Inequality in Quality? The selection and use of quality indicators to investigate ethnic disparities in the quality of hospital care, Aotearoa New Zealand.

There are well documented differences in health outcomes between Māori and New Zealand (NZ) Europeans. Jones (2002) describes differential treatment within the health system as one determinant of ethnic inequalities: is it possible that New Zealand’s health services contribute to the differences in health status between Māori and NZ Europeans? Aim and objectives: This thesis describes an investigation into the quality of care for Māori compared with NZ Europeans in public hospitals nationally. The objectives of this study were: 1. To identify measures applicable to this study context with validity as indicators of the quality of health care. 2. To employ this/these measure(s) to compare the quality of inpatient hospital care between NZ Māori and NZ European patients, with consideration of confounding and mediating factors in order to estimate the net effect of ethnic group on the quality indicator. 3. To offer recommendations in light of the findings of this study. Methods: Literature review and three ‘study context’ criteria were used to select two indicators to represent inpatient quality of care - unplanned readmission/death within thirty days of discharge (‘readmission’) and patient satisfaction. Phase One of the research used data from the National Minimum Data Set to calculate and compare the rate of readmission for Māori and NZ European inpatients at NZ public hospitals. Characteristics of the two ethnic groups were compared with age-sex adjusted proportions, and variation in the likelihood of readmission with patient and clinical factors was explored with rate ratios. The odds of readmission for NZ Māori compared to NZ European patients (n=89,090) were calculated from a logistic regression model, with variables representing age, comorbidity, index procedure, hospital volume and socio-economic position included. In Phase Two, Māori and NZ Europeans recently discharged from one of three NZ hospitals were approached to complete the Client Satisfaction Questionnaire-8 (CSQ-8). Descriptive analyses explored the characteristics of the respondents (n=1103) according to ethnic group and mean satisfaction score. A linear regression model including variables for age and health status estimated the difference in mean CSQ-8 score for Māori compared to NZ European respondents. Results: The Phase One analyses found 16% higher odds of readmission for NZ Māori compared to NZ European patients (odds ratio (OR) 1.16, 95% CI 1.08 – 1.24; adjusted for age, index procedure, comorbidity, hospital volume, and deprivation), and 19% higher odds (OR 1.19, 95% CI 1.11 – 1.27) when the model did not include a deprivation term. Readmission was also associated with older age (OR 1.33; 95% CI 1.19-1.48, for >79 yrs compared with 18-39 yrs), higher comorbidity (OR 2.08; 95% CI 1.89-2.31 for Charlson score 3+ compared with 0) and higher hospital volume (OR 0.81; 95% CI 0.76-0.86 for lowest volume facility compared with highest). Measurement error of quality of care by readmission was the primary source of bias in this phase; sensitivity analyses suggest the contribution of ‘poor quality’ to the increased odds of readmission for Māori may be small. That is, unmeasured factors may have a comparatively greater role than quality of care in the ethnic difference of this outcome. The Phase Two multivariable model showed comparable satisfaction for Māori and NZ European respondents, with the difference in mean scores only -0.02 (95% CI -0.36 - 0.57). However, bias from differential non-response is possible – participation for Māori was 37% compared to 60% for NZ Europeans. These results may also be affected by differential or non-differential measurement error. That is, CSQ-8 score may have lower validity as a measure of health care quality in this setting and population. Conclusions: A valid measurement of quality by readmission or satisfaction is difficult, as both are highly vulnerable to error. In particular, ethnic differences in readmission may be predominantly influenced by factors other than the inpatient quality of care. However, given supporting evidence and the plausibility of quality as a component cause for health outcomes inequalities, it is likely that the increased odds of readmission for Māori compared to NZ Europeans is in part due to poorer quality of care. This study recommends protocols be developed to guide the calculation and interpretation of readmission as a proxy for quality, and suggests further research to explore the measurement of patient satisfaction in the NZ setting

Inequality in Quality? The selection and use of quality indicators to investigate ethnic disparities in the quality of hospital care, Aotearoa New Zealand.

