Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Investigating lupus retention in care to inform interventions for disparities reduction: an observational cohort study

Arthritis Research & Therapy volume 22, Article number: 35 (2020) Cite this article

Abstract

Background

Systemic lupus erythematous (SLE) disproportionately impacts patients of color and socioeconomically disadvantaged patients. Similar disparities in HIV were reduced through a World Health Organization-endorsed Care Continuum strategy targeting “retention in care,” defined as having at least two annual visits or viral load lab tests. Using similar definitions, this study aimed to examine predictors of lupus retention in care, to develop an SLE Care Continuum and inform interventions to reduce disparities. We hypothesized that Black patients and those residing in disadvantaged neighborhoods would have lower retention in care.

Methods

Abstractors manually validated 545 potential adult cases with SLE codes in 2013–2014 using 1997 American College of Rheumatology (ACR) or 2012 Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) criteria. We identified 397 SLE patients who met ACR or SLICC criteria for definite lupus, had at least one baseline rheumatology visit, and were alive through 2015. Retention in care was defined as having two ambulatory rheumatology visits or SLE labs (e.g., complement tests) during the outcome year 2015, analogous to HIV retention definitions. Explanatory variables included age, sex, race, ethnicity, smoking status, neighborhood area deprivation index (ADI), number of SLE criteria, and nephritis. We used multivariable logistic regression to test our hypothesis and model predictors of SLE retention in care.

Results

Among 397 SLE patients, 91% were female, 56% White, 39% Black, and 5% Hispanic. Notably, 51% of Black versus 5% of White SLE patients resided in the most disadvantaged ADI neighborhood quartile. Overall, 60% met visit-defined retention and 27% met complement lab-defined retention in 2015. Retention was 59% lower for patients in the most disadvantaged neighborhood quartile (adjusted OR 0.41, CI 0.18, 0.93). No statistical difference was seen based on age, sex, race, or ethnicity. More SLE criteria and non-smoking predicted greater retention.

Conclusions

Disadvantaged neighborhood residence was the strongest factor predicting poor SLE retention in care. Future interventions could geo-target disadvantaged neighborhoods and design retention programs with vulnerable populations to improve retention in care and reduce SLE outcome disparities.

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease that disproportionately impacts young women, patients of color, and the socioeconomically disadvantaged, making SLE an important target for health disparity measurement and research [1]. Despite many effective lupus monitoring strategies and treatments [2, 3], significant healthcare disparities and outcome gaps remain [4,5,6]. Compared to White women, the US Black women are up to seven times more likely to develop renal failure, and two to three times as likely to die prematurely [4, 7]. SLE is also a leading chronic disease cause of death in the US women ages 18–25 [8]. Arguing against a purely biological mechanism, higher SLE damage was noted in the US African descendants but not in other African descendants in ten other countries in the SLICC cohort [9]. High-quality clinical care can reduce risk for many poor lupus outcomes [10, 11]. Yet, some data suggest that Black or low SES patients with milder onset SLE are more likely to ultimately die of their disease, suggesting that differences in healthcare follow-up might be to blame [7, 12].

Human immunodeficiency virus (HIV), which also disproportionately impacts young, Black, and socioeconomically disadvantaged populations, is an analogous condition where healthcare gaps have been targeted as a means by which to reduce disparities. As a framework, experts at the World Health Organization (WHO) [13], US Institute of Medicine (IOM) [14], and Centers for Disease Control and Prevention (CDC) [15] have defined an “HIV Care Continuum” outlining five critical healthcare steps to achieve successful viral suppression. The Care Continuum offers standard definitions to measure gaps at each step from (1) diagnosis to (2) linkage with specialty HIV care, (3) retention in care, and (4) retention on antiretroviral therapy, and finally to (5) control with suppressed HIV viral loads [16]. Using this continuum, HIV researchers determined that gaps in retention in care account for most (61%) failures to control HIV [16]. Moreover, continuum research has led to a compendium of evidence-based interventions [15] to close gaps at each step including many interventions to improve retention in HIV care. A similar approach holds promise for improving disparities in SLE. As such, we proposed an SLE Care Continuum from (1) diagnosis to (2) linkage with specialty lupus care, (3) retention in care, (4) retention on immune therapy, and finally (5) low lupus disease activity (Fig. 1). In this study, we first focus on predictors of retention in care.

