Mental Health Services Utilization Among Women Experiencing Intimate Partner Violence

Objective:

To examine the patient and provider characteristics associated with utilization of mental health services (MHS) among women experiencing intimate partner violence (IPV).

Study Design:

Cross-sectional study among 6870 women aged 18 to 65 years with first IPV identification between May 2004 and December 2009 in Kaiser Permanente Northern California.

Methods:

Utilization of MHS within 60 days after first IPV identification was determined. Multivariate generalized estimating equation logistic regression models that controlled for patient and provider characteristics were used to determine predictors of utilization.

Results:

Thirty-seven percent of women utilized MHS. In multivariate generalized estimating equation models, the strongest predictor of utilization was electronic referral (odds ratio [OR], 4.40; 95% confidence interval [CI], 3.66-5.28). Odds of utilization were lower among black (OR, 0.71; 95% CI, 0.57-0.89), Latina (OR, 0.62; 95% CI, 0.41-0.95), and Spanish-speaking (OR, 0.71; 95% CI, 0.57-0.89) patients and were higher among those with prior posttraumatic stress disorder (OR, 2.38; 95% CI, 1.17-3.44) or depression (OR, 1.35; 95% CI, 1.17-1.57). Emergency department identification of IPV was associated with lower odds of MHS utilization (OR, 0.46; 95% CI, 0.37-0.59), while older provider identification of IPV was associated with higher odds of MHS utilization (OR, 1.33; 95% CI, 1.07-1.65).

Conclusions:

Additional training for providers, particularly those who are younger or are practicing in emergency departments, may be needed to increase rates of MHS utilization among patients affected by IPV. Addressing language barriers to care and cultural appropriateness may improve MHS utilization.

(Am J Manag Care. 2010;16(10):731-738)

Intimate partner violence is a common event in women's lives.

• Lower rates of utilization of mental health services among women after intimate partner violence diagnosis by younger providers and in emergency department settings suggest these as points of intervention to improve referral rates.

• Utilization of mental health services is lower among nonwhite and non-English-speaking patients, suggesting language barriers to care and possibly the need to tailor intimate partner violence programs to minority patients.

Intimate partner violence (IPV) is a common experience in the lives of women^1-3 and is associated with deleterious effects on physical and mental health^4-6 and with substantial costs.^7,8 Professional medical organizations support routine IPV screening in medical care settings^9,10 and recommend that clinicians offer support and provide information about safety planning and community-based advocacy resources to IPV-affected patients.¹¹ The implementation of a coordinated approach to IPV prevention has demonstrated a dramatic and sustained increase in identification, intervention, and referral in a large healthcare delivery system.^12,13 Provision of follow-up referral for mental health or social services is often suggested, as women who are experiencing IPV also experience psychological distress^4,14-18 and want mental health services (MHS).¹⁴ Assessment of utilization patterns is important because the ultimate goal of IPV identification is the receipt of assistance that the patient can use to address her mental health needs, improve her safety, and change her situation. However, few data are available about the frequency or predictors of utilization of MHS among IPV-affected women. The objective of this study was to determine the predictors of women’s utilization of MHS in the managed care setting.

METHODS

Setting

This cross-sectional study was conducted among the membership of Kaiser Permanente Northern California Medical Services Plan (hereafter, Kaiser), an integrated healthcare delivery system that provides comprehensive care, including MHS, for approximately 3.1 million members in a 14-county region of northern California. Kaiser membership comprises almost one-third of the population in the catchment area and is representative of the underlying population, except for slightly lower membership at the extremes of income. Its racial/ethnic composition is similar to that of the US census—enumerated population in the San Francisco Bay metropolitan statistical area, namely, 7% African American in the Kaiser membership and 9% African American in the underlying metropolitan statistical area population, with 10% and 11% Asian or Pacific Islander, 10% and 14% Hispanic, and 71% and 64% white, respectively. Since 2002, Kaiser providers have had hard-copy and Internet access to clinical practice recommendations on screening, identification, and referral of IPV-affected patients. Training and continuing medical education credit has been offered annually at most Kaiser facilities. In addition, each clinical facility has an IPV physician champion, and most have a multidisciplinary team that provides facility clinician education and oversees IPV quality improvement efforts. Patient education materials are available in all clinics. This study was approved by the Kaiser Foundation Research Institute Institutional Review Board.

