Does potentially inappropriate prescribing predict an increased risk of admission to hospital and mortality? A longitudinal study of the ‘oldest old’.

doi:10.21203/rs.2.16434/v1

Background Potentially inappropriate prescribing (PIP) is associated with negative health outcomes, including hospitalisation and mortality. Using data from Life and Living in Advanced Age: a Cohort Study in New Zealand (LiLACS NZ), we reported the prevalence of PIP in Māori, the indigenous population of New Zealand, and non-Māori octogenarians and determined the association with hospitalisations and mortality prospectively over a 2-year time period.

Methods PIP, from pharmacist applied criteria, was reported as potentially inappropriate medicines (PIMs) and potential prescribing omissions (PPOs). The association between PIP, and continuous PIP (i.e. PIP at baseline and 24-months) and hospitalisations and mortality was determined throughout a series of 12-month follow-ups using binary logistic (hospitalisations) and Cox (mortality) regression analysis, reported as odds ratios (ORs) and hazard ratios (HRs), respectively, and the corresponding confidence intervals (CIs).

Results Data were included from 510 and 403 participants at 12-months and 24-months respectively. Māori participants were 83.2 (±2.6) and non-Māori were 85.5 (±0.5) years at 12-months. The mean number of medicines prescribed was 5.4 ±3.6 (Māori) and 5.3 ±3.3 (non-Māori) at 12-months and at 24-months was 5.7 ±3.5 (Māori) and 5.6 ±3.3 (non-Māori). Total prevalence of ≥1 PIM was 36.7% and 38.0%, of ≥1 PPO was 61.4% and 62.5%, and of any PIP (i.e. ≥1 PIM and/or PPO) was 72.2% and 73.0% at 12-months and 24-months, respectively. At 24-months, the overall prevalence of ≥1 continuous PIM, ≥1 continuous PPO and ≥1 continuous PIM and/or PPO was 18.7%, 37.9% and 51.8%, respectively. In Māori: PIMs were associated with a four-fold increase in hospitalisations; PPOs were associated with a two to three-fold increase in hospitalisations; continuous PIMs were associated with a four-fold increase in hospitalisations. In non-Māori: PIMs were associated with a double risk of hospitalisations; continuous PIMs were associated with a double risk of mortality.

Conclusions PIP was associated with an increased risk of hospitalisation and mortality in this cohort. Omissions appear more important for Māori in predicting hospitalisations, whereas PIMs were associated with hospitalisations and mortality for non-Māori. These results suggest understanding prescribing outcomes across and between population groups is needed and emphasises prescribing quality assessment is useful.

Geriatrics & Gerontology

Potentially inappropriate prescribing

adults aged ≥80 years

STOPP/START criteria

longitudinal study

health outcomes.

Population ageing has led to the inception of several longitudinal studies of advanced ageing, e.g. Leiden 85+ (1), Newcastle 85+ (2) and Life and Living in Advanced Age: a Cohort Study in New Zealand (LiLACS NZ) (3). These studies investigate the associations between health and socioeconomic factors and outcomes as people transition through older age (i.e. ≥80 years). LiLACS NZ, which commenced in 2010, was conceived as a bicultural study by Māori and non-Māori academics from several universities. This study involves two discrete cohorts, one Māori (the indigenous population of New Zealand) cohort and one non-Māori (3). The aim of LiLACS NZ is to establish the health status of octogenarians living in New Zealand and explore the importance of various factors (e.g. social contact and living conditions, nutritional status, disease diagnosis, prescribed medications) in predicting health outcomes such as hospitalisations and mortality, thus enabling health services to plan and individuals to prepare for living with advanced age (3).

As people age, they are more likely to be living with a number of chronic conditions (multimorbidity) and be prescribed a number of medications (polypharmacy) either appropriately or inappropriately. Prescribing in older people, in the context of multimorbidity, is complex as adverse effects relating to medications, e.g. drug-drug interactions and adverse drug reactions (ADRs), are more common in older age groups (4). Consequently, potentially inappropriate prescribing (PIP) (defined as ‘over-’, ‘under-’ or ‘mis-prescribing’) has received considerable attention in the research literature and been noted as problematic for patients and healthcare systems. However, there is inconclusive evidence relating to the long-term impact of PIP (4).

Undoubtedly, prescribing appropriateness is implicitly considered by the clinician at the point of prescribing. However, an explicit assessment of prescribing appropriateness has also evolved, through the development of prescribing tools such as Beers’ Criteria (5), Screening tool of Older Person’s Prescriptions (STOPP) and Screening Tool to Alert doctors to Right Treatment (START) version 1 (6) and version 2 (7). Using these tools, a number of studies have reported the association between PIP in older people (primarily aged 65 years and older) and health-related outcomes (4). PIP and its effect on health trajectories may be even more significant for octogenarians due to their increased vulnerability and susceptibility to ADRs. However, this has not been extensively reported in advanced age due to a paucity of clinical evidence. Using data from LiLACS NZ (3), this study reports the prevalence of PIP in a cohort of individuals aged ≥80 years and determines the association between PIP and subsequent hospitalisations and mortality over 36-months’ follow-up.

Study population

Data from LiLACS NZ were used in this study; the complete study protocol has been published elsewhere (3). The cohort consists of Māori (aged 80–90 years in 2010) and non-Māori (aged 85 years in 2010) recruited using multiple overlapping sampling frames to attempt a total population sample frame from a geographically defined region. Baseline data collection involved a face-to-face standardised questionnaire (including medication data), a health assessment, an audit of general practitioner (GP) medical records and review of hospitalisation records prior to enrolment in LiLACS NZ (3).

