Socioeconomic status and hospital utilization among younger adult pneumonia admissions at a Canadian hospital

Incidence of moral hazards among health care providers in the implementation of social health insurance toward universal health coverage: evidence from rural province hospitals in Indonesia

Objective

To identify the incidence of moral hazards among health care providers and its determinant factors in the implementation of national health insurance in Indonesia.

Methods

Data were derived from 360 inpatient medical records from six types C public and private hospitals in an Indonesian rural province. These data were accumulated from inpatient medical records from four major disciplines: medicine, surgery, obstetrics and gynecology, and pediatrics. The dependent variable was provider moral hazards, which included indicators of up-coding, readmission, and unnecessary admission. The independent variables are Physicians' characteristics (age, gender, and specialization), coders' characteristics (age, gender, education level, number of training, and length of service), and patients' characteristics (age, birth weight, length of stay, the discharge status, and the severity of patient's illness). We use logistic regression to investigate the determinants of moral hazard.

Results

We found that the incidences of possible unnecessary admissions, up-coding, and readmissions were 17.8%, 11.9%, and 2.8%, respectively. Senior physicians, medical specialists, coders with shorter lengths of service, and patients with longer lengths of stay had a significant relationship with the incidence of moral hazard.

Conclusion

Unnecessary admission is the most common form of a provider's moral hazard. The characteristics of physicians and coders significantly contribute to the incidence of moral hazard. Hospitals should implement reward and punishment systems for doctors and coders in order to control moral hazards among the providers.

"I Had Bills to Pay": a Mixed-Methods Study on the Role of Income on Care Transitions in a Public-Payer Healthcare System

BACKGROUND

Income disparities may affect patients' care transition home. Evidence among patients who have access to publicly funded healthcare coverage remains limited.

OBJECTIVE

To evaluate the association between low income and post-discharge health outcomes and explore patient and caregiver perspectives on the role of income disparities.

DESIGN

Mixed-methods secondary analysis conducted among participants in a double-blind randomized controlled trial.

PARTICIPANTS

Participants from a multicenter study in Ontario, Canada, were classified as low income if annual self-reported salary was below $29,000 CAD, or between $30,000 and $50,000 CAD and supported ≥ 3 individuals.

MAIN MEASURES

The associations between low income and the following self-reported outcomes were evaluated using multivariable logistic regression: patient experience, adherence to medications, diet, activity and follow-up, and the aggregate of emergency department (ED) visits, readmission, or death up to 3 months post-discharge. A deductive direct content analysis of patient and caregivers on the role of income-related disparities during care transitions was conducted.

KEY RESULTS

Individuals had similar odds of reporting high patient experience and adherence to instructions regardless of reported income. Compared to higher income individuals, low-income individuals also had similar odds of ED visits, readmissions, and death within 3 months post-discharge. Low-income individuals were more likely than high-income individuals to report understanding their medications completely (OR 1.9, 95% CI: 1.0-3.4) in fully adjusted regression models. Two themes emerged from 25 interviews which (1) highlight constraints of publicly funded services and costs incurred to patients or their caregivers along with (2) the various ways patients adapt through caregiver support, private services, or prioritizing finances over health.

CONCLUSIONS

There were few quantitative differences in patient experience, adherence, ED visits, readmissions, and death post-discharge between individuals reporting low versus higher income. Several hidden costs for transportation, medications, and home care were reported however and warrant further research.

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Background

Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.

Methods

This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.

Results

Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.