There are well documented differences in health outcomes between Māori and New Zealand (NZ) Europeans. Jones (2002) describes differential treatment within the health system as one determinant of ethnic inequalities: is it possible that New Zealand’s health services contribute to the differences in health status between Māori and NZ Europeans? Aim and objectives: This thesis describes an investigation into the quality of care for Māori compared with NZ Europeans in public hospitals nationally. The objectives of this study were: 1. To identify measures applicable to this study context with validity as indicators of the quality of health care. 2. To employ this/these measure(s) to compare the quality of inpatient hospital care between NZ Māori and NZ European patients, with consideration of confounding and mediating factors in order to estimate the net effect of ethnic group on the quality indicator. 3. To offer recommendations in light of the findings of this study. Methods: Literature review and three ‘study context’ criteria were used to select two indicators to represent inpatient quality of care - unplanned readmission/death within thirty days of discharge (‘readmission’) and patient satisfaction. Phase One of the research used data from the National Minimum Data Set to calculate and compare the rate of readmission for Māori and NZ European inpatients at NZ public hospitals. Characteristics of the two ethnic groups were compared with age-sex adjusted proportions, and variation in the likelihood of readmission with patient and clinical factors was explored with rate ratios. The odds of readmission for NZ Māori compared to NZ European patients (n=89,090) were calculated from a logistic regression model, with variables representing age, comorbidity, index procedure, hospital volume and socio-economic position included. In Phase Two, Māori and NZ Europeans recently discharged from one of three NZ hospitals were approached to complete the Client Satisfaction Questionnaire-8 (CSQ-8). Descriptive analyses explored the characteristics of the respondents (n=1103) according to ethnic group and mean satisfaction score. A linear regression model including variables for age and health status estimated the difference in mean CSQ-8 score for Māori compared to NZ European respondents. Results: The Phase One analyses found 16% higher odds of readmission for NZ Māori compared to NZ European patients (odds ratio (OR) 1.16, 95% CI 1.08 – 1.24; adjusted for age, index procedure, comorbidity, hospital volume, and deprivation), and 19% higher odds (OR 1.19, 95% CI 1.11 – 1.27) when the model did not include a deprivation term. Readmission was also associated with older age (OR 1.33; 95% CI 1.19-1.48, for >79 yrs compared with 18-39 yrs), higher comorbidity (OR 2.08; 95% CI 1.89-2.31 for Charlson score 3+ compared with 0) and higher hospital volume (OR 0.81; 95% CI 0.76-0.86 for lowest volume facility compared with highest). Measurement error of quality of care by readmission was the primary source of bias in this phase; sensitivity analyses suggest the contribution of ‘poor quality’ to the increased odds of readmission for Māori may be small. That is, unmeasured factors may have a comparatively greater role than quality of care in the ethnic difference of this outcome. The Phase Two multivariable model showed comparable satisfaction for Māori and NZ European respondents, with the difference in mean scores only -0.02 (95% CI -0.36 - 0.57). However, bias from differential non-response is possible – participation for Māori was 37% compared to 60% for NZ Europeans. These results may also be affected by differential or non-differential measurement error. That is, CSQ-8 score may have lower validity as a measure of health care quality in this setting and population. Conclusions: A valid measurement of quality by readmission or satisfaction is difficult, as both are highly vulnerable to error. In particular, ethnic differences in readmission may be predominantly influenced by factors other than the inpatient quality of care. However, given supporting evidence and the plausibility of quality as a component cause for health outcomes inequalities, it is likely that the increased odds of readmission for Māori compared to NZ Europeans is in part due to poorer quality of care. This study recommends protocols be developed to guide the calculation and interpretation of readmission as a proxy for quality, and suggests further research to explore the measurement of patient satisfaction in the NZ setting

Working and hypertension: gaps in employment not associated with increased risk in 13 European countries, a retrospective cohort study

Abstract Background There is growing evidence to suggest unemployment has a role in the development and incidence of cardiovascular disease. This study explores the contribution of breaks in employment to the development of hypertension, a key risk factor for coronary heart disease. Methods We use data from the Survey of Health, Ageing, and Retirement in Europe to estimate the association between gaps in employment of 6 months or more (‘Not Working’, NW) and the incidence of hypertension in 9,985 individuals aged 50 or over across 13 European countries. Life history information including transitions in and out of employment was used to create a panel dataset where each visit represented one year of life between age 30 and incident hypertension or censoring (whichever came first). Pooled logistic models estimated the odds of hypertension according to the experience of not working, controlling for age at interview, age at each visit, gender, childhood socio-economic position, and country. Results We consistently found no association between NW and hypertension, irrespective of the metrics used in defining the exposure or model specification. Conclusion There is the possibility of bias contributing to the null findings. However, given the relatively consistent evidence for an association between unemployment and cardiovascular outcomes in the literature, our results suggest there may be mechanisms - outside of hypertension – that have a comparatively greater contribution to this association

Under the same roof: co-location of practitioners within primary care is associated with specialized chronic care management

Abstract Background International and national bodies promote interdisciplinary care in the management of people with chronic conditions. We examine one facilitative factor in this team-based approach - the co-location of non-physician disciplines within the primary care practice. Methods We used survey data from 330 General Practices in Ontario, Canada and New Zealand, as a part of a multinational study using The Quality and Costs of Primary Care in Europe (QUALICOPC) surveys. Logistic and linear multivariable regression models were employed to examine the association between the number of disciplines working within the practice, and the capacity of the practice to offer specialized and preventive care for patients with chronic conditions. Results We found that as the number of non-physicians increased, so did the availability of special sessions/clinics for patients with diabetes (odds ratio 1.43, 1.25–1.65), hypertension (1.20, 1.03–1.39), and the elderly (1.22, 1.05–1.42). Co-location was also associated with the provision of disease management programs for chronic obstructive pulmonary disease, diabetes, and asthma; the equipment available in the centre; and the extent of nursing services. Conclusions The care of people with chronic disease is the ‘challenge of the century’. Co-location of practitioners may improve access to services and equipment that aid chronic disease management

Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand

Aotearoa New Zealand’s response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country’s pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice.</p