Applying Lessons From Behavioral Health Integration to Social Care Integration in Primary Care.

Interest and incentives are increasing around strategies whereby the health care sector can better identify and address patients' social and economic needs in the context of primary care delivery. This interest is likely to accelerate during the economic recession following the OVID-19 pandemic. Yet effective and sustainable strategies for integrating social care practices (eg, patient-facing social risk screening and activities to address identified needs) have not been clearly established. Lessons learned from more than 2 decades of research on behavioral health integration could be applied to efforts to integrate social care into primary care. In this article, we synthesize learnings from primary care and behavioral health care integration, and translate them into organizing principles with the goal of advancing social care integration practices to improve the health of both patients and communities.

Impact of Social Needs Navigation on Utilization Among High Utilizers in a Large Integrated Health System: a Quasi-experimental Study.

BACKGROUND: Programs addressing social determinants of health for high-utilizing patients are gaining interest among health systems as an avenue to promote health and decrease utilization. OBJECTIVE: To evaluate impacts of a social needs screening and navigation program for adult predicted high utilizers on total medical visit utilization. DESIGN: A prospective, quasi-experimental study using an intent-to-treat propensity-weighted difference-in-differences approach. Stratified analyses assessed intervention effects among three low-socioeconomic status sub-samples: patients in low-income areas, in low-education areas, and with Medicaid insurance. PARTICIPANTS: Predicted high utilizers-patients predicted to be in the highest 1% for total utilization in a large integrated health system. INTERVENTION: A telephonic social needs screening and navigation program. MAIN MEASURES: Primary difference-in-difference analyses compared total visit count utilization, including outpatient, emergency department (ED), and inpatient utilization, between the intervention and control groups at both in-network and out-of-network facilities. Prevalence of social needs among sample patients and their connection rates to social needs resources are also described. KEY RESULTS: The study included 34,225 patients (7107 intervention, 27,118 control). Most (53%) patients screened reported social needs, but only a minority (10%) of those with a need were able to connect with resources to address these needs. Primary analysis found total utilization visits decreased 2.2% (95% CI - 4.5%, 0.1%; p = 0.058) in the intervention group. Stratified analyses showed decreases in total utilization for all low-socioeconomic status subgroups receiving the intervention compared with controls: - 7.0% (95% CI - 11.9%, - 1.9%; p = 0.008) in the low-income area group, - 11.5% (- 17.6%, 5.0%; p < 0.001) in the low-education area group, and - 12.1% (- 18.1%, - 5.6%; p < 0.001) in the Medicaid group. CONCLUSIONS: Social needs navigation programs for high-utilizing patients may have modest effects on utilization for the population overall. However, significant decreases in utilization were found among low-socioeconomic status patients more likely to experience social needs.

Health facility-based malaria surveillance: the effects of age, area of residence and diagnostics on test positivity rates.

BACKGROUND: The malaria test positivity rate (TPR) is increasingly used as an indicator of malaria morbidity because TPR is based on laboratory-confirmed cases and is simple to incorporate into existing surveillance systems. However, temporal trends in TPR may reflect changes in factors associated with malaria rather than true changes in malaria morbidity. This study examines the effects of age, area of residence and diagnostic test on TPR at two health facilities in regions of Uganda with differing malaria endemicity. METHODS: The analysis included data from diagnostic blood smears performed at health facilities in Walukuba and Aduku between January 2009 and December 2010. The associations between age and time and between age and TPR were evaluated independently to determine the potential for age to confound temporal trends in TPR. Subsequently, differences between observed TPR and TPR adjusted for age were compared to determine if confounding was present. A similar analysis was performed for area of residence. Temporal trends in observed TPR were compared to trends in TPR expected using rapid diagnostic tests, which were modelled based upon sensitivity and specificity in prior studies. RESULTS: Age was independently associated with both TPR and time at both sites. At Aduku, age-adjusted TPR increased relative to observed TPR due to the association between younger age and TPR and the gradual increase in age distribution. At Walukuba, there were no clear differences between observed and age-adjusted TPR. Area of residence was independently associated with both TPR and time at both sites, though there were no clear differences in temporal trends in area of residence-adjusted TPR and observed TPR at either site. Expected TPR with pLDH- and HRP-2-based rapid diagnostic tests (RDTs) was higher than observed TPR at all time points at both sites. CONCLUSIONS: Adjusting for potential confounders such as age and area of residence can ensure that temporal trends in TPR due to confounding are not mistakenly ascribed to true changes in malaria morbidity. The potentially large effect of diagnostic test on TPR can be accounted for by calculating and adjusting for the sensitivity and specificity of the test used.

Use of the slide positivity rate to estimate changes in malaria incidence in a cohort of Ugandan children.

