A snapshot of families engaged with Whānau Ora services in Aotearoa New Zealand: a retrospective cross-sectional study

Pacific peoples make up 8.9% of the total Aotearoa New Zealand population.¹ There are at least 17 distinct Pacific ethnicities, all with unique languages and cultures.^2,3 Two-thirds of the population live in Auckland and make up around 17% of the total Auckland population.

Compared with Aotearoa New Zealand’s total population, Pacific peoples are more likely to live in large households, with over half living in households with five or more people.⁴ Although larger household size is associated with a reduced risk of loneliness and higher Pacific language competency⁴ (i.e., fostering cultural identity and better mental wellbeing⁵), it is also associated with overcrowding and lower levels of various socio-economic and health outcomes.^6,7 The 2023 Census found that 40% of Pacific peoples live in overcrowded housing. One of the contributing factors is an insufficient rental market, with 65% of Pacific families renting, and yet it is estimated that only 4% of rentals have five or more bedrooms.⁴ Overcrowding has been linked to negative impacts on health. For example, Pacific families who lived in dwellings with at least one major issue such as cold, mould, damp or need for repairs gave substantially lower self-ratings for physical health, mental wellbeing and life-satisfaction compared to those with no housing issues.⁴ Additionally, Pacific people living in overcrowded housing are more likely to have higher rates of hospitalisations due to preventable illnesses.⁸

Pacific communities face greater socio-economic challenges, which contribute to poorer health. In comparison to the total population, Pacific peoples are more likely to have higher debt, higher unemployment rates, lower income and greater material hardship.⁹ Further, Pacific children are more likely to live in a household experiencing material hardship, food insecurity or housing issues.⁹ These disparities partly stem from factors such as lower educational attainment, lower English proficiency and experiences of discrimination and inequality.⁹

In 2014, Pasifika Futures Ltd (PFL), Aotearoa New Zealand’s Whānau Ora commissioning agency for Pacific communities was established by the Pasifika Medical Association to provide comprehensive services that would meet the health and social needs of Pacific communities in a family-centred and co-designed approach.¹⁰ Supported by programme navigators, families created plans and set goals to achieve their aspirations in four areas of wellbeing: economic independence (financial freedom), education (lifelong learning), health and community connection. Depending on each family’s goals and needs, navigators connected them to the relevant resources and services. Nationally, the programme has maintained a broad influence currently reaching about 90% of Pacific families in Aotearoa New Zealand. Over a 10-year span, PFL has worked alongside and funded over 100 Whānau Ora commissioned partners and supported over 83,000 Pacific families, translating to 395,300 individuals.¹¹

Conversely, other behaviour change interventions developed for Pacific communities are traditionally more likely to be based on health behaviour models premised on the central theory that behaviour is individual and rational.¹² These frameworks are not designed to consider wellbeing values that go beyond physical health as the only measure of wellbeing, compared to holistic Pacific models of health such as the Fonofale model.¹³ Whānau Ora was set up to address the wellbeing of families from a broader, holistic perspective and develop programmes that are community led and not constrained by conventional service paradigms.

Previous analyses of Pacific Whānau Ora data outcomes used the Measurement Assessment Scoring Tool (MAST) to create “needs” scores for families enrolled in the programme.¹¹ The most recent PFL report found that needs scores reduced from entry into the programme to exit. In 2020/2021 over 40% of families who entered the service had moderate to high levels of need compared to 15–20% of families subsequently exiting the services in the following years.¹¹ While these findings support the effectiveness of the programme, more detailed analyses regarding particular subsets of the Pacific population and their level of needs are required to understand who may require targeted support. The aim of this current study was to examine the association between socio-demographic factors and needs (as measured by the MAST score) in families enrolled in Whānau Ora over an 8-year period.

Method

This observational, national cross-sectional study describes the families engaged with Whānau Ora services over 8 years at their first MAST score assessment. Whānau Ora services focus on Pacific families in Aotearoa New Zealand. The study period spans from July 2015 to June 2023, using data from families enrolled in Whānau Ora Core Navigation services commissioned by Pasifika Futures Ltd. As this study is based on a programme where evaluation is routinely performed to monitor its progress, ethical approval was not required.

