente Center for Health Research, Portland, Oregon; and 2Northwest Permanente, Continuum of Care Department, Portland, Oregon Address correspondence to: David M. Mosen, PhD, MPH, 3800 N. Interstate Avenue, Portland OR 97227. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.10.004
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.
Am J Prev Med 2019;000(000):1−4
1
ARTICLE IN PRESS Mosen et al / Am J Prev Med 2019;000(000):1−4
2
METHODS This study was a retrospective case/control analysis of Kaiser Permanente Northwest (KPNW) members with and without EHRcoded social or economic needs using ICD-10-CM z-codes from October 1, 2016 to November 31, 2017. Since approximately 2016, KPNW has implemented a healthcare approach to document SENs in the EHR using a taxonomy of ICD-10-CM z-codes as a mechanism to identify and refer patients to needed social services. This study compared rates of hospitalization and ED utilization in the 12 months following an index appointment between case and control patients, adjusting for sociodemographic and clinical covariates and prior healthcare utilization. All variables were identified using the KPNW EHR databases. This analysis was not considered human subjects’ research by the KPNW IRB. Individuals were eligible for inclusion if they were aged ≥18 years; had at least 1 primary care, ED, or urgent care visit from October 1, 2016 to November 30, 2017 (index visit); and had continuous healthcare coverage (identified from EHR databases) through KPNW for the 12 months before and 12 months following their index visit. Case patients included all individuals who met these criteria and had 1 or more select social or economic ICD-10-CM z-code documented in a primary care, ED, or urgent care setting during this time period (Table 1). The index visit for case patients was the date of the first qualifying visit within the identified time period in which an ICD-10-CM SEN z-code was documented. Control patients were a randomly selected sample of 10% of eligible individuals who had no documented SEN z-codes during the exposure period, despite having at least 1 qualifying visit. For control patients, the index visit was the date of the first visit to primary care, ED, or urgent care within the identified time period. Case and control patients were compared on the following 2 binary outcome measures: (1) presence or absence of any hospitalization (i.e., 1 or more hospital admission, yes versus no) and (2) presence or absence of any ED visit (i.e., 1 or more ED visit,
Table 1. Social Determinants of Health Z-Codes ICD-10 z-code Social codes Z63.8 Z59.7 Z75.4 Z63.4 Z62.5 Economic codes Z59.9 Z59.4 Z59.0 Z91.120 Z56.0 Z59.6
Definition Caregiver stress/family stress Insufficient social insurance or welfare support Ineligibility for community resources Disappearance and/or death of family member Problems related to needed legal resources and/or release from prison Problems related to housing or economic circumstances Lack of adequate food and/or safe drinking water Homelessness Patient’s intentional underdosing of medication because of financial hardship Unemployment Low income
yes versus no) in the 12 months following the index visit. All utilization data was obtained from KPNW’s EHR system. Within the study population, select sociodemographic measures available from the EHR, medical comorbidities, and prior utilization were used to compare case and control patients and to adjust for these measures in the regression analysis. Select sociodemographic measures included age at index visit, sex, race (white or nonwhite), and area deprivation index (ADI), a measure of socioeconomic deprivation within given Census block−level data.9 The ADI is reported on a 10-point scale where higher values indicate more disadvantage. ADI values were grouped into the following 3 categories: 1−3 (least disadvantaged neighborhoods), 4−6 (moderately disadvantaged neighborhoods), and 7−10 (most disadvantaged neighborhoods). Use of the ADI allowed the authors to separate out the effects of living in a socially deprived environment from those of personally facing specific social or economic hardships (as indicated by the z-codes). Medical comorbidities were measured using the Charlson comorbidity index (CCI), identifying the number of qualifying conditions that appeared in the EHR as diagnosis codes in the 12 months before the index visit. CCI was categorized as 0, 1, or 2 or more conditions.10,11 Healthcare utilization before the index visit was assessed as presence or absence of hospital admissions, ED visits, and primary care visits in the 12 months before the index visit. Statistical analysis was completed in April 2019. Descriptive statistical analyses were conducted comparing case and control patients. Comparisons were made across sociodemographic measures, CCI, and prior healthcare utilization measures. To assess the independent effect of SENs on subsequent healthcare utilization, multiple logistic regression models were fit for each utilization outcome variable (hospitalizations and ED visits) with case versus control patients (i.e., presence or absence of SENs at the index visit) as the independent variable of interest. All aforementioned covariate measures were included.