Fig. 1
figure 1

Proposed SLE Care Continuum adapted with permission from the HIV.gov Care Continuum

Full size image

Rheumatologists often observe that keeping some lupus patients, particularly young, Black, or socioeconomically disadvantaged lupus patients [17], in follow-up care is a challenge, yet no metrics exist to identify who is or is not retained in lupus care. In HIV, retention in care is defined by WHO [13] and CDC [15] experts as having two annual HIV visits or one viral load every 6 months. Creating such metrics was pivotal for identifying gaps and testing strategies to reduce HIV health disparities [15]. Despite an urgent need to reduce SLE disparities and known concerns regarding retention, we have no established methods to measure gaps in retention in care or other disparities across an SLE Care Continuum.

We were particularly interested in investigating relationships between race, socioeconomic disadvantage, and retention in lupus care in this cohort [18]. Black patients and the socioeconomically disadvantaged are more likely to reside in disadvantaged US neighborhoods, which are linked to high disease rates and mortality offering geographic targets for policy, practice change, and research [19, 20].

We hypothesized that Black patients and those residing in highly disadvantaged neighborhoods would have lower retention in care because of the multitude of socioeconomic, access, and other adverse social determinants of health that such residents face [21,22,23]. The objectives of this study were to validate an urban lupus cohort, to create metrics for retention in care, and to examine how race and other social determinants of health predict lupus retention in care. Defining lupus retention in care and its predictors are key steps toward designing future interventions to improve retention and reduce lupus outcome disparities.

Methods

Inclusion and study population

For this cohort study, we first searched all Jan 2013–June 2014 electronic health records (EHR) at an urban US academic center for all inpatient or outpatient visits with the International Classification of Diseases ninth or tenth edition (ICD-9 or 10) codes representing possible SLE [ICD-9 710.0 or ICD-10 M32, M32.1, M32.8, M32.9]. Inclusion required being age 18 or over, living through 2015 to assure eligibility for retention in care, and having at least one ambulatory visit in rheumatology [physician (MD, DO), nurse practitioner (NP), physician assistant (PA), resident, or fellow] and one in primary care (family medicine, internal medicine, geriatrics, obstetrics and gynecology, or pediatrics) in 2013–2014 to assure equal capture of baseline comorbidity and healthcare utilization information and follow-up. Recognizing that not all patients with lupus diagnosis codes meet clinical criteria, trained health professionals manually validated all potential SLE cases using a standard REDCap abstraction tool to assess the 1997-modified 1982 American College of Rheumatology (ACR) classification criteria and 2012 Systemic Lupus Collaborating Clinics (SLICC) SLE classification criteria [24]. Laboratory, pathology, imaging, and clinical note data were reviewed for all patients with possible lupus. Those with drug-induced lupus or not meeting either definite classification of SLE were excluded. All patients meeting either the 1997 ACR or 2012 SLICC SLE classification criteria and other inclusion criteria were included in the study. Patients were followed from the study start date of their first lupus diagnosis code, through death, or the end of 2015 as a study end date. Data elements for each individual were linked via a unique pseudo identifier, and direct identifiers were removed for a final limited dataset for analysis.

The Institutional Review Board approved of this minimal risk medical record review study with a waiver of individual informed consent and use of a limited dataset.

Data sources

In addition to manually abstracted items, EHR data were electronically extracted for definite SLE cases including (1) patient-level sociodemographic variables (age, sex, race, ethnicity, rural-urban commuting area (RUCA) classification, date of death), (2) visit-level information (all rheumatology encounter dates and all encounter ICD codes), and (3) select laboratory data (e.g., C3/C4 complement and anti-double-stranded DNA antibody testing dates). In addition to SLE classification criteria, sociodemographic and behavioral factors also reviewed during manual abstraction were nine-digit ZIP postal code and smoking history. Disease severity was noted using the number of SLE criteria, and a history of lupus nephritis was also manually validated.