Measures: gender, age and GP visits were ascertained by self-report at interview; prior hospitalisation from Ministry of Health records. Socioeconomic deprivation was assessed using the New Zealand Deprivation Index 2006 (8). Medications were recorded as taken from medication containers at time of interview. PIP prevalence was ascertained by a pharmacist trained in the application of the criteria (KC) examining all available clinical and medication data. Functional status was assessed using the Nottingham Extended Activities of Daily Living (NEADL) scale (9); a score ≥18 was classified as physically independent. Follow-up data collection involved an annual interview and health assessment. Diagnoses were ascertained using self-report, GP record review, hospitalisation discharge data and blood test analyses (10).

Data analysis

The results reported in this paper relate to the analysis of prescribed medication data collected at 12-months and 24-months; building on analysis of prescribed medication data collected at baseline (11). Rongoā medicines (Māori medicines), nutritional supplements, vitamins, topical creams and those containing inactive ingredients (e.g. aqueous cream) were excluded from the analysis as they were not included in the appropriateness assessment of prescribing. Included medications were coded using the World Health Organisation Anatomical Therapeutic Chemical Classification system (12).

PIP prevalence was reported as potentially inappropriate medicines (PIMs) and potential prescribing omissions (PPOs) identified by STOPP and START version 1, respectively (6) as version 2 (7) had not been published at the time this study was conceived. The prevalence of PIP (i.e. ≥1 PIM, ≥1 PPO and ≥1 PIM and/or PPO) was reported at two time-points (12-months and 24-months). Continuous PIMs (i.e. ≥1 PIM at baseline and 24-months), continuous PPOs (i.e. ≥1 PPO at baseline and 24-months) and any continuous PIP (i.e. ≥1 PIM and/or PPO at baseline and 24-months) were also calculated. Due to limited availability of clinical information, datasets could not be analysed for all instances of PIP noted in STOPP/START. Therefore, a sub-set of the criteria were applied to the data and some assumptions were made to facilitate the application of various criteria; see Supplementary File 1 Table 1 and Table 2.

Outcomes measured

Outcomes (hospitalisations and mortality) were evaluated at 24-months’ and 36-months’ follow-up. Following consent, hospitalisation and mortality data were obtained annually (until death) by matching the National Health Index number (a unique identifier) with routine data on hospitalisations and mortality held by the New Zealand Ministry of Health (3). Cardiovascular disease (CVD) is the leading cause of mortality in New Zealand. For this reason, hospitalisations were classified as all-cause, CVD-specific (identified using ICD–10 codes) and ambulatory-sensitive hospitalisations; the latter refers to hospitalisations for which effective management and treatment may prevent the admission. A standard list of diagnoses potentially susceptible to good primary care management was used to identify ambulatory-sensitive admissions (13).

Statistical analysis

Data were analysed using Statistical Package for the Social Sciences Version 21^®. Descriptive statistics provided an overview of the cohort. Attrition rates and the prevalence of PIMs and PPOs observed at each time-point were calculated. Reproducibility of results (i.e. the prevalence of PIMs and PPOs identified) by two independent researchers (trained in application of the criteria) was evaluated through calculation of Cohen’s Kappa statistic (κ) as a measure of inter-rater reliability (14). Inter-rater reliability was interpreted as ‘poor’ if ≤0.2, ‘fair’ if 0.21–0.40, ‘moderate’ if 0.41–0.60, ‘substantial’ if 0.61–0.80 and ‘good’ if 0.81–0.99 (15).

Differences in the prevalence of PIP (i.e. PIMs and PPOs), as well as differences in the prevalence of continuous PIP (i.e. continuous PIMs and continuous PPOs), between Māori and non-Māori were assessed using the Pearson Chi-Square (χ²) test or, for small numbers, Fisher’s Exact test (significance p<0.05); differences in age and number of medicines prescribed were tested using the two-sample t-test; differences in the proportion of males and females, and socioeconomic deprivation, were tested using the χ² test (significance p<0.05).

The association between PIP (as three individual binary variables i.e. ≥1 PIM, ≥1 PPO and ≥1 PIM and/or PPO) and hospitalisations, and between continuous PIP (as three individual binary variables i.e. ≥1 continuous PIM, ≥1 continuous PPO and ≥1 continuous PIM and/or PPO) and hospitalisations, was determined using binary logistic regression analysis, reported as odds ratios (ORs) and 95% confidence intervals (CIs), (significance p<0.05). Similarly, the association between PIP and mortality, and between continuous PIP and mortality, was determined using Cox regression analysis, measured by hazard ratios (HRs) and 95% CIs, (significance p<0.05). Regression models were adjusted for age (Māori only), gender, prior hospitalisation (any hospital admission within the previous 12 months), GP visits, socioeconomic deprivation, number of medications taken and functional status as assessed by the NEADL (9).