RESULTS This study identified 12,054 case patients who had documented SENs, along with 31,175 randomly selected control patients who had at least 1 primary care, ED, or urgent care visit but did not have any documented SENs during the study period. Characteristics of both samples are shown in Table 2. Case patients were significantly older than control patients and were significantly more likely to be female, white, and live in disadvantaged neighborhoods. Case patients were also more likely to have comorbidities and had higher baseline rates of healthcare utilization than control patients. About 79.0% (n=9,517) of case patients had 1 or more social need, whereas 30.5% (n=3,641) had at least 1 economic need and 9.2% (n=1,104) had both needs (results not shown). In logistic regression analyses, case patients were significantly more likely to have a hospitalization and an ED visit in the 12 months following the index date, adjusting for sociodemographic measures, CCI, and prior healthcare utilization. As shown in Table 3, the www.ajpmonline.org
ARTICLE IN PRESS Mosen et al / Am J Prev Med 2019;000(000):1−4
3
Table 2. Population Characteristics
Characteristic Sociodemographic measures Age, mean §SD Sex, n (%) Female Race, n (%) Nonwhite ADI, n (%) 1−3 (least disadvantaged neighborhoods) 4−6 7−10 (most disadvantaged neighborhoods) CCI, n (%) 0 1 ≥2 Utilization measures 12 months before index date, n (%) ≥1 hospital admissions (versus none) ≥1 ED visits (versus none) ≥1 PC visits (versus none)
Had social or economic needs (n=12,054)
Did not have social or economic needs (n=31,175)
48.2 §26.0
43.8 §23.5
8,155 (67.7)
3,705 (54.2)
1,291 (10.7)
3,705 (11.9)
3,469 (28.8)
11,439 (36.7)
4,123 (34.2) 4,298 (35.7)
10,559 (33.9) 8,754 (28.1)
7,274 (60.4) 2,090 (17.3) 2,690 (22.3)
2,453 (78.7) 3,733 (12.0) 2,903 (9.3)
1,243 (10.3) 3,258 (27.0) 9,121 (75.7)
1,261 (4.0) 3,432 (11.0) 17,609 (56.5)
p-value <0.001 <0.001 0.001 <0.001
<0.001
<0.001 <0.001 <0.001
Note: Boldface indicates statistical significance (p<0.05). ADI, area deprivation index; CCI, Charlson comorbidity index; ED, emergency department; PC, primary care.
Table 3. Logistic Regression Results: Association of SENs With Utilization Outcomes Characteristic SENs (versus none) Sociodemographic measures Age (continuous) Female (versus male) White (versus nonwhite) ADI 1−3 (ref) 4−6 7−10 CCI 0 (ref) 1 ≥2 Utilization measures 12 months before index date ≥1 hospital admissions (versus none) ≥1 ED visits (versus none) ≥1 PC visits (versus none)
Hospital admissionsa OR (95% CI)
p-value
ED visitsb OR (95% CI)
p-value
2.33 (2.16, 2.51)
<0.001
2.32 (2.21, 2.45)
<0.001
1.02 (1.02, 1.02) 0.90 (0.83, 0.97) 1.12 (1.01, 1.24)
<0.001 0.008 0.039
1.00 (1.00, 1.01) 0.96 (0.91, 1.01) 0.99 (0.93, 1.06)
<0.001 0.120 0.841
1.00 (—) 1.10 (1.01, 1.21) 1.19 (1.09, 1.30)
— 0.030 <0.001
1.00 (—) 1.06 (1.00, 1.13) 1.17 (1.10, 1.24)
— 0.049 <0.001
1.00 (—) 1.48 (1.33, 1.64) 2.41 (2.17, 2.67)
— <0.001 <0.001
1.00 (—) 1.37 (1.28, 1.47) 2.15 (1.99, 2.32)
— <0.001 <0.001
1.97 (1.75, 2.21) 1.87 (1.71, 2.05) 0.96 (0.88, 1.06)
<0.001 <0.001 0.442
1.24 (1.12, 1.36) 3.10 (2.91, 3.31) 1.16 (1.10, 1.23)
<0.001 <0.001 <0.001
Note: Boldface indicates statistical significance (p<0.05). a Area under the ROC curve=0.79. b Area under the ROC curve=0.72. ADI, area deprivation index; CCI, Charlson comorbidity index; ED, emergency department; PC, primary care; ROC, receiver operating characteristic; SEN, social or economic need.
