Measuring the Circle: Emerging Trends in Philanthropy for First Nations

The Circle had the opportunity to undertake a multi-part research project to gain a more robust understanding of non-governmental funding to Aboriginal beneficiaries and causes in Canada over the past few years. The year-long knowledge gathering process included three inter-related activities: (a) mining Canada Revenue Agency data to map the Aboriginal funding economy in Canada from 2005 to 2011; (b) a set of Key Informant interviews with representatives from a sample of grantmakers surfaced through the mapping activity; and (c) a series of case studies to showcase some leading funders in the Aboriginal funding sphere or initiatives dedicated to building community capacity as well as supporting Aboriginal beneficiaries and causes. This report contains the key findings from the three-part research initiative

Quality target negotiation in health care : evidence from the English NHS

We examine how public sector third-party purchasers and hospitals negotiate quality targets when a fixed proportion of hospital revenue is required to be linked to quality. We develop a bargaining model linking the number of quality targets to purchaser and hospital characteristics. Using data extracted from 153 contracts for acute hospital services in England in 2010/11, we find that the number of quality targets is associated with the purchaser’s population health and its budget, the hospital type, whether the purchaser delegated negotiation to an agency, and the quality targets imposed by the supervising regional health authority

Uses of population census data for monitoring geographical imbalance in the health workforce: snapshots from three developing countries

BACKGROUND: Imbalance in the distribution of human resources for health (HRH), eventually leading to inequities in health services delivery and population health outcomes, is an issue of social and political concern in many countries. However, the empirical evidence to support decision-making is often fragmented, and many standard data sources that can potentially produce statistics relevant to the issue remain underused, especially in developing countries. This study investigated the uses of demographic census data for monitoring geographical imbalance in the health workforce for three developing countries, as a basis for formulation of evidence-based health policy options. METHODS: Population-based indicators of geographical variations among HRH were extracted from census microdata samples for Kenya, Mexico and Viet Nam. Health workforce statistics were matched against international standards of occupational classification to control for cross-national comparability. Summary measures of inequality were calculated to monitor the distribution of health workers across spatial units and by occupational group. RESULTS: Strong inequalities were found in the geographical distribution of the health workforce in all three countries, with the highest densities of HRH tending to be found in the capital areas. Cross-national differences were found in the magnitude of distributional inequality according to occupational group, with health professionals most susceptible to inequitable distribution in Kenya and Viet Nam but less so in Mexico compared to their associate professional counterparts. Some discrepancies were suggested between mappings of occupational information from the raw data with the international system, especially for nursing and midwifery specializations. CONCLUSIONS: The problem of geographical imbalance among HRH across countries in the developing world holds important implications at the local, national and international levels, in terms of constraints for the effective deployment, management and retention of HRH, and ultimately for the equitable delivery of health services. A number of advantages were revealed of using census data in health research, notably the potential for producing detailed statistics on health workforce characteristics at the sub-national level. However, lack of consistency in the compilation and processing of occupational information over time and across countries continues to hamper comparative analyses for HRH policy monitoring and evaluation

Cardiovascular disease types and corresponding definitions in province-wide health administrative databases.

<p>ICD: International Classification of Diseases, version 9 or 10; OHIP: Ontario Health Insurance Plan; DAD: Discharge Abstract Database; NACRS: National Ambulatory Care Reporting System; PPV: Positive predictive value; MRD: most responsible diagnosis; N/A: Not available</p><p>¹ A similar validated algorithm published (using [OHIP + DAD claim] in place of [NACRS + 2^nd NACRS (2 claims in any study year)]: Schultz SE, Rothwell DM, Chen Z, Tu K. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records. Chronic Dis Inj Can 2013; 33(3): 160–6.</p><p>² Single NACRS I480 main diagnosis: Atzema CL, Austin PC, Miller E, Chong AC, Yun L, Dorian P. A population-based description of atrial fibrillation in the emergency department, 2002–2010. Ann Emerg Med 2013;62(6):570–7</p><p>³ Validated algorithm published at: Tu K, Campbell NR, Chen Z, Cauch-Dudek K, McAlister FA. Accuracy of administrative databases in identifying patients with hypertension. Open Med 2007; 1(1): 18–26.</p><p>Cardiovascular disease types and corresponding definitions in province-wide health administrative databases.</p

Age- and sex-adjusted all-cause and cardiovascular-related¹ one-year mortality (from incident diagnosis) by cardiovascular disease, per 100 persons, in the Métis and general Ontario population, April 1 2006 to March 31 2012.

<p>CI: Confidence Interval</p><p>¹ ICD 9/10 diagnostic codes to define cardiovascular-related mortality were obtained from: Statistics Canada. Comparability of ICD-10 and ICD-9 for Mortality Statistics in Canada. Ottawa ON, 2005. ICD-9 codes: 390–448. ICD-10 codes: I00-I78.</p><p>Age- and sex-adjusted all-cause and cardiovascular-related^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121779#t004fn002" target="_blank">1</a>} one-year mortality (from incident diagnosis) by cardiovascular disease, per 100 persons, in the Métis and general Ontario population, April 1 2006 to March 31 2012.</p

Quality of care measures after incident cardiovascular disease diagnosis in the Métis and the rest of the Ontario population, April 1 2006 to March 31 2011.

<p>CI: Confidence Interval</p><p>¹ Defined as frequency of beta-blocker use within 3 months after incident diagnosis in persons aged 65+</p><p>² Defined as frequency of outpatient echocardiogram within 6 months of incident diagnosis</p><p>³ Defined as age- and sex-adjusted rate ratio of emergency department visits in the year after incident diagnosis</p><p>* p<0.05</p><p>Quality of care measures after incident cardiovascular disease diagnosis in the Métis and the rest of the Ontario population, April 1 2006 to March 31 2011.</p

Age- and sex-adjusted rate ratios of one-year disease-specific hospitalizations (from incident diagnosis) by cardiovascular disease, in the Métis and general Ontario population, April 1 2006 to March 31 2012.

<p>CI: Confidence Interval</p><p>* p <0.05</p><p>Age- and sex-adjusted rate ratios of one-year disease-specific hospitalizations (from incident diagnosis) by cardiovascular disease, in the Métis and general Ontario population, April 1 2006 to March 31 2012.</p

Demographic characteristics of the Métis and the general Ontario population as of April 1, 2006.

<p>IQR: Interquartile Range</p><p>¹Income quintile was determined from postal codes obtained from the Registered Persons Database and neighbourhood-level median household income from Statistics Canada census data. Quintiles range from poorest (Q1) to wealthiest (Q5).</p><p>Demographic characteristics of the Métis and the general Ontario population as of April 1, 2006.</p

Study flow diagram of Métis and the general Ontario population.

<p>Study flow diagram of Métis and the general Ontario population.</p

Age- and sex-adjusted prevalence and incidence of cardiovascular diseases, per 100 persons, in the Métis and the general Ontario population, April 1 2006 to March 31 2011.

<p>CI: Confidence Interval; IQR: Interquartile Range</p><p>* p <0.05</p><p>Age- and sex-adjusted prevalence and incidence of cardiovascular diseases, per 100 persons, in the Métis and the general Ontario population, April 1 2006 to March 31 2011.</p