Demographic overview and prevalence of potentially inappropriate prescribing (PIP)

Baseline results relating to PIP prevalence and the association with outcomes at 12-months’ follow-up are reported elsewhere (11). Using the κ statistic, the inter-rater reliability for the identification of PIMs and PPOs (at 24-months) indicated a ‘good’ level of agreement for application of both STOPP (κ = 0.88) and START (κ = 0.80). Table 1 provides a demographic overview of the cohort and reports the prevalence of PIP and continuous PIP observed. Demographic data were obtained for 510 at 12-months and 403 participants at 24-months. Māori were significantly younger than non-Māori (p<0.01). Overall, 55.7% and 55.6% of the cohort were female at 12-months and 24-months, respectively. The mean number of medicines prescribed, and the prevalence of PIP reported at each time-point was similar for Māori and non-Māori. For the combined cohort (i.e. Māori and non-Māori) the prevalence of ≥1 PIM was 36.7% and 38.0% at 12-months and 24-months, respectively; the corresponding prevalence of ≥1 PPO was 61.4% and 62.5%, and for ≥1 PIM and/or PPO, the prevalence was 72.2% and 73.0% at 12-months and 24-months, respectively. At 24-months, the prevalence of ≥1 continuous PIM, ≥1 continuous PPO and ≥1 continuous PIM and/or PPO was 18.7%, 37.9% and 51.8%, respectively; see Table 1 for Māori and non-Māori data presented separately.

Table 1 Demographics and prevalence of potentially inappropriate prescribing (PIP) identified by STOPP and START

Overall, the most common PIMs at 12-months and 24-months were ‘a proton pump inhibitor (PPI) at full therapeutic dose for more than 8 weeks’, and ‘long-term opiates in those with recurrent falls’. Two prescribing scenarios were more common in Māori participants: ‘a thiazide in those with a history of gout’ and ‘diltiazem or verapamil in those with New York Heart Association class III or IV heart failure’; see Supplementary File 1, Table 3.

In both Māori and non-Māori, the most common PPO at 12-months and 24-months was the omission of ‘a calcium and vitamin D supplement in patients with known osteoporosis’. In Māori participants, omission of 'an angiotensin-converting-enzyme inhibitor (ACE) inhibitor in those with congestive heart failure (CHF)' was more common than in non-Māori participants. Conversely, the omission of ‘statin therapy in those with a documented history of coronary, cerebral or peripheral vascular disease’ was more common in non-Māori participants compared to Māori participants; see Supplementary File 1, Table 4.

Potentially inappropriate prescribing (PIP) and health-related outcomes

Māori cohort

Table 2 provides an overview of the association between exposure to PIP, and exposure to continuous PIP, and health-related outcomes in the Māori cohort. Exposure to ≥1 PPO (at 12-months) was associated with an increased risk of hospitalisations (all-cause, CVD-specific, ambulatory-sensitive) at 24-months’ follow-up. Exposure to ≥1 PIM (at 12-months) was associated with increased all-cause hospitalisation at 24-months’ follow up and ≥1 PIM (at 24-months) was associated with an increased risk of ambulatory-sensitive hospitalisation at 36-months’ follow-up. Māori were more likely to have a CVD-specific hospitalisation at 36-months’ follow-up if they had continuous exposure to ≥1 PIM. There were no associations observed between PIP and mortality for Māori; see Table 2.

Table 2 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for Māori participants

Non-Māori cohort

Table 3 provides an overview of the association between exposure to PIP and health-related outcomes in the non-Māori cohort. Exposure to ≥1 PIM (at 12-months) was associated with an increased risk of an all-cause, CVD-specific and ambulatory-sensitive hospitalisation at 24-months’ follow-up. Exposure to ≥1 continuous PIM was associated with an increased risk of mortality at 36-months’ follow-up; see Table 3.

Table 3 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for non-Māori participants

Using data from LiLACS NZ, this study reported the prevalence of PIP (defined by STOPP/START) at two time-points (12-months and 24-months) and explored the association of PIP with outcomes (all-cause, CVD-specific and ambulatory-sensitive hospitalisations and mortality) at 12-month intervals (24-months’ and 36-months’ follow-up). PIP was highly prevalent, and PPOs were more common than PIMs. In Māori, PIMs and PPOs were associated with an increased risk of hospitalisation; in non-Māori, PIMs were associated with an increased risk of hospitalisations and mortality. This is one of a few studies to find a prospective association between PIP (defined by STOPP/START) and health outcomes. In the context of population ageing, such data are increasingly relevant to forward planning of health services. This study adds evidence from the southern hemisphere to that from Europe, the USA and Taiwan (4) for the utility of STOPP/START in identifying PIP associated prospectively with adverse outcomes. We add that the associations between inappropriate prescribing and increased risk of hospitalisation and mortality persist into advanced age (≥80 years) and that PPOs may be as, and perhaps more, important than PIMs.

Considering that there were few differences between Māori and non-Māori in relation to the levels of polypharmacy observed, it is intriguing that the association between PIP and the described health-related outcomes differ. This could potentially be explained by the differing patterns of multimorbidity observed in the two ethnic groups (16). The association between prescribing omissions and CVD hospitalisations is particularly relevant. The prevalence of CHF and Diabetes mellitus were greater amongst Māori (17) and the increased prevalence of PPOs potentially suggests under-treatment with ACE inhibitor in CHF of Māori in advanced age. Disparities in access and outcomes related to CVD for Māori are well known (18–22) and these appear to persist into advanced age. This finding supports the need to individualise approaches to treatment for older people from diverse backgrounds with a call for more specific research in different ethnic groups. Moreover, the issue of institutional racism in treatment also needs to be raised where outcomes differ through systematic differences in treatment patterns between ethnic groups (23,24).

