View Article PDF

Multimorbidity is the presence of two or more long-term conditions that collectively influence a person’s health status, often requiring complex care and management.1,2 Multimorbidity is more common among older people,3–5 although a study of two million people registered in primary care practices found the absolute number of people with multimorbidity was higher for those under 65 than those over 65.3 While there is little published evidence on multimorbidity prevalence in New Zealand, one study of 1,326 hospitalised patients found that a third had comorbid disease (defined as disease occurring in addition to their primary reason for admission), and this was associated with poorer outcomes.6 The burden of multimorbidity is generally higher in those in lower socioeconomic groups and other underserved populations such as indigenous and ethnic minority groups, and the onset tends to be at a younger age.3,7–11

The impact of multimorbidity on those affected is far reaching: it often involves taking multiple medications, may adversely affect employment and can involve frequent but fragmented healthcare.12, 3 These patients are at high risk of poor outcomes such as disability, functional decline and poor quality of life.2 Multimorbidity also comes at a cost for both individuals and the healthcare system, with healthcare utilisation and costs increasing with each additional condition.2 Two recent qualitative New Zealand studies found that multimorbidity often has a substantial negative impact on people’s lives, including considerable difficulty with managing medications and difficulties accessing and navigating appropriate healthcare.14,15 In New Zealand, the impact of multimorbidity may be greatest for Māori, who have higher rates of many long-term conditions, poorer access to primary care, are more likely to experience discrimination and experience a lower socio-economic status than non-Māori.16–18

This study aimed to assess the frequency, pattern and impact of physical and mental health, and issues regarding support, employment and finance for people with multimorbidity across different ethnic groups in New Zealand.

Methods

Survey population and eligibility criteria

The study population was adults aged 18+ with multimorbidity, enrolled with one of three primary health organisations (PHOs) in New Zealand.

Multimorbidity was defined using retrospective hospital discharge data (via ICD-10 coded diagnosis codes). All recorded diagnoses from hospital discharge records were coded for 61 long-term conditions drawn from the M3 multimorbidity index.19 To be eligible for this study, participants needed to have two or more identified conditions in the five years prior to the data extract date (1 January 2016), with at least one being a physical health condition.

Other sampling eligibility criteria were enrolment in one of the study Primary Health Organisation’s (PHO) at time of data extraction, and recorded as alive at time of data extraction. Data were provided by the Ministry of Health, drawing from the National Health Index (NHI) master table and the National Minimum Dataset (NMDS),20 linked by NHI number (unique identifier for individuals engaged with healthcare system in New Zealand).

Sampling process

Sampling was stratified by respondent ethnicity (Māori, Pacific and Non-Māori/Non-Pacific, based on ethnicity recorded in the NHI master record). The target sample size was set at 200 participants per stratum to achieve a margin of error (half-width of 95% confidence interval) of +/- 7% for stratified estimates. We assumed a 40% response rate, and selected a list of 1,500 patients to invite in order to achieve a final sample size of 600 participants.

Following a pilot study of the survey recruitment methods, which identified a need for additional resources for recruitment, a decision was made to focus on two of the original PHOs: a new sample of patients was drawn for one PHO (Compass Health) where in-depth recruitment processes could be used (n=999, stratified by ethnicity), and the original sampling list was retained for the second PHO (Pegasus Health) (n=472).

Invitation lists were reviewed by each PHO to check patients were alive and still enrolled with the PHO. The resulting practice-level lists were subsequently reviewed by the participating general practices who removed patients deemed inappropriate to invite (due to acute poor health or impairment from conditions like dementia; see Supplementary Table 1).

Recruitment

Strategies for gaining the support of primary healthcare practices and recruiting individual patients within those practices were developed in conjunction with an advisory group of research-active GPs, PHOs and a Māori health provider.

Once a primary care practice agreed to participate, participant packs were prepared for all eligible patients (including invitation letters addressed from the patient’s practice, information sheet, paper copy of the survey and post-paid return envelope). Participants were able to complete the paper survey or had the option of completing the survey online or over the telephone.

A research company (Research New Zealand) was contracted to coordinate data collection, including design of the web-based version of the survey, data entry for returned paper surveys and conduct of telephone interviews using a Computer Assisted Telephone Interview (CATI) system.

Measures

The survey combined original questions alongside items from existing questionnaires including: New Zealand Health Survey,21 Work Productivity and Impairment Questionnaire (adapted),22 Bayliss23 and Social Provisions Scale (three questions only).24 The survey included five key topics: social support, financial implications, access to healthcare, health literacy, and coordination and continuity of care. These areas were chosen based on a literature review which identified key themes around patients’ experiences of living with multimorbidity, and themes emerging from our earlier qualitative study on multimorbidity.15 The survey also included demographic questions. A draft survey was piloted with 11 patients with multimorbidity, with subsequent amendments made and reviewed by the research and clinical advisory teams before the survey was finalised.

Data analysis

To account for the stratified sampling design, we calculated inverse sampling weights for each participant (by ethnicity and PHO) so that total estimates for the sample were weighted back to represent the eligible population (ie, people with multimorbidity in the two participating PHOs). These inverse sampling weights were used in all analyses: for categorical outcomes, we have reported unweighted frequencies (actual number of respondents in each category) alongside weighted percentages and their 95% confidence intervals.

Crude descriptive analyses for each survey question include frequencies and weighted proportions, both for the total cohort and stratified by ethnicity, calculated using Proc Surveyfreq in SAS v9.3. Mean scores on the SF-12 Mental and Physical health scales were calculated using Proc Surveymeans. General population figures for questions drawn from the 2015/16 New Zealand Health Survey (NZHS)25 were based on analysis NZHS data that was then directly standardised to the age- and sex-profile of our survey respondents. Socioeconomic deprivation (NZDep) was measured using NZDep2013, a small-area based index calculated using aggregated census data on residents’ socioeconomic characteristics.26 For the sake of brevity, we have only presented ethnicity-stratified results where there was notable variation.

Data management and analysis was performed in SAS v9.3 and Microsoft Excel. Ethical approval for the study was granted by the Southern Region Ethics Committee (16/STH/16); the study was also considered by the University of Otago Ngāi Tahu Research Consultation Committee.

Results

Patients were drawn from 75 primary care practices (Supplementary Table 2). Of 1,471 potential participants, 758 (51.5%) were deemed eligible and sent study information packs by the practices. Of these, 234 participants completed the survey (response rate: 31%), with 167 respondents from PHO 1 (37% response rate) and 67 from PHO 2 (22% response rate) (Supplementary Table 1). Of the 234 returned surveys, 219 were self-completed by paper survey, seven were completed online and eight by telephone.

Study participants characteristics

Participant characteristics are presented in Table 1. Over half of the participants (52%) were 65 years or older (mean age = 65.2, SD=13.9) with equal numbers of male and female participants (n=117 for both). Although we aimed to recruit similar numbers in each ethnic group, 25% of participants were Māori, 19% Pacific and the majority (56%) were Non-Māori/non-Pacific (NMNP). Most participants (74%) reported living with other people (partner, children, family, flatmates/non-family), 25% were living alone and only three participants were living in a home/care facility. Only 12% of participants were from NZ Dep Quintile 5 (most deprived) neighbourhoods. Half of the participants (50%) had a secondary school or similar level qualification, while a small proportion (15%) held a bachelor’s degree or higher and the remainder (29%) reported having no qualifications. While the majority (57.9%, 95% CI 48.7–67.1) were retired, homemakers or volunteers) a sizable minority were working in paid employment (38%, 95% CI 28.8–47.2).

Table 1: Sociodemographic characteristics of survey participants.

1Missing data for nine participants.
2Participants could choose more than one option.
3Missing data for four participants.

More than half of all participants had been diagnosed with three or more long-term conditions, with nearly 10% having five or more (9.2%, 95% CI 4.8–13.6). When stratifying comorbid conditions by ethnicity, we observed that Māori and Pacific participants appeared to have marginally greater numbers of comorbid conditions (Supplementary Table 3). Cardiovascular conditions were among the most common (cardiac arrhythmia: 5.5%, cardiac disease other: 5.4%, uncomplicated hypertension 4.3%, myocardial infarction 4.0%, angina 3.6%, cerebrovascular disease 3.6%; Table 2).

Table 2: Prevalence of multiple long-term conditions within the cohort.

1Only conditions with a weighted proportion of 2% or greater are reported.
2Residual category of cardiac conditions not counted under other specified cardiac categories.

Hauora: Physical, mental and social wellbeing

When asked to rate their general health, many participants reported only ‘fair’ or ‘poor’ health (41.2%) (Table 3). Māori (47.7%) and Pacific (53.2%) respondents were more likely to report having only fair or poor health compared with NMNP living with multimorbidity (40.2%). These figures were much higher than general population estimates from the NZHS, where only 13.5% rated their health as fair or poor (95% CI 10.6–16.4; age- and sex-standardised from the 2015/16 NZHS to match our sample).

Table 3: General self-rated health.

c


1Missing data for three participants.
2Missing data for one participant.
3Missing data for two participants.

The mean aggregate SF-12 Physical Health Score was 38.5 (95% CI 37.0–40.1) for survey participants, which was substantially lower than the general population mean score (mean=46.5, 95% CI 45.4–47.5, age- and sex-standardised from 2015/16 NZHS;). Health was reported by many participants (72.4%) to limit their ability to climb several flights of stairs (Supplementary Table 3).

The mean aggregate SF-12 Mental Health score for survey participants was 48.8 (95% CI 47.1–50.4), again lower than for the general population (mean=55.0, 95% CI 54.4–55.7; age- and sex-standardised from 2015/16 NZHS; Supplementary Table 4). Nearly half (48%) of participants reported accomplishing less than they would have liked as a result of their emotional problems (eg, feeling depressed or anxious) over the previous four weeks.

The majority (97.3%) of survey participants reported having participated in different social interactions in the two weeks prior to completing the survey (Table 4). Access to help was readily available with the majority (85.8%, 95% CI 79.1–92.4) of participants having people in their lives who they could depend on for help. However, half of participants (50.3%, 95% CI 40.5–60.1) also reported having other people who depended on them for help (Table 4).

Table 4: Socialisation and support.

1Participants could select more than one option.
2Missing data for nine participants.
3Missing data for 17 participants.

Employment, work and financial impacts

Of the 119 participants who had been employed in the past five years, many had made changes to their working conditions as a result of their health, including decreasing working hours (31.3%, 95% CI 18.4–44.2), taking lighter duties (17%, 95% CI, 6.6–27.4) or changing job (11.8%, 95% CI 2.5–21.1), while some had stopped working altogether (8.3%, 95% CI 2–14.7) (Table 5). A majority (70%, 95% CI 56.8-83.3) of these participants reported their health had affected their productivity over the previous seven days. Pain was reported as interfering with work over the previous four weeks for most of these participants (75.7%, 95% CI 67.8–83.7), while more than a quarter (26.1%, 95% CI 13.6–38.5) had taken time off work due to problems associated with their health (eg, to attend doctor’s appointments, sick days, left work early).