Outcome definitions for lupus retention in care

EHR dates of rheumatology visits attended and complete blood count (CBC), creatinine anti-double-stranded DNA (dsDNA), or C3 or C4 complement tests during the calendar year 2015 were used to test potential retention definitions. Thresholds of one, two, or four visits or complement or dsDNA lab tests were compared. We focused on two visit-defined and two lab-defined SLE retention in care in those without nephritis analogous to HIV definitions and consistent with guidelines for nephritis screening and quality measures [2, 11]. We also tested a four lab-defined threshold consistent with SLE guidelines for those with lupus nephritis [2, 3]. Non-specific tests such as complete blood counts and creatinine were tested but not used as final definitions as they may be ordered by other providers for other non-SLE reasons and may not indicate lupus retention in care. Tests such as urinalyses, while important to most lupus care, were avoided given that SLE patients on dialysis may forgo such testing or have results at external dialysis centers and thus not present in EHR data.

Predictors and covariates

Consistent with the National Institute of Minority Health and Health Disparities research framework [21], multilevel predictors of interest included individual-level age, sex, race, ethnicity, and smoking status, and ZIP code linked contextual-level rural urban (RUCA) classification, and neighborhood disadvantage quartile. We used the Area Deprivation Index (ADI), a neighborhood disadvantage metric encompassing 17 education, employment, housing quality, transportation, and poverty measures to match from 69 million nine-digit ZIP postal code census block groups, i.e., “neighborhoods” of approximately 1500 people [19, 20, 25]. Twenty-five patients were missing residential ADI data and were excluded from multivariable modeling. The number of ACR criteria and any history of nephritis were included as markers of lupus severity.

Statistical analysis

Baseline lupus cohort characteristics were presented as frequencies and proportions for the overall and the cohort stratified by race. Differences between proportions were examined by the chi-square test. Retention rates during the 2015 period were calculated as percent of eligible patients meeting visit or laboratory testing definitions. We also examined definitions specific to guideline-recommended lab frequencies for those with lupus nephritis [2, 12]. Next, to determine predictors of retention, univariate and multivariable logistic regression analyses were performed, and results are presented as odds ratios (ORs) along with 95% confidence intervals (CIs). Potential interactions between ADI neighborhood disadvantage and other covariates (e.g., race or ethnicity) were tested as sensitivity analyses. We a priori estimated 80% power to detect an odds ratio of 0.60 if retention in care is 65% in White compared to 53% in Black patients using a two-sided Z test, significance level 0.05, similar to a 15% reported race gap in medication adherence [26]. Analyses were completed using SAS version 9.4 (Cary, NC) and STATA version 13.1 (College Station, TX).

Results

Cohort description

Of 528 abstracted potential lupus cases, 397 patients met ACR or SLICC classification criteria for definite lupus and were included in our study (Fig. 2). Among definite lupus patients, 91% were female, 39% Black, 5% Other, and 4% Hispanic (see Table 1).

Fig. 2
figure 2

Flow diagram of project design and inclusion

Full size image
Table 1 Prevalent systemic lupus erythematosus cohort description (n = 397)
Full size table

Compared to White patients with lupus, Black patients were younger, more likely to reside in urban areas, and 10 times more likely to reside in a neighborhood within the most disadvantaged quartile (51% Blacks vs. 5% Whites). Black patients were also more likely to meet ACR SLE criteria (95% vs. 87%). Payer varied significantly by race (75% of Black versus 40% of White patients received public insurance), and only 2% of patients were uninsured or had unknown payer status.

Testing retention definitions

When examining definitions of lupus retention in care, we first examined visit-based definitions similar to those employed in HIV. Overall, as shown in Table 2, 83% (n = 331) of eligible lupus patients had at least one ambulatory rheumatology visit in 2015, and 60% (n = 238) had at least two visits. This was comparable to 74% with two or more visits in the baseline year 2014 (data not shown).

Table 2 Examining visit- and lab-defined retention in care thresholds in SLE cases with and without nephritis (n = 397)
Full size table

When testing lab-defined retention in care, we sequentially examined C3 and C4 complement, CBC, creatinine, and anti-dsDNA testing (Table 2). In 2015 follow-up, 54% of patients had at least one CBC and 46% of patients had at least one complement result recorded in the EHR. Only 27% and 41% had two annual complement and creatinine tests, respectively, compared to 43% with at least two complement tests in the 2014 baseline year (data not shown). Any single anti-dsDNA result was available on only 29% of the cohort during 2015, with only 19% having two dsDNA tests, leading us to operationalize the equally specific two complement test definition for lab-defined lupus retention in care for multivariable modeling although other definitions could be assessed in future studies.

