Introduction
HIV treatment is a contemporary example of how medical advancements has transformed the lives of people. First described in the 1980s [1], HIV infection shifted from a life-threatening disease to a chronic infection by the early 2000s. Although there is still no cure for HIV, patients with access to quality HIV care can have a near normal life expectancy [2–6] compared with the 2-year survival expectancy of the 1980s [7]. Although most of the advancements in HIV care are based on pharmacotherapeutics, quality HIV care requires a multifaceted approach as comorbidity and opportunistic illnesses need to be taken into account. Accordingly, there are multiple indicators for quality HIV care in addition to appropriate drug therapy.
Healthcare quality measures are performance indicators used to rate the quality of care provided to patients within the healthcare system. They are typically applied in quality improvement initiatives, performance-based reimbursements, and public reporting programs. A number of organizations, such as the US Department of Health and Human Services (DHHS) [8], Veteran's Administration [9], the New York State Department of Health AIDS Institute (NYSDHAI) [10], and Kaiser Permanente [11] have developed quality measures for HIV care. The most coordinated work was undertaken in 2007 by DHHS in collaboration with the HIV Medicine Association, National Committee on Quality Assurance, and American Medical Association. Through this partnership, 17 measures were adopted with 15 endorsed by the National Quality Forum (NQF) [12].
NQF is a consensus-based healthcare organization that develops evidence-based quality measures and sets standards for healthcare measurement in the United States. The endorsed measures for HIV care include: retention in care (seen at least twice at least 60 days apart), CD4+ cell count (at least twice annually), influenza immunization and screening for syphilis, injection drug use, and high-risk sexual behavior (each annually), pneumococcal immunization and screening for gonorrhea/chlamydia, tuberculosis, and hepatitis B and hepatitis C infections (each at least once), hepatitis B vaccination order (if appropriate), PCP prophylaxis (if CD4+ cell count less than 200 cells/μl), appropriate antiretroviral prescription, viral control (if prescribed antiretroviral therapy). Screening for high-risk sexual behaviors covers sexual encounters versus abstinence, number and sex of partners, partners’ HIV serostatus, type of sexual activity (oral, vaginal, or anal), and condom use [13].
Evidence-based quality measures are intended to improve health outcomes. Therefore, the HIV care quality indicators are expected to be associated with improved health outcomes for patients with HIV. The association between HIV care and health outcomes have been accessed in several studies with health outcomes defined using both disease-specific measures, such as viral suppression [14,15], AIDS incidence [14,16], or averted transmissions [14] and general measures, such as all-cause mortality [17,18]. Healthcare resource utilization and costs may also be a general measure of health outcomes as this has been shown to be inversely related to health status or healthcare need, such as disease or symptom severity [19–21]. The purpose of our study was to determine the association between individual HIV care quality indicators and health outcomes measured as healthcare resource utilization and costs. We hypothesized that HIV care will be associated with reduced healthcare resource utilization and costs.
Methods
Texas Medicaid medical and prescription claims data from January 2012 to August 2016 were used. January 2012 was chosen as the start date because antiretroviral treatment recommendations were updated to include all patients irrespective of CD4+ cell count in 2012 [22]. August 2016 was the latest date for which data was available at the time of data acquisition. The cohort included patients diagnosed with HIV [ICD-9-CM code = 042.XX (HIV disease), 043.XX (HIV infection causing other specified conditions), 044.XX (other HIV infection), V08 (asymptomatic HIV); ICD-10-CM code = B20.XX (HIV disease resulting in infections and parasitic infections), B21.XX (HIV disease resulting in malignant neoplasms), B22.XX (HIV disease resulting other specified diseases), B23.XX (HIV disease resulting in other conditions), B24.XX (unspecified HIV disease), Z21 (asymptomatic HIV)] between July 2012 and August 2014. This HIV diagnosis identification window was chosen to allow for a baseline period prior to HIV diagnosis and a post-diagnosis period to measure access to HIV care and outcomes. The initial diagnosis date or index date is used as the reference point throughout this article. The baseline period was the 6-month pre-diagnosis period. Quality indexes were measured during the first post-diagnosis year. Outcomes were measured in the second post-diagnosis year, which is, henceforth, referred to as the follow-up period.