In accordance with previous studies of PIP (4), the most frequently encountered PIMs were the prolonged use of high-dose PPIs, as well as opiates in those at risk of falling. Although PPIs have a favourable risk-benefit ratio, their use should be reviewed regularly as there are concerns surrounding an increased risk of infections and reduced absorption of nutrients with long term PPI use, in particular vitamin B12 and calcium (25). In this study, the prescription of benzodiazepines, tricyclic antidepressants, anticholinergics and opiates increased with time. Such drugs are a significant problem in older people due to the possibility of dependence and association with side effects such as falls, confusion, dizziness and constipation (26); this study exemplifies the challenges of safely managing multimorbidity in the elderly.

The omission of a calcium and vitamin D supplement in this study also increased substantially with time, which may reflect the local uncertainty surrounding its safety in the presence of CVD. Current evidence regarding vitamin D and, particularly, calcium supplementation is inconclusive; New Zealand has been the source of strong debate (27,28) and local prescribers may have been more influenced than international trends suggest. The suggestion that medicines (particularly those associated with CVD) cause more harm than benefit in older people is a clinical conundrum when prescribing for this population group. However, the use of antihypertensives (in those aged 80 years) (29) and statins (in those aged 40–80 years and 70–82 years) (30) have been shown to be beneficial in secondary prevention of CVD in older people. Potentially, this study suggests that conservative prescribing for CVD risk may not be in the best interests of those in advanced age, given the omission of CVD related medicines observed in this cohort. Clinical trials of conservative versus comprehensive prescribing for multimorbidity are needed before causality can be claimed.

In this cohort, prescribing omissions were more common in Māori than non-Māori. Reasons for this disparity have not been investigated in this study but are complex and associated with system-based issues such as access (18). A large body of evidence has also identified institutional racism as a cause of health inequalities for Māori in New Zealand. Thus, there is a need for on-going strategies to ensure Māori are not marginalised in health (23,24). The association between PIP and mortality observed in this cohort is not consistent over time, nevertheless, it suggests the need for trials to test the efficacy of prescribing strategies.

Recruitment to LiLACS NZ was favourable; of those contacted, 64% agreed to participate (n = 937) at baseline. The inability to engage ethnic minority groups in research is common; this may have been overcome by the support of a Māori oversight group, 'Rōpū Kaitiaki o tikanga Māori’ (31). Comparisons between LiLACS NZ and other population-based samples suggests that LiLACS NZ data largely reflects the older population of New Zealand. However, it should be noted that non-Māori living in residential care may be underrepresented (32). Moreover, prescribing practices have been shown to differ across New Zealand and globally. Therefore, the generalisability of the results may be limited. Despite this, these results serve as an important comparator for other longitudinal studies of PIP. LiLACS NZ data collection was comprehensive and data collectors were trained by researchers who were experienced in engaging with older people. However, data collection incurred a high participation burden and as a result, 28% of those recruited opted to complete a shorter interview that did not include medication use. Length of follow-up is a major strength of this study since previous studies of PIP have had short follow-up periods (33). The attrition rate between the two time-points was 21% and is an inevitable limitation of ageing research, i.e. attrition rates are higher than in studies of younger populations. Overall small numbers will limit this analysis and the possibility of a missing a significant association (Type II error) is high.

The LiLACS NZ dataset was information-rich and included medication data as well as clinical information. Medication use was ascertained from medication containers provided by study participants, which provides a more reliable indication of medication use compared to electronic dispensing records. However, Rongoā medicines (Māori medicines) were omitted from the analysis, thus the association between these medicines and outcomes was not assessed. The use of clinical information, in addition to medication data, helped prevent the overestimation of PIP as participants’ co-morbidities and clinical picture were taken into consideration. The diagnosis of chronic conditions was verified using GP records, but since this was completed at baseline only, data collectors relied on the ability of participants to report any clinical diagnoses made thereafter. Consequently, the true incidence of clinical conditions may have been underestimated, and thus the prevalence of PIP. Increasingly, patient involvement in the prescribing process is being advocated. However, due to the design of this study it was not possible to account for patient preferences when identifying issues of PIP. Other limitations include the inability to apply all STOPP/START criteria and the use of proxies (assumptions) to facilitate the application of certain criteria; these limitations are common to most studies of PIP. The estimation of ambulatory-sensitive hospitalisations may be inaccurate in this age group as criteria were developed for use in those aged up to 75 years (13). Finally, although this study reports a significant association between exposure to PIP and an increased risk of admission to hospital, this does not infer causality due to the potential influence of residual confounding (34).

PIP was highly prevalent in this cohort of individuals, aged ≥80 years living in New Zealand, and associated with an increased risk of hospitalisations and mortality. Omissions in medication were more common than inappropriate medication use, particularly for Māori. Given the predicted change in global demographics, these results are important in full understanding of the relationship between PIP and poor health outcomes.

ACEAngiotensin converting enzyme

ADRAdverse drug reaction

CHFChronic heart failure

CIConfidence interval

CVDCardiovascular disease

HRHazard ratio

LiLACS NZLife and Living in Advanced Age: a Cohort Study in New Zealand

NEADLNottingham Extended Activities of Daily Living

OROdds ratio

PIMPotentially inappropriate medicine

PIPPotentially inappropriate prescribing

PPIProton pump inhibitor

PPOPotential prescribing omission

STARTScreening Tool to Alert doctors to Right Treatment

STOPPScreening Tool of Older Persons’ Prescriptions

Ethics approval and consent to participate

The principal study was granted ethical approval by the Northern X Regional Ethics Committee of New Zealand in December 2009 (NTX/09/09/088).