Source Population

After excluding 4277 women whose first IPV identification (ie, International Classification of Diseases, Ninth Revision, Clinical Modification visit code) occurred in mental health clinics, our study sample comprised 6870 adult women aged 18 to 65 years with first IPV identification between May 2004 and December 2009. May 2004 was the month during which all Kaiser facilities implemented electronic referrals. To ensure that we recorded the first identification of IPV within Kaiser, we excluded women with prior IPV identification any time after January 2000 or who did not have at least 1 year of continuous Kaiser membership before identification. Furthermore, because the probability of utilization among inpatient referrals is 100% (because the physician comes to see the patient while hospitalized), we excluded inpatient IPV codes.

Study Variables and Data Sources

Kaiser maintains an electronic medical record in which each patient is identified by a unique medical record number. The electronic medical record includes patient demographic information, clinic visit diagnoses, and the provider associated with each visit. Since May 2004, an electronic referral system has allowed the referring provider to enter the service to which a patient is referred and the reason for referral; referring provider information is automatically populated into the referral, which is then delivered electronically.

We defined the index date as the date of first outpatient or emergency department (ED) IPV identification. The outcome, mental health utilization (hereafter, utilization), was defined as a patient attending an MHS visit (including psychiatry, behavioral medicine, or substance use treatment) within 60 days after the index date.

Figure

Putative confounding or explanatory variables were assessed at patient and provider levels. Patient age, sex, self-reported race/ethnicity, and primary language were obtained from administrative databases. Patient psychiatric comorbidities (posttraumatic stress disorder [PTSD] or depression) diagnosed during the 2 years before the index date at any outpatient visit were assessed from electronic records. Prior utilization was defined as any utilization during the 12 months before the index date. Provider age, race/ ethnicity, and sex were determined from medical group records. The diagnosing clinic was defined as primary care (internal medicine or family practice), obstetrics and gynecology (OB/GYN), ED (or urgent care), or other. Language concordance was defined as the provider speaking the patient’s preferred language. Mental health services referral (hereafter, e-referral) was defined as entry of electronic referral by the diagnosing provider within 60 days after the index date. Within Kaiser, members may access MHS directly by calling the mental health intake line, as well as through provider-initiated referral. As shown in the , our definition is conservative, as it excludes cases in which providers called the MHS intake line with the patient in the office, advised patients to call MHS on their own, or escorted the patient to colocated MHS providers in primary care clinics.

Statistical Analysis

We calculated the frequency of utilization among the entire cohort and in strata defined by referral and by patient and provider characteristics. The c2 test was used to examine differences in utilization by level of each characteristic.

We then used multivariate generalized estimating equation (GEE) logistic regression models that included patient and provider variables to determine which patient or provider factors explained the differences observed in utilization. We used the GEE method to account for clustering (correlation) of data by provider. Some providers identified multiple IPV-affected women. Therefore, provider characteristics (age, sex, race/ ethnicity, and clinic type) may be correlated within providers (concordance). Our outcome (ie, how likely a provider is to ensure that IPV-affected women are referred to and use MHS) may also be correlated within providers. The GEE provides a way to build a generalized linear model using correlated data and returning robust standard errors, even when the covariance matrix specified in the model is not correctly specified.19

Patient-level variables included age, race/ethnicity (white, black, Latina, Asian, or other or missing), primary language (English, Spanish, or other), prior mental health diagnosis (PTSD with or without depression, depression only, or none), and prior utilization. Provider-level variables included provider age (quartiles), sex, race/ethnicity (white, black, Latina, Asian, or other or missing), clinic type (primary care, OB/GYN, ED, or other), and e-referral. In addition, models included dummy variables for calendar year of IPV identification to control for any temporal trends.

