Disease burden calculation

Exposures to many environmental risks and risk factor-disease links have to date not

been quantified (3). Examples include various adverse health effects that can plausibly

be linked to inadequate WASH (12). Therefore, environmental burden of disease

assessments are likely underestimating the true disease burden attributable to the

environment. Burden of disease estimates attributable to various environmental risks

or to inadequate WASH presented in this critical analysis are sometimes based on

scarce evidence. Therefore, they are not always calculated using comparative risk

assessments but using more limited exposure or exposure response information or

even expert opinion (3).

Disease burden estimates are based on many assumptions that are made while

assessing exposures and exposure-response relationships and calculating the PAF and

are therefore quite uncertain. The assumptions that are made in exposure and

exposure-response relationship assessment have been listed in previous sections.

Another important source of uncertainty is the choice of the counterfactual exposure

level, i.e., the level of exposure with the lowest assumed risk for health. The

counterfactual importantly determines the size and the interpretation of the risk

factor-attributable disease burden. Different counterfactual exposure distributions

have been proposed including the theoretical, the plausible, the feasible and the cost-

effective minimum risk (16). Usually, risk factor-attributable burden of disease

assessments aim for a theoretical minimum risk exposure level to fully gauge the size of

health impacts related to the risk factor (12,24). The theoretical minimum risk WASH

exposure level may however be defined in different ways. There is evidence from

studies that implementing Water Safety Plans on piped water in high-income countries

resulted in reduced gastrointestinal disease (107,108). These findings indicate that

even high levels of service provision, such as high-quality piped water, may exhibit

residual WASH-attributable disease burden and thus do not constitute the “true”

theoretical minimum risk. Often current WASH counterfactual exposure scenarios do

not represent the theoretical minimum risk exposure level but more interim levels that

likely pose considerable risks to health. One example would be basic WASH services

(12). Additionally, counterfactual exposures and their related risk estimates are based

on intervention studies with sometimes many limitations such as poor implementation

and poor compliance. Often these interventions targeted individual households instead

of whole communities. Such interventions are unlikely to reduce exposure with faecal

or other pathogens to a large extent (74). Results from such interventions therefore

likely underestimate the exposure-response relationships between WASH and health.

Due to the different sources of uncertainty when estimating risk factor-attributable

disease burden, validation of estimates becomes very important. Validation of

estimates seeks to determine how well the estimates correspond to the true exposure,

the true exposure-response relationship or the true risk factor-attributable disease

burden. Validation of exposure estimates is especially important when estimates are

made for countries with no own source data and can be achieved with cross-validation

validation of the exposure-response relationships and of the risk-factor attributable

disease burden estimates is not possible at present as the true exposure-response

relationship or the true risk factor-attributable disease burden in the target population

is not known. This is an important limitation especially regarding the variability of

disease burden estimates over time and between institutions.

The PAF is usually interpreted as the fraction of disease that would have been

preventable through removal or reduction of the risk factor, however certain

assumptions need to apply (22,109). There needs to be a causal relationship between

exposure and disease. The relative risks describing the exposure-response relationship

need to be free from confounding and bias. Truly effective interventions are required

that are able to move everyone to the chosen counterfactual exposure distribution

(110). Another assumption is that the formerly exposed group immediately attains

disease risk of the unexposed group after removal or reduction of the exposure

(22,109). Furthermore, a disease can usually be caused by more than one exposure.

While the PAF for each risk factor is bounded by one (or 100%) the sum of the

individual relevant exposures can (and usually does) exceed one (8,109). Whether the

PAF of one exposure equals the preventable proportion of disease is therefore

conditional on the assumption that all other relevant exposures are kept constant

(109).

