Neighborhood Socioeconomic Status and Quality of Kidney Care: Data From Electronic Health Records.
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ABSTRACT: Rational & objective
Electronic health records can be leveraged to assess quality-of-care measures in patients with chronic kidney disease (CKD). Neighborhood socioeconomic status could be a potential barrier to receiving appropriate evidence-based therapy and follow-up. We examined whether neighborhood socioeconomic status is independently associated with quality of care received by patients with CKD.Study design
Observational study using electronic health record data.Setting & participants
Retrospective study of patients seen at a health care system in the 7-county Minneapolis/St Paul area.Exposures
Census tract socioeconomic status measures (wealth, income, and education).Outcomes
Indicators of CKD quality of care: (1) prescription for angiotensin-converting enzyme inhibitor/angiotensin receptor blocker in patients with stage ≥ 3 CKD or stage 1 or 2 CKD with urinary albumin-creatinine ratio (UACR) > 300 mg/d, (2) UACR measurement among patients with laboratory-based CKD (estimated glomerular filtration rate < 60 mL/min/1.72 m2), and (3) CKD identified on the problem list or coded for at an encounter among patients with laboratory-based CKD.Analytic approach
Multilevel Poisson regression with robust error variance with a random intercept at the census tract level.Results
Of the 16,776 patients who should be receiving an angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 65% were prescribed these medications. Among patients with laboratory-based CKD (n = 25,097), UACR was measured in 27% and CKD was identified in the electronic health record in 55%. We found no independent association between any neighborhood socioeconomic status measures and CKD quality-of-care indicators.Limitations
1 health care system and selection bias.Conclusions
We found no association of neighborhood socioeconomic status with quality of CKD care in our cohort. However, adherence to CKD guidelines is low, indicating an opportunity to improve care for all patients regardless of neighborhood socioeconomic status.
SUBMITTER: Ghazi L
PROVIDER: S-EPMC8350826 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportancePrediction models are widely used in health care as a way of risk stratifying populations for targeted intervention. Most risk stratification has been done using a small number of predictors from insurance claims. However, the utility of diverse nonclinical predictors, such as neighborhood socioeconomic contexts, remains unknown.ObjectiveTo assess the value of using neighborhood socioeconomic predictors in the context of 1-year risk prediction for mortality and 6 different health care use outcomes in a large integrated care system.Design, setting, and participantsDiagnostic study using data from all adults age 18 years or older who had Kaiser Foundation Health Plan membership and/or use in the Kaiser Permantente Northern California: a multisite, integrated health care delivery system between January 1, 2013, and June 30, 2014. Data were recorded before the index date for each patient to predict their use and mortality in a 1-year post period using a test-train split for model training and evaluation. Analyses were conducted in fall of 2019.Main outcomes and measuresOne-year encounter counts (doctor office, virtual, emergency department, elective hospitalizations, and nonelective), total costs, and mortality.ResultsA total of 2 951 588 patients met inclusion criteria (mean [SD] age, 47.2 [17.4] years; 47.8% were female). The mean (SD) Neighborhood Deprivation Index was -0.32 (0.84). The areas under the receiver operator curve ranged from 0.71 for emergency department use (using the LASSO method and electronic health record predictors) to 0.94 for mortality (using the random forest method and electronic health record predictors). Neighborhood socioeconomic status predictors did not meaningfully increase the predictive performance of the models for any outcome.Conclusions and relevanceIn this study, neighborhood socioeconomic predictors did not improve risk estimates compared with what is obtainable using standard claims data regardless of model used.
| S-EPMC7582126 | biostudies-literature
Project description:The US Federal Government is investing up to $29 billion in incentives for meaningful use of electronic health records (EHRs). However, the effect of EHRs on ambulatory quality is unclear, with several large studies finding no effect.To determine the effect of EHRs on ambulatory quality in a community-based setting.Cross-sectional study, using data from 2008.Ambulatory practices in the Hudson Valley of New York, with a median practice size of four physicians.We included all general internists, pediatricians and family medicine physicians who: were members of the Taconic Independent Practice Association, had patients in a data set of claims aggregated across five health plans, and had at least 30 patients per measure for at least one of nine quality measures selected by the health plans.Adoption of an EHR.We compared physicians using EHRs to physicians using paper on performance for each of the nine quality measures, using t-tests. We also created a composite quality score by standardizing performance against a national benchmark and averaging standardized performance across measures. We used generalized estimation equations, adjusting for nine physician characteristics.We included 466 physicians and 74,618 unique patients. Of the physicians, 204 (44 %) had adopted EHRs and 262 (56 %) were using paper. Electronic health record use was associated with significantly higher quality of care for four of the measures: hemoglobin A1c testing in diabetes, breast cancer screening, chlamydia screening, and colorectal cancer screening. Effect sizes ranged from 3 to 13 percentage points per measure. When all nine measures were combined into a composite, EHR use was associated with higher quality of care (sd 0.4, p?=?0.008).This is one of the first studies to find a positive association between EHRs and ambulatory quality in a community-based setting.