Predictors of lupus retention in care

In adjusted multivariable models, residing in the neighborhood quartile with worst disadvantage was the strongest predictor of lower retention in care, predicting nearly 60% lower odds (OR 0.41, CI 0.18, 0.93; Table 3). Smoking likewise significantly predicted lower visit-defined retention in care (OR 0.63, 0.40, 0.99). Neither Black race nor Hispanic ethnicity was predictive of lupus retention in care.

Table 3 Predictors of two visit-defined lupus retention in care (n = 372, multivariable analysis included 372 of 397 SLE patients with complete data)
Full size table

Examining unadjusted odds using the two complement definitions of retention, nephritis predicted greater retention (OR 1.70, CI 1.12, 2.59; Table 4); small town residence predicted lower lab-defined retention. However, in multivariate models, nephritis patients did not have significantly different retention (OR 1.41, CI 0.82, 2.41) and only a higher number of SLE criteria predicted greater retention (OR 1.17, CI 1.01, 1.35).

Table 4 Predictors of two complement lab-defined retention in care (n = 372, multivariable analysis included 372 of 397 SLE patients with complete data)
Full size table

Among 145 patients with lupus nephritis, slightly more (66 vs. 60%) met the two visit-defined retention criteria. Likewise, 34% vs. 27% had at least two complement tests, but in such patients, only 14% had four tests per year as recommended per ACR guidelines for lupus nephritis [2].

Discussion

Using definitions analogous to those used by the WHO, IOM, and CDC [13,14,15] to define HIV retention in care, we were able to define and examine predictors of lupus retention in care. We examined both visit and lab-defined retention in care noting that 40% of patients lacked recommended visits and more than half of patients lacked recommended lupus labs. Notably, our visit-based models revealed that neighborhood disadvantage was the strongest predictor of gaps in lupus retention in care. Patients with lupus nephritis performed slightly better using basic measures, but only 14% met guideline recommended quarterly lab-defined retention.

More broadly, our data, and those of others [27, 28], support defining and investigating a Care Continuum across lupus care to inform the design of targeted interventions to eliminate disparities. Supporting the idea that the SLE Care Continuum would likewise correlate with outcomes, prior studies have shown retention on therapy for instance links to patient outcomes in SLE. Specifically, hydroxychloroquine improves outcomes [6], and nonadherence gaps correlate with worse outcomes, particularly, in disparities populations [27, 28]. A Canadian study correlated better hydroxychloroquine adherence with 5-year reductions in cumulative steroid use, disease activity, and lupus damage [28]. Conversely, a US Medicaid study reported that hydroxychloroquine therapy gaps (as defined by < 80% medication possession) were seen in 83% of patients and correlated with increased emergency visits and hospitalizations by 4 months [27]. We focused upstream in the Care Continuum noting that retention in care is critical to receive prescriptions for such treatments, to decrease treatment interruptions, and to signal treatment escalation when necessary.

Based on prior literature describing outcome disparities and follow-up gaps in Black patients or those from socioeconomic disadvantaged backgrounds [17], we predicted lower retention in care in those groups. We observed that neighborhood disadvantage was most strongly predictive, independent of race. Geographically, our site for this study, urban Milwaukee, WI, USA, may be more segregated than other cities [29] influencing the relative weights of predictors. We observed that 51% of Black patients with SLE resided in the most disadvantaged quartile neighborhoods compared to only 5% of White patients. Both race and neighborhood disadvantage in addition to other factors may be predictive of retention in care in other places, yet data suggest that neighborhood disadvantage predicts health independent of individual socioeconomic status as well [30]. Neighborhood context can directly affect access to healthcare as well as access to transportation, food, education, health behaviors, safety, discrimination, and chronic stress influencing many health outcomes [22, 23, 31]. Mechanistically, if transportation is less available or healthcare facilities are farther from disadvantaged populations, as shown in a US SLE Medicaid study [32], then getting routine visits and lab tests is more challenging. A strength of neighborhood disadvantage as a disparities predictor is that it can be calculated using already collected addresses within EHRs to identify patients who may need additional resources. It can also be used as a first step toward additional targeted research to disentangle the myriad of mechanisms that may underlie lupus disparities and to specifically target these actionable factors for interventions across policy, research, and clinical domains.