Consent for publication

Not applicable.

Availability of data and material

The datasets generated and/or analysed during the current study are not publicly available as data collection and analysis is on-going but are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This analysis was supported by the Department for Employment and Learning (DEL), Northern Ireland, through a postgraduate studentship to KC.

Authors’ contributions

KC analysed the data and led the writing of the paper. CH (Principal Investigator) and CR co-developed the research programme and contributed to the review of results and writing of the paper. NK conceived the LiLACS NZ study, led its design and the development of outcome measures and contributed to the review of results and writing of the paper. RT, SM, OM, AR and JB contributed to the review of results and writing of the paper.

Acknowledgements

We thank all the participants and their whanau/families for their time and contribution to the principal study. The Rōpū Kaitiaki (Hone Kameta, Florence Kameta, Betty McPherson, Te Kaanga Skipper, Paea Smith, Laiana Reynolds) oversaw the project from feasibility and throughout recruitment. Elizabeth Robinson guided the biostatistical planning and Rudi Westendorp gave advice at the planning stages.

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Table 1 Demographics and prevalence of potentially inappropriate prescribing (PIP), identified by STOPP and START

12-months

(n=510)

24-months

(n=403)

Māori

(n=178)

Non-Māori

(n=332)

P value

Māori

(n=122)

Non-Māori

(n=281)

P value

Age (years; mean ±SD)

83.2 ±2.6

85.5 ±0.5

<0.01^*

84.2 ±2.6

86.5 ±0.5

<0.01^*

Male

n (%)

71 (39.9)

155 (46.7)

0.14^†

44 (36.1)

135 (48.0)

0.03^†

Female

n (%)

107 (60.1)

177 (53.3)

78 (63.9)

146 (52.0)

Number of medicines prescribed

(mean ±SD)

5.4 ±3.6

5.3 ±3.3

0.78^*

5.69 ±3.5

5.6 ±3.3

0.68^*

Socioeconomic deprivation (NZDep)

n (%)

1-4

5-7

8-10

27 (15.2)

46 (25.8)

105 (59.0)

83 (25.0)

143 (43.1)

106 (31.9)

<0.01^†

16 (13.1)

35 (28.7)

71 (58.2)

71 (25.3)

124 (44.1)

86 (30.6)

<0.01^†

Individuals with ≥1 PIM

n (%)

62 (34.8)

125 (37.7)

0.53^†

43 (35.3)

110 (39.2)

0.46^†

Individuals with ≥1 PPO

n (%)

119 (66.9)

194 (58.4)

0.06^†

80 (65.6)

172 (61.2)

0.41^†

Individuals with ≥1 PIM and/or PPO

n (%)

134 (75.3)

234 (70.5)

0.25^†

88 (72.1)

206 (73.3)

0.81^†

Individuals with ≥1 continuous PIM^‡

n (%)

17 (14.2)

57 (20.7)

0.17^†

Individuals with ≥1 continuous PPO^‡

n (%)

49 (41.7)

100 (36.2)

0.24^†

Individuals with ≥1 continuous PIM and/or PPO^‡

n (%)

60 (50.8)

144 (52.2)

0.98^†

Key: NZDep, New Zealand Deprivation Index score (higher NZDep score is more deprived); ^*Two-samples t-test (significance p<0.05); ^†Chi-squared (χ2) test (significance p<0.05); ^‡Continuous PIP exposure defined as exposure to PIP at baseline and at 24-months.

Table 2 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for Māori participants

All-cause hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=98 (%)]	Individuals with ≥1 hospitalisation [n=70 (%)]	OR (95% CI)	P Value^†
PIM	0	73 (74.5)	35 (50.0)	2.49 (1.24, 5.01)	0.01
	≥1	25 (25.5)	35 (50.0)
PPO	0	43 (43.9)	13 (18.6)	3.02 (1.36, 6.73)	0.01
	≥1	55 (56.1)	57 (81.4)
PIM and/or PPO	0	34 (34.7)	7 (10.0)	3.65 (1.42, 9.39)	0.01
	≥1	64 (65.3)	63 (90.0)
CVD-specific hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=132 (%)]	Individuals with ≥1 hospitalisation [n=36 (%)]	OR (95% CI)	P Value^†
PIM	0	88 (66.7)	20 (55.6)	1.26 (0.57, 2.83)	0.57
	≥1	44 (33.3)	16 (44.4)
PPO	0	51 (38.6)	5 (13.9)	3.08 (1.04, 9.17)	0.04
	≥1	81 (61.4)	31 (86.1)
PIM and/or PPO	0	38 (28.8)	3 (8.3)	3.20 (0.88, 11.63)	0.08
	≥1	94 (71.2)	33 (91.7)
Ambulatory-sensitive hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=115 (%)]	Individuals with ≥1 hospitalisation [n=53 (%)]	OR (95% CI)	P Value^†
PIM	0	80 (69.6)	28 (52.8)	2.05 (0.98, 4.27)	0.06
	≥1	35 (30.4)	25 (47.2)
PPO	0	47 (40.9)	9 (17.0)	2.61 (1.11, 6.14)	0.03
	≥1	68 (59.1)	44 (83.0)
PIM and/or PPO	0	36 (31.3)	5 (9.4)	3.49 (1.21, 10.00)	0.02
	≥1	79 (68.7)	48 (90.6)
Mortality at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		No [n=157 (%)]	Yes [n=11 (%)]	HR (95% CI)	P Value^‡
PIM	0	102 (65.0)	6 (54.5)	1.63 (0.37, 7.22)	0.52
	≥1	55 (35.0)	5 (45.5)
PPO	0	54 (34.4)	2 (18.2)	0.99 (0.20, 4.99)	0.99
	≥1	103 (65.6)	9 (81.8)
PIM and/or PPO	0	40 (25.5)	1 (9.1)	1.47 (0.17, 12.52)	0.72
	1	117 (74.5)	10 (90.9)
All-cause hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=61 (%)]	Individuals with ≥1 hospitalisation [n=55 (%)]	OR (95% CI)	P Value^†
PIM	0	43 (70.5)	30 (54.5)	1.43 (0.60, 3.38)	0.42
	≥1	18 (29.5)	25 (45.5)
PPO	0	25 (41.0)	13 (23.6)	1.81 (0.73, 4.47)	0.20
	≥1	36 (59.0)	42 (74.4)
PIM and/or PPO	0	21 (34.4)	9 (16.4)	2.16 (0.78, 5.97)	0.14
	≥1	40 (65.6)	46 (83.6)
CVD-specific hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=88 (%)]	Individuals with ≥1 hospitalisation [n=28 (%)]	OR (95% CI)	P Value^†
PIM	0	58 (65.9)	15 (53.6)	1.43 (0.56, 3.63)	0.45
	≥1	30 (34.1)	13 (46.4)
PPO	0	29 (33.0)	9 (32.1)	0.93 (0.34, 2.60)	0.90
	≥1	59 (67.0)	19 (67.9)
PIM and/or PPO	0	24 (27.3)	6 (21.4)	1.27 (0.41, 3.96)	0.68
	≥1	64 (72.7)	22 (78.6)
Ambulatory-sensitive hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=74 (%)]	Individuals with ≥1 hospitalisation [n=42(%)]	OR (95% CI)	P Value^†
PIM	0	52 (70.3)	21 (50.0)	4.29 (1.44, 12.77)	0.01
	≥1	22 (29.7)	21 (50.0)
PPO	0	28 (37.8)	10 (23.8)	1.72 (0.61, 4.85)	0.30
	≥1	46 (62.2)	32 (76.2)
PIM and/or PPO	0	24 (32.4)	6 (14.3)	2.66 (0.81, 8.73)	0.11
	≥1	50 (67.6)	36 (85.7)
Mortality at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		No [n=90 (%)]	Yes [n=26 (%)]	HR (95% CI)	P Value^‡
PIM	0	58 (64.4)	15 (57.7)	1.25 (0.53, 2.96)	0.61
	≥1	32 (35.6)	11 (42.3)
PPO	0	31 (34.4)	7 (26.9)	1.08 (0.42, 2.77)	0.88
	≥1	59 (65.6)	19 (73.1)
PIM and/or PPO	0	23 (25.6)	7 (26.9)	0.69 (0.26, 1.83)	0.45
	≥1	67 (74.4)	19 (73.1)
All-cause hospitalisations^ϒ at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=61 (%)]	Individuals with ≥1 hospitalisation [n=55 (%)]	OR (95% CI)	P value^†
PIM	0	55 (90.2)	44 (80.0)	1.65 (0.50, 5.48)	0.41
	≥1	6 (9.8)	11 (20.0)
PPO	0	40 (65.6)	27 (49.1)	1.57 (0.64, 3.81)	0.32
	≥1	21 (34.4)	28 (50.9)
PIM and/or PPO	0	35 (57.4)	19 (34.5)	1.84 (0.74, 4.55)	0.19
	≥1	26 (42.6)	36 (65.5)
CVD specific hospitalisations^ϒ at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=88 (%)]	Individuals with ≥1 hospitalisation [n=28 (%)]	OR (95% CI)	P value^†
PIM	0	79 (89.8)	20 (71.4)	3.57 (1.09, 11.74)	0.04
	≥1	9 (10.2)	8 (28.6)
PPO	0	53 (60.2)	14 (50.0)	1.64 (0.61, 4.42)	0.33
	≥1	35 (39.8)	14 (50.0)
PIM and/or PPO	0	45 (51.1)	9 (32.1)	2.23 (0.79, 6.31)	0.13
	≥1	43 (48.9)	19 (67.9)
Ambulatory-sensitive hospitalisations^ϒ at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=74 (%)]	Individuals with ≥1 hospitalisation [n=42 (%)]	OR (95% CI)	P value^†
PIM	0	68 (91.9)	31 (73.8)	3.40 (0.98, 11.73)	0.05
	≥1	6 (8.1)	11 (26.2)
PPO	0	46 (62.2)	21 (50.0)	1.46 (0.57, 3.75)	0.43
	≥1	28 (37.8)	21 (50.0)
PIM and/or PPO	0	41 (55.4)	13 (31.0)	2.10 (0.81, 5.47)	0.13
	≥1	33 (44.6)	29 (69.0)
Mortality at 36-months’ follow-up				Adjusted*
Continuous PIP exposure^¥		No [n=90 (%)]	Yes [n=26 (%)]	HR (95% CI)	P Value^‡
PIM	0	78 (86.7)	21 (80.8)	1.20 (0.42, 3.44)	0.74
	≥1	12 (13.3)	5 (19.2)
PPO	0	54 (60.0)	13 (50.0)	1.12 (0.47, 2.67)	0.80
	≥1	36 (40.0)	13 (50.0)
PIM and/or PPO	0	43 (47.8)	11 (42.3)	0.71 (0.29, 1.79)	0.47
	≥1	47 (52.2)	15 (57.7)