BACKGROUND: As malaria control efforts intensify, it is critical to monitor trends in disease burden and measure the impact of interventions. A key surveillance indicator is the incidence of malaria. Yet measurement of incidence is challenging. The slide positivity rate (SPR) has been used as a surrogate measure of malaria incidence, but limited data exist on the relationship between SPR and the incidence of malaria. METHODS: A cohort of 690 children aged 1-10 years at enrollment were followed for all their health care needs over a four-year period in Kampala, Uganda. All children with fever underwent laboratory testing, allowing us to measure the incidence of malaria and non-malaria fevers. A formula was derived to estimate relative changes in the incidence of malaria (rDeltaIm) based on changes in the SPR and the assumption that the incidence of non-malaria fevers was consistent over time. Observed and estimated values of rDeltaIm were compared over two, six, and 12 month time intervals after restricting the analysis to children contributing observation time between the ages of 4-10 years to control for aging of the cohort. RESULTS: Over the four-year observation period the incidence of malaria declined significantly from 0.93 episodes per person-year in 2005 to 0.39 episodes per person-year in 2008 (p < 0.0001) and the incidence of non-malaria fevers declined significantly from 2.31 episodes per person-year in 2005 to 1.31 episodes per person-year in 2008 (p < 0.0001). Younger age was associated with a significantly greater incidence of malaria and the incidence of malaria was significantly higher during seasonal peaks occurring each January-February and May-June. Changes in SPR produced reasonably accurate estimates of rDeltaIm over all time intervals. The average absolute difference in observed and estimated values of rDeltaIm was lower for six-month intervals (0.13) than it was for two-month (0.21) or 12 month intervals (0.21). CONCLUSION: Changes in SPR provided a useful estimate of changes in the incidence of malaria in a well defined cohort; however, a gradual decline in the incidence of non-malaria fevers introduced some bias in these estimates.

Uses and Misuses of Patient- and Neighborhood-level Social Determinants of Health Data.

Health care leaders in the US are actively exploring strategies to identify and address patients' social and economic hardships as part of high-quality clinical care. The result has been a proliferation of screening tools and interventions related to patients' social determinants of health, but little guidance on effective strategies to implement them. Some of these tools rely on patient- or household-level screening data collected from patients during medical encounters. Other tools rely on data available at the neighborhood-level that can be used to characterize the environment in which patients live or to approximate patients' social or economic risks. Four case examples were selected from different health care organizations to illustrate strengths and limitations of using patient- or neighborhood-level social and economic needs data to inform a range of interventions. This work can guide health care investments in this rapidly evolving arena.

Effect of HIV-1 infection on antimalarial treatment outcomes in Uganda: a population-based study.

BACKGROUND: Human immunodeficiency virus (HIV) infection may increase the burden of malaria by increasing susceptibility to infection or by decreasing the response to antimalarial treatment. We investigated the seroprevalence rate of HIV-1 infection and its effect on antimalarial treatment outcomes in adults and children with uncomplicated falciparum malaria in Uganda. METHODS: This retrospective study included 1965 patients > or =18 months old who were randomized to receive 1 of 3 antimalarial regimens at 7 sites in Uganda. HIV-1 testing was performed using 2 enzyme-linked immunosorbent assays and Western blot analysis of stored blood spots. The primary study outcome was clinical treatment failure at 28 days after antimalarial treatment. Molecular genotyping was used to distinguish clinical treatment failures due to new infections from those due to recrudescences. RESULTS: The HIV-1 seroprevalence rate was 2.5% in 1802 patients <18 years old and 31% in 163 patients > or =18 years old presenting with malaria. HIV-1 infection was associated with a >3-fold (hazard ratio [HR], 3.28 [95% confidence interval [CI], 1.25-8.59]) increased risk of clinical treatment failure for adults, but there was no increased risk for HIV-1-infected children. Molecular genotyping revealed that clinical treatment failures were due to new infections (HR, 6.35 [95% CI, 1.64-24.5]) rather than to recrudescences (HR, 1.51 [95% CI, 0.27-8.58]). CONCLUSIONS: The HIV-1 seroprevalence rate was surprisingly high in adults presenting with malaria. This finding supports the implementation of routine HIV counseling and testing for adults with uncomplicated falciparum malaria. HIV-1 infection increased the susceptibility to new malarial infections but did not increase the risk of recrudescences in adults.

Geographic differences in antimalarial drug efficacy in Uganda are explained by differences in endemicity and not by known molecular markers of drug resistance.

BACKGROUND: Recent clinical trials from Uganda have shown that the risk of failure following antimalarial therapy varies geographically. We tested the hypothesis that geographic differences in the response to therapy could be explained by differences in the prevalence of known molecular markers of drug resistance. METHODS: Samples from 2084 patients treated with chloroquine (CQ) plus sulfadoxine-pyrimethamine (SP) and amodiaquine (AQ) plus SP were tested for the presence of known molecular markers of resistance. Differences in the risk of treatment failure across 6 sites were compared, and age and complexity of infection were controlled for. RESULTS: The prevalence of molecular markers of drug resistance was high at all of the sites: 61%-91% of patients were infected with parasites containing the pfcrt Thr-76 mutation and dhfr/dhps quintuple mutation. The risk of treatment failure decreased with increasing transmission intensity for both CQ plus SP (73% to 19%) and AQ plus SP (38% to 2%). Restricting the analyses to patients infected with parasites containing all 6 mutations of interest did not affect these trends. CONCLUSIONS: The risk of treatment failure was inversely proportional to transmission intensity and was not explained by differences in molecular markers of antimalarial drug resistance. Our findings strongly suggest that geographic differences in response to antimalarial therapy in Uganda are primarily mediated by acquired immunity associated with malaria transmission intensity, rather than by parasite factors.