Patients included had at least two HIV-related medical claims in the identification window (Appendix 1, https://links.lww.com/QAD/B560), were between 13 and 62 years at initial diagnosis, and were continuously enrolled from 6 months prior to 12 months after the initial diagnosis date. Enrolment eligibility throughout the follow-up year was not required in order to minimize the exclusion of patients who do not survive this period. Patients were excluded if they had a HIV-related medical claim in the pre-diagnosis period, or had no medical or prescription claim in the follow up period.
Outcomes variables were emergency department visits, inpatient hospitalization, prescription count, and all-cause healthcare costs. Emergency department visit and inpatient hospitalization were operationalized as the total number of visits and hospital days from any cause in the follow-up year, respectively. Prescription count was operationalized as the total number of prescription claims filled in the follow-up year, factoring in all medications filled and the number of times the medications were filled. All-cause healthcare costs were operationalized as the total healthcare expenditure for all health services in the follow-up year. To account for patients with less than 12 months of follow-up, the outcomes variables were annualized by dividing by the number of follow-up months and multiplying by 12.
The main independent variables were 11 quality indexes based on measures endorsed by the NQF [12] (Appendix 2, https://links.lww.com/QAD/B561). They were CD4+ cell count monitoring, syphilis screening, chlamydia screening, gonorrhea screening, hepatitis B screening, hepatitis C screening, tuberculosis (TB) screening, influenza vaccination, pneumococcal vaccination, retention in care, and HAART initiation. We operationalized syphilis, chlamydia, gonorrhea, hepatitis B (HBV), hepatitis C (HCV), and TB screenings, influenza and pneumococcal vaccinations as having at least one relevant claim for the indicator in the first post-HIV diagnosis year. We defined CD4+ cell count monitoring and retention in care as having at least two relevant claims and two claims for office visits in the first post-HIV diagnosis year at least 60 days apart, respectively. HAART initiation was defined as having prescriptions for three or more antiretroviral drugs in the index year filled within 30 days of each other with at least two being from the nucleoside reverse transcriptase inhibitor class. Covariates included age at index [23,24], sex [23,24], AIDS diagnosis [25], chronic HCV infection in the index year [26,27], and baseline healthcare cost [28,29] as these have been shown to be associated with general and HIV-related health outcomes. AIDS diagnosis was a composite variable defined as having a CD4+ cell count less than 200 cells/μl or a diagnostic claim for one or more AIDS-defining illnesses in the first post-HIV diagnosis year [30] (Appendix 3, https://links.lww.com/QAD/B562). As the Medicaid claim data does not provide laboratory values, having prescription claims (sulfamethoxazole or pentamidine) for Pneumocystis carinii pneumonia (PCP) prophylaxis served as a proxy for CD4+ cell count less than 200 cells/μl based on clinical recommendations for preventing opportunistic illnesses [31].
Statistical analyses
Means/standard deviations and frequencies/percentages were used to describe the continuous and dichotomous/categorical variables. All models involving healthcare utilization had significant dispersion parameters. As such, generalized linear models (GLMs) with log link and negative binomial distribution were used to address related objectives. For outcomes where greater than 20% of cases had zero values, a zero-inflated negative binomial model was used. GLMs with log link and gamma distribution were used to address objectives involving healthcare costs. Separate models were run for each quality indicator before (model 1) and after (model 2) controlling for covariates. To account for variable supply of antiretrovirals, a sensitivity analysis for HAART initiation using proportion of days covered with standard three-drug HAART combination during the first post-HIV diagnosis year was compared with the original adjusted model for total number of prescription claims. A sensitivity analysis for retention in care was also conducted with retention in care redefined as having at least one office visit in each quarter. The a priori global alpha level of statistical significance was set at P less than 0.05. As there were 44 GLM tests (11 quality indexes multiplied by four outcome variables) for models 1 and 2 each, the level of significance for each test was determined using Bonferroni correction and was set at P less than 0.001 (0.05/44). Analyses were conducted using SAS 9.4.