& 2019
ARTICLE IN PRESS 4
Mosen et al / Am J Prev Med 2019;000(000):1−4
adjusted odds of having at least 1 hospitalization in the year after the index date were 2.33 times higher for case versus control patients (95% CI=2.16, 2.51), and the adjusted odds of an ED visit in the year after index were 2.32 times higher (95% CI=2.21, 2.45). Other variables that significantly predicted hospitalization included older age, male sex, white race, ADI, CCI, and prior hospitalizations and ED visits. Other variables that significantly predicted ED visits included older age; ADI; CCI; and prior hospitalizations, ED visits, and primary care visits.
DISCUSSION Patients with EHR-coded SENs were significantly more likely to use the ED or be admitted to the hospital in the year following their index visit than patients who did not have EHR-coded needs, even when controlling for covariate measures. The effects of having social or economic need were larger than the effects of most demographic and clinical factors, except for multiple comorbidities (for hospitalizations) and prior ED visits (for ED visits).
Limitations A limitation of this work is that the documentation of ICD-10-CM codes is likely underutilized. Nevertheless, this may suggest an even greater effect of SENs on future healthcare utilization than that observed in this study.
CONCLUSIONS These findings suggest that SENs are an important indicator of future hospitalization and ED use. More research is warranted to determine whether addressing SENs in the healthcare setting can reduce future healthcare utilization. These data also demonstrate the value of documenting SENs within the EHR. Future work should provide validation of EHR-coded SENs and determine if ICD-10-CM social and economic z-codes are undercounted. More research is needed to better understand the extent to which patients identified with SENs receive the resources to address their needs and the timing in which these SENs are addressed.
ACKNOWLEDGMENTS No financial disclosures were reported by the authors of this paper.
REFERENCES 1. Booske B, Athens J, Kindig D, Park H, Remington P. County Health Rankings Working paper. Different Perspectives for Assigning Weights to Determinants of Health. Madison, WI: University of Wisconsin, Population Health Institute. www.countyhealthrankings.org/sites/ default/files/differentPerspectivesForAssigningWeightsToDeterminantsOfHealth.pdf. Published 2010. Accessed November 8, 2019. 2. Woolf SH, Braveman P. Where health disparities begin: the role of social and economic determinants—and why current policies may make matters worse. Health Aff (Millwood). 2011;30(10):1852–1859. https://doi.org/10.1377/hlthaff.2011.0685. 3. Krieger J, Higgins DL. Housing and health: time again for public health action. Am J Public Health. 2002;92(5):758–768. https://doi. org/10.2105/ajph.92.5.758. 4. Mansfield C, Novick LF. Poverty and health: focus on North Carolina. N C Med J. 2012;73(5):366–373. 5. Seligman HK, Laraia BA, Kushel MB. Food insecurity is associated with chronic disease among low-income NHANES participants. J Nutr. 2010;140(2):304–310. https://doi.org/10.3945/jn.109.112573. 6. Artiga S, Hinton E. Beyond health care: the role of social determinants in promoting health and health equity. https://www.kff.org/disparities-policy/issue-brief/beyond-health-care-the-role-of-social-determinants-in-promoting-health-and-health-equity/. Accessed on November 8, 2019. 7. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood). 2016;35(11):2116–2123. https://doi. org/10.1377/hlthaff.2016.0723. 8. Friedman NL, Banegas MP. Toward addressing social determinants of health: a health care system strategy. Perm J. 2018;22:18–095. https:// doi.org/10.7812/TPP/18-095. 9. Tuliani TA, Shenoy M, Parikh M, Jutzy K, Hilliard A. Impact of area deprivation index on coronary stent utilization in a medicare nationwide cohort. Popul Health Manag. 2017;20(4):329–334. https://doi. org/10.1089/pop.2016.0086. 10. Kieszak SM, Flanders WD, Kosinski AS, Shipp CC, Karp H. A comparison of the Charlson comorbidity index derived from medical record data and administrative billing data. J Clin Epidemiol. 1999;52 (2):137–142. https://doi.org/10.1016/s0895-4356(98)00154-1. 11. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. https://doi.org/10.1016/0895-4356(92) 90133-8.
www.ajpmonline.org