Because odds ratios (ORs) do not approximate the risk ratio when the outcome is common, as is the case in our study, we also calculated predicted probabilities for each category of each covariate, using least squares means. We present these results to facilitate interpretation of the predicted difference in probability of the outcome (utilization) in multivariate models. Commercially available statistical software (SAS, version 9.1; SAS Institute Inc, Cary, NC) was used for all analyses.

RESULTS

Study Subject Characteristics

Table 1

Among 6870 adult women with first IPV identification between May 2004 and December 2009, the mean (SD) age was 37.8 (11.8) years, and there was significant racial/ethnic diversity (). Almost 90% spoke English as their primary language, whereas 8% spoke Spanish and 2% other languages. A language-concordant physician identified 92%. Prior mental health diagnoses were common, with 25% having prior PTSD or depression. The mean age of diagnosing providers was 46 years; 64% of diagnosing providers were female. Most IPV-affected women were identified by providers working in primary care (48%), with fewer in OB/GYN (30%) or EDs (18%). The identifying provider performed e-referral to MHS within 60 days of IPV identification in 849 (12%) of 6870 women.

Utilization

Table 2

Among 6870 women with first IPV identification, 2527 (37%) used MHS, including 550 patients with e-referral and 1977 patients without e-referral (). All patient and provider characteristics were significantly associated (P <.003) with utilization among the entire cohort and among patients without e-referral. For example, among the entire cohort, having a prior PTSD (61%) or depression (46%) diagnosis was associated with higher probability of utilization than having no previously diagnosed mental health comorbidity (33%). English-speaking patients (37%) were more likely to use MHS than those who spoke Spanish (32%) or other languages (28%), and patient—physician language concordance was associated with a 6% absolute higher utilization frequency. Patient age was associated with utilization, with highest utilization among patients aged 45 to 65 years (40%) and lowest utilization among those aged 25 to 34 years (34%). Among provider characteristics, older provider age was associated with higher frequency of utilization (45% vs 37% for the youngest quartile, P <.001). Patients of female providers (40%) were more likely to use MHS than those of male providers (32%). Utilization was higher among patients diagnosed in primary care (41%) and OB/GYN (37%) clinics than among those diagnosed in EDs (21%).

Among patients with e-referral, utilization varied by patient race/ethnicity (P = .06), with white women most likely (72%) and Latinas least likely (58%) to use MHS (Table 2). Provider age was associated with utilization (P = .05), being highest among providers aged 46 to 54 years (69%) and lowest among providers younger than 37 years (62%). Utilization was highest among cases identified in primary care (68%), followed by OB/GYN (63%) and EDs (53%) (P = .007). Although the associations of prior mental health diagnosis, patient language, patient—provider language concordance, and provider sex did not reach statistical significance, the absolute differences in rates of utilization between levels of each of these variables were similar to differences among patients without e-referral.

Multivariate GEE Models

Table 3

In multivariate GEE models of utilization that controlled for clustering of observations by provider (), e-referral was the strongest predictor of utilization (OR, 4.40; 95% confidence interval [CI], 3.66-5.28). Odds of utilization were lower among blacks (OR, 0.71; 95% CI, 0.57-0.89) and Latinas (OR, 0.62; 95% CI, 0.41-0.95) compared with whites. Furthermore, Spanish speakers had lower odds of utilization compared with English speakers (OR, 0.71; 95% CI, 0.57-0.89). Odds of utilization were higher among patients with prior mental health morbidity. Patients identified by the oldest 2 quartiles of providers had modestly increased odds of utilization (OR, 1.33; 95% CI, 1.07-1.65 for the oldest providers). Utilization was not significantly different between those identified in Ob/gyn settings compared with those identified in primary care, but identification in EDs was associated with an OR for utilization of 0.46 (95% CI, 0.37-0.59).