Key: ^ϒPermission to access hospital records granted by 168 and 116 Māori individuals at 24-months’ and 36-months’ follow-up, respectively; ^¥Continuous PIP exposure defined as exposure to PIP at baseline and at 24-months; CI, Confidence Interval; CVD, Cardiovascular disease; HR, hazard ratio; PIM, potentially inappropriate medicine; PIP, potentially inappropriate prescribing; PPO, potential prescribing omission; OR, odds ratio; Reference category ‘0’; ^†Binary logistic regression (significance p<0.05); ^‡Cox regression (significance p<0.05); ^*Adjusted for age, gender, GP visits, prior hospitalisation (within previous 12 months), socioeconomic deprivation, number of medicines prescribed, functional status (Nottingham Extended Activities of Daily Living score).

Table 3 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for non-Māori participants

All-cause hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=199 (%)]	Individuals with ≥1 hospitalisation [n=130 (%)]	OR (95% CI)	P Value^†
PIM	0	132 (66.3)	72 (55.4)	1.84 (1.12, 3.02)	0.02
	≥1	67 (33.7)	58 (44.6)
PPO	0	82 (41.2)	55 (42.3)	0.87 (0.55, 1.40)	0.56
	≥1	117 (58.8)	75 (57.7)
PIM and/or PPO	0	58 (29.1)	39 (30.0)	0.89 (0.54, 1.49)	0.66
	≥1	141 (70.9)	91 (70.0)
CVD-specific hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=279 (%)]	Individuals with ≥1 hospitalisation [n=50 (%)]	OR (95% CI)	P Value^†
PIM	0	180 (64.5)	17 (34.0)	2.22 (1.14, 4.29)	0.02
	≥1	99 (35.5)	33 (66.0)
PPO	0	120 (43.0)	17 (34.0)	1.35 (0.70, 2.61)	0.37
	≥1	159 (57.0)	33 (66.0)
PIM and/or PPO	0	87 (31.2)	10 (20.0)	1.74 (0.81, 3.73)	0.16
	≥1	192 (68.8)	40 (80.0)
Ambulatory-sensitive hospitalisation^ϒ at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		Individuals with 0 hospitalisations [n=245 (%)]	Individuals with ≥1 hospitalisation [n= 84 (%)]	OR (95% CI)	P Value^†
PIM	0	161 (65.7)	43 (51.2)	1.88 (1.08, 3.26)	0.03
	≥1	84 (34.3)	41 (48.8)
PPO	0	108 (44.1)	29 (34.5)	1.48 (0.86, 2.56)	0.15
	≥1	137 (55.9)	55 (65.5)
PIM and/or PPO	0	76 (31.0)	21 (25.0)	1.30 (0.72, 2.36)	0.38
	≥1	169 (69.0)	63 (75.0)
Mortality at 24-months’ follow-up				Adjusted*
Exposure to PIP at 12-months		No [n=307 (%)]	Yes [n=22 (%)]	HR (95% CI)	P Value^‡
PIM	0	195 (63.5)	9 (40.9)	1.97 (0.81, 4.81)	0.14
	≥1	112 (36.5)	13 (59.1)
PPO	0	129 (42.0)	8 (36.4)	1.25 (0.51, 3.05)	0.62
	≥1	178 (58.0)	14 (63.6)
PIM and/or PPO	0	92 (30.0)	5 (22.7)	1.32 (0.48, 3.62)	0.60
	1	215 (70.0)	17 (77.3)
All-cause hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=157 (%)]	Individuals with ≥1 hospitalisation [n=121 (%)]	OR (95% CI)	P Value^†
PIM	0	103 (65.6)	65 (53.7)	1.19 (0.69, 2.06)	0.52
	≥1	54 (34.4)	56 (46.3)
PPO	0	63 (40.1)	44 (36.4)	0.88 (0.52, 1.49)	0.63
	≥1	94 (59.9)	77 (63.6)
PIM and/or PPO	0	45 (28.7)	28 (23.1)	0.91 (0.50, 1.67)	0.76
	≥1	112 (71.3)	93 (76.9)
CVD-specific hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=230 (%)]	Individuals with ≥1 hospitalisation [n=48 (%)]	OR (95% CI)	P Value^†
PIM	0	144 (62.6)	24 (50.0)	0.89 (0.43, 1.84)	0.75
	≥1	86 (37.4)	24 (50.0)
PPO	0	92 (40.0)	15 (31.3)	0.94 (0.45, 1.97)	0.88
	≥1	138 (60.0)	33 (68.8)
PIM and/or PPO	0	65 (28.3)	8 (16.7)	0.96 (0.39, 2.34)	0.93
	≥1	165 (71.7)	40 (83.3)
Ambulatory-sensitive hospitalisation^ϒ at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		Individuals with 0 hospitalisations [n=196 (%)]	Individuals with ≥1 hospitalisation [n=82 (%)]	OR (95% CI)	P Value^†
PIM	0	126 (64.3)	42 (51.2)	1.07 (0.59, 1.95)	0.82
	≥1	70 (35.7)	40 (48.8)
PPO	0	84 (42.9)	23 (28.0)	1.41 (0.77, 2.58)	0.27
	≥1	112 (57.1)	59 (72.0)
PIM and/or PPO	0	59 (30.1)	14 (17.1)	1.35 (0.66, 2.76)	0.41
	≥1	137 (69.9)	68 (82.9)
Mortality at 36-months’ follow-up				Adjusted*
Exposure to PIP at 24-months		No [n=232 (%)]	Yes [n=46 (%)]	HR (95% CI)	P Value^‡
PIM	0	149 (64.2)	19 (41.3)	1.87 (0.99, 3.52)	0.05
	≥1	83 (35.8)	27 (58.7)
PPO	0	92 (39.7)	15 (32.6)	1.12 (0.57, 2.19)	0.74
	≥1	140 (60.3)	31 (67.4)
PIM and/or PPO	0	62 (26.7)	11 (23.9)	0.81 (0.38, 1.76)	0.60
	≥1	170 (73.3)	35 (76.1)
All-cause hospitalisations at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=157 (%)]	Individuals with ≥1 hospitalisation [n=121 (%)]	OR (95% CI)	P value^†
PIM	0	126 (80.3)	95 (78.5)	0.75 (0.38, 1.47)	0.40
	≥1	31 (19.7)	26 (21.5)
PPO	0	102 (65.0)	73 (60.3)	1.02 (0.59, 1.77)	0.93
	≥1	55 (35.0)	48 (39.7)
PIM and/or PPO	0	78 (49.7)	52 (43.0)	1.05 (0.62, 1.79)	0.86
	≥1	79 (50.3)	69 (57.0)
CVD specific hospitalisations at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=230 (%)]	Individuals with ≥1 hospitalisation [n=48 (%)]	OR (95% CI)	P value^†
PIM	0	186 (80.9)	35 (72.9)	0.76 (0.33, 1.79)	0.54
	≥1	44 (19.1)	13 (27.1)
PPO	0	145 (63.0)	30 (62.5)	0.79 (0.38, 1.65)	0.53
	≥1	85 (37.0)	18 (37.5)
PIM and/or PPO	0	112 (48.7)	18 (37.5)	0.90 (0.43, 1.89)	0.78
	≥1	118 (51.3)	30 (62.5)
Ambulatory-sensitive hospitalisations at 36-months’ follow-up				Adjusted^*
Continuous PIP exposure^¥		Individuals with 0 hospitalisations [n=196 (%)]	Individuals with ≥1 hospitalisation [n=82 (%)]	OR (95% CI)	P value^†
PIM	0	162 (82.7)	59 (72.0)	1.11 (0.55, 2.25)	0.77
	≥1	34 (17.3)	23 (28.0)
PPO	0	128 (65.3)	47 (57.3)	1.11 (0.61, 2.02)	0.72
	≥1	68 (34.7)	35 (42.7)
PIM and/or PPO	0	101 (51.5)	29 (35.4)	1.42 (0.79, 2.57)	0.25
	≥1	95 (48.5)	53 (64.6)
Mortality at 36-months’ follow-up				Adjusted*
Continuous PIP exposure^¥		No [n=232 (%)]	Yes [n=46 (%)]	HR (95% CI)	P Value^‡
PIM	0	193 (83.2)	28 (60.9)	2.25 (1.16, 4.35)	0.02
	≥1	39 (16.8)	18 (39.1)
PPO	0	150 (64.7)	25 (54.3)	1.56 (0.85, 2.85)	0.15
	≥1	82 (35.3)	21 (45.7)
PIM and/or PPO	0	115 (49.6)	15 (32.6)	1.65 (0.86, 3.16)	0.13
	≥1	117 (50.4)	31 (67.4)