Results
The initial cohort had 5075 patients with index date between July 2012 and August 2014 and aged between 13 and 62 years. An additional 2074 were excluded based on enrolment eligibility (1561), gender miscoding (6), and lack of follow-up data (507). Compared with the excluded 2074 patients, the final cohort (N = 3001) were older (39.5 ± 13.1 vs. 35.7 ± 12.6 years, P < 0.0001), more likely to be male (49.6 vs. 46.5%, P = 0.03), and more likely to be symptomatic at index (73.3 vs. 70.5%, P = 0.03). Table 1 summarizes the characteristics of the final cohort and rate of access to HIV care in the index period. The average age at index was 39.5 ± 13.1 years with the majority (69.6%) between 25 and 54 years. About half the patients were women (50.4%) and had indications for AIDS diagnosis (50.7%). Eleven percent of the included patients had chronic HCV infection. Sixty-eight percent and 47% of the patients were retained in care and monitored for CD4+ cell count, respectively. Fifty-six percent of the patients received any antiretroviral therapy with 46% meeting the HAART initiation criterion. Sixty-five percent, 39, and 41% of the patients received syphilis, chlamydia, and gonorrhea screenings, respectively, and 46, 46, and 25% received hepatitis B, hepatitis C, and tuberculosis screenings. Regarding vaccination, 33 and 13% of the patients received influenza and pneumococcal vaccinations, respectively.
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Table 1: Characteristics of Texas Medicaid patients diagnosed with HIV and access to HIV care for according to HIV care quality indicators (N = 3001).
In the second post-diagnosis year, patients were followed for study outcomes for an average of 10.4 ± 3.0 months (median = 12 months). Annualized average all-cause healthcare cost was $27 168.34 ± $45 509.37 (median = $19 927.00). Patients were hospitalized (annualized) for 6.0 ± 18.7 (median = 0.0) days on average. There were 2.7 ± 5.4 (median = 1.0) emergency department visits and 40.1 ± 45.1 (median = 25.0) prescription claims per patient (annualized).
Forty-two, 67, and 14% of patients had no emergency department visits, hospitalizations, or prescription claims, respectively. Hence, zero-inflated Poisson modeling was used for emergency department visits and hospitalizations whereas generalized linear modeling with Poisson distribution and log-link function regression was used for prescription count.
Tables 2 and 5 show the unadjusted and adjusted models of the associations between the individual quality indexes and emergency department visits, inpatient hospitalizations, prescription count, and all-cause healthcare costs, respectively. CD4+ cell count monitoring, syphilis, chlamydia, and HAART initiation were significantly associated with reduced emergency department visits in the unadjusted models (P < 0.001). Association with reduced emergency department visits persisted for CD4+ cell count monitoring and HAART initiation after controlling for covariates (P < 0.001). Patients who were monitored for CD4+ cell count had 27% lower rate of emergency department visits compared with those who were not [incidence rate ratio (IRR) = 0.73, 95% confidence interval (95% CI) = 0.64–0.84]. Patients who initiated HAART had 26% lower rate of emergency department visit compared with those who did not (IRR = 0.74, 95% CI = 0.65–0.83).
Table 2: Association between individual HIV care quality indicators and emergency department visita (N = 3001).
Table 3: Association between individual HIV care quality indicators and inpatient hospitalizationa (N = 3001).
Table 4: Association between individual HIV care quality indicators and number of prescription claimsa (N = 3001).
Table 5: Association between individual HIV care quality indicators and all-cause healthcare costa (N = 3001).
Syphilis, chlamydia, and gonorrhea screenings and influenza vaccination were significantly associated with reduced inpatient hospitalization in the unadjusted model (P < 0.001) but only influenza vaccination was associated with reduced inpatient hospitalization after controlling for covariates. Hospitalization rate was 42% lower for those who received influenza vaccination compared with those who did not (IRR = 0.58, 95% CI = 0.46–0.73).