Retention in care is a well-developed metric in the HIV literature that has been central to the WHO and US National HIV/AIDS Strategy [15] and for measuring progress in reducing disparities [33]. Among those retained in care, 89% received treatment and 80% achieved viral suppression control, highlighting that retention in care is a key target. Encouragingly, a meta-analysis [34] and the compendium [15] highlight numerous evidence-based interventions proven to improve retention in HIV care and reduce disparities. Policy-driven programs have used such HIV Care Continuum strategies to increase viral suppression from 69 to 81% among Black participants between 2012 and 2016 [35]. Individual patients with HIV now also know that two viral load lab tests per year define a minimum standard to share accountability. Overall, success reducing disparities in HIV highlights the need to measure SLE retention in care as a necessary first step toward designing interventions, policies, and building patient partnerships to eliminate lupus outcome disparities.

Despite strengths of carefully validating lupus cases, applying a novel Care Continuum from HIV, and testing associations using multivariable regression, we also acknowledge limitations. First, our analysis included only one health system in one US city. Future projects will test retention definitions in other health systems or regions. Second, patients with nephritis or skin predominant SLE may be followed more closely in nephrology or dermatology which was not examined in this study. Future studies could examine retention in care including visits provided by nephrologists or dermatologists, who may share care for patients with SLE. A strength of our EHR-derived visit-based approach to define retention is the possibility of reproducing this metric across or between health systems. It is possible that compared to single-system EHR assessments, payer claims datasets might show higher retention in care given capture of visit or lab claims anywhere, including labs performed in other local health systems. For instance, we noted that small town residents had lower univariate odds of complement tests; some may have had lab testing in local clinics while others simply did not have tests. Likewise, only 2% of our cohort was uninsured and retention rates may vary in populations with more patients who are uninsured. We also acknowledge that some patients may have been lost to follow-up—migrating or intentionally changing insurance or providers, without true gaps in lupus retention in care. Residence may also change more frequently in patients with greatest disadvantage or adverse life experiences [36, 37]. Such time-varying factors could be examined in future multi-site studies.

Conclusions

Defining lupus retention in care and its predictors are key steps toward future evidence-based interventions to improve retention and reduce some disparities in lupus outcomes. Residence in a disadvantaged neighborhood was the strongest factor predicting poor SLE retention in care. Constructing future interventions could leverage geo-targeting disadvantaged neighborhoods, as well as designing programs specifically with and for vulnerable populations residing within these neighborhoods. Ultimately, we aim to use the SLE Care Continuum measures to design evidence-based care strategies and policies to reduce health disparities and advance outcome equity among patients with lupus.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to limited patient health information but may be reviewed with the corresponding author on reasonable request.

Abbreviations

ACR:

American College of Rheumatology

ADI:

Neighborhood area deprivation index

CDC:

Centers for Disease Control and Prevention

DNA :

Deoxyribonucleic acid

dsDNA :

Double stranded DNA antibody test

EHR:

Electronic health record

HIV:

Human immunodeficiency virus

ICD:

International Classification of Diseases

IOM:

US Institute of Medicine

RUCA:

Rural-urban commuting area

SLE:

Systemic lupus erythematosus

SLICC:

Systemic Lupus Erythematosus International Collaborating Clinics

WHO:

World Health Organization

References

  1. U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases. The future directions of lupus research. Bethesda; 2007. https://www.niams.nih.gov/about/future-directions-lupus-research. Accessed 30 Sept 2019

  2. Hahn BH, Mcmahon MA, Wilkinson A, Wallace WD, Daikh DI, Fitzgerald JD, et al. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res. 2012;64(6):797–808.

    Article  Google Scholar 

  3. Fanouriakis A, Kostopoulou M, Alunno A, Aringer M, Bajema I, Boletis JN, et al. 2019 update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis. 2019;78(6):736–45.