Conclusion

The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Associations between Multimorbidity Patterns and Subsequent Labor Market Marginalization among Refugees and Swedish-Born Young Adults-A Nationwide Registered-Based Cohort Study

Background: Young refugees are at increased risk of labor market marginalization (LMM). We sought to examine whether the association of multimorbidity patterns and LMM differs in refugee youth compared to Swedish-born youth and identify the diagnostic groups driving this association. Methodology: We analyzed 249,245 individuals between 20–25 years, on 31 December 2011, from a combined Swedish registry. Refugees were matched 1:5 to Swedish-born youth. A multimorbidity score was computed from a network of disease co-occurrences in 2009–2011. LMM was defined as disability pension (DP) or >180 days of unemployment during 2012–2016. Relative risks (RR) of LMM were calculated for 114 diagnostic groups (2009–2011). The odds of LMM as a function of multimorbidity score were estimated using logistic regression. Results: 2841 (1.1%) individuals received DP and 16,323 (6.5%) experienced >180 annual days of unemployment during follow-up. Refugee youth had a marginally higher risk of DP (OR (95% CI): 1.59 (1.52, 1.67)) depending on their multimorbidity score compared to Swedish-born youth (OR (95% CI): 1.51 (1.48, 1.54)); no differences were found for unemployment (OR (95% CI): 1.15 (1.12, 1.17), 1.12 (1.10, 1.14), respectively). Diabetes mellitus and influenza/pneumonia elevated RR of DP in refugees (RRs (95% CI) 2.4 (1.02, 5.6) and 1.75 (0.88, 3.45), respectively); most diagnostic groups were associated with a higher risk for unemployment in refugees. Conclusion: Multimorbidity related similarly to LMM in refugees and Swedish-born youth, but different diagnoses drove these associations. Targeted prevention, screening, and early intervention strategies towards specific diagnoses may effectively reduce LMM in young adult refugees.

Prevalence of Social Risks on Inpatient Screening and Their Impact on Pediatric Care Use

OBJECTIVES

Screening for social determinants of health in the inpatient setting is uncommon. However, social risk factors documented in billing and electronic medical record data are associated with increased pediatric care use. We sought to describe (1) the epidemiology of social risks and referral acceptance and (2) association between social risks identified through routine inpatient screening and care use.

METHODS

Parents of children ages 0 to 18 admitted to a general pediatric floor at an academic children's hospital completed a psychosocial screening survey from October 2017 to June 2019. The survey covered the following domains: finances, housing, food security, medications, and benefits. Patient characteristics and care use outcomes were abstracted from the electronic medical record and compared by using Pearson's χ² or the Wilcoxon rank test and logistic regression analyses.

RESULTS

Of 374 screened families, 141 (38%) had a positive screen result, of whom 78 (55%) reported >1 need and 64 (45%) accepted a community resource. In bivariate analyses, patients with a positive screen result had higher 30-day readmission (10% vs 5%; P = .05), lower median household income ($62 321 vs $71 460; P < .01), lower parental education (P < .01), public insurance (57% vs 43%; P < .01), lived in a 1-parent household (30 vs 12%; P < .01), and had a complex chronic condition (35% vs 23%; P = .01) compared with those with a negative screen result. There was no difference in care reuse by screening status in adjusted analyses.

CONCLUSIONS

Social risks are common in the pediatric inpatient setting. Children with medical complexity offer a good target for initial screening efforts.

Poverty as an independent risk factor for in-hospital mortality in community-acquired pneumonia: A study in a developing country population

BACKGROUND

Community-acquired pneumonia (CAP) is one of the most severe lower respiratory tract infections with a high in-hospital mortality. The aim of this study was to investigate the socioeconomic and medical risk factors affecting the prognosis of acute pneumonia. The results of this study can mention the value of socioeconomic backgrounds like poverty and illiteracy in clinical practice, even in a well-known biological phenomenon (eg acute pneumonia).

METHODS

In this cross-sectional study, all admitted patients to a tertiary teaching hospital with a diagnosis of community acquired pneumonia in a 12-month period were enrolled. Socioeconomic and demographic characteristics, underlying conditions, clinical manifestations and para-clinical test results at admission registered prospectively. A logistic regression model was conducted using in-hospital mortality as the dependent variable.