| S-EPMC3599037 | biostudies-literature
Project description:ContextEarly childhood caries (ECC) is a persistent public health challenge, affecting more than 56% of US toddlers and preschool-aged children. Despite this, ECC is largely preventable with routine oral hygiene practices, diet, and application of topical fluoride.ObjectiveThis study assessed the utilization of preventive oral health care in primary care practices and evaluated the variation in patient characteristic and geographic disparities.DesignWe conducted a retrospective study using electronic health records (EHRs) over a 2-year period. Patients' home addresses were geocoded and linked to census-based neighborhood statistics and fluoridated water accessibility. Multiple logistic regression modeling was used to assess the risk of ECC in patients with fluoride preventive care, controlled for demographics, comorbid conditions, and neighborhood risk factors.ParticipantsPatients aged 6 to 71 months who had primary care providers at family medicine and general pediatric clinics in a large academic medical center.Main outcome measureThe presence of dental caries based on diagnoses in EHRs.ResultsThe study consisted of 10 836 patients: 17% treated with topical fluoride varnish (TFV), 12% prescribed oral fluoride supplement, 6.1% with both TFV and supplement, and 64% without fluoride treatment. Patients with fluoride treatment were 24% to 53% less likely to have ECC. Children living in rural and nonfluoridated water communities had 1.7 to 1.8 times greater risk of developing ECC. Minority, under/uninsured, and low-income patients also were at an increased risk of ECC.ConclusionDespite continuing efforts to improve access to dental care for vulnerable populations, substantial disparities remain among socioeconomically disadvantaged children. To address dental care shortage, primary care clinicians should serve as the safety net to care for vulnerable and underserved children who have no or limited access to oral health services. Future research into the collaboration between primary care and dental providers at the level of both practice and professional education should be considered.
| S-EPMC9897270 | biostudies-literature
Project description:ImportanceData from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies.ObjectiveTo quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models.Design, setting, and participantsCohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation.Main outcomes and measuresOutcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke.ResultsAmong the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome.Conclusions and relevanceSocial determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.
| S-EPMC6324505 | biostudies-literature
Project description:Rational & objectiveNeighborhood socioeconomic status (SES) and health insurance status may be important upstream social determinants of chronic kidney disease (CKD), but their relationship remains unclear. The aim of this study was to determine whether neighborhood SES and individual-level health insurance status were independently associated with CKD prevalence.Study designObservational study using electronic health records (EHRs).Setting & participantsEHRs of patients (n = 185,269) seen at a health care system in the 7-county Minneapolis/St Paul area (2017-2018).ExposuresCensus tract neighborhood SES measures (median value of owner-occupied housing units [wealth], percentage of residents aged >25 years with bachelor's degree or higher [education]) and individual-level health insurance status (aged <65 years: Medicaid vs other insurance; ≥65 years: Medicare vs Medicare and supplemental insurance plan) were obtained from the American Community Survey and EHR data. Neighborhood SES was operationalized into quartiles, comparing low (first quartile) versus high (fourth quartile) neighborhood SES.OutcomesCKD prevalence: estimated glomerular filtration rate < 60 mL/min/1.73 m2 or proteinuria.Analytic approachMultilevel Poisson regression with robust error variance with a random intercept at the census-tract level, adjusted for demographic and clinical covariates, was used to estimate the association between neighborhood SES, insurance, and CKD.ResultsNeighborhood SES and insurance were independently associated with CKD prevalence. In covariate-adjusted models, patients living in low versus high neighborhood SES had a higher CKD prevalence among both younger and older patients. For example, the prevalence ratios of CKD in low versus high neighborhood SES as defined by education among patients younger than 65 and 65 years and older were 1.11 (95% CI, 1.05-1.18) and 1.08 (95% CI, 1.04-1.12), respectively. Patients younger than 65 years receiving Medicaid had higher CKD prevalence versus those with other insurance (1.51 [95% CI, 1.43-1.6]). For patients 65 years and older, insurance was not associated with prevalence of CKD in the fully adjusted model.LimitationsOne health care system and selection bias.ConclusionsLiving in low neighborhood SES as defined by wealth and education and having Medicaid for patients younger than 65 years were associated with higher CKD prevalence.
