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Background: Disparities in cardiovascular outcomes between Māori and non-Māori persist despite technological advances in the treatment of cardiovascular disease and improved service provision. Little is known about how social determinants of health, such as income [in]security affect Māori men's access, treatment, and recovery from cardiovascular disease. This paper explores the contexts within which cardiovascular disease is experienced and healthcare becomes embedded. Methods: This study utilized a case-comparative narrative approach to document and make sense of the patient experiences of four male Māori patients who, in the previous 6 months, had come through cardiac investigation and treatment at Waikato Hospital, a large tertiary cardiac center in New Zealand. Participant accounts were elicited using a culturally patterned narrative approach to case development, informed by Kaupapa Māori Research practices. It involved three repeat 1–3-hour interviews recorded with participants (12 interviews); the first interviews took place 5–16 weeks after surgery/discharge. Results: Each of the four case studies firstly details a serious cardiac event(s) before describing the varying levels of financial worry they experienced. Major financial disruptions to their lives were at the forefront of the concerns of those facing financial insecurity—as opposed to their medical problems. Financial hardship within the context of an unresponsive welfare system impacted the access to care and access to funding contributed to psychological distress for several participants. Economic security and reciprocal relationships between employers and employees facilitated positive treatment experiences and recovery. Conclusion: Findings suggest that although multiple factors influence participant experiences and treatment outcomes, financial [in]security, and personal income is a key determinant. The heterogeneity in participant narratives suggests that although general inequities in health may exist for Māori as a population group, these inequities do not appear to be uniform. We postulate diverse mechanisms, by which financial insecurity may adversely affect outcomes from treatment and demonstrate financial security as a significant determinant in allowing patients to respond to and recover from cardiovascular disease more effectively. [ABSTRACT FROM AUTHOR]

Introduction: The Revised Cardiac Risk Index (RCRI) is a six-parameter model that is commonly used in assessing individual 30-day perioperative cardiovascular risk before general surgery, but its use in patients on chronic kidney replacement therapy (KRT) is unvalidated. This study aimed to externally validate RCRI in this patient group over a 15-year period. Methods: Data linkage was used between the the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry and jurisdictional hospital admisisons data across Australia and New Zealand to identify all incident and prevalent patients on chronic KRT between 2000 and 2015 who underwent elective abdominal surgery. Chronic KRT was categorised as haemodialysis (HD), peritoneal dialysis (PD), home haemodialysis (HHD) and kidney transplant. The outcome of interest was major adverse cardiovascular event (MACE) which was defined as nonfatal myocardial infarction, nonfatal stroke, non-fatal cardiac arrest and cardiovascular mortality at 30 days. Logistic regression was used with the RCRI score included as a continuous variable to estimate discrimination by area under the receiver operating curve (AUROC). Calibration was evaluated using a calibration plot. Clinical utility was assessed using a decision curve analysis to determine the net benefit. Results: A total of 5094 elective surgeries were undertaken, and MACE occurred in 153 individuals (3.0%). Overall, RCRI had poor discrimination in patients on chronic KRT undergoing elective surgery (AUROC 0.67), particularly in patients aged greater than 65 years (AUROC 0.591). A calibration plot showed that RCRI overestimated risk of MACE. The expected-to-observed outcome ratio was 6.0, 5.1 and 2.5 for those with RCRI scores of 1, 2 and ≥ 3, respectively. Discrimination was moderate in patients under 65 years and in kidney transplant recipients, with AUROC values of 0.740 and 0.718, respectively. Overestimation was common but less so for kidney transplant recipients. Decision curve analysis showed that there was no net benefit of using the tool in neither the overall cohort nor patients under 65 years, but a slight benefit associated with threshold probability > 5.5% in kidney transplant recipients. Conclusions: The RCRI tool performed poorly and overestimated risk in patients on chronic dialysis, potentially misinforming patients and clinicians about the risk of elective surgery. Further research is needed to define a more comprehensive means of estimating risk in this unique population. [ABSTRACT FROM AUTHOR]