CD4+ cell count monitoring, influenza vaccination, and HAART initiation were associated with increased prescription claims in both the unadjusted and adjusted models (P < 0.001). Prescription count was 30% higher for those who were monitored for CD4+ cell count compared with those who were not (IRR = 1.30, 95% CI = 1.18–1.42). Prescription count was 25% higher for those who received influenza vaccination compared with those who did not (IRR = 1.25, 95% CI = 1.13–1.37). Prescription count was 41% higher for those who initiated HAART compared with those who did not (IRR = 1.41, 95% CI = 1.28–1.54). There was no association between TB screening and prescription count but a significant association was observed after controlling for covariates (IRR = 1.20, 95% CI = 1.08–1.33, P < 0.001).
Discussion
In our study, we assessed access to HIV care among newly diagnosed Medicaid-enrolled patients, which we operationalized based on 11 HIV-specific quality measures endorsed by the National Quality Forum (NQF). Overall, HIV care was sub-optimal even though rates were higher compared with some other settings. For example, a study using 2011 to 2012 Truven MarketScan data showed the screening rate for syphilis and chlamydia/gonorrhea to be 51 and 22% [32], respectively, compared with 64 and 40% recorded in our study. Although Medicaid-enrolled patients have historically had poorer access to care when compared with other insurance types, the sub-optimal quality of care received by our study population may also be a reflection of patients’ unwillingness to initiate therapy as they come to terms with their diagnosis.
Screening for STIs has been shown to vary by HIV risk group with MSM having higher syphilis screening rates and women having comparable or higher gonorrhea/chlamydia screening rates [33,34]. We compared screening rates by sex in a subgroup analysis. Compared with men, gonorrhea (49.7 vs 31.7%, P < 0.0001) and chlamydia (47.6 vs. 30.5%, P < 0.0001) screening rates were higher among women whereas there was no significant difference (65.1 vs. 64.4%, P = 0.688) by sex in syphilis screening rates. Compared with the 2012–2013 rates in the Medical Monitoring Project (MMP) [33], gonorrhea/chlamydia (47–49 vs. 36–39%) and syphilis (65 vs. 52–59%) screening rates were higher among Texas Medicaid women. We did not have access to data on sexual orientation to compare MSMs with males who have sex with women (MSWs). However, figures from the MMP indicate that gonorrhea/chlamydia (37–39 vs. 32–33%) and syphilis (65–69 vs 52–59%) screening rates are higher among MSMs compared with MSWs.
We found CD4+ cell count monitoring and HAART initiation to be associated with reduced emergency department visits in the follow-up period and influenza vaccination associated with reduced inpatient hospitalization. CD4+ cell count monitoring is a complimentary service as it is necessary to evaluate the effectiveness of first-line drug therapy and make decisions on whether to switch or maintain therapy in newly diagnosed patients. Reductions in emergency department visits is, therefore, more likely an effect of HAART initiation. The benefits of early initiation of treatment on morbidity and mortality have been established in previous studies [35–37], including a landmark study by Lundgren et al.[38]. The current study underscores the role of early treatment in reducing morbidity by showing an association between HAART initiation and emergency department visits.
Our finding of an association between influenza vaccination and inpatient hospitalization is more impressive than findings in the literature, which indicate that, although influenza vaccination is effective in reducing the incidence of influenza-like illness, associations with hospitalization may be debatable. For example, a cross-sectional study found that only 5% of influenza-vaccinated HIV-infected persons compared with 22% of unvaccinated persons required hospitalization and emergency department evaluation for influenza-like illness [39]. However, the sample size was too small to detect significance (N = 65). Similarly, two other studies (N = 102 and 328) found significantly higher rates of influenza symptoms in vaccinated persons compared with unvaccinated persons (P < 0.05) but no hospitalization was recorded [40,41]. This difference between the cited studies and the current study may be because of differences in settings and timing. The aforementioned studies were small clinical studies conducted prior to the 2009 influenza pandemic. Although absolute estimates fluctuate from year to year, a study conducted using 2005 through 2011 data from the Centers for Disease Control and Prevention (CDC) Emerging Infections Program suggests that influenza hospitalizations were averted nationally by vaccination, thereby supporting findings of the present study that influenza vaccination reduced inpatient hospitalization [42].
