key: cord-1023789-dut4z0kl
authors: Steyn, N.; Binny, R. N.; Hannah, K.; Hendy, S.; James, A.; Lustig, A.; Ridings, K.; Plank, M. J.; Sporle, A.
title: Maori and Pacific People in New Zealand have higher risk of hospitalisation for COVID-19
date: 2020-12-28
journal: nan
DOI: 10.1101/2020.12.25.20248427
sha: e47a691df4faa1f6ae07b965b372b0676ad1bb78
doc_id: 1023789
cord_uid: dut4z0kl

Aims. We aim to quantify differences in clinical outcomes from COVID-19 infection by ethnicity with a focus on risk of hospitalisation. Methods. We used data on age, ethnicity, deprivation index, pre-existing health conditions, and clinical outcomes on 1,829 COVID-19 cases reported in New Zealand. We used a logistic regression model to calculate odds ratios for the risk of hospitalisation by ethnicity. We also consider length of hospital stay and risk of fatality. Results. M[a]ori have 2.5 times greater odds of hospitalisation than non-M[a]ori, non-Pacific people, after controlling for age and pre-existing conditions. Similarly, Pacific people have 3 times greater odds. Conclusions. Structural inequities and systemic racism in the healthcare system mean that M[a]ori and Pacific communities face a much greater health burden from COVID-19. Older people and those with pre-existing health conditions are also at greater risk. This should inform future policy decisions including prioritising groups for vaccination.

Up to 25 September 2020, New Zealand reported 1,829 confirmed and probable cases of COVID-19, a disease caused by a novel coronavirus originating in Wuhan, China. The majority of these cases were associated with one of two outbreaks of sustained community transmission: the first in March/April 2020 and the second in August/September 2020. Up to 22 May, there 1,504 confirmed and probable cases. Between 22 May and 11 August, there were 65 cases, all of which were in detected in international arrivals and contained in government-managed isolation facilities. Between 11 August and 25 September, 260 cases were reported, with the majority linked to a large cluster in Auckland.

The August cluster differed substantially from the initial outbreak in March/April 2020. The vast majority of cases resulted from workplace, community, public transport and household transmission, rather than being associated with international travel 1 . The August cluster had a higher proportion of cases under 20 years old and a lower proportion of cases over 60 years old than the earlier outbreak ( Figure 1) . It also contained a much higher proportion of cases among the Pacific and Māori populations that the first outbreak 1, 2 . The Pacific community values multi-generational living, but the lack of high-quality suitable housing means that this is often overcrowded 3 . Pacific people also experience poorer access to healthcare 4, 5 and are at greater risk of clinically severe outcomes from COVID-19 infection 6 .

Historically, Māori, and Pacific communities both in New Zealand and in the Pacific have had worse experiences of pandemics. During the 2009 H1N1 influenza pandemic, the rate of infection for Māori was twice that of Pākehā, with increased severity 7 . Our recent research estimated similar inequities would occur in the infection fatality rate for COVID-19 6 . New Zealand's effective public health response to the pandemic has so far limited the number of COVID-19 fatalities to 25 8 , which corresponds to a fatality rate of 5 deaths per million people. This means that there is, thankfully, insufficient empirical data at present to reliably estimate the effect of ethnicity on the infection fatality rate. Here, we aim to estimate the effect of ethnicity on the risk of clinically severe outcomes from COVID-19 measured by the hospitalisation rate and length of hospital stay. The results are important for future policy decisions and pandemic planning, for example identification of priority groups for vaccination against COVID-19. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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We developed three separate risk models to quantify the risk of hospitalisation, length of hospital stay and fatality risk. Each model used the same methodology and set of predictor variables.

Case data was obtained on all 1,829 confirmed and probable cases of COVID-19 reported in New Zealand up to 25 September 2020. The data included hospitalisation status and dates, clinical outcome (i.e. recovered, death), age, sex, underlying health conditions, StatsNZ meshblock of current home address, and ethnicity (see Table 1 ).