Table 5: Health impact on work.

1Among those who had been in paid employment in the past five years (n=119).
2Participants could select more than one option.
3Among the total cohort (n=234).

Nearly one in five participants (18%, 95% CI 10.7–25.0) reported financial difficulty taking care of all their healthcare needs including prescriptions (Table 6). Māori (37%, 95% CI 19.3–53.7) and Pacific people (29%, 95% CI 12.8–44.9) were more likely to report financial healthcare difficulties than non-Māori/non-Pacific (16%, 95% CI 8.0–23.7). Nearly a quarter of participants (24%, 95% CI 16.4–32.2) reported difficulties covering other basic living costs (eg, rent/mortgage, food, power) in addition to healthcare costs, and more than a quarter cut down on other purchases (eg, clothing) because of their healthcare expenses (26%, 95% CI 17.4–34).

Table 6: Experiences of financial difficulty among patients with multimorbidity.

c


Discussion

This study describes the self-reported health status and experiences of people living with multiple long-term conditions in New Zealand. Participants with multimorbidity experienced poorer self-reported general health, physical health and mental health than the general population; their conditions also impacted adversely on their employment and financial wellbeing. Māori and Pacific participants reported poorer health and financial healthcare difficulties than Non-Māori/non-Pacific (NMNP).

Our observation that patients with multimorbidity report poorer health than a similar cohort of the general New Zealand population is unsurprising. Over half of our study cohort were living with three or more long-term conditions, and health-related quality of life scores have been found to decrease as the number of concurrent conditions increases.27

However, this study also suggests that Māori and Pacific people with multimorbidity may experience greater impact on their health than NMNP people with multimorbidity. There are a number of potential reasons for this difference. When stratifying comorbid condition by ethnicity, we observed that Māori and Pacific people appeared to have a marginally greater number of comorbid conditions, which may at least partially explain why Māori and Pacific people tend to report poorer overall health. Māori are also more likely to have poorer health arising from the breach of indigenous rights, manifesting in differential access to the determinants of health, differential access to healthcare and differences in the quality of care received.28 Māori are also more likely to have experienced racism (interpersonal and institutional), which is associated with poor health.17 In addition, and likely partially as a result of these factors, Māori and Pacific people experience higher rates of, and have been found to have, worse outcomes for a number of long-term conditions.28

These results are in line with previous studies where people living with multimorbidity experienced poorer mental health compared with a similar cohort of the general population.29 Multimorbidity places a substantial psychological burden on those who live with it, and so these people are at higher risk of poor mental health compared with those without multimorbidity.2,30 As an additional burden for those with multimorbidity, poor mental health can make self-management difficult.3,31 Other studies have suggested that depression in conjunction with other long-term conditions results in greater decrements in health.32

We observed a high degree of socialisation and support among respondents, which can influence the mental health of those with multimorbidity.14,33–35 Patient social support networks are important for practical reasons, emotional wellbeing and are key in supporting people to self-manage their health.14,36,37 High levels of social support are also associated with perceived improved quality of life for those living with multimorbidity.34

Employment issues were identified as a problem, with participants reporting having to make changes to their employment as a result of poor health. Our findings are consistent with those from other studies, which show multimorbidity adversely affects employment by acting as a barrier to employment and resulting in time off work due to illness or injury.13 Employment limitations and financial treatment burdens associated with multimorbidity also make self-management challenging.13,38 To maintain workplace productivity and employee wellness, it is necessary for workplaces to take a proactive approach to the health of their workers.

Our observation that nearly 20% of participants experienced financial difficulties taking care of their healthcare needs (including prescriptions in addition to basic living costs) was unexpected given the small proportion (12%) of respondents living in quintile 5 (most deprived). This impact was greater for Māori and Pacific participants, with nearly half of Māori participants reporting that they find it hard to cover basic living costs in addition to their healthcare expenses. The financial impact of long-term conditions is important and under-researched. The relationship between financial hardship and higher levels of multimorbidity is consistent both with the hypothesis that those with lower incomes are at higher risk of multimorbidity, and that multimorbidity may result in loss of income. Both are likely to be operating, and the association is important regardless of the direction of causality.

Another financial element is the likelihood that patients require multiple different prescriptions, although we do note that many participants accessed one of two government subsidy schemes which should reduce the cost of multiple prescriptions. When patients cannot afford to collect some or all of their prescribed medications they may be forced to prioritise which medications are most essential, or ration the medication they can afford, resulting in unnecessary suffering, deterioration in health and increased costs to the patient and healthcare system.15,39,40 This research supports previous suggestions for the need to reconsider prescription costs to better enable optimal self-management to maintain health among people with multimorbidity.41

This population-based study is among the first to report on the impact of multimorbidity on patients from New Zealand primary care. The survey questionnaire was developed using a rigorous process, using validated or existing questions wherever possible, which allowed for broader comparison with the general population. Unfortunately the overall response rate was lower than anticipated, which limits the statistical precision of our estimates (ie, confidence intervals are wide) and raises the potential of selection bias. Since our sample only included those who had been hospitalised in the last five years, the included participants may have been less well than the broader population of people with multimorbidity. Poor recruitment of Māori and Pacific peoples meant we were unable to generate substantial evidence for these populations. Future studies in this setting may benefit from identifying alternative and complementary recruitment strategies, and factoring in a greater allowance for the screening out of unsuitable patients by PHOs and GPs.

Conclusions

The results of this study provide a picture of the substantial impact that multimorbidity has on individuals in terms of their physical, mental and social wellbeing, their employment, and financial wellbeing. Furthermore, these impacts appear to be greater for Māori and Pacific people. Taken together, these results support a partnership approach to improving the lives of people with multimorbidity, including supporting patients to self-manage their conditions; society level support involving support people and employers; and healthcare providers taking person-centred approaches using holistic care models.15,42–45 Finally, future research should concentrate on the investigation of potential explanations and interventions for ethnic disparities in New Zealand.

Appendix

Supplementary Table 1: Participant sample and participation

*Not all practices in PHO 2 returned information about the number of patients invited into the study so the figure is the maximum possible.

Supplementary Table 2: Practice sample and participation.

Supplementary Table 3:

Supplementary Table 4: Impact of health on ability to climb several flights of stairs.

Supplementary Table 5: Accomplished less than would have liked due to emotional problems, such as feeling depressed or anxious in the past four weeks.

Summary

Abstract

Aim

To describe the experiences of people living with multimorbidity in New Zealand.

Method

We conducted a cross-sectional survey of adults with multimorbidity enrolled in two primary health organisations in New Zealand. Potential participants with multimorbidity were identified using retrospective hospital discharge data coded for long-term conditions. Sampling was stratified by ethnicity (Mori, Pacific and non-Mori/non-Pacific). Analysis was descriptive, with some responses compared to the general population estimates from the New Zealand Health Survey.

Results

A total of 234 participants completed the survey (mean age 65.2). Self-reported physical health was poor among the cohort: forty-one percent of participants reported only fair or poor general health, compared to 13.5% in the general population (age and sex standardised), with similar results for both self-reported mental health and physical health. Self-reported health was poorer for Mori and Pacific participants. The majority (70%) of those who were working reported their health had affected their productivity, while nearly 20% of participants reported financial difficulty in taking care of their health needs.

Conclusion

These results emphasise the serious impact multimorbidity has on patients health status compared to the general population. This research supports the development of holistic patient-centred care models designed to improve patient outcomes.

Author Information

James Stanley, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Elinor Millar, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Kelly Semper, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Cheryl Davies, Tu Kotahi Asthma Trust, Lower Hutt; Anthony Dowell, Department of Primary Health Care and General Practice, University of Otago, Wellington; Dee Mangin, Department of General Practice, University of Otago, Christchurch; Ross Lawrenson, University of Waikato and Waikato District Health Board, Hamilton; Diana Sarfati, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington.

Acknowledgements

#NAME?

Correspondence

Jeannine Stairmand, Cancer and Chronic Conditions (C3) Research Group, Department of Public Health, University of Otago, Mein St, Newtown, Wellington 6021.

Correspondence Email

jeannine.stairmand@otago.ac.nz

Competing Interests

Professor Lawrenson is both an employee of the University of Waikato and Waikato District Health Board; Dr Sarfati and Dr Millar reports grants from the Health Research Council during the conduct of the study.

