CONTAMINANTES DEL AIRE

Increasing hospitalisation levels for CAP have been well described, but what is driving these increases is less well understood. The results of a detailed literature review of risk factors for hospitalisation after a CAP episode are presented in Chapter 6. An overview of possible reasons for the increasing trends seen is provided below.

Increasing incidence

It is important to contextualise any trends in hospitalisation against the underlying trends in pneumonia incidence. For example, if CAP incidence increases over a period and the proportion of CAP patients hospitalised remains stable, the number of hospitalisations would also increase. Thus, increasing hospitalisations may simply reflect (at least in part) rising CAP rates. Use of stand-alone hospital admission data such as HES (used in the English hospitalisation studies) does not enable the distinction between increases in underlying incidence of a disease, and increases in hospital admissions over and above any increase in incidence.

Increasing prevalence of co-morbidities

Increasing levels of co-morbidity among the older population may also have played a key role in rising CAP admissions, as co-morbidities may affect both patients’ susceptibility to CAP, and the severity of their illness. A commonly used method to summarise the extent of a patient’s co-morbidities is to calculate their Charlson index score.

The Charlson Index

The Charlson index was originally published in 1987 as a new method to classify prognostic co-morbidity in longitudinal studies. The index was developed using the medical information of 604 patients admitted to the medical service at a New York hospital over a one-month period in 1984. The vital status of these patients was established at one year, including deaths during the initial hospitalisation and post- discharge. Cox regression was used to quantify the association between a range of

37

diseases and mortality at one year, and the resulting hazard ratios (HRs) were rounded and used to create the score that is still in use today. Conditions with HRs of 1.2 or less were not included in the final model, HRs ≥1.2 and < 1.5 were assigned a weight of 1; conditions with a HR ≥1.5 and < 2.5 a weight of 2; conditions with a HR of ≥2.5 to < 3.5 a weight of 3; and two conditions with weights of 6 or more were assigned a weight of 6.

Seventeen co-morbidities were included in the final score, two of which had scores which increased with increasing severity of disease (liver disease and diabetes), resulting in 19 factors to be considered. These co-morbidities and their associated scores are presented in Table 1-1.

Table 1-1 Scores assigned to co-morbidities when using the Charlson index

Score Co-morbidity

1 Myocardial infarction Congestive heart failure Peripheral vascular disease Cerebrovascular disease Dementia

Chronic lung disease Connective tissue disease Peptic ulcer disease Mild liver disease Diabetes

2 Hemiplegia

Severe renal disease

Diabetes with complications Solid cancer

Leukaemia Lymphoma

3 Moderate/severe liver disease 6 Metastatic cancer

AIDS

The total score was calculated for each patient by summing the scores for each co- morbid condition that they had, and their final co-morbidity score was then categorised as none (0), mild (1-2), moderate (3-4), or severe (≥5).[68] External validation of the score was performed in a cohort of 685 women first treated for breast cancer between 1962 and 1969 at Yale hospital.[68] In this cohort, 10-year mortality was the outcome of interest (although as the cohort were all breast cancer patients those who died of breast cancer were categorised as having left the study rather than having died). Formal

38

assessment of the model’s performance (using methods outlined in section 7.3) was only assessed graphically in the original paper.

As mentioned previously, the majority of the UK’s older population are now thought to be living with at least one long-term condition.[37] Of the European papers reporting increasing CAP hospitalisations above, only two studies investigated the possible contribution of rising co-morbidity levels through use of the Charlson index.[61, 64] When assessing the suitability of the Charlson index to adjust for co-morbidity status, it is important to consider its development and intended application as a score to predict mortality. While hospitalisation and death are both considered severe outcomes of disease, risk factors for these two events may differ, and therefore a score developed to predict mortality may not be the best tool to explain hospitalisation trends. The use of a score to adjust for co-morbidities also precludes identification of the risk of hospitalisation associated with individual co-morbidities. Among the growing older population it would be useful to better understand the role individual conditions such as dementia and chronic respiratory disease play in CAP hospitalisation trends, in order to inform clinicians and plan future resource allocation. Furthermore, stand-alone hospitalisation data (as used in the two English studies) have suboptimal recording of patients’ co-morbidities, including only those pertinent to patients’ care.

Medications and vaccinations

A consequence of the rising prevalence of co-morbidities is increasing prescription medication use. Some of these drugs, such as immunosuppressive medications used to treat conditions such as rheumatoid arthritis and chronic lung disease, increase patients’ risk of CAP.[30] Conversely, some medicines such as statins may offer some protective effect,[32] as does prompt treatment of LRTI with antibiotics.[28] Interestingly, hospitalisations for CAP have risen despite the introduction of vaccinations for influenza and pneumococcal disease for older or at risk groups.[53, 48] There is evidence that influenza vaccine protects older individuals against hospitalisation after pneumonia,[69- 71] however, PPV23 has not been shown to have such an effect.[72] The effect of medications or vaccinations on the rising hospitalisation levels has been relatively little studied. Vaccination and medication use are simply not recorded in HES, and as a result their effect on CAP hospitalisation levels in England has not been quantified.

39

Increasing prevalence of frailty

As highlighted in section 1.1.7, older adults are more prone to a general vulnerability in underlying health, called frailty. Frail patients are identified using a number of factors (due to the multiple systems involved), and many studies have developed models to this end. The inclusion of a range of factors, rather than simply summing the number of co- morbidities an older adult has, enables identification of frail older adults who do not have life threatening diseases but who have experienced physiological changes which make them more susceptible to adverse events. Two frequently discussed measures of frailty are the frailty phenotype and the frailty index.[73, 74] The ‘phenotype’ hypothesises that frailty can be recognised from a set of five deficits: measured slow walking speed, measured impaired grip strength, self-reports of declining activity levels, exhaustion and unintended weight loss. Patients with a score of three or more deficits are categorised as frail, and those with one or two deficits are pre-frail.[73] While the phenotype method is simple to use and extensively validated, the factors it contains are not routinely collected in primary or secondary care, limiting its usefulness in either setting.[39]

An alternative methodology is the ‘index’ (or cumulative deficit approach) whereby information on a large number of deficits is collected across co-morbidities, clinical signs and symptoms, disabilities or abnormal test findings. To be considered for inclusion, deficits must accumulate with age, be biologically plausible, and not saturate too early (i.e. the deficit cannot have a prevalence of 100% before older age).[74] Several indexes have been developed, and their ability to predict adverse outcomes found to be high as long as more than 30 deficits are included.[74] Patients’ scores are calculated as the proportion of deficits they have, with higher proportions correlated with increasing susceptibility to adverse outcomes. Across these models, deficits have been found to accumulate within patients at on average 0.03/year.[74] The proportion of deficits tolerated by patients seems to be limited at around 0.67, after which no further accumulation is sustainable and death becomes likely.[75]

Despite the abundance of frailty measures available, frailty is not commonly included in studies assessing severe outcomes following CAP among older adults. However, it certainly plays a large role in clinicians’ treatment location decisions for some patients.

40