    Article  CAS  Google Scholar 

  4. Lim SS, Bayakly AR, Helmick CG, Gordon C, Easley KA, Drenkard C. The incidence and prevalence of systemic lupus erythematosus, 2002-2004: the Georgia lupus registry. Arthritis Rheumatol. 2014;66(2):357–68.

    Article  Google Scholar 

  5. Drenkard C, Rask KJ, Easley KA, Bao G, Lim SS. Primary preventive services in patients with systemic lupus erythematosus: study from a population-based sample in Southeast U.S. Semin Arthritis Rheum. 2013;43(2):209–16.

    Article  Google Scholar 

  6. Urowitz MB, Gladman DD, Anderson NM, Su J, Romero-Diaz J, Bae SC, et al. Cardiovascular events prior to or early after diagnosis of systemic lupus erythematosus in the systemic lupus international collaborating clinics cohort. Lupus Sci Med. 2016;3(1):e000143.

    Article  CAS  Google Scholar 

  7. Gonzalez LA, Toloza SM, Alarcon GS. Impact of race and ethnicity in the course and outcome of systemic lupus erythematosus. Rheum Dis Clin N Am. 2014;40(3):433–54 vii-viii.

    Article  Google Scholar 

  8. Yen EY, Singh RR. Brief report: lupus-an unrecognized leading cause of death in young females: a population-based study using nationwide death certificates, 2000-2015. Arthritis Rheumatol. 2018;70(8):1251–5.

    Article  Google Scholar 

  9. Bruce IN, O'Keeffe AG, Farewell V, Hanly JG, Manzi S, Su L, et al. Factors associated with damage accrual in patients with systemic lupus erythematosus: results from the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort. Ann Rheum Dis. 2015;74(9):1706–13.

    Article  CAS  Google Scholar 

  10. Alarcon GS, McGwin G, Bertoli AM, Fessler BJ, Calvo-Alen J, Bastian HM, et al. Effect of hydroxychloroquine on the survival of patients with systemic lupus erythematosus: data from LUMINA, a multiethnic US cohort (LUMINA L). Ann Rheum Dis. 2007;66(9):1168–72.

    Article  CAS  Google Scholar 

  11. Yazdany J, Panopalis P, Gillis JZ, Schmajuk G, MacLean CH, Wofsy D, et al. A quality indicator set for systemic lupus erythematosus. Arthritis Rheum. 2009;61(3):370–7.

    Article  Google Scholar 

  12. Anderson E, Nietert PJ, Kamen DL, Gilkeson GS. Ethnic disparities among patients with systemic lupus erythematosus in South Carolina. J Rheumatol. 2008;35(5):819–25.

    PubMed  PubMed Central  Google Scholar 

  13. Medland NA, McMahon JH, Chow EP, Elliott JH, Hoy JF, Fairley CK. The HIV care cascade: a systematic review of data sources, methodology and comparability. J Int AIDS Soc. 2015;18:20634.

    Article  Google Scholar 

  14. Rebeiro PF, Horberg MA, Gange SJ, Gebo KA, Yehia BR, Brooks JT, et al. Strong agreement of nationally recommended retention measures from the Institute of Medicine and Department of Health and Human Services. PLoS One. 2014;9(11):e111772.

    Article  Google Scholar 

  15. Centers for Disease Control and Prevention: Recommendations for HIV prevention with adults and adolescents with HIV in the United States. 2014. https://stacks.cdc.gov/view/cdc/44064. Accessed 30 Sept 2019.

  16. Skarbinski J, Rosenberg E, Paz-Bailey G, Hall HI, Rose CE, Viall AH, et al. Human immunodeficiency virus transmission at each step of the care continuum in the United States. JAMA Intern Med. 2015;175(4):588–96.

    Article  Google Scholar 

  17. Bertoli AM, Fernandez M, Calvo-Alen J, Vila LM, Sanchez ML, Reveille JD, et al. Systemic lupus erythematosus in a multiethnic U.S. cohort (LUMINA) XXXI: factors associated with patients being lost to follow-up. Lupus. 2006;15(1):19–25.

    Article  CAS  Google Scholar 

  18. Harper S, MacLehose RF, Kaufman JS. Trends in the black-white life expectancy gap among US states, 1990-2009. Health Aff (Millwood). 2014;33(8):1375–82.