  1. Almirall J, Fortin M. The coexistence of terms to describe the presence of multiple concurrent diseases. J Comorb. 2013 Oct 8; 3:4–9.
  2. Xu XL, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Res Rev. 2017 Aug; 37:53–68. doi: 10.1016/j.arr.2017.05.003.
  3. Barnett K, Mercer SW, Norbury M, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012 Jul 7; 380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2.
  4. Garin N, Olaya B, Perales J, et al. Multimorbidity patterns in a national representative sample of the Spanish adult population. PLoS One. 2014 Jan 20;9(1):e84794. doi: 10.1371/journal.pone.0084794. eCollection 2014. Erratum in: PLoS One. 2015;10(4):e0123037.
  5. Kirchberger I, Meisinger C, Heier M, et al. Patterns of multimorbidity in the aged population. Results from the KORA-Age study. PLoS One. 2012; 7(1):e30556. doi: 10.1371/journal.pone.0030556.
  6. Davis P1, Lay-Yee R, Fitzjohn J, et al. Co-morbidity and health outcomes in three Auckland hospitals. N Z Med J. 2002 May 10; 115(1153):211–5.
  7. Robinson PC, Merriman TR, Herbison P, Highton. Hospital admissions associated with gout and their comorbidities in New Zealand and England 1999–2009. Rheumatology (Oxford). 2013 Jan; 52(1):118–26. doi: 10.1093/rheumatology/kes253. Epub 2012 Sep 18.
  8. Sarfati D, Gurney J, Lim BT, et al. Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival. Asia Pac J Clin Oncol. 2016 Mar; 12(1):e47–56. doi: 10.1111/ajco.12130. Epub 2013 Dec 19.
  9. Sarfati D, Tan L, Blakely T, Pearce N. Comorbidity among patients with colon cancer in New Zealand. N Z Med J. 2011 Jul 8; 124(1338):76–88.
  10. Johnson-Lawrence V, Zajacova A, Sneed R. Education, race/ethnicity, and multimorbidity among adults aged 30–64 in the National Health Interview Survey. SSM - Population Health. 2017 Dec; 3:366–372. doi.org/10.1016/j.ssmph.2017.03.007.
  11. Robson B, Purdie G, Cormack D. Unequal Impact II: Māori and Non-Māori Cancer Statistics by Deprivation and Rural-Urban Status, 2002-2006. 2010, Ministry of Health: Wellington.
  12. Jokanovic N, Tan EC, Dooley MJ, et al. Prevalence and Factors Associated With Polypharmacy in Long-Term Care Facilities: A Systematic Review. J Am Med Dir Assoc. 2015 Jun 1; 16(6):535.e1–12. doi: 10.1016/j.jamda.2015.03.003. Epub 2015 Apr 11. Review.
  13. Ward BW. Multiple chronic conditions and labor force outcomes: A population study of U.S. adults. Am J Ind Med. 2015 Sep; 58(9):943–54. doi: 10.1002/ajim.22439. Epub 2015 Jun 23.
  14. McKinlay EM, McDonald J, Darlow B, Perry M. Social networks of patients with multimorbidity: a qualitative study of patients’ and supporters’ views. J Prim Care. 2017; 9(2):153–161. https://doi.org/10.1071/HC1606215.
  15. Signal L, Semper K, Stairmand J, et al. A walking stick in one hand and a chainsaw in the other: patients’ perspectives of living with multimorbidity. N Z Med J. 2017 May 12; 130(1455):65–76.
  16. Jansen P, Bacal K, Buetow S. A comparison of Maori and non-Maori experiences of general practice. N Z Med J. 2011 Mar 4; 124(1330):24–9.
  17. Harris R, Tobias M, Jeffreys M, et al. Racism and health: the relationship between experience of racial discrimination and health in New Zealand. Soc Sci Med. 2006; 63(6):1428–41. doi.org/10.1016/j.socscimed.2006.04.009
  18. Ministry of Health. Tatau Kahukura: Māori Health Chart Book 2015 (3rd edition). 2015. Wellington: Ministry of Health.
  19. Stanley J, Sarfati D. The new measuring multimorbidity index predicted mortality better than Charlson and Elixhauser indices among the general population. J Clin Epidemiol. 2017 Aug 24. pii: S0895-4356(17)30441-9. doi: 10.1016/j.jclinepi.2017.08.005.
  20. Ministry of Health. Collections (Health statistics, National collections and surveys). 2017 [cited 26/9/2017]; Available from: http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections
  21. Ministry of Health. Content Guide 2014/15 New Zealand Health Survey. 2015. Ministry of Health: Wellington.
  22. Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics. 1993 Nov; 4(5):353–65.
  23. Bayliss EA, Ellis JL, Steiner JF. Subjective assessments of comorbidity correlate with quality of life health outcomes: initial validation of a comorbidity assessment instrument. Health Qual Life Outcomes. 2005 Sep 1; 3:51.
  24. Cutrona C, Russell DW. The provisions of social relationships and adaptation to stress. Adv Pers Relation. 1987; 1(1):37–67.
  25. Ministry of Health. Methodology Report 2015/16: New Zealand Health Survey. 2016, Ministry of Health: Wellington.
  26. Salmond CE, Crampton P. Development of New Zealand’s deprivation index (NZDep) and its uptake as a national policy tool. Can J Public Health. 2012 May 9; 103(8 Suppl 2):S7–11.
  27. Ramond-Roquin A, Haggerty J, Lambert M, et al. Different Multimorbidity Measures Result in Varying Estimated Levels of Physical Quality of Life in Individuals with Multimorbidity: A Cross-Sectional Study in the General Population. Biomed Res Int. 2016;2016:7845438. doi: 10.1155/2016/7845438.
  28. Robson B, Harris R (editors). Hauora: Maori Standards of Health IV. A study of the years 2000–2005. 2007, Te Rōpū Rangahau Hauora a Eru Pōmare: Wellington.
  29. Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017 Oct 15; 221:36–46. doi: 10.1016/j.jad.2017.06.009.
  30. Slightam CA, Brandt K, Jenchura EC, et al. “I had to change so much in my life to live with my new limitations”: Multimorbid patients’ descriptions of their most bothersome chronic conditions. Chronic Illn. 2017 Jan 1:1742395317699448. doi: 10.1177/1742395317699448.
  31. Coventry PA, Hays R, Dickens C, et al. Talking about depression: a qualitative study of barriers to managing depression in people with long term conditions in primary care. BMC Fam Pract. 2011 Mar 22; 12:10. doi: 10.1186/1471-2296-12-10.
  32. Moussavi S, Chatterji S, Verdes E, et al. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007 Sep 8; 370(9590):851–8.
  33. McKinlay E, Graham S, Horrill P. Culturally and linguistically diverse patients’ views of multimorbidity and general practice care. J Prim Health Care. 2015 Sep 1; 7(3):228–35.
  34. Vogel I, Miksch A, Goetz K, et al. The impact of perceived social support and sense of coherence on health-related quality of life in multimorbid primary care patients. Chronic Illn. 2012 Dec; 8(4):296–307. doi: 10.1177/1742395312445935.
  35. Roberto KA, Gigliotti CM, Husser EK. Older Women’s Experiences with Multiple Health Conditions: Daily Challenges and Care Practices. Health Care Women Int. 2005 Sep; 26(8):672–92.
  36. Duguay C, Gallagher F, Fortin M. The experiences of adults with multimorbidity: a qualitative study. J Comorb. 2014 May 28; 4:11–21. doi: 10.15256/joc.2014.4.31.
  37. Noël PH, Parchman ML, Williams JW Jr, et al. The challenges of multimorbidity from the patient perspective. J Gen Intern Med. 2007 Dec; 22 Suppl 3:419–24.
  38. Eton DT, Ramalho de Oliveira D, Egginton JS, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Relat Outcome Meas. 2012; 3:39–49. doi: 10.2147/PROM.S34681.
  39. Jatrana S, Crampton P, Norris P. Ethnic differences in access to prescription medication because of cost in New Zealand. J Epidemiol Community Health. 2011 May; 65(5):454–60. doi: 10.1136/jech.2009.099101.
  40. Tseng CW, Brook RH, Keeler E, Mangione CM. Impact of an annual dollar limit or “cap” on prescription drug benefits for Medicare patients. JAMA. 2003 Jul 9; 290(2):222–7.
  41. Jatrana S, Crampton P, Richardson K, Norris P. Increasing prescription part charges will increase health inequalities in New Zealand. N Z Med J. 2012 May 25; 125(1355):78–80.
  42. Salisbury C, Johnson L, Purdy S, et al. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. British Journal of General Practice, 2011; 61(582):p. e12–21.
  43. Smith SM, Soubhi H, Fortin M, et al. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ, 2012; 345:p. e5205.
  44. Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012 Jul 7; 380(9836):7–9. doi: 10.1016/S0140-6736(12)60482-6.
  45. Pitama S, Huria T, Lacey C. Improving Maori health through clinical assessment: Waikare o te Waka o Meihana. N Z Med J. 2014 May 2; 127(1393):107–19.

Contact diana@nzma.org.nz
for the PDF of this article

View Article PDF

Multimorbidity is the presence of two or more long-term conditions that collectively influence a person’s health status, often requiring complex care and management.1,2 Multimorbidity is more common among older people,3–5 although a study of two million people registered in primary care practices found the absolute number of people with multimorbidity was higher for those under 65 than those over 65.3 While there is little published evidence on multimorbidity prevalence in New Zealand, one study of 1,326 hospitalised patients found that a third had comorbid disease (defined as disease occurring in addition to their primary reason for admission), and this was associated with poorer outcomes.6 The burden of multimorbidity is generally higher in those in lower socioeconomic groups and other underserved populations such as indigenous and ethnic minority groups, and the onset tends to be at a younger age.3,7–11

The impact of multimorbidity on those affected is far reaching: it often involves taking multiple medications, may adversely affect employment and can involve frequent but fragmented healthcare.12, 3 These patients are at high risk of poor outcomes such as disability, functional decline and poor quality of life.2 Multimorbidity also comes at a cost for both individuals and the healthcare system, with healthcare utilisation and costs increasing with each additional condition.2 Two recent qualitative New Zealand studies found that multimorbidity often has a substantial negative impact on people’s lives, including considerable difficulty with managing medications and difficulties accessing and navigating appropriate healthcare.14,15 In New Zealand, the impact of multimorbidity may be greatest for Māori, who have higher rates of many long-term conditions, poorer access to primary care, are more likely to experience discrimination and experience a lower socio-economic status than non-Māori.16–18

This study aimed to assess the frequency, pattern and impact of physical and mental health, and issues regarding support, employment and finance for people with multimorbidity across different ethnic groups in New Zealand.

Methods

Survey population and eligibility criteria

The study population was adults aged 18+ with multimorbidity, enrolled with one of three primary health organisations (PHOs) in New Zealand.

Multimorbidity was defined using retrospective hospital discharge data (via ICD-10 coded diagnosis codes). All recorded diagnoses from hospital discharge records were coded for 61 long-term conditions drawn from the M3 multimorbidity index.19 To be eligible for this study, participants needed to have two or more identified conditions in the five years prior to the data extract date (1 January 2016), with at least one being a physical health condition.

Other sampling eligibility criteria were enrolment in one of the study Primary Health Organisation’s (PHO) at time of data extraction, and recorded as alive at time of data extraction. Data were provided by the Ministry of Health, drawing from the National Health Index (NHI) master table and the National Minimum Dataset (NMDS),20 linked by NHI number (unique identifier for individuals engaged with healthcare system in New Zealand).

Sampling process

Sampling was stratified by respondent ethnicity (Māori, Pacific and Non-Māori/Non-Pacific, based on ethnicity recorded in the NHI master record). The target sample size was set at 200 participants per stratum to achieve a margin of error (half-width of 95% confidence interval) of +/- 7% for stratified estimates. We assumed a 40% response rate, and selected a list of 1,500 patients to invite in order to achieve a final sample size of 600 participants.

Following a pilot study of the survey recruitment methods, which identified a need for additional resources for recruitment, a decision was made to focus on two of the original PHOs: a new sample of patients was drawn for one PHO (Compass Health) where in-depth recruitment processes could be used (n=999, stratified by ethnicity), and the original sampling list was retained for the second PHO (Pegasus Health) (n=472).

Invitation lists were reviewed by each PHO to check patients were alive and still enrolled with the PHO. The resulting practice-level lists were subsequently reviewed by the participating general practices who removed patients deemed inappropriate to invite (due to acute poor health or impairment from conditions like dementia; see Supplementary Table 1).

Recruitment

Strategies for gaining the support of primary healthcare practices and recruiting individual patients within those practices were developed in conjunction with an advisory group of research-active GPs, PHOs and a Māori health provider.