The data on underlying health conditions were simplified into a binary variable indicating if the individual had at least one of the following conditions: chronic lung disease, cardiovascular disease, diabetes, immunodeficiency, asthma, or malignancy. These are all known to be associated with increased risk of COVID-19 hospitalisation 9 .

The meshblock number of residential address was used to allocate a measure of geographic socioeconomic deprivation based on the New Zealand deprivation index (NZDep18) 10 . This was not available for 34 cases, so any models that include deprivation index had a smaller sample size of 1,795 cases and 114 hospitalisations.

Total ethnicity data was used to assign individuals into one or more of the following groups: Māori, Pacific, Asian, NZ European/Other. We assigned individuals whose ethnicity was recorded as Middle Eastern/Latin American/African (n=54 cases, 0 hospitalisations) or Unknown (n=8 cases, 0 hospitalisations) to the NZ European/Other ethnicity group. Individuals for whom total ethnicity data was missing (n=29 cases, 1 hospitalisation) were assigned to the ethnicity recorded in the "prioritised ethnicity" field. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. Table 1 . Summary of case data. Deprivation index was used in its raw index form in the model but has been presented as quintiles for ease of interpretation, with the 1 st quintile representing those that reside in a meshblock with the lowest socioeconomic deprivation and the 5 th quintile the highest deprivation. Age is also presented in discretised brackets. The use of total ethnicity statistics means sums over these rows will be greater than the totals where some cases are recorded as having multiple ethnicities.

Of the 120 hospitalised cases, only 102 had listed discharge dates, which were required for analysis on the length of hospital stay. Five of the 18 cases without discharge dates recorded resulted in death, so their discharge date was set to the date of their death. The remaining 13 cases (two who had not recovered by 25 September 2020 and 11 with no discharge date recorded) were excluded from the length of stay analysis. One additional case was excluded as the discharge date recorded was prior to the hospitalisation date. This resulted in a sample of 106 cases with a recorded length of stay in hospital (see Figure 2 ). Of the 11 cases with no discharge date or date of death, 5 (45%) were in Pacific is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. ; https://doi.org/10.1101/2020.12.25.20248427 doi: medRxiv preprint people, despite Pacific people only making up 18% of hospitalisations. This reduced the sample size for Pacific people and likely biased the results. 

For each of the three models, we carried out a simple analysis to determine which predictor variables to include in the model. We used a logistic regression to determine which of ethnicity, underlying health conditions, sex, age, and deprivation should be included. We used Akaike information criterion (AIC) and the area under the receiver operating characteristic curve (AUC) for model selection 11, 12 .

The complete model was:

( (hospitalised)) ~ age + ethnicity + sex + has underlying conditions + dep index Ethnicity was treated as a categorical variable with individuals belonging to one of Māori, Pacific, Asian, or NZ European/Other. In the case of multiple recorded ethnicities, as there was insufficient data to consider all ethnicity combinations, the standard prioritisation was used 13 for the model selection phase (see below for estimation of effect sizes using multiple ethnicity data) . The NZ . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. ; https://doi.org/10.1101/2020.12.25.20248427 doi: medRxiv preprint European/Other group is used as the baseline group so that resulting odds-ratios are interpreted as "difference in risk relative to NZ European/Other". AIC requires all models to have the same sample size, so during model selection the 34 records with missing deprivation index were removed.

During the model selection phase, ethnicity (using prioritised ethnicity) was consistently identified as a significant predictor variable in all three models. Using priority ethnicity neglects important information on individuals who were in multiple ethnicity groups 14 . For example, there were 19 individuals who were recorded as Māori and Pacific, none of whom were hospitalised. In the standard prioritisation routine, these individuals were classified as Māori and did not, therefore, contribute to model estimates for Pacific people. This undercounted Pacific cases and potentially created agerelated biases in the results for Pacific people, as younger Pacific people are more likely to report multiple ethnicities 15 . To correct for this, we reran each model using different ethnicity prioritisation orderings (see Table 2 ). Odds-ratios and confidence intervals on the odds-ratios were obtained by exponentiating the coefficient estimates and confidence intervals on the coefficient estimates for each risk factor.

