Pediatric primary care services in Manitoba: is the health of the next generation of children at risk?

OBJECTIVES: Physician resource planning is an important part of health policy, but to date there are no studies measuring the primary care service needs of a particular population. The aim of this study was to project the expected provision of physician services for the pediatric population of one Canadian province for 2020. METHODS: A novel standardized measure of physician service provision, the equivalent services measure, was developed using mathematical modeling. Population projections and past use of services were used to calculate the projected service needs for the pediatric population of Manitoba. RESULTS: Despite projecting a small increase in the pediatric population (2.8%), our model predicted a decrease of 13.4% in the services that would be provided. CONCLUSIONS: The findings of this study indicate that the health of future generations of children may be at risk. Further research is needed to determine the effect of the reduction in pediatric service provision on the health of the pediatric population.

Frequent users of emergency departments: developing standard definitions and defining prominent risk factors.

STUDY OBJECTIVE: We identify factors that define frequent and highly frequent emergency department (ED) users. METHODS: Administrative health care records were used to define less frequent (1 to 6 visits), frequent (7 to 17 visits), and highly frequent (≥18 visits) ED users. Analyses were conducted to determine the most unique demographic, disease, and health care use features of these groups. RESULTS: Frequent users composed 9.9% of all ED visits, whereas highly frequent users composed 3.6% of visits. Compared with less frequent users, frequent users were defined most strongly by their substance abuse challenges and by their many visits to primary care and specialist physicians. Substance abuse also distinguished highly frequent from frequent ED users strongly; 67.3% versus 35.9% of these patient groups were substance abusers, respectively. Also, 70% of highly frequent versus only 17.8% of frequent users had a long history of frequent ED use. Last, highly frequent users did not use other health care services proportionally more than their frequent user counterparts, suggesting that these former patients use EDs as a main source of care. CONCLUSION: This research develops objective thresholds of frequent and highly frequent ED use. Although substance abuse is prominent in both groups, only highly frequent users seem to visit EDs in place of other health care services. Future analyses can investigate these patterns of health care use more closely, including how timely access to primary care affects ED use. Cluster analysis also has value for defining frequent user subgroups who may benefit from different yet equally effective treatment options.

Learning from the census: the Socio-economic Factor Index (SEFI) and health outcomes in Manitoba.

OBJECTIVES: Using data from the Canadian census, researchers at the Manitoba Centre for Health Policy sought to create an area-based socio-economic measure (ABSM). The degree of association between the ABSM and health was evaluated. METHODS: Values on several census variables (including income, education, employment and family structure) were captured at the enumeration-area or dissemination-area level and submitted to a principal components factor analysis to create three ABSMs: an updated version of the Socio-economic Factor Index (SEFI-2) and modified versions of Pampalon's material deprivation and social deprivation indices. Factor scores from these analyses were then compared with several population health measures: Premature Mortality Rate (PMR), Potential Years of Life Lost (PYLL), life expectancy, and self-rated health. RESULTS: SEFI-2 scores were strongly related not only to the other ABSMs but also to every measure of health status. The strongest correlations between an ABSM and health measure were for SEFI-2 and PYLL(r=0.85), and SEFI-2 and PMR (r=0.80). The weakest correlations were found with the social deprivation ABSM measure and the self-rated health measure. CONCLUSIONS: ABSMs based on measures from the Canadian census are a valuable resource to population health researchers. Importantly, depending on the research question and reason for the inclusion of an ABSM, these composite measures may perform better than a simple measure of income alone. The ability to adjust for socio-economic status when assessing population health status or population health interventions contributes to the validity of conclusions drawn when conducting this type of research, and ABSMs may be able to substitute for area health status where it may not be easily determined.

Can the quality of care in family practice be measured using administrative data?

OBJECTIVE: To explore the feasibility of using administrative data to develop process indicators for measuring quality in primary care. DATA SOURCES/STUDY SETTING: The Population Health Research Data Repository (Repository) housed at the Manitoba Centre for Health Policy which includes physician claims, hospital discharge abstracts, pharmaceutical use (Drug Program Information Network (DPIN)), and the Manitoba Immunization Monitoring Program (MIMS) for all residents of Manitoba, Canada who used the health care system during the 2001/02 fiscal year. Family physicians were identified from the Physician Resource Database. Indicators were developed based on a literature review and focus group validation. DATA COLLECTION/EXTRACTION METHODS: Data files were extracted from administrative data available in the Repository. We extracted data based on the ICD-9-CM codes and ATC-class drugs prescribed and then linked them to the Physician Resource Database. Physician practices were defined by allocating patients to their most responsible physician. Every family physician in Manitoba that met the inclusion criteria (having either 5 or 10 eligible patients depending on the indicator) was 'scored' on each indicator. Physicians were then grouped according to the proportion of the patients allocated to their practice who received the recommended care for the specific indicator. PRINCIPAL FINDINGS: Using administrative health data we were able to develop and measure eight indicators of quality of care covering both preventive care services and chronic disease management. The number of eligible physicians and patients varied for each indicator as did the percent of patients with recommended care, per physician. For example, the childhood immunization indicator included 544 physicians who, on average, provided immunization for 65 percent of their patients. CONCLUSIONS: Quality of care provided by family physicians can be measured using administrative data. Despite the limitations addressed in this paper, this work establishes a practical methodology to measure quality of care provided by family physicians that can be used for quality improvement initiatives.