Once a primary care practice agreed to participate, participant packs were prepared for all eligible patients (including invitation letters addressed from the patient’s practice, information sheet, paper copy of the survey and post-paid return envelope). Participants were able to complete the paper survey or had the option of completing the survey online or over the telephone.

A research company (Research New Zealand) was contracted to coordinate data collection, including design of the web-based version of the survey, data entry for returned paper surveys and conduct of telephone interviews using a Computer Assisted Telephone Interview (CATI) system.

Measures

The survey combined original questions alongside items from existing questionnaires including: New Zealand Health Survey,21 Work Productivity and Impairment Questionnaire (adapted),22 Bayliss23 and Social Provisions Scale (three questions only).24 The survey included five key topics: social support, financial implications, access to healthcare, health literacy, and coordination and continuity of care. These areas were chosen based on a literature review which identified key themes around patients’ experiences of living with multimorbidity, and themes emerging from our earlier qualitative study on multimorbidity.15 The survey also included demographic questions. A draft survey was piloted with 11 patients with multimorbidity, with subsequent amendments made and reviewed by the research and clinical advisory teams before the survey was finalised.

Data analysis

To account for the stratified sampling design, we calculated inverse sampling weights for each participant (by ethnicity and PHO) so that total estimates for the sample were weighted back to represent the eligible population (ie, people with multimorbidity in the two participating PHOs). These inverse sampling weights were used in all analyses: for categorical outcomes, we have reported unweighted frequencies (actual number of respondents in each category) alongside weighted percentages and their 95% confidence intervals.

Crude descriptive analyses for each survey question include frequencies and weighted proportions, both for the total cohort and stratified by ethnicity, calculated using Proc Surveyfreq in SAS v9.3. Mean scores on the SF-12 Mental and Physical health scales were calculated using Proc Surveymeans. General population figures for questions drawn from the 2015/16 New Zealand Health Survey (NZHS)25 were based on analysis NZHS data that was then directly standardised to the age- and sex-profile of our survey respondents. Socioeconomic deprivation (NZDep) was measured using NZDep2013, a small-area based index calculated using aggregated census data on residents’ socioeconomic characteristics.26 For the sake of brevity, we have only presented ethnicity-stratified results where there was notable variation.

Data management and analysis was performed in SAS v9.3 and Microsoft Excel. Ethical approval for the study was granted by the Southern Region Ethics Committee (16/STH/16); the study was also considered by the University of Otago Ngāi Tahu Research Consultation Committee.

Results

Patients were drawn from 75 primary care practices (Supplementary Table 2). Of 1,471 potential participants, 758 (51.5%) were deemed eligible and sent study information packs by the practices. Of these, 234 participants completed the survey (response rate: 31%), with 167 respondents from PHO 1 (37% response rate) and 67 from PHO 2 (22% response rate) (Supplementary Table 1). Of the 234 returned surveys, 219 were self-completed by paper survey, seven were completed online and eight by telephone.

Study participants characteristics

Participant characteristics are presented in Table 1. Over half of the participants (52%) were 65 years or older (mean age = 65.2, SD=13.9) with equal numbers of male and female participants (n=117 for both). Although we aimed to recruit similar numbers in each ethnic group, 25% of participants were Māori, 19% Pacific and the majority (56%) were Non-Māori/non-Pacific (NMNP). Most participants (74%) reported living with other people (partner, children, family, flatmates/non-family), 25% were living alone and only three participants were living in a home/care facility. Only 12% of participants were from NZ Dep Quintile 5 (most deprived) neighbourhoods. Half of the participants (50%) had a secondary school or similar level qualification, while a small proportion (15%) held a bachelor’s degree or higher and the remainder (29%) reported having no qualifications. While the majority (57.9%, 95% CI 48.7–67.1) were retired, homemakers or volunteers) a sizable minority were working in paid employment (38%, 95% CI 28.8–47.2).

Table 1: Sociodemographic characteristics of survey participants.

1Missing data for nine participants.
2Participants could choose more than one option.
3Missing data for four participants.

More than half of all participants had been diagnosed with three or more long-term conditions, with nearly 10% having five or more (9.2%, 95% CI 4.8–13.6). When stratifying comorbid conditions by ethnicity, we observed that Māori and Pacific participants appeared to have marginally greater numbers of comorbid conditions (Supplementary Table 3). Cardiovascular conditions were among the most common (cardiac arrhythmia: 5.5%, cardiac disease other: 5.4%, uncomplicated hypertension 4.3%, myocardial infarction 4.0%, angina 3.6%, cerebrovascular disease 3.6%; Table 2).

Table 2: Prevalence of multiple long-term conditions within the cohort.

1Only conditions with a weighted proportion of 2% or greater are reported.
2Residual category of cardiac conditions not counted under other specified cardiac categories.

Hauora: Physical, mental and social wellbeing

When asked to rate their general health, many participants reported only ‘fair’ or ‘poor’ health (41.2%) (Table 3). Māori (47.7%) and Pacific (53.2%) respondents were more likely to report having only fair or poor health compared with NMNP living with multimorbidity (40.2%). These figures were much higher than general population estimates from the NZHS, where only 13.5% rated their health as fair or poor (95% CI 10.6–16.4; age- and sex-standardised from the 2015/16 NZHS to match our sample).

Table 3: General self-rated health.

c


1Missing data for three participants.
2Missing data for one participant.
3Missing data for two participants.

The mean aggregate SF-12 Physical Health Score was 38.5 (95% CI 37.0–40.1) for survey participants, which was substantially lower than the general population mean score (mean=46.5, 95% CI 45.4–47.5, age- and sex-standardised from 2015/16 NZHS;). Health was reported by many participants (72.4%) to limit their ability to climb several flights of stairs (Supplementary Table 3).

The mean aggregate SF-12 Mental Health score for survey participants was 48.8 (95% CI 47.1–50.4), again lower than for the general population (mean=55.0, 95% CI 54.4–55.7; age- and sex-standardised from 2015/16 NZHS; Supplementary Table 4). Nearly half (48%) of participants reported accomplishing less than they would have liked as a result of their emotional problems (eg, feeling depressed or anxious) over the previous four weeks.

The majority (97.3%) of survey participants reported having participated in different social interactions in the two weeks prior to completing the survey (Table 4). Access to help was readily available with the majority (85.8%, 95% CI 79.1–92.4) of participants having people in their lives who they could depend on for help. However, half of participants (50.3%, 95% CI 40.5–60.1) also reported having other people who depended on them for help (Table 4).

Table 4: Socialisation and support.

1Participants could select more than one option.
2Missing data for nine participants.
3Missing data for 17 participants.

Employment, work and financial impacts

Of the 119 participants who had been employed in the past five years, many had made changes to their working conditions as a result of their health, including decreasing working hours (31.3%, 95% CI 18.4–44.2), taking lighter duties (17%, 95% CI, 6.6–27.4) or changing job (11.8%, 95% CI 2.5–21.1), while some had stopped working altogether (8.3%, 95% CI 2–14.7) (Table 5). A majority (70%, 95% CI 56.8-83.3) of these participants reported their health had affected their productivity over the previous seven days. Pain was reported as interfering with work over the previous four weeks for most of these participants (75.7%, 95% CI 67.8–83.7), while more than a quarter (26.1%, 95% CI 13.6–38.5) had taken time off work due to problems associated with their health (eg, to attend doctor’s appointments, sick days, left work early).

Table 5: Health impact on work.

1Among those who had been in paid employment in the past five years (n=119).
2Participants could select more than one option.
3Among the total cohort (n=234).

Nearly one in five participants (18%, 95% CI 10.7–25.0) reported financial difficulty taking care of all their healthcare needs including prescriptions (Table 6). Māori (37%, 95% CI 19.3–53.7) and Pacific people (29%, 95% CI 12.8–44.9) were more likely to report financial healthcare difficulties than non-Māori/non-Pacific (16%, 95% CI 8.0–23.7). Nearly a quarter of participants (24%, 95% CI 16.4–32.2) reported difficulties covering other basic living costs (eg, rent/mortgage, food, power) in addition to healthcare costs, and more than a quarter cut down on other purchases (eg, clothing) because of their healthcare expenses (26%, 95% CI 17.4–34).

Table 6: Experiences of financial difficulty among patients with multimorbidity.

c


Discussion

This study describes the self-reported health status and experiences of people living with multiple long-term conditions in New Zealand. Participants with multimorbidity experienced poorer self-reported general health, physical health and mental health than the general population; their conditions also impacted adversely on their employment and financial wellbeing. Māori and Pacific participants reported poorer health and financial healthcare difficulties than Non-Māori/non-Pacific (NMNP).

Our observation that patients with multimorbidity report poorer health than a similar cohort of the general New Zealand population is unsurprising. Over half of our study cohort were living with three or more long-term conditions, and health-related quality of life scores have been found to decrease as the number of concurrent conditions increases.27

However, this study also suggests that Māori and Pacific people with multimorbidity may experience greater impact on their health than NMNP people with multimorbidity. There are a number of potential reasons for this difference. When stratifying comorbid condition by ethnicity, we observed that Māori and Pacific people appeared to have a marginally greater number of comorbid conditions, which may at least partially explain why Māori and Pacific people tend to report poorer overall health. Māori are also more likely to have poorer health arising from the breach of indigenous rights, manifesting in differential access to the determinants of health, differential access to healthcare and differences in the quality of care received.28 Māori are also more likely to have experienced racism (interpersonal and institutional), which is associated with poor health.17 In addition, and likely partially as a result of these factors, Māori and Pacific people experience higher rates of, and have been found to have, worse outcomes for a number of long-term conditions.28

These results are in line with previous studies where people living with multimorbidity experienced poorer mental health compared with a similar cohort of the general population.29 Multimorbidity places a substantial psychological burden on those who live with it, and so these people are at higher risk of poor mental health compared with those without multimorbidity.2,30 As an additional burden for those with multimorbidity, poor mental health can make self-management difficult.3,31 Other studies have suggested that depression in conjunction with other long-term conditions results in greater decrements in health.32

We observed a high degree of socialisation and support among respondents, which can influence the mental health of those with multimorbidity.14,33–35 Patient social support networks are important for practical reasons, emotional wellbeing and are key in supporting people to self-manage their health.14,36,37 High levels of social support are also associated with perceived improved quality of life for those living with multimorbidity.34

Employment issues were identified as a problem, with participants reporting having to make changes to their employment as a result of poor health. Our findings are consistent with those from other studies, which show multimorbidity adversely affects employment by acting as a barrier to employment and resulting in time off work due to illness or injury.13 Employment limitations and financial treatment burdens associated with multimorbidity also make self-management challenging.13,38 To maintain workplace productivity and employee wellness, it is necessary for workplaces to take a proactive approach to the health of their workers.