As expected, we found similar point estimates but wider CIs using GEE models vs using multivariate logistic regressionmodels that did not account for clustering of observations.¹⁹

The finding that each association remained significant when using GEE models suggests that the findings are robust.

DISCUSSION

Among 6870 adult women with first identification of IPV, 37% used MHS within 60 days of identification. In multivariate models that included patient-level and provider-level variables, receiving provider-initiated electronic referral was the strongest predictor of utilization. Odds of utilization were lower among black and Latina patients, Spanish-speaking patients, and those identified in EDs. Provider demographic characteristics were unassociated with utilization, other than a modest association with older provider age.

To the best of our knowledge, this is the first published study to assess predictors of MHS utilization among IPV-affected women identified in the healthcare setting. It is also the only study to date offering information on both provider and patient characteristics associated with utilization. The literature on referral rates of patients with other types of trauma is sparse. In one study²⁰ of patients hospitalized because of traumatic physical injury, 19% received MHS referral, and 51% of those used MHS. These data were based on patient self-report and excluded patients with injuries caused by domestic violence. The lower referral rate in our study (12%) likely reflects our conservative definition of referral (Figure). The high utilization rate among referred patients may reflect the straightforward patient access to MHS within Kaiser.²⁰

Two prior studies^21,22 examined patient factors related to help-seeking behavior, including obtaining MHS, among IPV-affected patients. In a study²³ of men and women with PTSD,

factors such as patient need (symptoms causing distress) and predisposing characteristics (age, sex, race/ethnicity, and language) were found to be important in determining receipt of MHS treatment. Patients experiencing high levels of distress were more likely to receive treatment. Our study may reflect similar patterns of help-seeking behavior, as patients with prior diagnosis of PTSD or depression were more likely to use MHS.

There are several potential explanations for our finding that women who were Spanish speaking, black, or Latina were less likely than English-speaking or white women to use MHS. It was previously found that the most common reasons for IPV-affected women to accept referral were unhappiness with their situation, fear for their physical well-being, concern about their children who witnessed abuse, and the suggestion by a healthcare provider that their symptoms could be related to the abuse.¹⁸ Although many persons who experience mental health disorders do not seek treatment, this is particularly true among minority populations.²⁴ Racial/ethnic minorities may differ in their tendency to associate mental health problems with their symptoms. If clinicians do not discuss connections between IPV and a patient’s symptoms, patients may not anticipate benefit in MHS follow-up. One study²⁵ of patients with depression showed less clinician—patient communication and rapport building among some racial/ethnic minority patients. This could also affect the likelihood of patients’ using MHS. In addition, minority or non–English-speaking groups may be more likely than white English-speaking patients to choose other support networks (eg, clergy or advocacy agencies) as alternatives to MHS.²⁴ It is possible that women with prior mental health diagnosis were more likely to accept care because of higher levels of psychological distress. Alternately, higher utilization might reflect return to a previously established mental health provider.

Our study has several limitations. First, some women who experience IPV are never identified in the medical setting. Our study did not investigate this phenomenon. Second, our definition of referral is conservative, as we were unable to determine which women were referred through in-clinic telephone calls for immediate MHS intake or through a provider’s verbal directions to call the MHS intake line. Third, we lack data to identify women who obtained MHS or social services outside of Kaiser. Our estimation of predictors of utilization would be biased if certain groups of women were more likely to seek outside services. However, because MHS within Kaiser are a covered benefit for most members, there is a cost incentive for women to use services within Kaiser. Fourth, we did not collect information from women about why they did or did not use MHS. Fifth, data on sex, race/ethnicity, and age are missing from 15% of diagnosing providers. Almost all of these were working per diem in EDs. Sixth, we lacked data on duration, type (physical, sexual, or emotional), and severity of IPV. It is possible that these characteristics affected the likelihood of utilization.