RESULTS

A total of 621 patients was included in this study. Among them, 47 patients (7.6%) died during the hospitalisation period. In multiple logistic regression analysis, pleural effusion, a higher CURB-65 score, hyponatremia, hyperglycaemia and poverty (being in the lower economic class) were identified as independent risk factors for in-hospital mortality in community-acquired pneumonia.

CONCLUSION

Numerous factors can influence the prognosis of CAP. In addition to the CURB-65 score and some other medical risk factors, socioeconomic backgrounds can also affect the early outcome in CAP. In this study, being in the lower economic class (as an indicator of poverty) is interpreted as an independent risk factor for a poor prognosis in CAP.

A before-after study of hospital use in two frail populations receiving different home-based services over the same time in Vancouver, Canada

Background

As individuals age, they are more likely to experience increasing frailty and more frequent use of hospital services. First, we explored whether initiating home-based primary care in a frail homebound cohort, influenced hospital use. Second, we explored whether initiating regular home care support for personal care with usual primary care, in a second somewhat less frail cohort, influenced hospital use.

Methods

This was a before-after retrospective cohort study of two frail populations in Vancouver, Canada using administrative data to assess the influence of two different services started in two different cohorts over the same time period. The participants were 246 recipients of integrated home-based primary care and 492 recipients of home care followed between July 1st, 2008 and June 30th, 2013 before and after starting their respective services. Individuals in each group were linked to their hospital emergency department visit and discharge abstract records. The main outcome measures were mean emergency department visit and hospital admission rates per 1000 patient days for 21 months before versus the period after receipt of services, and the adjusted incidence rate ratios (IRRs) on these outcomes post receipt of service.

Results

Before versus after starting integrated home-based primary care, emergency department visit rates per 1000 patient days (95% confidence intervals) were 4.1 (3.8, 4.4) versus 3.7 (3.3, 4.1), and hospital admissions rates were 2.3 (2.1, 2.5) versus 2.2 (1.9, 2.5). Before versus after starting home care, emergency department visit rates per 1000 patient days (95% confidence intervals) were 3.0 (2.8, 3.2) versus 4.0 (3.7, 4.3) visits and hospital admissions rates were 1.3 (1.2, 1.4) versus 1.9 (1.7, 2.1). Home-based primary care IRRs were 0.91 (0.72, 1.15) and 0.99 (0.76, 1.27) and home care IRRs were 1.34 (1.15, 1.56) and 1.46 (1.22, 1.74) for emergency department visits and hospital admissions respectively.

Conclusions

After enrollment in integrated home-based primary care, emergency department visit and hospital admission rates stabilized. After starting home care with usual primary care, emergency department visit and hospital admission rates continued to rise.

Investigating the effect of sociodemographic factors on 30-day hospital readmission among medical patients in Toronto, Canada: a prospective cohort study

OBJECTIVE

To examine the influence of patient-level sociodemographic factors on the incidence of hospital readmission within 30 days among medical patients in a large Canadian metropolitan city.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

Patients admitted to the General Internal Medicine service of an urban teaching hospital in Toronto, Canada participated in a survey of sociodemographic information. Patients were not surveyed if deemed medically unstable, receiving care in medical/surgical step-down beds or were isolated for infection control. Included in the final analysis was a diverse cohort of 1427 adult, non-palliative, patients who were discharged home.

MEASURES

Thirteen patient-level sociodemographic variables were examined in relation to time to unplanned all-cause readmission within 30 days. Illness level was accounted for by the following covariates: self-perceived health status, previous hospital utilisation, primary diagnosis case mix group, Charlson Comorbidity Index score and inpatient length of stay.

RESULTS

Approximately, 14.4% (n=205) of patients experienced readmission within 30 days. Sociodemographic factors were not significantly associated with time to readmission in unadjusted and adjusted analyses. Indicators of illness level, namely, previous hospitalisations, were the strongest risk factors for readmission within this cohort. One previous admission (adjusted HR 1.78; 95% CI 1.22 to 2.59, P<0.01) and at least four previous emergency department visits (adjusted HR 2.33; 95% CI 1.46 to 4.43, P<0.01) were associated with increased hazard of readmission within 30 days.

