Suchergebnisse

Objective and ApproachThis study examined the impact of two sources of bias on differences in ethnic disparities in non-communicable disease rates in New Zealand (NZ). Data were sourced from Stats NZ's Integrated Data Infrastructure (IDI), a collection of deidentified whole-population administrative (eg, health, justice, housing) and survey datasets (eg, NZ Census) linked at the individual level using probabilistic linkage procedures. Linking several datasets reduces the size of the population available for study because not all individuals can be linked. In addition, there are several sources of ethnicity information that may disagree with each other. We will illustrate the impact of population loss due to linkage and ethnicity data source on Māori-European gaps in lung cancer and cardiovascular disease.
ResultsOur results showed that the choice of ethnicity information source and the population used had an impact on the size of ethnic disparities in lung cancer and cardiovascular disease. For lung cancer the age standardised rate ratio for Māori:European ranged from 2.88 to 3.21, and for CVD 1.70 to 1.87. Population loss and ethnicity data source each had independent effects on the size of ethnic differences.
ConclusionsDifferent combinations of population and ethnicity information source produced different estimates of ethnic gaps in lung cancer and CVD prevalence. Population and source of ethnicity data both had independent effects on the size of ethnic differences.
ImplicationsComparisons of ethnic differences in disease prevalence between studies, or over time, may be misleading if they do not use the same population and ethnicity data source.

BackgroundLiving with a family member who has a long-term health condition (HC) is associated with poorer health and well-being outcomes, but the number and socio-demographics of people and families impacted by a family member who has an HC is unknown.
MethodsUsing the Integrated Data Infrastructure (IDI), a collection of linked administrative datasets for the full New Zealand (NZ) population, we identified n= 1,043,172 families using 2013 NZ Census data, and used health data over the previous 5-years to ascertain whether people in these families (n=3,137,517) had cancer, chronic obstructive pulmonary disease, coronary health disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI) or a mental health/behavioural condition (MHBC).
ResultsOver 60% of families included at least one person with a HC. The most common HCs were MHBCs (39.4% of families), diabetes (16.0%), and TBI (13.9%). A high proportion of multi-generation families (73.9%) included a member with a HC. Two-thirds (67.7%) of Pacific Peoples either had a HC themselves or were living with a family member who had a HC, compared with 62.3% of Europeans and 62.5% of Māori (the indigenous peoples of NZ). At the highest level of socioeconomic deprivation, 57.6% of children lived with a family member who had a HC.
ConclusionsThree in five NZ families were living with a HC, with differences in the proportion affected according to family composition, socio-economic status and an individuals' ethnicity. This suggests that there are a substantial number of people at risk of the associated poor outcomes.

Aims To examine the incidence of type 2 diabetes in people with newly diagnosed prediabetes and the factors that protect against this progression. Methods The study population was 14,043 adults with pre-diabetes enrolled in a primary health organization in the upper North Island of New Zealand. Glycated hemoglobin (HbA1c) and body mass index (BMI) were linked to government health, census and social datasets in the Statistics New Zealand Integrated Data Infrastructure. Adults with a first diagnosis of pre-diabetes between 2009 and 2017 (HbA1c in range 5.9-6.6% [41-49 mmol/mol]) were followed-up for type 2 diabetes incidence. Cox regression was used to examine protective factors and adjust for potential confounding. Results Cumulative diabetes incidence was 5.0% after three years. Progression was greater in younger adults, men, people with higher HbA1c, greater BMI and a more recent diagnosis. Progression was lower in people treated with metformin, and Indigenous language speakers. Higher progression rates for Māori (Indigenous population) and Pacific peoples (migrants to New Zealand) were related to higher baseline HbA1c. Conclusions This is the first study to identify Indigenous language as a protective factor against diabetes, and results confirm obesity as a key target for population prevention. People with identified risk factors should be prioritized for pre-diabetes interventions. ; Peer Reviewed