Our study has multiple strengths. First, we present data from a large cohort of women identified as IPV survivors at Kaiser, which provides medical care to more than 3 million individuals. Our findings have applicability to a large segment of the population. Second, our electronic databases allow complete ascertainment of clinically recognized IPV, electronic referral, and utilization within Kaiser. Our ability to fully capture utilization data obviates the possibility of recall bias.

Our findings suggest several potential targets for interventions to increase rates of utilization among women experiencing IPV who present to the healthcare system. First, there is a particular need to focus on improving rates of provider referral, as the present study and previous investigations have found that provider referral has the strongest relationship to utilization, far outweighing that of patient characteristics.²⁰ Ongoing training for providers about what the health consequences of IPV are and how to refer to appropriate services may improve referral rates. Second, interventions need to focus on increasing utilization among non—English-speaking and nonwhite women. Although providers are more likely to refer these patients, they are less likely to use services. This suggests that addressing language barriers to care may be a useful intervention. Alternately, these patients may prefer community-based rather than medical system–based care. Third, lower utilization among black and Latina women suggests that services might be, or might be perceived to be, culturally inappropriate. Fourth, additional training of providers, particularly those who are younger or are practicing in EDs, might increase rates at which providers refer IPV survivors to MHS. After controlling for e-referral, utilization did not differ between OB/GYN and primary care clinics, although it remained significantly lower for EDs.

Additional study should determine the comparative effectiveness of strategies to improve provider referral, which is primarily dependent on provider knowledge and behavior, as well as how to ensure timely access to culturally and linguistically appropriate services. Furthermore, future community-based participatory research might best determine how to deliver appropriate services to members of racial/ethnic, cultural, or linguistic minorities and to women of different ages. In particular, it will be important to further examine MHS utilization by race/ethnicity and by other social factors. There is a need to interview women who do not use MHS after referral, to determine whether they are seeking medical services outside of the Kaiser system, obtaining services from community-based organizations, or seeking help from social networks, including family, friends, or clergy. It is important to determine if these findings can be duplicated in other healthcare systems that have implemented a coordinated approach to IPV prevention.

Author Affiliations: From the Division of Research (ATA) and the Family Violence Prevention Program (BRM), Kaiser Permanente, Oakland, CA.

Funding Source: This study was funded by Kaiser Permanente Community Benefits Fund.

Author Disclosures: The authors (ATA, BRM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ATA, BRM); acquisition of data (ATA); analysis and interpretation of data (ATA, BRM); drafting of the manuscript (ATA, BRM); critical revision of the manuscript for important intellectual content (ATA, BRM); obtaining funding (ATA, BRM); and supervision (BRM).

Address correspondence to: Ameena T. Ahmed, MD, MPH, Division of Research, Kaiser Permanente, 200 Broadway, Oakland, CA 94612. E-mail: ameena.t.ahmed@kp.org.

1. Tjaden P, Thoennes N. Prevalence, Incidence, and Consequences of Violence Against Women: Findings From the National Violence Against Women Survey. Washington, DC: US Dept of Justice; 1998. Publication NCJ 172837.

2. Tjaden P, Thoennes N. Extent, Nature, and Consequences of Intimate Partner Violence: Findings From the National Violence Against Women Survey. Washington, DC: US Dept of Justice; 2000. Publication NCJ 181867.

3. Breiding MJ, Black MC, Ryan GW. Prevalence and risk factors of intimate partner violence in eighteen U.S. states/territories: 2005. Am J Prev Med. 2008;34(2):112-118.

4. Bonomi AE, Thompson RS, Anderson M, et al. Intimate partner violence and women's physical, mental, and social functioning. Am J Prev Med. 2006;30(6):458-466.

5. Campbell JC. Health consequences of intimate partner violence. Lancet. 2002;359(9314):1331-1336.

6. Coker AL, Smith PH, Bethea L, King MR, McKeown RE. Physical health consequences of physical and psychological intimate partner violence. Arch Fam Med. 2000;9(5):451-457.

7. Rivara FP, Anderson ML, Fishman P, et al. Healthcare utilization and costs for women with a history of intimate partner violence. Am J Prev Med. 2007;32(2):89-96.

8. National Center for Injury Prevention and Control. Costs of Intimate Partner Violence Against Women in the United States. Atlanta, GA: Centers for Disease Control and Prevention; 2003.

9. American Congress of Obstetricians and Gynecologists. Screening tools: domestic violence. http://www.acog.org/departments/dept_ notice.cfm?recno=17&bulletin=585. Accessed September 15, 2010.

10. Basile KC, Hertz MF, Back SE. Intimate Partner Violence and Sexual Violence Victimization Assessment Instruments for Use in Healthcare Settings. Version 1.0. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention; 2007.

11. Family Violence Prevention Fund. National Consensus Guidelines on Identifying and Responding to Domestic Violence Victimization in Health Care Settings. San Francisco, CA: Family Violence Prevention Fund; 2004.

12. McCaw B, Berman WH, Syme SL, Hunkeler EF. Beyond screening for domestic violence: a systems model approach in a managed care setting. Am J Prev Med. 2001;21(3):170-176.

13. McCaw B, Kotz K. A 'systems model' response to intimate partner violence in the healthcare setting. In: Michell C, ed. Intimate Partner Violence: A Health-Based Perspective. New York, NY: Oxford University Press; 2009:419-428.

14. Libet M. Frequent mental distress and desire for help among California women experiencing intimate partner violence: 2003-2004. In: Data Points: Results From the California Women's Health Survey. Sacramento, CA: California Dept of Health Services Maternal, Child and Adolescent Health/Office of Family Planning Branch Office of Women's Health; 2006:4.

15. Coker AL, Davis KE, Arias I, et al. Physical and mental health effects of intimate partner violence for men and women. Am J Prev Med. 2002;23(4):260-268.

16. Caetano R, Cunradi C. Intimate partner violence and depression among whites, blacks, and Hispanics. Ann Epidemiol. 2003;13(10): 661-665.

17. Scholle SH, Rost KM, Golding JM. Physical abuse among depressed women. J Gen Intern Med. 1998;13(9):607-613.

18. McCaw B, Bauer HM, Berman WH, Mooney L, Holmberg M, Hunkeler E. Women referred for on-site domestic violence services in a managed care organization. Women Health. 2002;35(2-3):23-40.

19. Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol. 2003;157(4):364-375.

20. Wong EC, Schell TL, Marshall GN, Jaycox LH, Hambarsoomians K, Belzberg H. Mental health service utilization after physical trauma: the importance of physician referral. Med Care. 2009;47(10):1077-1083.

21. Schreiber V, Renneberg B, Maercker A. Seeking psychosocial care after interpersonal violence: an integrative model. Violence Vict. 2009;24(3):322-336.

22. Liang B, Goodman L, Tummala-Narra P, Weintraub S. A theoretical framework for understanding help-seeking processes among survivors of intimate partner violence. Am J Community Psychol. 2005;36(1-2):71-84.

23. Koenen KC, Goodwin R, Struening E, Hellman F, Guardino M. Posttraumatic stress disorder and treatment seeking in a national screening sample. J Trauma Stress. 2003;16(1):5-16.

24. Zuvekas SH, Fleishman JA. Self-rated mental health and racial /ethnic disparities in mental health service use. Med Care.2008;46(9): 915-923.

25. Ghods BK, Roter DL, Ford DE, Larson S, Arbelaez JJ, Cooper LA. Patient-physician communication in the primary care visits of African Americans and whites with depression. J Gen Intern Med. 2008;23(5):600-606.

Download PDF: Mental Health Services Utilization Among Women Experiencing Intimate Partner Violence