    Article  Google Scholar 

  19. Kind AJ, Jencks S, Brock J, Yu M, Bartels C, Ehlenbach W, et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann Intern Med. 2014;161(11):765–74.

    Article  Google Scholar 

  20. Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible - the neighborhood atlas. N Engl J Med. 2018;378(26):2456–8.

    Article  Google Scholar 

  21. Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D. The National Institute on Minority Health and Health Disparities research framework. Am J Public Health. 2019;109(S1):S16–20.

    Article  Google Scholar 

  22. Badley EM, Canizares M, Gunz AC, Davis AM. Visits to rheumatologists for arthritis: the role of access to primary care physicians, geographic availability of rheumatologists, and socioeconomic status. Arthritis Care Res (Hoboken). 2015;67(2):230–9.

    Article  Google Scholar 

  23. Feldman CH, Hiraki LT, Liu J, Fischer MA, Solomon DH, Alarcon GS, et al. Epidemiology and sociodemographics of systemic lupus erythematosus and lupus nephritis among US adults with Medicaid coverage, 2000-2004. Arthritis Rheum. 2013;65(3):753–63.

    Article  Google Scholar 

  24. Petri M, Orbai AM, Alarcon GS, Gordon C, Merrill JT, Fortin PR, et al. Derivation and validation of the systemic lupus international collaborating clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012;64(8):2677–86.

    Article  Google Scholar 

  25. Bidaut-Russell M, Gabriel SE, Scott CG, Zinsmeister AR, Luthra HS, Yawn B. Determinants of patient satisfaction in chronic illness. Arthritis Rheum. 2002;47(5):494–500.

    Article  Google Scholar 

  26. Feldman CH, Yazdany J, Guan H, Solomon DH, Costenbader KH. Medication nonadherence is associated with increased subsequent acute care utilization among Medicaid beneficiaries with systemic lupus erythematosus. Arthritis Care Res (Hoboken). 2015;67(12):1712–21.

    Article  CAS  Google Scholar 

  27. Feldman CH, Collins J, Zhang Z, Subramanian SV, Solomon DH, Kawachi I, et al. Dynamic patterns and predictors of hydroxychloroquine nonadherence among Medicaid beneficiaries with systemic lupus erythematosus. Semin Arthritis Rheum. 2018;48(2):205–13.

    Article  Google Scholar 

  28. Pakchotanon R, Gladman DD, Su J, Urowitz MB. More consistent antimalarial intake in first 5 years of disease is associated with better prognosis in patients with systemic lupus erythematosus. J Rheumatol. 2018;45(1):90–4.

    Article  CAS  Google Scholar 

  29. Massey DS, Tannen J. A research note on trends in black hypersegregation. Demography. 2015;52(3):1025–34.

    Article  Google Scholar 

  30. Ludwig J, Duncan GJ, Gennetian LA, Katz LF, Kessler RC, Kling JR, et al. Neighborhood effects on the long-term well-being of low-income adults. Science. 2012;337(6101):1505–10.

    Article  CAS  Google Scholar 

  31. Arcaya MC, Tucker-Seeley RD, Kim R, Schnake-Mahl A, So M, Subramanian SV. Research on neighborhood effects on health in the United States: a systematic review of study characteristics. Soc Sci Med. 2016;168:16–29.

    Article  Google Scholar 

  32. Gillis JZ, Yazdany J, Trupin L, Julian L, Panopalis P, Criswell LA, et al. Medicaid and access to care among persons with systemic lupus erythematosus. Arthritis Rheum. 2007;57(4):601–7.

    Article  Google Scholar 

  33. Hall HI, Frazier EL, Rhodes P, Holtgrave DR, Furlow-Parmley C, Tang T, et al. Differences in human immunodeficiency virus care and treatment among subpopulations in the United States. JAMA Intern Med. 2013;173(14):1337–44.

    Article  Google Scholar 

  34. Higa DH, Crepaz N, Mullins MM. Identifying best practices for increasing linkage to, retention, and re-engagement in HIV medical care: findings from a systematic review, 1996–2014. AIDS Behav. 2016;20(5):951–66.