Ethnicity effect being estimated Prioritisation ordering Māori Māori, Pacific, Asian, NZ European/Other Pacific Pacific, Māori, Asian, NZ European/Other Asian Asian, Māori, Pacific, NZ European/Other Table 2 . Ethnicity prioritisation ordering depending on the ethnicity effect being estimated.

In addition to the risk of hospitalisation, we used a linear model to consider the effect of these variables on length of hospital stay:

length of hospital stay ~ age + ethnicity + sex + has underlying conditions + dep index is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. ; https://doi.org/10.1101/2020.12.25.20248427 doi: medRxiv preprint Finally, despite very limited data, we also considered fatality risk under the same framework:

In this final model, as there were no fatalities in Asian people, we combined the Asian and NZ European/Other ethnicity groups. For both these models, we used the same methodology as for the risk of hospitalisation model, i.e. we used standard ethnicity prioritisation to identify significant predictor variables then re-analysed the contribution of these predictor variables under different ethnicity prioritisation orderings.

For risk of hospitalisation, the model containing age, ethnicity, and the presence of underlying health conditions as predictor variables gave the most parsimonious fit (lowest AIC). This model also has the same predictive power (similar AUC) as more complex models (see Table 3 ). Including interaction terms did not improve the model fit as measured by AIC. Age was always the strongest predictor of hospitalisation and was included in all models. After age has been accounted for, the best two-variable model also included ethnicity.

Coefficient estimates associated with sex were always close to zero and had consistently large pvalues, indicating that sex was not a strong predictor of hospitalisation in New Zealand's COVID-19

cases. This is contrary to some international evidence that suggests men suffer worse clinical outcomes on average 9 . Deprivation index was only statistically significant when considered alongside age but not ethnicity. Deprivation index and ethnicity were slightly correlated, so this suggests the effect of deprivation index was partially captured by ethnicity. Different age groups were represented differently across different levels of deprivation index, suggesting a model containing a deprivation index-age interaction term may be suitable. This was tested and the resulting coefficients were not statistically significant. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted December 28, 2020. ; Table 4 and Figure 3 show the results for the preferred model for risk of hospitalisation. Age was associated with a 4.5% increase in odds of hospitalisation per additional year. The presence of at least one underlying health condition increased the odds of hospitalisation by 1.74 times. After controlling for age and underlying conditions, Māori and Pacific people had substantially higher odds of being hospitalised for COVID-19 than other ethnicities: Māori 2.5 times higher odds ( = 0.002) and Pacific people 3.06 times higher odds ( = 8 × 10 −5 ). Asian people were also at higher risk, with 1.35 times higher odds, although this result was not statistically significant.

The odds ratios for different ethnicities shown in Figure 3 represent the increase in risk after controlling for underlying health conditions, which are present in higher rates in Māori and Pacific people 16 . In the 1,829 cases in the data, there was only a very small correlation between having underlying conditions recorded and either Māori ethnicity (Pearson's r-squared 2 = 0.07) or Pacific ethnicity ( 2 = 0.02), so the results were not affected by multi-collinearity in these variables.

The model can be used to estimate the probability of hospitalisation following infection with COVID-19 for an individual of a given age, ethnicity, and presence/absence of underlying health conditions (see Figure 4 ). These estimates should be used with caution as they assume that age has the same . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted December 28, 2020. ;

proportional effect in each ethnicity. Māori and Pacific people have lower life expectancy, higher rates of multi-morbidity and respiratory illness, higher rates of under-reporting of comorbid conditions, and typically experience adverse health outcomes at an earlier age 16 17 18 . These factors have not been accounted for in the model and may exacerbate the risk of clinically severe outcomes from COVID-19

for Māori and Pacific people. 