CONCLUSIONS

Patient-level sociodemographic factors did not influence the incidence of unplanned all-cause readmission within 30 days. Further research is needed to understand the generalisability of our findings and investigate whether contextual factors, such as access to universal health insurance coverage, attenuate the effects of sociodemographic factors.

Hospital Readmission and Social Risk Factors Identified from Physician Notes

OBJECTIVE

To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.

STUDY SETTING

A multihospital academic health system in southeastern Massachusetts.

STUDY DESIGN

An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.

DATA COLLECTION/EXTRACTION METHODS

All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry.

PRINCIPAL FINDINGS

All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).

CONCLUSIONS

The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

Impact of socio-economic status on unplanned readmission following injury: A multicenter cohort study

BACKGROUND

Unplanned readmissions cost the US economy approximately $17 billion in 2009 with a 30-day incidence of 19.6%. Despite the recognised impact of socio-economic status (SES) on readmission in diagnostic populations such as cardiovascular patients, its impact in trauma patients is unclear. We examined the effect of SES on unplanned readmission following injury in a setting with universal health insurance. We also evaluated whether additional adjustment for SES influenced risk-adjusted readmission rates, used as a quality indicator (QI).

STUDY DESIGN

We conducted a multicenter cohort study in an integrated Canadian trauma system involving 56 adult trauma centres using trauma registry and hospital discharge data collected between 2005 and 2010. The main outcome was unplanned 30-day readmission; all cause, due to complications of injury and due to subsequent injury. SES was determined using ecological indices of material and social deprivation. Odds ratios of readmission and 95% confidence intervals adjusted for covariates were generated using multivariable logistic regression with a correction for hospital clusters. We then compared a readmission QI validated previously (original QI) to a QI with additional adjustment for SES (SES-adjusted QI) using the mean absolute difference.

RESULTS

The cohort consisted of 52,122 trauma admissions of which 6.5% were rehospitalised within 30 days of discharge. Compared to patients in the lowest quintile of social deprivation, those in the highest quintile had a 20% increase in the odds of all-cause unplanned readmission (95% CI=1.06-1.36) and a 27% increase in the odds of readmission due to complications of injury (95% CI=1.04-1.54). No association was observed for material deprivation or for readmissions due to subsequent injuries. We observed a strong agreement between the original and SES-adjusted readmission (mean absolute difference= 0.04%).

CONCLUSIONS

Patients admitted for traumatic injury who suffer from social deprivation have an increased risk of unplanned rehospitalisation due to complications of injury in the 30 days following discharge. Better discharge planning or follow up for such patients may improve patient outcome and resource use for trauma admissions. Despite observed associations, results suggest that the trauma QI based on unplanned readmission does not require additional adjustment for SES.

Has variation in length of stay in acute hospitals decreased? Analysing trends in the variation in LOS between and within Dutch hospitals

Background

We aimed to get better insight into the development of the variation in length of stay (LOS) between and within hospitals over time, in order to assess the room for efficiency improvement in hospital care.

Methods

Using Dutch national individual patient-level hospital admission data, we studied LOS for patients in nine groups of diagnoses and procedures between 1995 and 2010. We fitted linear mixed effects models to the log-transformed LOS to disentangle within and between hospital variation and to evaluate trends, adjusted for case-mix.

Results

We found substantial differences between diagnoses and procedures in LOS variation and development over time, supporting our disease-specific approach. For none of the diagnoses, relative variance decreased on the log scale, suggesting room for further LOS reduction. Except for two procedures in the same specialty, LOS of individual hospitals did not correlate between diagnoses/procedures, indicating the absence of a hospital wide policy. We found within-hospital variance to be many times greater than between-hospital variance. This resulted in overlapping confidence intervals across most hospitals for individual hospitals’ performances in terms of LOS.

Conclusions

The results suggest room for efficiency improvement implying lower costs per patient treated. It further implies a possibility to raise the number of patients treated using the same capacity or to downsize the capacity. Furthermore, policymakers and health care purchasers should take into account statistical uncertainty when benchmarking LOS between hospitals and identifying inefficient hospitals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-015-1087-6) contains supplementary material, which is available to authorized users.

Impact of socio-economic status on hospital length of stay following injury: a multicenter cohort study

BACKGROUND

Injury is second only to cardiovascular disease in terms of acute care costs in North America. One key to improving injury care efficiency is to generate knowledge on the determinants of resource use. Socio-economic status (SES) is a documented risk factor for injury severity and mortality but its impact on length of stay (LOS) for injury admissions is unknown. This study aimed to examine the relationship between SES and LOS following injury. This multicenter retrospective cohort study was based on adults discharged alive from any trauma center (2007-2012; 57 hospitals; 65,486 patients) in a Canadian integrated provincial trauma system. SES was determined using ecological indices of material and social deprivation. Mean differences in LOS adjusted for age, gender, comorbidities, and injury severity were generated using multivariate linear regression.

RESULTS

Mean LOS was 13.5 days. Patients in the highest quintile of material/social deprivation had a mean LOS 0.5 days (95 % CI 0.1-0.9)/1.4 days (1.1-1.8) longer than those in the lowest quintile. Patients in the highest quintiles of both social and material deprivation had a mean LOS 2.6 days (1.8-3.5) longer than those in the lowest quintiles.

CONCLUSIONS

Results suggest that patients admitted for traumatic injury who suffer from high social and/or material deprivation have longer acute care LOS in a universal-access health care system. The reasons behind observed differences need to be further explored but may indicate that discharge planning should take patient SES into consideration.

Is socioeconomic status associated with utilization of health care services in a single-payer universal health care system?

Objectives

To assess an association of Socio-economic status with utilization of health care services between years 2002 and 2008 in Israel.

Methods

We retrospectively analyzed the utilization of health care services in a cohort of 100,000 members, 21 years and older, of a Clalit Health Services. The research compared utilization according to the neighborhood SES status; and clinic’s location as another SES proxy. Data included: Charlson Score morbidity factor, utilization of health care services (visits to primary physicians and specialists, purchase of pharmaceuticals, number of hospitalization days, visits to ED, utilization of laboratory tests and imaging). The analysis was performed using Generalized Linear Model (GLM) technique.

Results

People with lower SES visited more the ED and primary physicians and were hospitalized for longer periods. People with higher SES visited more specialists, bought more prescription drugs and used more medical imaging. The associations between SES and most of the services we analyzed did not change between 2002 and 2008. However, the gap between lower and higher SES levels in ED visits and the use of prescription drugs slightly increased over time, while the gap in visits to specialists decreased.

Conclusions

The research shows that even in a universal health care system SES is associated with utilization of health care services. In order to improve equity in utilization of services the Israeli public health should reduce economic barriers and in parallel invest in making information accessible to improve “navigation skills” for all.

Community household income and resource utilization for common inpatient pediatric conditions

BACKGROUND AND OBJECTIVE

Child health is influenced by biomedical and socioeconomic factors. Few studies have explored the relationship between community-level income and inpatient resource utilization for children. Our objective was to analyze inpatient costs for children hospitalized with common conditions in relation to zip code-based median annual household income (HHI).

METHODS

Retrospective national cohort from 32 freestanding children's hospitals for asthma, diabetes, bronchiolitis and respiratory syncytial virus, pneumonia, and kidney and urinary tract infections. Standardized cost of care for individual hospitalizations and across hospitalizations for the same patient and condition were modeled by using mixed-effects methods, adjusting for severity of illness, age, gender, and race. Main exposure was median annual HHI. Posthoc tests compared adjusted standardized costs for patients from the lowest and highest income groups.