Our observation that nearly 20% of participants experienced financial difficulties taking care of their healthcare needs (including prescriptions in addition to basic living costs) was unexpected given the small proportion (12%) of respondents living in quintile 5 (most deprived). This impact was greater for Māori and Pacific participants, with nearly half of Māori participants reporting that they find it hard to cover basic living costs in addition to their healthcare expenses. The financial impact of long-term conditions is important and under-researched. The relationship between financial hardship and higher levels of multimorbidity is consistent both with the hypothesis that those with lower incomes are at higher risk of multimorbidity, and that multimorbidity may result in loss of income. Both are likely to be operating, and the association is important regardless of the direction of causality.

Another financial element is the likelihood that patients require multiple different prescriptions, although we do note that many participants accessed one of two government subsidy schemes which should reduce the cost of multiple prescriptions. When patients cannot afford to collect some or all of their prescribed medications they may be forced to prioritise which medications are most essential, or ration the medication they can afford, resulting in unnecessary suffering, deterioration in health and increased costs to the patient and healthcare system.15,39,40 This research supports previous suggestions for the need to reconsider prescription costs to better enable optimal self-management to maintain health among people with multimorbidity.41

This population-based study is among the first to report on the impact of multimorbidity on patients from New Zealand primary care. The survey questionnaire was developed using a rigorous process, using validated or existing questions wherever possible, which allowed for broader comparison with the general population. Unfortunately the overall response rate was lower than anticipated, which limits the statistical precision of our estimates (ie, confidence intervals are wide) and raises the potential of selection bias. Since our sample only included those who had been hospitalised in the last five years, the included participants may have been less well than the broader population of people with multimorbidity. Poor recruitment of Māori and Pacific peoples meant we were unable to generate substantial evidence for these populations. Future studies in this setting may benefit from identifying alternative and complementary recruitment strategies, and factoring in a greater allowance for the screening out of unsuitable patients by PHOs and GPs.

Conclusions

The results of this study provide a picture of the substantial impact that multimorbidity has on individuals in terms of their physical, mental and social wellbeing, their employment, and financial wellbeing. Furthermore, these impacts appear to be greater for Māori and Pacific people. Taken together, these results support a partnership approach to improving the lives of people with multimorbidity, including supporting patients to self-manage their conditions; society level support involving support people and employers; and healthcare providers taking person-centred approaches using holistic care models.15,42–45 Finally, future research should concentrate on the investigation of potential explanations and interventions for ethnic disparities in New Zealand.

Appendix

Supplementary Table 1: Participant sample and participation

*Not all practices in PHO 2 returned information about the number of patients invited into the study so the figure is the maximum possible.

Supplementary Table 2: Practice sample and participation.

Supplementary Table 3:

Supplementary Table 4: Impact of health on ability to climb several flights of stairs.

Supplementary Table 5: Accomplished less than would have liked due to emotional problems, such as feeling depressed or anxious in the past four weeks.

Summary

Abstract

Aim

To describe the experiences of people living with multimorbidity in New Zealand.

Method

We conducted a cross-sectional survey of adults with multimorbidity enrolled in two primary health organisations in New Zealand. Potential participants with multimorbidity were identified using retrospective hospital discharge data coded for long-term conditions. Sampling was stratified by ethnicity (Mori, Pacific and non-Mori/non-Pacific). Analysis was descriptive, with some responses compared to the general population estimates from the New Zealand Health Survey.

Results

A total of 234 participants completed the survey (mean age 65.2). Self-reported physical health was poor among the cohort: forty-one percent of participants reported only fair or poor general health, compared to 13.5% in the general population (age and sex standardised), with similar results for both self-reported mental health and physical health. Self-reported health was poorer for Mori and Pacific participants. The majority (70%) of those who were working reported their health had affected their productivity, while nearly 20% of participants reported financial difficulty in taking care of their health needs.

Conclusion

These results emphasise the serious impact multimorbidity has on patients health status compared to the general population. This research supports the development of holistic patient-centred care models designed to improve patient outcomes.

Author Information

James Stanley, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Elinor Millar, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Kelly Semper, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Cheryl Davies, Tu Kotahi Asthma Trust, Lower Hutt; Anthony Dowell, Department of Primary Health Care and General Practice, University of Otago, Wellington; Dee Mangin, Department of General Practice, University of Otago, Christchurch; Ross Lawrenson, University of Waikato and Waikato District Health Board, Hamilton; Diana Sarfati, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington.

Acknowledgements

#NAME?

Correspondence

Jeannine Stairmand, Cancer and Chronic Conditions (C3) Research Group, Department of Public Health, University of Otago, Mein St, Newtown, Wellington 6021.

Correspondence Email

jeannine.stairmand@otago.ac.nz

Competing Interests

Professor Lawrenson is both an employee of the University of Waikato and Waikato District Health Board; Dr Sarfati and Dr Millar reports grants from the Health Research Council during the conduct of the study.

  1. Almirall J, Fortin M. The coexistence of terms to describe the presence of multiple concurrent diseases. J Comorb. 2013 Oct 8; 3:4–9.
  2. Xu XL, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Res Rev. 2017 Aug; 37:53–68. doi: 10.1016/j.arr.2017.05.003.
  3. Barnett K, Mercer SW, Norbury M, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012 Jul 7; 380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2.
  4. Garin N, Olaya B, Perales J, et al. Multimorbidity patterns in a national representative sample of the Spanish adult population. PLoS One. 2014 Jan 20;9(1):e84794. doi: 10.1371/journal.pone.0084794. eCollection 2014. Erratum in: PLoS One. 2015;10(4):e0123037.
  5. Kirchberger I, Meisinger C, Heier M, et al. Patterns of multimorbidity in the aged population. Results from the KORA-Age study. PLoS One. 2012; 7(1):e30556. doi: 10.1371/journal.pone.0030556.
  6. Davis P1, Lay-Yee R, Fitzjohn J, et al. Co-morbidity and health outcomes in three Auckland hospitals. N Z Med J. 2002 May 10; 115(1153):211–5.
  7. Robinson PC, Merriman TR, Herbison P, Highton. Hospital admissions associated with gout and their comorbidities in New Zealand and England 1999–2009. Rheumatology (Oxford). 2013 Jan; 52(1):118–26. doi: 10.1093/rheumatology/kes253. Epub 2012 Sep 18.
  8. Sarfati D, Gurney J, Lim BT, et al. Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival. Asia Pac J Clin Oncol. 2016 Mar; 12(1):e47–56. doi: 10.1111/ajco.12130. Epub 2013 Dec 19.
  9. Sarfati D, Tan L, Blakely T, Pearce N. Comorbidity among patients with colon cancer in New Zealand. N Z Med J. 2011 Jul 8; 124(1338):76–88.
  10. Johnson-Lawrence V, Zajacova A, Sneed R. Education, race/ethnicity, and multimorbidity among adults aged 30–64 in the National Health Interview Survey. SSM - Population Health. 2017 Dec; 3:366–372. doi.org/10.1016/j.ssmph.2017.03.007.
  11. Robson B, Purdie G, Cormack D. Unequal Impact II: Māori and Non-Māori Cancer Statistics by Deprivation and Rural-Urban Status, 2002-2006. 2010, Ministry of Health: Wellington.
  12. Jokanovic N, Tan EC, Dooley MJ, et al. Prevalence and Factors Associated With Polypharmacy in Long-Term Care Facilities: A Systematic Review. J Am Med Dir Assoc. 2015 Jun 1; 16(6):535.e1–12. doi: 10.1016/j.jamda.2015.03.003. Epub 2015 Apr 11. Review.
  13. Ward BW. Multiple chronic conditions and labor force outcomes: A population study of U.S. adults. Am J Ind Med. 2015 Sep; 58(9):943–54. doi: 10.1002/ajim.22439. Epub 2015 Jun 23.
  14. McKinlay EM, McDonald J, Darlow B, Perry M. Social networks of patients with multimorbidity: a qualitative study of patients’ and supporters’ views. J Prim Care. 2017; 9(2):153–161. https://doi.org/10.1071/HC1606215.
  15. Signal L, Semper K, Stairmand J, et al. A walking stick in one hand and a chainsaw in the other: patients’ perspectives of living with multimorbidity. N Z Med J. 2017 May 12; 130(1455):65–76.
  16. Jansen P, Bacal K, Buetow S. A comparison of Maori and non-Maori experiences of general practice. N Z Med J. 2011 Mar 4; 124(1330):24–9.
  17. Harris R, Tobias M, Jeffreys M, et al. Racism and health: the relationship between experience of racial discrimination and health in New Zealand. Soc Sci Med. 2006; 63(6):1428–41. doi.org/10.1016/j.socscimed.2006.04.009
  18. Ministry of Health. Tatau Kahukura: Māori Health Chart Book 2015 (3rd edition). 2015. Wellington: Ministry of Health.
  19. Stanley J, Sarfati D. The new measuring multimorbidity index predicted mortality better than Charlson and Elixhauser indices among the general population. J Clin Epidemiol. 2017 Aug 24. pii: S0895-4356(17)30441-9. doi: 10.1016/j.jclinepi.2017.08.005.
  20. Ministry of Health. Collections (Health statistics, National collections and surveys). 2017 [cited 26/9/2017]; Available from: http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections
  21. Ministry of Health. Content Guide 2014/15 New Zealand Health Survey. 2015. Ministry of Health: Wellington.
  22. Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics. 1993 Nov; 4(5):353–65.
  23. Bayliss EA, Ellis JL, Steiner JF. Subjective assessments of comorbidity correlate with quality of life health outcomes: initial validation of a comorbidity assessment instrument. Health Qual Life Outcomes. 2005 Sep 1; 3:51.
  24. Cutrona C, Russell DW. The provisions of social relationships and adaptation to stress. Adv Pers Relation. 1987; 1(1):37–67.
  25. Ministry of Health. Methodology Report 2015/16: New Zealand Health Survey. 2016, Ministry of Health: Wellington.
  26. Salmond CE, Crampton P. Development of New Zealand’s deprivation index (NZDep) and its uptake as a national policy tool. Can J Public Health. 2012 May 9; 103(8 Suppl 2):S7–11.
  27. Ramond-Roquin A, Haggerty J, Lambert M, et al. Different Multimorbidity Measures Result in Varying Estimated Levels of Physical Quality of Life in Individuals with Multimorbidity: A Cross-Sectional Study in the General Population. Biomed Res Int. 2016;2016:7845438. doi: 10.1155/2016/7845438.
  28. Robson B, Harris R (editors). Hauora: Maori Standards of Health IV. A study of the years 2000–2005. 2007, Te Rōpū Rangahau Hauora a Eru Pōmare: Wellington.
  29. Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017 Oct 15; 221:36–46. doi: 10.1016/j.jad.2017.06.009.
  30. Slightam CA, Brandt K, Jenchura EC, et al. “I had to change so much in my life to live with my new limitations”: Multimorbid patients’ descriptions of their most bothersome chronic conditions. Chronic Illn. 2017 Jan 1:1742395317699448. doi: 10.1177/1742395317699448.
  31. Coventry PA, Hays R, Dickens C, et al. Talking about depression: a qualitative study of barriers to managing depression in people with long term conditions in primary care. BMC Fam Pract. 2011 Mar 22; 12:10. doi: 10.1186/1471-2296-12-10.
  32. Moussavi S, Chatterji S, Verdes E, et al. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007 Sep 8; 370(9590):851–8.
  33. McKinlay E, Graham S, Horrill P. Culturally and linguistically diverse patients’ views of multimorbidity and general practice care. J Prim Health Care. 2015 Sep 1; 7(3):228–35.
  34. Vogel I, Miksch A, Goetz K, et al. The impact of perceived social support and sense of coherence on health-related quality of life in multimorbid primary care patients. Chronic Illn. 2012 Dec; 8(4):296–307. doi: 10.1177/1742395312445935.
  35. Roberto KA, Gigliotti CM, Husser EK. Older Women’s Experiences with Multiple Health Conditions: Daily Challenges and Care Practices. Health Care Women Int. 2005 Sep; 26(8):672–92.
  36. Duguay C, Gallagher F, Fortin M. The experiences of adults with multimorbidity: a qualitative study. J Comorb. 2014 May 28; 4:11–21. doi: 10.15256/joc.2014.4.31.
  37. Noël PH, Parchman ML, Williams JW Jr, et al. The challenges of multimorbidity from the patient perspective. J Gen Intern Med. 2007 Dec; 22 Suppl 3:419–24.
  38. Eton DT, Ramalho de Oliveira D, Egginton JS, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Relat Outcome Meas. 2012; 3:39–49. doi: 10.2147/PROM.S34681.
  39. Jatrana S, Crampton P, Norris P. Ethnic differences in access to prescription medication because of cost in New Zealand. J Epidemiol Community Health. 2011 May; 65(5):454–60. doi: 10.1136/jech.2009.099101.
  40. Tseng CW, Brook RH, Keeler E, Mangione CM. Impact of an annual dollar limit or “cap” on prescription drug benefits for Medicare patients. JAMA. 2003 Jul 9; 290(2):222–7.
  41. Jatrana S, Crampton P, Richardson K, Norris P. Increasing prescription part charges will increase health inequalities in New Zealand. N Z Med J. 2012 May 25; 125(1355):78–80.
  42. Salisbury C, Johnson L, Purdy S, et al. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. British Journal of General Practice, 2011; 61(582):p. e12–21.
  43. Smith SM, Soubhi H, Fortin M, et al. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ, 2012; 345:p. e5205.
  44. Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012 Jul 7; 380(9836):7–9. doi: 10.1016/S0140-6736(12)60482-6.
  45. Pitama S, Huria T, Lacey C. Improving Maori health through clinical assessment: Waikare o te Waka o Meihana. N Z Med J. 2014 May 2; 127(1393):107–19.