| S-EPMC8350830 | biostudies-literature
Project description:Evidence of the impact electronic health records (EHRs) have on clinical outcomes remains mixed. The impact of EHRs likely depends on the organizational context in which they are used. This study focuses on one aspect of the organizational context: cohesion of primary care teams. We examined whether team cohesion among primary care team members changed the association between EHR use and changes in clinical outcomes for patients with diabetes.Retrospective longitudinal study.We combined provider-reported primary care team cohesion with lab values for patients with diabetes collected during the staggered EHR implementation (2005-2009). We used multivariate regression models with patient-level fixed effects to assess whether team cohesion levels changed the association between outpatient EHR use and clinical outcomes for patients with diabetes. Subjects were comprised of 80,611 patients with diabetes, in whom we measured changes in glycated hemoglobin (A1C) and low-density lipoprotein cholesterol (LDL-C).For A1C, EHR use was associated with an average decrease of 0.11% for patients with higher-cohesion primary care teams compared with a decrease of 0.08% for patients with lower-cohesion teams (difference = 0.02% in A1C; 95% CI, 0.01%-0.03%). For LDL-C, EHR use was associated with a decrease of 2.15 mg/dL for patients with higher-cohesion primary care teams compared with a decrease of 1.42 mg/dL for patients with lower-cohesion teams (difference = 0.73 mg/dL; 95% CI, 0.41-1.11 mg/dL).Patients cared for by higher cohesion primary care teams experienced modest but statistically significantly greater EHR-related health outcome improvements, compared with patients cared for by providers practicing in lower cohesion teams.
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Project description:ObjectiveTo determine whether quality measures based on computer-extracted EHR data can reproduce findings based on data manually extracted by reviewers.Data sourcesWe studied 12 measures of care indicated for adolescent well-care visits for 597 patients in three pediatric health systems.Study designObservational study.Data collection/extraction methodsManual reviewers collected quality data from the EHR. Site personnel programmed their EHR systems to extract the same data from structured fields in the EHR according to national health IT standards.Principal findingsOverall performance measured via computer-extracted data was 21.9 percent, compared with 53.2 percent for manual data. Agreement measures were high for immunizations. Otherwise, agreement between computer extraction and manual review was modest (Kappa = 0.36) because computer-extracted data frequently missed care events (sensitivity = 39.5 percent). Measure validity varied by health care domain and setting. A limitation of our findings is that we studied only three domains and three sites.ConclusionsThe accuracy of computer-extracted EHR quality reporting depends on the use of structured data fields, with the highest agreement found for measures and in the setting that had the greatest concentration of structured fields. We need to improve documentation of care, data extraction, and adaptation of EHR systems to practice workflow.
| S-EPMC4239847 | biostudies-literature
Project description:OBJECTIVE:To study the association between neighbourhood socioeconomic status and diabetes prevalence, incidence, and control in the entire population of northeastern Madrid, Spain. SETTING:Electronic health records of the primary-care system in four districts of Madrid (Spain). PARTICIPANTS:269?942 people aged 40 or older, followed from 2013 to 2014. EXPOSURE:Neighbourhoodsocioeconomic status (NSES), measured using a composite index of seven indicators from four domains of education, wealth, occupation and living conditions. PRIMARY OUTCOME MEASURES:Diagnosis of diabetes based on ICPC-2 codes and glycated haemoglobin (HbA1c %). RESULTS:In regression analyses adjusted by age and sex and compared with individuals living in low NSES neighbourhoods, men living in medium and high NSES neighbourhoods had 10% (95% CI: 6% to 15%) and 29% (95% CI: 25% to 32%) lower prevalence of diabetes, while women had 27% (95% CI: 23% to 30%) and 50% (95% CI: 47% to 52%) lower prevalence of diabetes. Moreover, the hazard of diabetes in men living in medium and high NSES neighbourhoods was 13% (95% CI: 1% to 23%) and 20% (95% CI: 9% to 29%) lower, while the hazard of diabetes in women living in medium and high NSES neighbourhoods was 17% (95% CI: 3% to 29%) and 31% (95% CI: 20% to 41%) lower. Individuals living in medium and high SES neighbourhoods had 8% (95% CI: 2% to 15%) and 15% (95% CI: 9% to 21%) lower prevalence of lack of diabetes control, and a decrease in average HbA1c % of 0.05 (95% CI: 0.01 to 0.10) and 0.11 (95% CI: 0.06 to 0.15). CONCLUSIONS:Diabetes prevalence, incidence and lack of control increased with decreasing NSES in a southern European city. Future studies should provide mechanistic insights and targets for intervention to address this health inequity.
| S-EPMC6173235 | biostudies-other
Project description:Importancerisk factors for delirium in hospital inpatients are well established, but less is known about whether delirium occurring in the community or during an emergency admission to hospital care might be predicted from routine primary-care records.Objectivesidentify risk factors in primary-care electronic health records (PC-EHR) predictive of delirium occurring in the community or recorded in the initial episode in emergency hospitalisation. Test predictive performance against the cumulative frailty index.DesignStage 1: case-control; Stages 2 and 3: retrospective cohort.Settingclinical practice research datalink: PC-EHR linked to hospital discharge data from England.SubjectsStage 1: 17,286 patients with delirium aged ≥60 years plus 85,607 controls. Stages 2 and 3: patients ≥ 60 years (n = 429,548 in 2015), split into calibration and validation groups.MethodsStage 1: logistic regression to identify associations of 110 candidate risk measures with delirium. Stage 2: calibrating risk factor weights. Stage 3: validation in independent sample using area under the curve (AUC) receiver operating characteristic.Resultsfifty-five risk factors were predictive, in domains including: cognitive impairment or mental illness, psychoactive drugs, frailty, infection, hyponatraemia and anticholinergic drugs. The derived model predicted 1-year incident delirium (AUC = 0.867, 0.852:0.881) and mortality (AUC = 0.846, 0.842:0.853), outperforming the frailty index (AUC = 0.761, 0.740:0.782). Individuals with the highest 10% of predicted delirium risk accounted for 55% of incident delirium over 1 year.Conclusionsa risk factor model for delirium using data in PC-EHR performed well, identifying individuals at risk of new onsets of delirium. This model has potential for supporting preventive interventions.
| S-EPMC7297278 | biostudies-literature
Project description:BackgroundDiagnosis of alcohol use disorder (AUD) in primary care is critical for increasing access to alcohol treatment. However, AUD is underdiagnosed and may be inequitably diagnosed due to societal structures that determine access to resources (e.g., structural racism that limits opportunities for some groups and influences interpersonal interactions in and beyond health care). This study described patterns of provider-documented AUD in primary care across intersections of race, ethnicity, sex, and community-level socioeconomic status (SES).MethodsThis cross-sectional study used EHR data from a regional healthcare system with 35 primary care clinics that included adult patients who completed alcohol screenings between 3/1/2015 and 9/30/2020. The prevalence of provider-documented AUD in primary care based on International Classification of Diseases-9 (ICD-9) and ICD-10 diagnoses was compared across intersections of race, ethnicity, sex, and community-level SES.ResultsAmong 439,375 patients, 6.6% were Latine, 11.0% Asian, 5.4% Black, 1.3% Native Hawaiian/Pacific Islander (NH/PI), 1.5% American Indian/Alaska Native (AI/AN), and 74.2% White, and 58.3% women. The overall prevalence of provider-documented AUD was 1.0% and varied across intersecting identities. Among women, the prevalence was highest for AI/AN women with middle SES, 1.5% (95% CI 1.0-2.3), and lowest for Asian women with middle SES, 0.1% (95% CI 0.1-0.2). Among men, the prevalence was highest for AI/AN men with high and middle SES, 2.0% (95% CI 1.1-3.4) and 2.0% (95% CI 1.2-3.2), respectively, and lowest for Asian men with high SES, 0.5% (95% CI 0.3-0.7). Black and Latine patients tended to have a lower prevalence of AUD than White patients, across all intersections of sex and SES except for Black women with high SES. There were no consistent patterns of the prevalence of AUD diagnosis that emerged across SES.ConclusionThe prevalence of provider-documented AUD in primary care was highest in AI/AN men and women and lowest in Asian men and women. Findings of lower prevalence of provider-documented AUD in Black and Hispanic than White patients across most intersections of sex and SES differed from prior studies. Findings may suggest that differences in access to resources, which vary in effects across these identity characteristics and lived experiences, influence the diagnosis of AUD in clinical care.
| S-EPMC11365182 | biostudies-literature