    Article  Google Scholar 

  35. US Health Resources and Services Administration. Ryan White HIV/AIDS Program Annual Client-Level Data Report 2016. http://hab.hrsa.gov/data/data-reports. Accessed 30 Sept 2019.

    Google Scholar 

  36. Dong M, Anda RF, Felitti VJ, Williamson DF, Dube SR, Brown DW, et al. Childhood residential mobility and multiple health risks during adolescence and adulthood: the hidden role of adverse childhood experiences. Arch Pediatr Adolesc Med. 2005;159(12):1104–10.

    Article  Google Scholar 

  37. Tseliou F, Maguire A, Donnelly M, O’Reilly D. The impact of childhood residential mobility on mental health outcomes in adolescence and early adulthood: a record linkage study. J Epidemiol Community Health. 2016;70(3):278–85.

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank Marit Johnson RN, for the chart review and data acquisition, Amanda Perez for the project management, Kristen Osinski for the electronic health record searches, Patrick Fergusson for the programming and analysis support, and Monica Messina for the manuscript support.

Funding

Primary funding for this project was provided by University of Wisconsin School of Medicine and Public Health through the Wisconsin Partnership Program New Investigator Program award. Support was also provided in part by the University of Wisconsin (UW) Clinical and Translational Science Award (CTSA), through the National Institutes of Health National Center for Advancing Translational Sciences (NIH NCATS grant UL1TR000427), and under National Institute on Minority Health and Health Disparities Research Award Number (NIH NIA R01MD010243 PI-Kind). Funders played no role in the design, conduct, analysis, or interpretation of results or writing of the manuscript. The manuscript content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information

Authors and Affiliations

  1. Department of Medicine, Rheumatology Division, University of Wisconsin School of Medicine and Public Health, 1485 Highland Ave, Rm 4132, Madison, WI, 53705, USA

    Christie M. Bartels & Shivani Garg

  2. Health Services & Care Research Program, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Christie M. Bartels & Amy Kind

  3. Medical College of Wisconsin, Milwaukee, WI, USA

    Ann Rosenthal, Umber Ahmad & Ian Chang

  4. Clement J. Zablocki Veterans Affairs Medical Center, Milwaukee, WI, USA

    Ann Rosenthal

  5. Department of Biostatistics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Xing Wang

  6. Seattle Children’s Hospital, Seattle, WI, USA

    Xing Wang

  7. Department of Medicine, Rheumatology, University of California Irvine Medical Center, Orange County, CA, USA

    Ian Chang

  8. University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Nnenna Ezeh & Maria Schletzbaum

  9. Geriatrics Division, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, USA

    Amy Kind

  10. VA Geriatrics Research Education and Clinical Center, William S Middleton VA Hospital, Madison, WI, USA

    Amy Kind

Authors
  1. Christie M. Bartels
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ann Rosenthal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Umber Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ian Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nnenna Ezeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shivani Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maria Schletzbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Amy Kind
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CMB, AKR, and XW had full access to the data in the study and take responsibility for the integrity of the data and statistical data analysis. CMB, AKR, and AJK conceptualized the study and interpreted the data. CMB and AKR obtained funding and oversaw data acquisition. UA and IC performed manual validation of the SLE cases and were contributors in writing the manuscript. CMB and XW performed and supervised the statistical analysis. CMB, AJK, XW, and AKR drafted the manuscript. UA, IC, NE, SG, and MS assisted result interpretation and substantively contributed to critical manuscript revisions for important intellectual content. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Christie M. Bartels.

Ethics declarations

Ethics approval and consent to participate

The Medical College of Wisconsin Institutional Review Board approved of this minimal risk medical record review study with waiver of individual informed consent and use of a limited dataset (PRO00029061).

Consent for publication

Not applicable

Competing interests

CMB receives peer-reviewed institutional grant funding from the Independent Grants for Learning and Change (Pfizer) for research unrelated to this study. All other authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bartels, C.M., Rosenthal, A., Wang, X. et al. Investigating lupus retention in care to inform interventions for disparities reduction: an observational cohort study. Arthritis Res Ther 22, 35 (2020). https://doi.org/10.1186/s13075-020-2123-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13075-020-2123-4

Keywords

Arthritis Research & Therapy

ISSN: 1478-6362

Contact us