Measurements

The programme navigators had performed the measurements (gave “needs” score ratings of families) using a standardised MAST (provided in the Appendix) protocol to assess baseline wellbeing and needs at the time of service engagement. Data on these measures were retrieved from the family databases. Wellbeing and needs scores measured wellbeing across four outcome domains: economic (financial freedom), education (lifelong learning), health (living longer, living better) and connection (leadership, culture and community). Altogether there are 47 items that are clustered into 20 areas of need, and these are each scored between one (lowest need level) and five (highest need level). These scores are further grouped together into four priority areas of need: living financially free; lifelong learning; living longer, living better; and leadership, culture and community. The priority area scores yield a maximum score of 25 in each. Thus, the overall summed MAST scores can range from five (minimum level of need) to 100 (maximum level of need). These data from overall MAST scores, developed specifically for the Whānau Ora Core Navigation programme, were analysed. A higher MAST score indicates lower wellbeing and higher level of need. Finally, families who registered a score of 80 or more are considered “high” needs. This was also identified as an outcome in our analysis. Demographic data included mean family age, male percentage (% of family members that were male), main family ethnicity (identified by the family), languages spoken, region and family size. Region is based on the following programme-defined groups: Auckland, Northland, Wellington, South Island and Midlands (all other regions located in the middle of the North Island).

Statistical analysis

Statistical analysis was performed at the family level, as the MAST scoring items relate to the family as a whole (Appendix). MAST score distribution was checked for normality. Linear regression was used to examine predictors of initial MAST score (continuous), measured at the first assessment. Logistic regression was used to examine predictors of high-needs initial MAST score, measured at the first assessment. Regression analyses were performed for univariable (unadjusted) and multivariable (adjusted for all other predictors) models. The independent variables in each multivariable model comprised age, male percentage, ethnic group, languages spoken, region and family size. Unstated answers to covariates were retained so that regression output was adjusted for non-responses. All analyses were performed using R (version 4.3.2). A two-sided P-value of less than 0.05 was considered statistically significant.

Results

There were 15,135 families representing 51,362 individuals—of whom 51,068 belonged to families who identified with a Pacific ethnicity as their main ethnic group—with an initial MAST assessment registered with Pasifika Futures between July 2015 and June 2023. Of these 15,135 families, 3,136 were excluded as they had missing MAST details. The remaining 11,999 families were included in this study. Table 1 shows the characteristics of the families included in the study. Over half (53%) of families had a mean age of less than 40 years. One-quarter had fewer than one in 10 members who were males. Nearly 40% were Sāmoan, and a further 31% were Tongan. Over a quarter (27%) of families spoke English only or were bilingual, while a further 25% of families spoke only their Pacific language. Around 30% of families had at least six members.

View Table 1–3.

Mean differences in initial MAST scores

Table 2 shows mean differences in initial MAST scores between groups of Whānau Ora families. The univariable analysis showed significant differences across all the comparison groups (p<0.0001). Compared with MAST scores for families living in Auckland, scores were lower (by up to 9.1 points) in three regions outside of Auckland: Midlands (−7.2, 95% CI: −8.0–−6.4), South Island (−7.9, 95% CI: −8.7–−7.1) and Wellington (−9.1, 95% CI: −9.9–−8.3).

Multivariable analysis, adjusted for other covariates, shows that average MAST scores remained significantly lower than in Auckland (by up to −6.8, 95% CI: −7.6–−6.0) in these three regions (p<0.0001). MAST scores were 4.0 points (95% CI: 1.6–6.3) higher in Northland than in Auckland. Living outside of Auckland, except for Northland, was associated with having a lower average initial MAST score and a higher level of wellbeing.

The univariable results showed increasing family size was associated with a higher average MAST score. Compared to the MAST score of families in the lowest tertile (one to three members), MAST scores were higher by 5.0 points (95% CI: 4.4–5.6) among those in tertile two (four to five members) and even higher (by 6.0 [95% CI: 5.4–6.6]) in those in tertile three (six to 26 members). Accounting for influences from other factors in the multivariable analysis, these differences were reduced but were still significant (p<0.0001). That is, compared to those in the lowest tertile of family size, those in the highest tertile had a MAST score 2.4 points (95% CI: 1.7–3.8) higher. Thus, larger families tend to have higher average initial MAST scores and a higher level of need (lower wellbeing).