For length of hospital stay, the model containing only age and ethnicity as predictor variables gave the most parsimonious fit (lowest AIC). Age was a more important factor than in the probability of hospitalisation model, with an additional year of age predicting an additional 0.224 days in hospital on average ( = 2 × 10 −6 ). When used as the priority ethnicity, Māori are expected to spend 4.88 days ( = 0.052) longer in hospital than NZ European/Other, and Pacific people are expected to spend 5.16 days ( = 0.049) longer in hospital than NZ European/Other. Length of hospital stay for Asian people was not significantly different from NZ European/Other. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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For risk of fatality, the model containing only age and deprivation index as predictor variables gave the most parsimonious fit (lowest AIC). In this model, an additional year of age increased the odds of fatality by 15.9% (11.5%, 20.4%) ( = 3 × 10 −14 ). A unit increase in deprivation index was associated with a 0.80% (0.33%, 1.27%) increase in the odds of fatality ( = 0.001). The difference in deprivation score between the 1 st and 4 th quintiles in the dataset was 146. This means that the model predicts that an individual at the 80 th percentile of deprivation has 3.19 times the risk of fatality as someone at the 20 th percentile in this dataset.

International evidence suggests a linear relationship between log infection fatality rates and age , with one paper estimating an increase in probability of death of 12.9% per year of age 19 . This is comparable to our results (although changes in the infection fatality rate are not identical to changes in odds, they are close at small probabilities).

The number of fatalities was, fortunately, too small to draw any concrete conclusions on the relationship between risk of fatality and ethnicity. There were no models where ethnicity was a consistently statistically significant predictor of fatality risk. However, this is most likely due to inadequate statistical power of the small sample. Furthermore, the majority of fatalities are linked to aged care facilities, so are not a representative sample of fatalities if COVID-19 were to become more widespread in the community.

New Zealand's experience with the COVID-19 epidemic indicates that Māori and Pacific people are at much greater risk of hospitalisation following infection with COVID-19. It is widely understood from overseas experience that the risk of hospitalisation for COVID-19 increases rapidly with age. However, the effects of ethnicity in New Zealand are not so well understood. Our results show that a healthy Māori person has the same risk of hospitalisation for COVID-19 as a healthy person in the NZ European/Other ethnic group who is 21 years older. A healthy Pacific person has the same risk of hospitalisation as a healthy person in the NZ European/Other ethnic group who is 25 years older.

These differences in age-specific risk are broadly consistent with earlier estimates of inequities in the . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted December 28, 2020. ; https://doi.org/10.1101/2020.12.25.20248427 doi: medRxiv preprint COVID-19 infection fatality rate 6 . Furthermore, Māori and Pacific people are more likely to have underlying health conditions, which puts them at even higher risk of hospitalisation.

We have only considered the risk of being hospitalised given an individual had COVID-19. The overall risk of being hospitalised also depends on the likelihood of infection. COVID-19 can spread quickly in communities with higher levels of workplace or community interaction, crowded housing, insecure employment, and decreased access to healthcare or COVID-19 testing. These are frequently the same communities that are at higher risk of hospitalisation and fatality if infected, meaning there is additional potential burden of the epidemic on these people. When fitting each model, we assigned each individual to only one ethnicity as the sample size precluded investigation of all ethnicity combinations. This means that our results cannot be used additively to estimate the risk of hospitalisation for an individual belonging to multiple ethnicities.

Other effects are additive in the log-odds. For example, an individual with underlying health conditions has log-odds of hospitalisation that is 0.553 greater than another individual of the same age and ethnicity without underlying health conditions.