RESULTS

From 116,636 hospitalizations, 4 of 5 conditions had differences at the hospitalization and at the patient level, with lowest-income groups having higher costs. The individual hospitalization level cost differences ranged from $187 (4.1%) to $404 (6.4%). Patient-level cost differences ranged from $310 to $1087 or 6.5% to 15% higher for the lowest-income patients. Higher costs were typically not for laboratory, imaging, or pharmacy costs. In total, patients from lowest income zip codes had $8.4 million more in hospitalization-level costs and $13.6 million more in patient-level costs.

CONCLUSIONS

Lower community-level HHI is associated with higher inpatient costs of care for 4 of 5 common pediatric conditions. These findings highlight the need to consider socioeconomic status in health care system design, delivery, and reimbursement calculations.

Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review

BACKGROUND

Readmission and mortality after hospitalization for community-acquired pneumonia (CAP) and heart failure (HF) are publically reported. This systematic review assessed the impact of social factors on risk of readmission or mortality after hospitalization for CAP and HF-variables outside a hospital's control.

METHODS

We searched OVID, PubMed and PSYCHINFO for studies from 1980 to 2012. Eligible articles examined the association between social factors and readmission or mortality in patients hospitalized with CAP or HF. We abstracted data on study characteristics, domains of social factors examined, and presence and magnitude of associations.

RESULTS

Seventy-two articles met inclusion criteria (20 CAP, 52 HF). Most CAP studies evaluated age, gender, and race and found older age and non-White race were associated with worse outcomes. The results for gender were mixed. Few studies assessed higher level social factors, but those examined were often, but inconsistently, significantly associated with readmissions after CAP, including lower education, low income, and unemployment, and with mortality after CAP, including low income. For HF, older age was associated with worse outcomes and results for gender were mixed. Non-Whites had more readmissions after HF but decreased mortality. Again, higher level social factors were less frequently studied, but those examined were often, but inconsistently, significantly associated with readmissions, including low socioeconomic status (Medicaid insurance, low income), living situation (home stability rural address), lack of social support, being unmarried and risk behaviors (smoking, cocaine use and medical/visit non-adherence). Similar findings were observed for factors associated with mortality after HF, along with psychiatric comorbidities, lack of home resources and greater distance to hospital.

CONCLUSIONS

A broad range of social factors affect the risk of post-discharge readmission and mortality in CAP and HF. Future research on adverse events after discharge should study social determinants of health.

Length of stay and hospital costs among patients admitted to hospital by family physicians

OBJECTIVE

To compare length of stay and total hospital costs among patients admitted to hospital under the care of family physicians who were their usual health care providers in the community (group A) and patients admitted to the same inpatient service under the care of family physicians who were not their usual health care providers (group B).

DESIGN

Retrospective observational study.

SETTING

A large urban hospital in Vancouver, BC.

PARTICIPANTS

All adult admissions to the family practice inpatient service between April 1, 2006, and June 30, 2008.

MAIN OUTCOME MEASURES

Ratio of length of stay to expected length of stay and total hospital costs per resource intensity weight unit. Multivariate linear regression was performed to determine the effect of admitting group (group A vs. group B) on the natural logarithm transformations of the outcomes.

RESULTS

The median acute length of stay was 8.0 days (interquartile range [IQR] 4.0 to 13.0 days) for group A admissions and 8.0 days (IQR 4.0 to 15.0 days) for group B admissions. The median (IQR) total hospital costs were $6498 ($4035 to $11,313) for group A admissions and $6798 ($4040 to $12,713) for group B admissions. Aftera djustment for patient characteristics, patients admitted to hospital under the care of their own family physicians did not significantly differ in terms of acute length of stay to expected length of stay ratio (percent change 0.6%, P = .942)or total hospital costs per resource intensity weight unit (percent change -2.0%, P = .722) compared with patients admitted under the care of other family physicians.

