Most arsenic cancer risk assessments have been based solely on epidemiological studies to characterize the dose-response relationship for arsenic-associated cancer and to perform risk calculations. However, current epidemiological evidence is too inconsistent and fraught with uncertainty regarding arsenic exposure to provide reliable estimates. This makes it hard to draw a firm conclusion about the shape and slope of the dose-response relationship from individual studies. Meta-analysis is a statistical approach to combining results across studies and offers expanded opportunities for obtaining an improved dose-response relationship. In this study, a meta-analysis of arsenic studies was conducted by combining seven epidemiological studies from different regions to get an overall dose-response relationship between the amount of arsenic intake and the excess probability of bladder cancer. Both the fixed-effect and random-effect models were used to calculate the averaged coefficient of the linear-logistic regression model. A homogeneity test was also conducted. The final product of this research is an aggregated dose-response model in the range of empirical observation of arsenic. Considering the most recent arsenic MCL (maximum contaminant level, i.e. 10μg/L), the associated bladder cancer risk (lifetime excess probability) at this MCL is 2.29 × 10^{−5}.

In risk-based regulation, data are needed to characterize the dose-response relationship for risk calculations. The accuracy of the data and the ability to fit them by an appropriate model in turn determine the scientific validity of a risk assessment [

Meta-analysis is a statistical tool for integrating and analyzing data from related but independent studies. Applying a set of statistical procedures, which quantitatively aggregate the results of multiple primary studies, an overall conclusion or summary of average properties such as risk coefficients across these studies may be reached [

The quantitatively-aggregating ability of meta-analysis allows it to examine relationships not investigated in the original primary studies [

The objective of this study is to use meta-analysis to combine several epidemiological datasets to produce an aggregated dose-response function for the relationship between bladder cancer risk and arsenic intake from drinking water.

The criteria for inclusion of epidemiological studies in the present meta-analysis are: all studies are of a case-control or cohort design, and evaluate the relationship between arsenic concentration in drinking water and bladder cancer; studies are of males, females or of both genders combined; studies examine incidence or mortality as the study outcome; studies provide information required for the statistical analysis; studies are published in English between 1970 and 2005; and studies are referenced in the U.S. EPA IRIS (Integrated Risk Information System), NRC’s (National Research Council) Reports [

The study outcomes varied. In cohort studies, relative risks were used as the study outcome; in case-control studies, odds ratios were the outcomes. Considering that bladder cancer is a rare disease, the odds ratio was assumed approximately the same as relative risk, and relative risk was used as the study outcome. Only one cohort mortality study [

Seven studies were included in the meta-analysis since they satisfied the criteria mentioned above. They were from different regions, including Taiwan, U.S., Argentina, Chile and Finland [

As for deciding the exposure midpoint assigned to a subpopulation, if the highest category of arsenic exposure was open-ended, its interval was set equal to the width between 0 and the lower bound of the open-ended boundary. For example, in the study of Chiou et al. [

For each study, using the information on RR (relative risk) and average arsenic exposure (X) for each subpopulation, the hazard as a function of exposure can be modeled as [

Where X is the exposure (in μg/L), ΔX is the difference in arsenic concentration intake between each category of exposure (X) and the reference category in each study (X_{0}). The coefficient b is the fitted slope factor in the linear-logistic regression model. This linear-logistic model estimates the logarithm of the observed relative risks (estimated as the odds ratio in some studies), and accounts for the correlation between risk estimates for separate exposure levels depending on the same reference group.