Contact diana@nzma.org.nz
for the PDF of this article

View Article PDF

Multimorbidity is the presence of two or more long-term conditions that collectively influence a person’s health status, often requiring complex care and management.1,2 Multimorbidity is more common among older people,3–5 although a study of two million people registered in primary care practices found the absolute number of people with multimorbidity was higher for those under 65 than those over 65.3 While there is little published evidence on multimorbidity prevalence in New Zealand, one study of 1,326 hospitalised patients found that a third had comorbid disease (defined as disease occurring in addition to their primary reason for admission), and this was associated with poorer outcomes.6 The burden of multimorbidity is generally higher in those in lower socioeconomic groups and other underserved populations such as indigenous and ethnic minority groups, and the onset tends to be at a younger age.3,7–11

The impact of multimorbidity on those affected is far reaching: it often involves taking multiple medications, may adversely affect employment and can involve frequent but fragmented healthcare.12, 3 These patients are at high risk of poor outcomes such as disability, functional decline and poor quality of life.2 Multimorbidity also comes at a cost for both individuals and the healthcare system, with healthcare utilisation and costs increasing with each additional condition.2 Two recent qualitative New Zealand studies found that multimorbidity often has a substantial negative impact on people’s lives, including considerable difficulty with managing medications and difficulties accessing and navigating appropriate healthcare.14,15 In New Zealand, the impact of multimorbidity may be greatest for Māori, who have higher rates of many long-term conditions, poorer access to primary care, are more likely to experience discrimination and experience a lower socio-economic status than non-Māori.16–18

This study aimed to assess the frequency, pattern and impact of physical and mental health, and issues regarding support, employment and finance for people with multimorbidity across different ethnic groups in New Zealand.

Methods

Survey population and eligibility criteria

The study population was adults aged 18+ with multimorbidity, enrolled with one of three primary health organisations (PHOs) in New Zealand.

Multimorbidity was defined using retrospective hospital discharge data (via ICD-10 coded diagnosis codes). All recorded diagnoses from hospital discharge records were coded for 61 long-term conditions drawn from the M3 multimorbidity index.19 To be eligible for this study, participants needed to have two or more identified conditions in the five years prior to the data extract date (1 January 2016), with at least one being a physical health condition.

Other sampling eligibility criteria were enrolment in one of the study Primary Health Organisation’s (PHO) at time of data extraction, and recorded as alive at time of data extraction. Data were provided by the Ministry of Health, drawing from the National Health Index (NHI) master table and the National Minimum Dataset (NMDS),20 linked by NHI number (unique identifier for individuals engaged with healthcare system in New Zealand).

Sampling process

Sampling was stratified by respondent ethnicity (Māori, Pacific and Non-Māori/Non-Pacific, based on ethnicity recorded in the NHI master record). The target sample size was set at 200 participants per stratum to achieve a margin of error (half-width of 95% confidence interval) of +/- 7% for stratified estimates. We assumed a 40% response rate, and selected a list of 1,500 patients to invite in order to achieve a final sample size of 600 participants.

Following a pilot study of the survey recruitment methods, which identified a need for additional resources for recruitment, a decision was made to focus on two of the original PHOs: a new sample of patients was drawn for one PHO (Compass Health) where in-depth recruitment processes could be used (n=999, stratified by ethnicity), and the original sampling list was retained for the second PHO (Pegasus Health) (n=472).

Invitation lists were reviewed by each PHO to check patients were alive and still enrolled with the PHO. The resulting practice-level lists were subsequently reviewed by the participating general practices who removed patients deemed inappropriate to invite (due to acute poor health or impairment from conditions like dementia; see Supplementary Table 1).

Recruitment

Strategies for gaining the support of primary healthcare practices and recruiting individual patients within those practices were developed in conjunction with an advisory group of research-active GPs, PHOs and a Māori health provider.

Once a primary care practice agreed to participate, participant packs were prepared for all eligible patients (including invitation letters addressed from the patient’s practice, information sheet, paper copy of the survey and post-paid return envelope). Participants were able to complete the paper survey or had the option of completing the survey online or over the telephone.

A research company (Research New Zealand) was contracted to coordinate data collection, including design of the web-based version of the survey, data entry for returned paper surveys and conduct of telephone interviews using a Computer Assisted Telephone Interview (CATI) system.

Measures

The survey combined original questions alongside items from existing questionnaires including: New Zealand Health Survey,21 Work Productivity and Impairment Questionnaire (adapted),22 Bayliss23 and Social Provisions Scale (three questions only).24 The survey included five key topics: social support, financial implications, access to healthcare, health literacy, and coordination and continuity of care. These areas were chosen based on a literature review which identified key themes around patients’ experiences of living with multimorbidity, and themes emerging from our earlier qualitative study on multimorbidity.15 The survey also included demographic questions. A draft survey was piloted with 11 patients with multimorbidity, with subsequent amendments made and reviewed by the research and clinical advisory teams before the survey was finalised.

Data analysis

To account for the stratified sampling design, we calculated inverse sampling weights for each participant (by ethnicity and PHO) so that total estimates for the sample were weighted back to represent the eligible population (ie, people with multimorbidity in the two participating PHOs). These inverse sampling weights were used in all analyses: for categorical outcomes, we have reported unweighted frequencies (actual number of respondents in each category) alongside weighted percentages and their 95% confidence intervals.

Crude descriptive analyses for each survey question include frequencies and weighted proportions, both for the total cohort and stratified by ethnicity, calculated using Proc Surveyfreq in SAS v9.3. Mean scores on the SF-12 Mental and Physical health scales were calculated using Proc Surveymeans. General population figures for questions drawn from the 2015/16 New Zealand Health Survey (NZHS)25 were based on analysis NZHS data that was then directly standardised to the age- and sex-profile of our survey respondents. Socioeconomic deprivation (NZDep) was measured using NZDep2013, a small-area based index calculated using aggregated census data on residents’ socioeconomic characteristics.26 For the sake of brevity, we have only presented ethnicity-stratified results where there was notable variation.

Data management and analysis was performed in SAS v9.3 and Microsoft Excel. Ethical approval for the study was granted by the Southern Region Ethics Committee (16/STH/16); the study was also considered by the University of Otago Ngāi Tahu Research Consultation Committee.

Results

Patients were drawn from 75 primary care practices (Supplementary Table 2). Of 1,471 potential participants, 758 (51.5%) were deemed eligible and sent study information packs by the practices. Of these, 234 participants completed the survey (response rate: 31%), with 167 respondents from PHO 1 (37% response rate) and 67 from PHO 2 (22% response rate) (Supplementary Table 1). Of the 234 returned surveys, 219 were self-completed by paper survey, seven were completed online and eight by telephone.

Study participants characteristics

Participant characteristics are presented in Table 1. Over half of the participants (52%) were 65 years or older (mean age = 65.2, SD=13.9) with equal numbers of male and female participants (n=117 for both). Although we aimed to recruit similar numbers in each ethnic group, 25% of participants were Māori, 19% Pacific and the majority (56%) were Non-Māori/non-Pacific (NMNP). Most participants (74%) reported living with other people (partner, children, family, flatmates/non-family), 25% were living alone and only three participants were living in a home/care facility. Only 12% of participants were from NZ Dep Quintile 5 (most deprived) neighbourhoods. Half of the participants (50%) had a secondary school or similar level qualification, while a small proportion (15%) held a bachelor’s degree or higher and the remainder (29%) reported having no qualifications. While the majority (57.9%, 95% CI 48.7–67.1) were retired, homemakers or volunteers) a sizable minority were working in paid employment (38%, 95% CI 28.8–47.2).