Background: Cardiometabolic disease (CMD) risk often begins early in life. Healthy lifestyle behaviors can mitigate risk, but the optimal combination of behaviors has not been determined. This cross-sectional study simultaneously examined the associations between lifestyle factors (fitness, activity behaviors, and dietary patterns) and CMD risk in preadolescent children. Methods: 1480 New Zealand children aged 8–10 years were recruited. Participants included 316 preadolescents (50% female, age: 9.5 ± 1.1 years, BMI: 17.9 ± 3.3 kg/m2). Fitness (cardiorespiratory fitness [CRF], muscular fitness), activity behaviors (physical activity, sedentary, sleep), and dietary patterns were measured. Factor analysis was used to derive a CMD risk score from 13 variables (adiposity, peripheral and central hemodynamics, glycemic control, and blood lipids). Results: Only CRF (β = −0.45, p < 0.001) and sedentary time (β = 0.12, p = 0.019) were associated with the CMD risk score in the adjusted multivariable analysis. CRF was found to be nonlinear (VO2 max ≤ ≈42 mL/kg/min associated with higher CMD risk score), and thus a CRF polynomial term was added, which was also associated (β = 0.19, p < 0.001) with the CMD risk score. Significant associations were not found with sleep or dietary variables. Conclusion: The findings indicate that increasing CRF and decreasing sedentary behavior may be important public health targets in preadolescent children. [ABSTRACT FROM AUTHOR]

Type 2 diabetes (T2DM), its related morbidities and entrenched diabetes‐related inequities pose significant challenges for health care delivery systems in Aotearoa New Zealand (NZ). Primary care services undertake the majority of diabetes prevention work by initially detecting and managing those with prediabetes. In this viewpoint, we present available NZ data to highlight NZ trends in prediabetes and consider the current NZ clinical guidelines and the prediabetes care pathway. Multiple areas for improvement are identified to optimise diabetes prevention, potentially reduce T2DM inequities, and sustain more effective prediabetes management in primary care in NZ. [ABSTRACT FROM AUTHOR]

Introduction. Type 2 diabetes mellitus (T2DM) is a highly prevalent and potentially preventable condition associated with significant health, social, and economic costs. The detection and management of pre-diabetes is an important opportunity to prevent or delay the onset of T2DM and associated morbidities; however, its importance is controversial as the health risks associated with pre-diabetes are poorly understood. Aim. To understand the cardio-metabolic health profile of a sample of adults with pre-diabetes in Aotearoa New Zealand. Methods. Secondary analyses of baseline data from all 153 adults recruited to an intervention trial for adults with pre-diabetes were carried out. A profile of cardio-metabolic risk was measured by describing the proportion with metabolic syndrome (MetS) calculated using Adult Treatment Panel III criteria, which includes blood pressure, lipids, and obesity in addition to glycaemic measures. The severity of MetS was calculated as MetS Z-scores. Subgroup analyses for sex, ethnicity and glycated haemoglobin (HbA1c) were performed. Results. Overall, 74% of this study population had MetS, and the proportion varied according to ethnicity and HbA1c level. The severity of MetS was highly variable, with MetS-Z-scores ranging from -1.0 to 2.8. Although mean MetS Z-scores differed according to ethnicity and HbA1c level, all subgroups included individuals with widely differing severity of MetS, suggesting likely quite different risks for progression to diabetes or cardiovascular disease across the range of pre-diabetes defined by HbA1c. Discussion. Single biochemical markers of glycaemia are insufficient to ascertain overall cardio-metabolic risk when prioritising clinical efforts for those with pre-diabetes, particularly in primary care, where the potential for preventing or delaying the onset of type 2 diabetes mellitus (T2DM) is significant. Findings indicate the importance of attending to all cardiometabolic risk factors when caring for people with pre-diabetes. The development of tools using multiple relevant variables and predicting a comprehensive range of outcomes would improve timely risk stratification and treatment effect monitoring of pre-diabetes populations. [ABSTRACT FROM AUTHOR]

Background: Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods: We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results: The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion: Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models. [ABSTRACT FROM AUTHOR]