Groups with high need

Table 3 shows odds ratios (OR) for predictors of having a “high need” MAST score (a MAST score of 80 or more out of 100). Perhaps not unsurprisingly, these results emulate those in Table 2, in that all the covariate groups are significantly associated with odds of receiving high-needs assessment scores (p<0.0001). Adjusting for other covariates, sub-groups with lower multivariable odds for registering a high-need score compared with living in Auckland were: living in the Midlands (OR: 0.65, 95% CI: 0.48–0.86), South Island (OR: 0.29, 95% CI: 0.22–0.37) or Wellington regions (OR: 0.37, 95% CI: 0.27–0.48); or older families, average age over 40 (OR: 0.66, 95% CI: 0.56–0.79). Conversely, sub-groups with greater odds were those who spoke a Pacific language only (OR: 1.47, 95% CI: 1.19–1.82 or higher); younger families (with an average age under 25); larger families (particularly those with six or more members, OR: 1.50, 95% CI: 1.28–1.75); or living in either Auckland or Northland. Compared with Sāmoan families most other ethnic groups had lower odds (OR: 0.71, 95% CI: 0.54–0.93 or less).

Discussion

This large-scale, national cross-sectional study examined socio-demographic group differences in Pacific family support needs, enabling the identification of key indicators that point to higher support requirements. Higher needs were observed in those with Sāmoan or Tuvaluan ethnicity, those who were non-English–speaking, those with larger family households, those living in Auckland or Northland and those with younger families. Regional findings showed that families living in Auckland and Northland have the highest rates of severe housing deprivation. These results relate to previous studies that consistently show that Pacific populations in Aotearoa New Zealand experience disproportionately high rates of housing deprivation, with contributing factors including socio-economic disparities, larger family household sizes and cultural norms.⁶ Pacific families experiencing severe housing deprivation were shown to be young, with nearly 50% aged under 25 years.⁶ Another study showed that those of Sāmoan and Tongan ethnicity were more likely to live in crowded households and had higher rates of child hospitalisations for respiratory-related illnesses compared to those of Cook Islands Māori and Niuean ethnicity.⁷

In many cases, we did not have data on age, male percentage and languages spoken (Tables 1–3). However, we adjusted for their non-responses by including these in our analysis as “not stated” groups. Missing data for male percentage and languages spoken was associated with higher needs (higher adjusted MAST score; Tables 2 and 3). A possible reason for this is that this missing information is reflective of families who had a low level of exposure to the programme: those who had minimal contact with the navigators or those who dropped out. Given this scenario, together with the assumption that less exposure to the programme is associated with more need, we would expect “not stated” groups to have higher MAST scores. In support of this assumption, prior analysis of the Pacific Whānau Ora data showed that needs scores reduce from entry into the programme.¹¹ Another possible explanation is that, given demographic variation in non-response rates in Aotearoa New Zealand research,¹⁴ the “not stated” groups may tend to be of demographics that are associated with higher needs. For example, speaking a Pacific language only or being bilingual were associated with having a higher MAST score (Tables 2 and 3), and if non-respondents were more likely to belong to these language groups, this would account for their higher MAST scores.

The current study was able to utilise data collected by health navigators who worked closely with families. This increased the validity of the data by being more likely to capture accurate and salient family needs at the time of measurement. Another strength of the study included the power to detect statistical differences between groups due to the large sample number, which enhances the study’s generalisability. The final population number represented around 11.5% of the current total Pacific population in Aotearoa New Zealand.

There are several limitations that are important when considering these findings. Although the MAST tool has been validated for content by experts and Pacific community members,¹¹ it has not undergone robust statistical validation, which may introduce bias (e.g., construct-validity bias).¹⁵ Additionally, subjective measures in the MAST score could introduce self-report bias. Another limitation is that, as this was an observational study, it was not able to establish causality. For instance, there was an association between larger family household size and higher needs scores, but we cannot confirm causation, and as these are scores assigned to families seen by Whānau Ora services, reverse causation is also a consideration. For the observed associations between region and MAST scores, we cannot determine if MAST scores influence where a family resides, or vice versa. Finally, there may be residual confounding from unmeasured variables unaccounted for in the models.

In conclusion, our study has identified socio-demographic factors associated with lower wellbeing in Pacific families, which can be used to create targeted interventions to drive improvements. It gives key evidence for supporting families whose primary language is a Pacific language, and developing housing assistance initiatives and financial literacy workshops. In addition, finding ways to give targeted support, particularly for younger families, could lead to overall better distribution of healthcare resources and improved access to vital social services to improve wellbeing for our Pacific families who have unmet needs.

View Appendix.