Table 1: Sociodemographic characteristics of survey participants.

1Missing data for nine participants.
2Participants could choose more than one option.
3Missing data for four participants.

More than half of all participants had been diagnosed with three or more long-term conditions, with nearly 10% having five or more (9.2%, 95% CI 4.8–13.6). When stratifying comorbid conditions by ethnicity, we observed that Māori and Pacific participants appeared to have marginally greater numbers of comorbid conditions (Supplementary Table 3). Cardiovascular conditions were among the most common (cardiac arrhythmia: 5.5%, cardiac disease other: 5.4%, uncomplicated hypertension 4.3%, myocardial infarction 4.0%, angina 3.6%, cerebrovascular disease 3.6%; Table 2).

Table 2: Prevalence of multiple long-term conditions within the cohort.

1Only conditions with a weighted proportion of 2% or greater are reported.
2Residual category of cardiac conditions not counted under other specified cardiac categories.

Hauora: Physical, mental and social wellbeing

When asked to rate their general health, many participants reported only ‘fair’ or ‘poor’ health (41.2%) (Table 3). Māori (47.7%) and Pacific (53.2%) respondents were more likely to report having only fair or poor health compared with NMNP living with multimorbidity (40.2%). These figures were much higher than general population estimates from the NZHS, where only 13.5% rated their health as fair or poor (95% CI 10.6–16.4; age- and sex-standardised from the 2015/16 NZHS to match our sample).

Table 3: General self-rated health.

c


1Missing data for three participants.
2Missing data for one participant.
3Missing data for two participants.

The mean aggregate SF-12 Physical Health Score was 38.5 (95% CI 37.0–40.1) for survey participants, which was substantially lower than the general population mean score (mean=46.5, 95% CI 45.4–47.5, age- and sex-standardised from 2015/16 NZHS;). Health was reported by many participants (72.4%) to limit their ability to climb several flights of stairs (Supplementary Table 3).

The mean aggregate SF-12 Mental Health score for survey participants was 48.8 (95% CI 47.1–50.4), again lower than for the general population (mean=55.0, 95% CI 54.4–55.7; age- and sex-standardised from 2015/16 NZHS; Supplementary Table 4). Nearly half (48%) of participants reported accomplishing less than they would have liked as a result of their emotional problems (eg, feeling depressed or anxious) over the previous four weeks.

The majority (97.3%) of survey participants reported having participated in different social interactions in the two weeks prior to completing the survey (Table 4). Access to help was readily available with the majority (85.8%, 95% CI 79.1–92.4) of participants having people in their lives who they could depend on for help. However, half of participants (50.3%, 95% CI 40.5–60.1) also reported having other people who depended on them for help (Table 4).

Table 4: Socialisation and support.

1Participants could select more than one option.
2Missing data for nine participants.
3Missing data for 17 participants.

Employment, work and financial impacts

Of the 119 participants who had been employed in the past five years, many had made changes to their working conditions as a result of their health, including decreasing working hours (31.3%, 95% CI 18.4–44.2), taking lighter duties (17%, 95% CI, 6.6–27.4) or changing job (11.8%, 95% CI 2.5–21.1), while some had stopped working altogether (8.3%, 95% CI 2–14.7) (Table 5). A majority (70%, 95% CI 56.8-83.3) of these participants reported their health had affected their productivity over the previous seven days. Pain was reported as interfering with work over the previous four weeks for most of these participants (75.7%, 95% CI 67.8–83.7), while more than a quarter (26.1%, 95% CI 13.6–38.5) had taken time off work due to problems associated with their health (eg, to attend doctor’s appointments, sick days, left work early).

Table 5: Health impact on work.

1Among those who had been in paid employment in the past five years (n=119).
2Participants could select more than one option.
3Among the total cohort (n=234).

Nearly one in five participants (18%, 95% CI 10.7–25.0) reported financial difficulty taking care of all their healthcare needs including prescriptions (Table 6). Māori (37%, 95% CI 19.3–53.7) and Pacific people (29%, 95% CI 12.8–44.9) were more likely to report financial healthcare difficulties than non-Māori/non-Pacific (16%, 95% CI 8.0–23.7). Nearly a quarter of participants (24%, 95% CI 16.4–32.2) reported difficulties covering other basic living costs (eg, rent/mortgage, food, power) in addition to healthcare costs, and more than a quarter cut down on other purchases (eg, clothing) because of their healthcare expenses (26%, 95% CI 17.4–34).

Table 6: Experiences of financial difficulty among patients with multimorbidity.

c


Discussion

This study describes the self-reported health status and experiences of people living with multiple long-term conditions in New Zealand. Participants with multimorbidity experienced poorer self-reported general health, physical health and mental health than the general population; their conditions also impacted adversely on their employment and financial wellbeing. Māori and Pacific participants reported poorer health and financial healthcare difficulties than Non-Māori/non-Pacific (NMNP).

Our observation that patients with multimorbidity report poorer health than a similar cohort of the general New Zealand population is unsurprising. Over half of our study cohort were living with three or more long-term conditions, and health-related quality of life scores have been found to decrease as the number of concurrent conditions increases.27

However, this study also suggests that Māori and Pacific people with multimorbidity may experience greater impact on their health than NMNP people with multimorbidity. There are a number of potential reasons for this difference. When stratifying comorbid condition by ethnicity, we observed that Māori and Pacific people appeared to have a marginally greater number of comorbid conditions, which may at least partially explain why Māori and Pacific people tend to report poorer overall health. Māori are also more likely to have poorer health arising from the breach of indigenous rights, manifesting in differential access to the determinants of health, differential access to healthcare and differences in the quality of care received.28 Māori are also more likely to have experienced racism (interpersonal and institutional), which is associated with poor health.17 In addition, and likely partially as a result of these factors, Māori and Pacific people experience higher rates of, and have been found to have, worse outcomes for a number of long-term conditions.28

These results are in line with previous studies where people living with multimorbidity experienced poorer mental health compared with a similar cohort of the general population.29 Multimorbidity places a substantial psychological burden on those who live with it, and so these people are at higher risk of poor mental health compared with those without multimorbidity.2,30 As an additional burden for those with multimorbidity, poor mental health can make self-management difficult.3,31 Other studies have suggested that depression in conjunction with other long-term conditions results in greater decrements in health.32

We observed a high degree of socialisation and support among respondents, which can influence the mental health of those with multimorbidity.14,33–35 Patient social support networks are important for practical reasons, emotional wellbeing and are key in supporting people to self-manage their health.14,36,37 High levels of social support are also associated with perceived improved quality of life for those living with multimorbidity.34

Employment issues were identified as a problem, with participants reporting having to make changes to their employment as a result of poor health. Our findings are consistent with those from other studies, which show multimorbidity adversely affects employment by acting as a barrier to employment and resulting in time off work due to illness or injury.13 Employment limitations and financial treatment burdens associated with multimorbidity also make self-management challenging.13,38 To maintain workplace productivity and employee wellness, it is necessary for workplaces to take a proactive approach to the health of their workers.

Our observation that nearly 20% of participants experienced financial difficulties taking care of their healthcare needs (including prescriptions in addition to basic living costs) was unexpected given the small proportion (12%) of respondents living in quintile 5 (most deprived). This impact was greater for Māori and Pacific participants, with nearly half of Māori participants reporting that they find it hard to cover basic living costs in addition to their healthcare expenses. The financial impact of long-term conditions is important and under-researched. The relationship between financial hardship and higher levels of multimorbidity is consistent both with the hypothesis that those with lower incomes are at higher risk of multimorbidity, and that multimorbidity may result in loss of income. Both are likely to be operating, and the association is important regardless of the direction of causality.

Another financial element is the likelihood that patients require multiple different prescriptions, although we do note that many participants accessed one of two government subsidy schemes which should reduce the cost of multiple prescriptions. When patients cannot afford to collect some or all of their prescribed medications they may be forced to prioritise which medications are most essential, or ration the medication they can afford, resulting in unnecessary suffering, deterioration in health and increased costs to the patient and healthcare system.15,39,40 This research supports previous suggestions for the need to reconsider prescription costs to better enable optimal self-management to maintain health among people with multimorbidity.41

This population-based study is among the first to report on the impact of multimorbidity on patients from New Zealand primary care. The survey questionnaire was developed using a rigorous process, using validated or existing questions wherever possible, which allowed for broader comparison with the general population. Unfortunately the overall response rate was lower than anticipated, which limits the statistical precision of our estimates (ie, confidence intervals are wide) and raises the potential of selection bias. Since our sample only included those who had been hospitalised in the last five years, the included participants may have been less well than the broader population of people with multimorbidity. Poor recruitment of Māori and Pacific peoples meant we were unable to generate substantial evidence for these populations. Future studies in this setting may benefit from identifying alternative and complementary recruitment strategies, and factoring in a greater allowance for the screening out of unsuitable patients by PHOs and GPs.

Conclusions

The results of this study provide a picture of the substantial impact that multimorbidity has on individuals in terms of their physical, mental and social wellbeing, their employment, and financial wellbeing. Furthermore, these impacts appear to be greater for Māori and Pacific people. Taken together, these results support a partnership approach to improving the lives of people with multimorbidity, including supporting patients to self-manage their conditions; society level support involving support people and employers; and healthcare providers taking person-centred approaches using holistic care models.15,42–45 Finally, future research should concentrate on the investigation of potential explanations and interventions for ethnic disparities in New Zealand.

Appendix

Supplementary Table 1: Participant sample and participation

*Not all practices in PHO 2 returned information about the number of patients invited into the study so the figure is the maximum possible.

Supplementary Table 2: Practice sample and participation.

Supplementary Table 3:

Supplementary Table 4: Impact of health on ability to climb several flights of stairs.

Supplementary Table 5: Accomplished less than would have liked due to emotional problems, such as feeling depressed or anxious in the past four weeks.

Summary

Abstract

Aim

To describe the experiences of people living with multimorbidity in New Zealand.

Method

We conducted a cross-sectional survey of adults with multimorbidity enrolled in two primary health organisations in New Zealand. Potential participants with multimorbidity were identified using retrospective hospital discharge data coded for long-term conditions. Sampling was stratified by ethnicity (Mori, Pacific and non-Mori/non-Pacific). Analysis was descriptive, with some responses compared to the general population estimates from the New Zealand Health Survey.

Results

A total of 234 participants completed the survey (mean age 65.2). Self-reported physical health was poor among the cohort: forty-one percent of participants reported only fair or poor general health, compared to 13.5% in the general population (age and sex standardised), with similar results for both self-reported mental health and physical health. Self-reported health was poorer for Mori and Pacific participants. The majority (70%) of those who were working reported their health had affected their productivity, while nearly 20% of participants reported financial difficulty in taking care of their health needs.