We have presented the results of a simple analysis that ignores several potential sources of bias and additional inequities. For example, the recording and the analysis of the effect of underlying health conditions are crude. Different health conditions have significantly different effects and the presence of multiple health conditions may increase risk further. We did not have a sufficient sample size to estimate the effect of individual health conditions or combinations of conditions. Māori are more likely to experience multiple health conditions and this could exacerbate the risk of clinically severe outcomes from COVID-19. Testing rates were much higher in the second outbreak in August/September 2020 than in the first outbreak in March/April 2020, meaning that more mild cases of COVID-19 would have been identified. As this second outbreak disproportionately affected Pacific and Māori people, the model may underestimate their relative risk of hospitalisation. Our model is fitted to data from a period in which the prevalence of COVID-19 was low and healthcare services had adequate capacity. Structural biases and systemic racism within the healthcare system could further exacerbate inequities in outcomes if COVID-19 prevalence increased and healthcare capacity was overstretched 20 16 21 . Deprivation index was assigned according to the meshblock of an individual's home address. This may be a good proxy for general socioeconomic deprivation on average, but our sample size may not be sufficient for this to apply. Finally, Asian and NZ European/Other are very . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.

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The copyright holder for this this version posted December 28, 2020. ; https://doi.org/10.1101/2020.12.25.20248427 doi: medRxiv preprint broad ethnicity groups that cover a wide range of lived experiences. Further work is needed to accurately capture their risk profiles.

We conclude that Māori and Pacific people have substantially higher risk of hospitalisation for COVID-19, after controlling for age, presence of underlying health conditions, and socioeconomic deprivation.

We have previously estimated that Māori and Pacific people would experience higher infection fatality rates from COVID-19 6 . Our new results add to the imperative for New Zealand's COVID-19 response to include a focus on measures to protect high-risk groups and to prevent the large-scale inequities in health outcomes that would result from widespread community transmission 22 . Our results also have clear implications for identifying priority groups for vaccination against COVID-19, for which planning is currently underway. They demonstrate that it will be essential to account for ethnicity when targeting vaccination to age groups based on their risk of clinically severe infection. For example, our results show that NZ European/Other people aged 80 have a comparable risk to that of Māori aged 59 and Pacific people aged 55.

Available from: nzcoviddashboard.esr.cri.nz. 2. StatsNZ. Census 2018 Usually Resident Population

Island Time -New Zealand's Pacific Futures: Bridget Williams Books

Racism and health in Aotearoa New Zealand: a systematic review of quantitative studies

Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100

Estimated inequities in COVID-19 infection fatality rates by ethnicity for Aotearoa New Zealand

Estimating the Impact of Control Measures to Prevent Outbreaks of COVID-19 Associated with Air Travel into a COVID-19-free country: A Simulation Modelling Study. medRxiv

COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. The Lancet Public Health

Factors associated with COVID-19-related death using OpenSAFELY

NZDep2018 Index of Deprivation

Model selection and multi-model inference

An introduction to ROC analysis

The politics and practice of counting: ethnicity in official statistics in Aotearoa/New Zealand: Te Rōpū Rangahau Hauora a Eru Pōmare; 2010. 15. StatsNZ. Census 2018 ethnic group summaries

Cohort morbidity hypothesis: health inequalities of older Māori and non-Māori in New Zealand

Towards a social-structural model for understanding current disparities in Maori health and well-being

Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications

Māori Standards of Health IV. A study of the years 2000-2005. Wellington: Te Ropu Rangahau Hauora a Eru Pomare

Ethnic bias amongst medical students in Aotearoa/New Zealand: Findings from the Bias and Decision Making in Medicine (BDMM) study

The authors acknowledge the support of StatsNZ, ESR, and the Ministry of Health in supplying data in support of this work. In particular, we would like to acknowledge Laura Cleary for her work in providing data on total ethnicity and meshblock of home address. This work was funded by the New Zealand Ministry of Business, Innovation and Employment and Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems.