Breast cancer is the most common cancer in New Zealand women, accounting for almost 30% of all new cancer cases and 14% of all cancer deaths in 2012, with a higher rate observed in Māori, Pacific women and those living in deprived area.1,2 New Zealand has poorer survival from breast cancer compared to some other developed nations,3 including its neighbour Australia.4,5

The outcomes of breast cancer can be influenced by a range of factors, including demographic, biological and treatment factors. One important factor is obesity, assessed by body mass index (BMI, weight/height2). A meta-analysis of 82 studies reported an increased risk of total mortality with a hazard ratio of 1.41 (95% CI: 1.29-1.53) for women with a BMI over 30 compared to those with normal weight (BMI 18.5-25.1).6 While a few studies have shown no effect, most studies show worse outcomes in patients with higher BMI, including metastatic disease and first recurrences.

In New Zealand, three in ten adults are obese, and the rate is significantly higher in Māori, Pacific women and those living in deprived areas.7 Yet the ability of researchers to explore the contribution of BMI to breast cancer outcomes is limited, as the national and regional cancer registries do not routinely collect information on patient height and weight, although some regional registries have started collecting the data recently. An exception is the Waikato Breast Cancer Register, which captures newly diagnosed breast cancer cases in the Waikato District Health Board Region, and has recorded patient height and weight at the time of diagnosis since 1991.

This paper assessed the completeness of data on patient height, weight and BMI in the Waikato Breast Cancer Register and its association with specific patient characteristics and clinical outcomes.

Methods

Data sources

This analysis used the data from the Waikato Breast Cancer Register and involved all women who were diagnosed with primary breast cancer in the Waikato District Health Board Region between January 2000 and June 2014. Compared with the national data sources, the register contains more comprehensive and accurate information on many factors,8,9 and records patient demographics such as age, ethnicity and health domicile code, height, weight, year of cancer diagnosis, mode of presentation (screen or symptomatic), tumour characteristics such as stage at diagnosis, grade, histological type and hormone receptor status, treatments undertaken such as surgery, radiotherapy, chemotherapy, hormonal therapy and biological treatment and health care facility where primary treatment was undertaken. Information on patient height and weight was obtained from the medical oncology new patient clinical letter or the surgical admission form or both, which record measured weight. If such information was not available, the patient history form was used, which records measured or self-completed (with help from a nurse) height and weight. The health domicile codes represent patients’ usual residential address, and were categorised as urban (main urban, satellite urban and rural with high urban influence) and rural areas (others) based on Statistics New Zealand’s Urban/Rural Profile.10 To assess the degree of neighbourhood deprivation, the domicile codes were also mapped on to the 2006 New Zealand Deprivation Index (NZDep),11 with decile ten the most deprived and decile one the least. Each woman was followed prospectively through public and private clinic follow-ups, and outcomes such as loco-regional recurrence, metastasis and death were recorded.

The data were linked to the National Minimum Dataset (NMDS) to obtain information on comorbidities. The NMDS contains information about all day patients and inpatients discharged from all public hospitals and over 90% of private hospitals in New Zealand.12 Comorbidity was measured using a C3 index score, which is a cancer-specific index of comorbidity based on the presence of 42 chronic conditions recorded in the NMDS for a period of five years prior to the diagnosis of cancer.13 Each condition was weighed to its impact on one-year non-cancer mortality in a cancer cohort, and the weights were then summed to get a final comorbidity score.

This analysis was undertaken as part of a wider project aiming to improve outcomes for women with breast cancer in New Zealand. Ethical approval for the project was obtained from the New Zealand Northern ‘A’ Ethics Committee (Ref. No. 12/NTA/42).

Analyses

All analyses were performed using SAS (release 9.4, SAS Institute Inc., Cary, North Carolina). Missing values except for BMI were computed using multiple imputation with ten complete datasets created by the Markov chain Monte Carlo method,14 incorporating all baseline characteristics and outcomes. Baseline data were presented as percentages, and compared between patients with recorded height, weight and BMI and those with missing data by using a χ2 test.

Cumulative incidences for specific outcomes (loco-regional recurrence, metastasis, breast cancer-specific mortality, death from other causes and overall mortality) in the presence of competing risks were computed. For loco-regional recurrence and metastasis, death from any cause as the first event was considered as a competing risk. For breast cancer-specific mortality, death from other causes as the first event was considered as a competing risk. For death from other causes, breast cancer-specific death as the first event was considered as a competing risk. Cox proportional hazards regression modelling was then performed and hazards of the specified outcomes associated with missing data on BMI were assessed. Hazard ratios (HRs) were adjusted for all baseline characteristics except HER-2 status (as about one-third of the records had missing values).

Results

There were 3,536 patients who were diagnosed with primary breast cancer between January 2000 and June 2014. Height was not recorded on 25.4% of patients and weight not recorded on 16.2% so that BMI was unavailable for 27.4% (Table 1). There were significant differences in baseline characteristics of patients with recorded vs. unrecorded height, weight and BMI. Generally, missing data was more frequent in patients who were older and of European ethnicity, resided in semi-urban or rural areas and had a higher comorbidity index. Missing data was also more common in screen-detected patients, patients with early stage (0 and 1) or low-grade cancer and hormone receptor-positive patients. BMI information was available on almost all patients who had adjuvant chemotherapy but was missing on about 40% of other patients. The amount of missing data has declined over time but was still 17.1% in the most recent period, 2012–14.

Table 1: Baseline characteristics of patients by missing height, weight and BMI.

Patients with missing data on BMI were less likely to experience loco-regional recurrence (crude HR: 0.56; 95% CI: 0.35, 0.90; adjusted HR: 0.61; 95% CI: 0.37, 1.02), metastasis (crude HR: 0.35; 95% CI: 0.24, 0.50; adjusted HR: 0.38; 95% CI: 0.25, 0.58) and breast cancer-specific mortality (crude HR: 0.44; 95% CI: 0.34, 0.57; adjusted HR: 0.64; 95% CI: 0.46, 0.88), but were more likely to experience death from other causes (crude HR: 2.19; 95% CI: 1.78, 2.70; adjusted HR: 1.28; 95% CI: 1.00, 1.63) (Figure 1 and Table 2). The HRs were adjusted for all baseline characteristics mentioned in Table 1, except HER2-status.

Figure 1: Cumulative incidence of specific outcomes in patients with known BMI vs. unknown BMI.

* Figure 1 (e) has two lines which are overlapping.

Table 2: Clinical outcomes in patients with recorded vs. unrecorded BMI.

* Adjusted for all baseline characteristics mentioned in Table 1 except HER2-status.

Discussion

In the Waikato Breast Cancer Register, height was not recorded on one in four patients and weight not recorded on one in six patients. Missing data was differential by several demographic, disease and treatment factors as well as specific outcomes.

In general, patients with missing data were older, had early-stage cancer, did not receive chemotherapy and had better cancer-specific outcomes. It is possible that older patients were less likely to have their BMI measured or to complete height and weight fields in the patient history form, and hence had more missing data. It is not surprising that BMI data is almost complete for patients who received chemotherapy, as BMI is important in the prescribing of chemotherapy. These patients also tend to have more aggressive cancer and hence have poorer outcomes. Importantly, our findings indicate that analyses restricted to patients with recorded BMI could be biased, possibly away from the null.

The amount of missing data in the register has been declining over time, reflecting efforts made by the registry staff to ensure that BMI data is collected. However, there is room for improvement as BMI was not available for about 17% of patients who were diagnosed between 2012 and 2014. Patient height and weight should be recorded in all population-based cancer registries for several reasons. First, obesity rates in New Zealand are among the highest in the OECD countries.15 In particular, two in three Pacific women and one in two Māori women are obese.7 Second, there is increasing evidence linking obesity to development and prognosis of breast cancer6,16 and also several other cancers.17 Possible mechanisms include hormonal imbalance, suboptimal treatment and related comorbidities,6,18,19 and may be different across population subgroups (eg, across racial/ethnic groups16). Yet the impact of obesity on breast cancer has rarely been evaluated in New Zealand. Such evaluation would benefit Māori and Pacific women most, as they bear a disproportionate burden of obesity and related diseases including cancer.

An initial step in New Zealand would be to routinely record height and weight in the NMDS, as hospital records are the primary source of information for cancer registries and contain data on objectively measured height and weight. An earlier US study found height and weight to be available in the hospital record of most cancer patients (more than 80%) at the time of diagnosis, but acknowledged that manually abstracting height and weight for each patient was resource-intensive.20 However, the data collection process should be simpler, quicker and cheaper with the growing movement toward electronic health records, advances in data linkage and availability of digital medical scales, which can be connected to a PC or smartphone.

Potential limitations of this analysis should be noted. Misclassification of the cause of death may occur, but such errors are likely to be similar in the two groups being compared, and will only act to reduce observed differences to a small extent. NZDep2006 used in this analysis measures area-level deprivation and may not reflect an individual’s actual socioeconomic status, although it has been validated previously.21 Tumour grade and ER/PR status were missing for some patients (9% and 7% respectively) as patients with stage 0 or in-situ cancer were included in this analysis. HER-2 status was missing for 29% of the patients and was excluded from this analysis, as most patients with missing HER-2 were diagnosed prior to 2006 when HER-2 testing was not routine in New Zealand.

To conclude, height or weight or both were not recorded for more than one quarter of the patients in the Waikato Breast Cancer Register. Importantly, missing data was differential by specific patient characteristics and clinical outcomes. To be able to evaluate the associations between BMI and breast cancer outcomes in New Zealand, patient height and weight should be recorded in hospital and computerised data systems.

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