No indicator was associated with reductions in prescription count or healthcare costs after adjustment for covariates. In fact, CD4+ cell count monitoring, HAART initiation, tuberculosis screening, and influenza immunization were associated with increased prescription count and costs. The observation with influenza immunization may be an indication of the healthy user effect where patients who receive one preventive service (annual influenza vaccination) are also likely to partake in other health behaviors (adhere to medication) [43]. The observed increase in prescription count with tuberculosis screening may be related to additional treatment for those who tested positive. In addition to antiretroviral therapy, isoniazid for 9 months, supplemented with pyridoxine, is recommended for individuals with latent TB infection to prevent active TB disease [31]. This has the potential to increase the number of prescriptions as well as overall healthcare costs. It is not surprising that patients who initiated HAART had a higher number of prescriptions as the filling of prescriptions implies that patients are receiving drug treatment for HIV. Our sensitivity analysis supports this as it showed a three-fold increase in the number of HIV-related claims when compared with all-cause prescription claims (IRR = 4.84 vs. 1.43). Overall, patients who initiated HAART in the index year had a greater proportion of HIV-related costs in the follow-up year (74 vs. 30%, full data not shown). Among the HAART initiators, 72% of HIV-related costs was on antiretroviral medication. US Medicaid spent $1.6 billion on antiretrovirals alone in 2007 [44] and estimates show that antiretroviral drugs constituted 30% of annual Medicaid spending [45]. Early treatment initiation has been shown to be associated with higher lifetime costs in various settings but the incremental costs have often been deemed cost-effective [46–48]. When indirect effects such as prevented transmissions are considered, early treatment can be cost-saving [49].
Because the NQF definition for retention may not sufficiently discriminate between patients especially if most patients had their office visits within 90-day window, we conducted a sensitivity analysis where retention was defined as having at least one office visit in each quarter. Similar to the NQF-based definition, retention in care was not associated with inpatient hospitalization, emergency department visit, or healthcare costs. However, it was associated with increased prescription count, which may be because of the healthy user effect. We also performed sensitivity analysis for HAART initiation using proportion of days covered in the index year. HAART use with this definition was significantly associated with reduced hospitalization and emergency department visit and increased prescription claims, and all-cause healthcare costs.
The inclusion of only Texas Medicaid-enrolled patients in our study implies that results may not be generalizable to Medicaid patients in other states or to commercially insured patients. The quality measures, pneumococcal immunization and screenings for gonorrhea, chlamydia, tuberculosis and hepatitis B and C infections may have been underestimated as these are recommended at least once. It is possible that patients had received these services prior to HIV diagnosis and their provider has access to this information. However, considering that these standards were set within the context of HIV care, it is reasonable to expect that these services will be provided, especially following HIV diagnosis. Although it was expected that the impact of screening would be greater for those who tested positive, lack of screening results precluded the conducting of subgroup analysis. In addition to the lack of data on sexual orientation and screening results, our dataset had more than 40% missing data on race/ethnicity, which is generally associated with health outcomes. We decided not to control for race/ethnicity as we did not expect reliable estimates with imputation because of the high level of missingness. Additionally, although previous studies have shown most of the quality indexes to be cost effective, the short follow-up period of the current study may have precluded observation of an association between most of the quality indexes and reduced healthcare resource utilization and costs. Our sensitivity analysis using 2-year follow-up mostly showed similar results but syphilis and chlamydia screening were significantly associated with reduced ED visits (Appendix 4, https://links.lww.com/QAD/B563).
In conclusion, HAART may reduce use of emergency care services as early as 1 year following initiation. Studies with longer follow-up are needed to determine the impact of access to quality HIV care on healthcare resource utilization/costs as a measure of health status. This will lend support to the body of literature showing associations with HIV-specific outcomes and mortality.
Acknowledgements
S.O.N. conceptualized the study. J.C.B. and K.H.F. contributed to the study design. J.N.B. provided clinical input. K.A.L. and J.P.W. provided statistical input. S.O.N., J.C.B., K.H.F., J.N.B., and K.A.L. contributed to the development of the manuscript.
Conflicts of interest
There are no conflicts of interest.
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