Article Figures & Data
Tables
Characteristic Income Quintilea Q1 (Lowest)
(n = 7,915)Q2
(n = 7,362)Q3
(n = 6,685)Q4
(n = 6,679)Q5 (Highest)
(n = 6,100)Hospitalized, % 3.6 2.5 1.7 1.3 1.0 Patient demographics Sex, male, % 39.1 41.5 42.4 44.4 44.5 Age, mean (SD), y 46.6 (14.6) 46.4 (14.5) 47.2 (14.5) 47.0 (14.3) 46.7 (14.4) Comorbidity level (major ADGs)b Low (0–1), % 57.3 67.5 69.8 71.7 74.3 Medium (2–3), % 35.2 28.0 26.7 25.0 23.3 High (≥4), % 7.5 4.5 3.5 3.3 2.4 Ambulatory care usec Physician visits, mean No. (SD) 29.1 (23.1) 22.6 (18.6) 22.1 (18.3) 20.8 (17.1) 20.0 (16.5) Usual physician index, mean (SD)d 0.59 (0.23) 0.63 (0.23) 0.64 (0.22) 0.64 (0.22) 0.62 (0.22) Received influenza vaccine, % 33.9 31.3 32.4 32.6 32.6 Referred to specialist, % 7.3 6.6 7.1 5.9 7.1 Usual physician characteristicse Type, family physician, % 91.6 92.3 91.7 91.8 90.7 Payment, fee for service, % 96.6 97.1 97.6 97.8 97.7 Sex, male, % 80.9 74.3 73.7 72.3 70.3 Time practicing in Manitoba 1–9 y 27.9 24.4 21.5 21.8 21.9 10–17 y 27.9 24.5 26.3 24.8 22.7 ≥18 y 44.2 51.1 52.2 53.5 55.4 Training, international, % 41.0 39.0 38.0 36.5 35.2 Daily patient load, mean No. (SD) 34.8 (16.0) 33.9 (14.3) 33.7 (14.0) 33.0 (13.3) 32.0 (12.7) -
ADG = aggregated diagnosis group.
Note: Data only available starting in 1991.
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↵a All differences between groups were significant at P <.05 on analysis of variance or χ2 analysis.
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↵b Of 8 major ADGs during 3 years: ADG3, ADG4, ADG9, ADG11, ADG16, ADG22, ADG25, or ADG32.
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↵c For the 3-year period before index hospitalization or median time to that event; 1-year period for influenza vaccine.
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↵d Possible range: 0 to 1.0. Higher values indicate greater continuity of care.
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↵e At the patient level; for example, 80.9% of patients in Q1 had a male usual physician.
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- Table 2
Socioeconomic Status as a Predictor of Obstructive Airway Disease–Related Hospitalization Across Models
Income Quintile Odds Ratio (95% Confidence Limit) Model A Model B Model C Model D Q1 (lowest) 3.72a (2.81, 4.92) 3.10a (2.33, 4.13) 3.15a (2.35, 4.21) 2.93a (2.19, 3.93) Q2 2.61a (1.95, 3.50) 2.45a (1.82, 3.30) 2.61a (1.93, 3.53) 2.51a (1.86, 3.40) Q3 1.70a (1.24, 2.33) 1.57a (1.14, 2.16) 1.60a (1.15, 2.21) 1.54a (1.11, 2.13) Q4 1.38 (0.99, 1.91) 1.33 (0.95, 1.85) 1.43a (1.02, 2.01) 1.41a (1.01, 1.98) Q5 (highest)b 1.0 1.0 1.0 1.0 -
↵a P <.05.
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↵b Reference group for odds ratio calculation.
Notes: model A = income quintile only; model B = income quintile + patient demographics; model C = income quintile + patient demographics + ambulatory care use; and model D = income quintile + patient demographics + ambulatory care use + usual physician characteristics.
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Additional Files
In Brief
Inequities in Ambulatory Care and the Relationship Between Socioeconomic Status and Respiratory Hospitalizations: A Population-Based Study of a Canadian City
Alan Katz , and colleagues
Background People who have lower socioeconomic status may have higher rates of hospitalization for ambulatory care-sensitive conditions (conditions for which some hospitalization can be avoided with successful management in the community). This study examines whether differences in patient demographics, ambulatory care use, or physician characteristics explain this disparity in avoidable hospitalizations.
What This Study Found Neither differences in the type of care nor the physicians providing the care are driving the disadvantaged to be hospitalized. Among 34,741 patients in Winnipeg, Manitoba, Canada, 2 percent of whom were hospitalized with an obstructive airway disease-related diagnosis during the two-year follow-up period, there was an approximately three-fold increase in the odds of being hospitalized in the lowest income group relative to the highest. After controlling for patient demographics, ambulatory care use and physician characteristics, the relationship between socioeconomic status and hospitalization remained virtually unchanged.
Implications
- Factors outside of direct contact with the health care system may lead to inequities in obstructive airway disease hospitalization.
- The authors conclude that these findings should remind clinicians and policy makers of the importance of social determinants of health and encourage the development of programs and policies that address the poverty associated with poor health outcomes