Conclusion

These results emphasise the serious impact multimorbidity has on patients health status compared to the general population. This research supports the development of holistic patient-centred care models designed to improve patient outcomes.

Author Information

James Stanley, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Elinor Millar, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Kelly Semper, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington; Cheryl Davies, Tu Kotahi Asthma Trust, Lower Hutt; Anthony Dowell, Department of Primary Health Care and General Practice, University of Otago, Wellington; Dee Mangin, Department of General Practice, University of Otago, Christchurch; Ross Lawrenson, University of Waikato and Waikato District Health Board, Hamilton; Diana Sarfati, Cancer and Chronic Conditions (C3) Research Group, University of Otago, Wellington.

Acknowledgements

#NAME?

Correspondence

Jeannine Stairmand, Cancer and Chronic Conditions (C3) Research Group, Department of Public Health, University of Otago, Mein St, Newtown, Wellington 6021.

Correspondence Email

jeannine.stairmand@otago.ac.nz

Competing Interests

Professor Lawrenson is both an employee of the University of Waikato and Waikato District Health Board; Dr Sarfati and Dr Millar reports grants from the Health Research Council during the conduct of the study.

  1. Almirall J, Fortin M. The coexistence of terms to describe the presence of multiple concurrent diseases. J Comorb. 2013 Oct 8; 3:4–9.
  2. Xu XL, Mishra GD, Jones M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews. Ageing Res Rev. 2017 Aug; 37:53–68. doi: 10.1016/j.arr.2017.05.003.
  3. Barnett K, Mercer SW, Norbury M, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012 Jul 7; 380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2.
  4. Garin N, Olaya B, Perales J, et al. Multimorbidity patterns in a national representative sample of the Spanish adult population. PLoS One. 2014 Jan 20;9(1):e84794. doi: 10.1371/journal.pone.0084794. eCollection 2014. Erratum in: PLoS One. 2015;10(4):e0123037.
  5. Kirchberger I, Meisinger C, Heier M, et al. Patterns of multimorbidity in the aged population. Results from the KORA-Age study. PLoS One. 2012; 7(1):e30556. doi: 10.1371/journal.pone.0030556.
  6. Davis P1, Lay-Yee R, Fitzjohn J, et al. Co-morbidity and health outcomes in three Auckland hospitals. N Z Med J. 2002 May 10; 115(1153):211–5.
  7. Robinson PC, Merriman TR, Herbison P, Highton. Hospital admissions associated with gout and their comorbidities in New Zealand and England 1999–2009. Rheumatology (Oxford). 2013 Jan; 52(1):118–26. doi: 10.1093/rheumatology/kes253. Epub 2012 Sep 18.
  8. Sarfati D, Gurney J, Lim BT, et al. Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival. Asia Pac J Clin Oncol. 2016 Mar; 12(1):e47–56. doi: 10.1111/ajco.12130. Epub 2013 Dec 19.
  9. Sarfati D, Tan L, Blakely T, Pearce N. Comorbidity among patients with colon cancer in New Zealand. N Z Med J. 2011 Jul 8; 124(1338):76–88.
  10. Johnson-Lawrence V, Zajacova A, Sneed R. Education, race/ethnicity, and multimorbidity among adults aged 30–64 in the National Health Interview Survey. SSM - Population Health. 2017 Dec; 3:366–372. doi.org/10.1016/j.ssmph.2017.03.007.
  11. Robson B, Purdie G, Cormack D. Unequal Impact II: Māori and Non-Māori Cancer Statistics by Deprivation and Rural-Urban Status, 2002-2006. 2010, Ministry of Health: Wellington.
  12. Jokanovic N, Tan EC, Dooley MJ, et al. Prevalence and Factors Associated With Polypharmacy in Long-Term Care Facilities: A Systematic Review. J Am Med Dir Assoc. 2015 Jun 1; 16(6):535.e1–12. doi: 10.1016/j.jamda.2015.03.003. Epub 2015 Apr 11. Review.
  13. Ward BW. Multiple chronic conditions and labor force outcomes: A population study of U.S. adults. Am J Ind Med. 2015 Sep; 58(9):943–54. doi: 10.1002/ajim.22439. Epub 2015 Jun 23.
  14. McKinlay EM, McDonald J, Darlow B, Perry M. Social networks of patients with multimorbidity: a qualitative study of patients’ and supporters’ views. J Prim Care. 2017; 9(2):153–161. https://doi.org/10.1071/HC1606215.
  15. Signal L, Semper K, Stairmand J, et al. A walking stick in one hand and a chainsaw in the other: patients’ perspectives of living with multimorbidity. N Z Med J. 2017 May 12; 130(1455):65–76.
  16. Jansen P, Bacal K, Buetow S. A comparison of Maori and non-Maori experiences of general practice. N Z Med J. 2011 Mar 4; 124(1330):24–9.
  17. Harris R, Tobias M, Jeffreys M, et al. Racism and health: the relationship between experience of racial discrimination and health in New Zealand. Soc Sci Med. 2006; 63(6):1428–41. doi.org/10.1016/j.socscimed.2006.04.009
  18. Ministry of Health. Tatau Kahukura: Māori Health Chart Book 2015 (3rd edition). 2015. Wellington: Ministry of Health.
  19. Stanley J, Sarfati D. The new measuring multimorbidity index predicted mortality better than Charlson and Elixhauser indices among the general population. J Clin Epidemiol. 2017 Aug 24. pii: S0895-4356(17)30441-9. doi: 10.1016/j.jclinepi.2017.08.005.
  20. Ministry of Health. Collections (Health statistics, National collections and surveys). 2017 [cited 26/9/2017]; Available from: http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections
  21. Ministry of Health. Content Guide 2014/15 New Zealand Health Survey. 2015. Ministry of Health: Wellington.
  22. Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmacoeconomics. 1993 Nov; 4(5):353–65.
  23. Bayliss EA, Ellis JL, Steiner JF. Subjective assessments of comorbidity correlate with quality of life health outcomes: initial validation of a comorbidity assessment instrument. Health Qual Life Outcomes. 2005 Sep 1; 3:51.
  24. Cutrona C, Russell DW. The provisions of social relationships and adaptation to stress. Adv Pers Relation. 1987; 1(1):37–67.
  25. Ministry of Health. Methodology Report 2015/16: New Zealand Health Survey. 2016, Ministry of Health: Wellington.
  26. Salmond CE, Crampton P. Development of New Zealand’s deprivation index (NZDep) and its uptake as a national policy tool. Can J Public Health. 2012 May 9; 103(8 Suppl 2):S7–11.
  27. Ramond-Roquin A, Haggerty J, Lambert M, et al. Different Multimorbidity Measures Result in Varying Estimated Levels of Physical Quality of Life in Individuals with Multimorbidity: A Cross-Sectional Study in the General Population. Biomed Res Int. 2016;2016:7845438. doi: 10.1155/2016/7845438.
  28. Robson B, Harris R (editors). Hauora: Maori Standards of Health IV. A study of the years 2000–2005. 2007, Te Rōpū Rangahau Hauora a Eru Pōmare: Wellington.
  29. Read JR, Sharpe L, Modini M, Dear BF. Multimorbidity and depression: A systematic review and meta-analysis. J Affect Disord. 2017 Oct 15; 221:36–46. doi: 10.1016/j.jad.2017.06.009.
  30. Slightam CA, Brandt K, Jenchura EC, et al. “I had to change so much in my life to live with my new limitations”: Multimorbid patients’ descriptions of their most bothersome chronic conditions. Chronic Illn. 2017 Jan 1:1742395317699448. doi: 10.1177/1742395317699448.
  31. Coventry PA, Hays R, Dickens C, et al. Talking about depression: a qualitative study of barriers to managing depression in people with long term conditions in primary care. BMC Fam Pract. 2011 Mar 22; 12:10. doi: 10.1186/1471-2296-12-10.
  32. Moussavi S, Chatterji S, Verdes E, et al. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007 Sep 8; 370(9590):851–8.
  33. McKinlay E, Graham S, Horrill P. Culturally and linguistically diverse patients’ views of multimorbidity and general practice care. J Prim Health Care. 2015 Sep 1; 7(3):228–35.
  34. Vogel I, Miksch A, Goetz K, et al. The impact of perceived social support and sense of coherence on health-related quality of life in multimorbid primary care patients. Chronic Illn. 2012 Dec; 8(4):296–307. doi: 10.1177/1742395312445935.
  35. Roberto KA, Gigliotti CM, Husser EK. Older Women’s Experiences with Multiple Health Conditions: Daily Challenges and Care Practices. Health Care Women Int. 2005 Sep; 26(8):672–92.
  36. Duguay C, Gallagher F, Fortin M. The experiences of adults with multimorbidity: a qualitative study. J Comorb. 2014 May 28; 4:11–21. doi: 10.15256/joc.2014.4.31.
  37. Noël PH, Parchman ML, Williams JW Jr, et al. The challenges of multimorbidity from the patient perspective. J Gen Intern Med. 2007 Dec; 22 Suppl 3:419–24.
  38. Eton DT, Ramalho de Oliveira D, Egginton JS, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Relat Outcome Meas. 2012; 3:39–49. doi: 10.2147/PROM.S34681.
  39. Jatrana S, Crampton P, Norris P. Ethnic differences in access to prescription medication because of cost in New Zealand. J Epidemiol Community Health. 2011 May; 65(5):454–60. doi: 10.1136/jech.2009.099101.
  40. Tseng CW, Brook RH, Keeler E, Mangione CM. Impact of an annual dollar limit or “cap” on prescription drug benefits for Medicare patients. JAMA. 2003 Jul 9; 290(2):222–7.
  41. Jatrana S, Crampton P, Richardson K, Norris P. Increasing prescription part charges will increase health inequalities in New Zealand. N Z Med J. 2012 May 25; 125(1355):78–80.
  42. Salisbury C, Johnson L, Purdy S, et al. Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study. British Journal of General Practice, 2011; 61(582):p. e12–21.
  43. Smith SM, Soubhi H, Fortin M, et al. Managing patients with multimorbidity: systematic review of interventions in primary care and community settings. BMJ, 2012; 345:p. e5205.
  44. Salisbury C. Multimorbidity: redesigning health care for people who use it. Lancet. 2012 Jul 7; 380(9836):7–9. doi: 10.1016/S0140-6736(12)60482-6.
  45. Pitama S, Huria T, Lacey C. Improving Maori health through clinical assessment: Waikare o te Waka o Meihana. N Z Med J. 2014 May 2; 127(1393):107–19.

Contact diana@nzma.org.nz
for the PDF of this article

Subscriber Content

The full contents of this pages only available to subscribers.

LOGINSUBSCRIBE