Key: ^ϒPermission to access hospital records granted by 329 and 278 non-Māori individuals at 24-months’ and 36-months’ follow-up, respectively; ^¥Continuous PIP exposure defined as exposure to PIP at baseline and at 24-months; CI, Confidence Interval; CVD, Cardiovascular disease; HR, hazard ratio; PIM, potentially inappropriate medicine; PIP, potentially inappropriate prescribing; PPO, potential prescribing omission; OR, odds ratio; Reference category ‘0’; ^†Binary logistic regression (significance p<0.05); ^‡Cox regression (significance p<0.05); ^*Adjusted for gender, GP visits, prior hospitalisation (within previous 12 months), socioeconomic deprivation, number of medicines prescribed, functional status (Nottingham Extended Activities of Daily Living score).

Does potentially inappropriate prescribing predict an increased risk of admission to hospital and mortality? A longitudinal study of the ‘oldest old’.

Status:

Journal Publication

Version 1

Abstract

Background

methods

Study population

Data analysis

Outcomes measured

Statistical analysis

Results

Demographic overview and prevalence of potentially inappropriate prescribing (PIP)

Table 1 Demographics and prevalence of potentially inappropriate prescribing (PIP) identified by STOPP and START

Potentially inappropriate prescribing (PIP) and health-related outcomes

Table 2 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for Māori participants

Table 3 The association of potentially inappropriate prescribing (PIP) with health-related outcomes for non-Māori participants

discussion

conclusions

abbreviations

declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Authors’ contributions

Acknowledgements

references

tables

Status:

Journal Publication

Version 1