CONCLUSION

These findings suggest that having networks of family physicians involved in hospital care for patients is not less efficient than having family physicians provide care for their own patients.

Influence of socioeconomic status on community-acquired pneumonia outcomes in elderly patients requiring hospitalization: a multicenter observational study

BACKGROUND

The associations between socioeconomic status and community-acquired pneumonia outcomes in adults have been studied although studies did not always document a relationship.The aim of this multicenter observational study was to determine the association between socioeconomic status and community-acquired pneumonia outcomes in the elderly, in the context of a public health system providing universal free care to the whole population.

METHODS

A total of 651 patients aged > or =65 years hospitalized due to community-acquired pneumonia through the emergency departments of five Spanish public hospitals were recruited and followed up between May 2005 and January 2007. The primary outcomes studied were: length of stay, intensive care unit admission, overall mortality and readmission. Socioeconomic status was measured using both individual and community data: occupation [categorized in six social groups (I, II, III, IVa, IVb and V)], educational level (< or = primary level or > or = secondary level) and disposable family income of the municipality or district of residence [>12,500 euro (high municipality family income) and < or =12,500 euro (low municipality family income)]. The six social groups were further categorized as upper/middle social class (groups I-IVb) and lower class (group V).Bivariate and multivariate analyses were performed. OR and their 95% confidence intervals were calculated. All statistical tests were two tailed and statistical significance was established as p < 0.05.

RESULTS

17.7% of patients lived in a municipality or district with a high municipality family income and 63.6% were upper/middle social class (I-IVb). Only 15.7% of patients had a secondary education. The adjusted analysis showed no association between pneumonia outcomes and social class, educational level or municipality family income. However, length of stay increased significantly in patients in whom the factors, living alone and being a smoker or ex-smoker coincided (p < 0.001).

CONCLUSIONS

We measured socioeconomic status using both individual and community data and found no association between social class, educational level or municipality family income and the variables of pneumonia outcomes. The lack of differences between social classes supports the provision of universal, equitable health care by the public health system.

Environmental risk factors for community-acquired pneumonia hospitalization in older adults

OBJECTIVES

To investigate the risk of hospitalization for pneumonia in older adults in relation to biophysical environmental factors.

DESIGN

Population-based case control study with collection of personal interview data.

SETTING

Hamilton, Ontario, and Edmonton, Alberta, Canada.

PARTICIPANTS

Seven hundred seventeen people aged 65 and older hospitalized for community-acquired pneumonia (CAP) from September 2002 to April 2005 and 867 controls aged 65 and older randomly selected from the same communities as the cases.

MEASUREMENTS

Odds ratios (ORs) for risk of pneumonia in relation to environmental and other variables.

RESULTS

Exposure to secondhand smoke in the previous month (OR=1.73, 95% confidence interval (CI)=1.04-2.90); poor nutritional score (OR=1.83, 95% CI=1.19-2.80); alcohol use per month (per gram; OR=1.69, 95% CI=1.08-2.61); history of regular exposure to gases, fumes, or chemicals at work (OR=3.69, 95% CI=2.37-5.75); history of regular exposure to fumes from solvents, paints, or gasoline at home (OR=3.31, 95% CI=1.59-6.87); and non-English language spoken at home (OR=5.31, 95% CI=2.60-10.87) were associated with a greater risk of pneumonia hospitalization in multivariable analysis. Age, congestive heart failure, chronic obstructive lung disease, dysphagia, renal disease, functional status, use of immunosuppressive disease medications, and lifetime history of smoking of more than 100 cigarettes were other variables associated with hospitalization for pneumonia.

CONCLUSION

In elderly people, present and past exposures in the physical environmental are associated with hospitalization for CAP.