The PAF calculates the attributable disease burden or so-called “excess cases” of a

disease or health outcome (2). Excess disease cases would not have occurred without

exposure to the risk factor and are potentially preventable (2,23,111), They need to be

differentiated from etiologic cases (2,23,104). Etiologic cases would have occurred

without exposure (2,110,112,113) but are still caused by the risk factor (e.g., the risk

factor led to an earlier disease onset). Often the number of excess cases is much smaller

than the number of etiologic cases (2). Therefore, it can be argued that the PAF usually

underestimates the health importance of a risk factor and that it indicates the lower

bound of attributable burden (2,110). This is less important for infectious disease

occurrence where probably all cases caused by the risk factor can be considered excess

cases compared to mortality and chronic diseases.

Studies examining the linkages between environmental risks and disease are often of

an observational design as it would be unethical to randomize a group of people to a

(potential) hazardous exposure. Therefore, there is likely confounding in the risk

factor-disease association and relative risks taken from the published literature are

usually confounder-adjusted. Adjusted relative risks should however not be used in the

presented PAF formula (Equation 1) but in alternative formulas (111). One formula

requires the proportion of exposed disease cases (Equation A4, Appendix 1 (A1.2)).

Another formula calculates stratum-specific (by confounder strata) attributable

fractions which are subsequently weighted by the proportion of cases in each stratum

and summed (Equation A5, Appendix 1 (A1.2))(25,111). Adjusted relative risks have

often been used in Equation 1 which has been called the “partially adjusted” method

(114). Using adjusted relative risks in Equation 1 was estimated to lead to a biased PAF

of between 10% and 20% (25). The use of the alternative formulas that are appropriate

for adjusted relative risks is often constrained by the additional data needs required for

their computation (25). In current risk factor-attributable burden of disease analyses,

usually stratum-specific PAFs for a range of potential confounders including age, sex

and location are calculated (12,24). However and due to data availability, often the

same exposure data and exposure-response relationships are applied across different

confounder strata. There are various other factors acting as potential confounders, one

notable example is socio-economic status. In WASH interventions in which a

confounder such as socio-economic status will often positively confound the exposure-

response relationship (i.e., crude relative risk > adjusted relative risk) the resulting PAF

will be underestimated (25). This will usually be less important in randomized

compared to non-randomized interventions as randomization aims for balanced

covariate distributions between intervention and control groups. Relative risks from

randomized studies are therefore usually less confounded than results from

observational studies and are therefore more appropriately used in the presented PAF

formula (Equation 1).

The exposure-response relationships used for calculating the PAFs were mostly

derived from intervention studies. These studies had usually been conducted in

population subgroups and not in the population for which the disease burden was

subsequently calculated. To assure portability of an exposure-response relationship

from a source to a target population, important assumptions are needed: Exposure is

defined alike and the distribution of relevant confounders and effect-modifiers is the

same in the source and the target population (23). It has been shown that small

differences in relative risks and in the distribution of confounders and effect modifiers

between the source and the target population resulted in considerable bias in the

estimation of the PAF and the risk factor-attributable disease burden (114).

The PAFs from different exposures are often combined, for example to derive the

overall environmental fraction of a disease (Equation 2). The application of this

formula assumes independence between risk factors (e.g., being exposed to one factor

does not increase the likelihood of being exposed to the other risk factor) and that the

joint effects of the individual risk factors are multiplicative (23). The application of

Equation 2 may have resulted in upward-biased estimates of the WASH-attributable

disease burden as inadequate water, sanitation and hygiene are likely to be positively

related to each other (115).

Priority setting for preventive action and choice of interventions should not be

exclusively based on environmental burden of disease assessments. Social and ethical

considerations need to be taken into account such as the priorities of a population and

its risk perceptions as well as social consequences of the disease burden and related

interventions (8). Another important issue is the availability and cost-effectiveness of

interventions strategies (18). Also burden of disease assessments do usually not

account for benefits other than health gains (8). A WASH intervention for example

might not only improve health but also lead to increased security, empowerment of

women and girls, dignity and time savings (116,117).

In document Estimating burden of disease due to environmental factors with an emphasis on inadequate water, sanitation and hygiene (Page 57-61)