After finding the coefficient (b_{i}) of each study, the summary estimate is the pooled coefficient (

The underlying statistical theory of meta-analysis is “Sample Error Theory”. The sample error stems from the variation of characteristics between samples and the original population, given that a sample typically can’t represent the whole population. There are two major sources of variation to be considered when conducting a meta-analysis: (i)

The fixed-effects model assumes there is only within-study variation in the mean outcomes of a study, and that inter-study variation can be excluded. It also assumes that the underlying population from which studies are generated is the same and has identical characteristics and study effect for all studies considered in the meta-analysis. [

The random-effects model assumes both within-study and between-study variations exist. The population from which studies are generated may have different characteristics and study effects. This assumption leads to wider and more conservative confidence intervals than the fixed effects model [

Meta-analysis uses a weighted average of the results from the individual studies:

Where w_{i} is the weight of each study, y_{i} is the parameter being estimated of each study (here, the slope factor), and _{w}

The weight usually is the inverse of the variance of the result for each study. The larger studies therefore have more influence than the smaller ones [

_{i}^{2} is the within-study variation and _{i}^{2} is the inter-study variation [

The fixed-effects model assumes that all studies are sampled from the same population, so the

The chi-square test can be employed as a basic statistical test of the homogeneity assumptions [

Where _{i}_{i}^{2}, _{i}_{i}_{i}

If H_{0} cannot be rejected, we have to accept the null hypotheses; _{0} is rejected, it may be concluded that these study means arose from different populations and are not homogeneous. Under this condition, Normand (1999) suggests to “…continue proceeding by either attempting to identify covariates that stratify studies into the homogeneous populations or estimating a random-effects model” [

From

The fixed-effects model was first used. The pooled estimate of slopes from seven studies was 0.00615 (95% CI: 0.00588, 0.00642), with the unit of lnRR per unit increase of exposure. But the chi-square statistic was quite large (i.e. Q= 3197.110 on 6 degrees of freedom, p= 0.00), which rejects the null hypothesis of homogeneity and means there was evidence of heterogeneity.

The fitted slope (with the unit of lnRR per unit increase of exposure) of each study and the combined estimate of slope by using fixed-effect model are presented as box plots in

To make sure the exclusion of the Finland study done by [

By using the random-effect model, the pooled estimate of the slopes from the seven studies was found to be 0.004 (in units of per μg/L) (95% CI: −0.03, 0.012). The results are shown in

The result of the meta-analysis supports the claim that there is a positive dose-response relationship between exposure to arsenic in drinking water and bladder cancer. Using the results presented above, the best estimate of the relative risk associated with an increase of arsenic exposure can be estimated as:

Were X is the waterborne arsenic concentration in units of μg/L. Using the upper 95% confidence limit, the plausible upper limit of the relative risk associated with an increase of arsenic exposure can be estimated as:

The absolute risk (AR) of bladder cancer is calculated by multiplying the excess relative risk (ERR) by the natural rate (NR) of bladder cancer. Excess relative risk equals the relative risk minus one (i.e. ERR=RR-1). Therefore, AR can be calculated as:

^{−5} by using the upper bound estimate of the slope factor.

From the upper bound result of the meta-analysis, the arsenic concentration corresponding to a lifetime excess probability of 10^{−3} is approximately 160 μg/L; the concentration corresponding to 10^{−4} is approximately 40 μg/L; and the concentration corresponding to 10^{−5} is 4.5 μg/L.

The slope factor was fitted using the equation of Pc = SF × ADRI, where Pc is the mean probability of cancer, SF is the slope factor, and ADRI is average daily rate of intake of arsenic (μg/kg/day). ADRI (μg/kg/day) was transformed from arsenic MCL (μg/L or ppb) by assuming a tap water ingestion rate of 0.023 L/kg-day. A linear function (characterized by a slope factor) was then fitted as an approximation to the dose-response curve for the meta-analysis results.

The best estimate of the slope factor from the meta-analysis is 3.0 × 10^{−5} (with unit of probability per μg//kg/day), with the upper bound of 1.27 × 10^{−4}. These slope factors from the meta-analysis are lower than the ones from the EPA (1.5 × 10^{−3}) and NRC (8.85 × 10^{−4}).

In this study, a meta-analysis of arsenic studies was conducted by combining several epidemiological studies from different regions (such as Taiwan, US, Argentina, Chile and Finland) to produce a composite dose-response relationship between the amount of arsenic exposure and the excess probability of cancer. Both the fixed-effect and random-effect models were used to calculate the averaged coefficient of the linear-logistic regression model. A homogeneity test was conducted first to check the heterogeneity among these studies. Because the heterogeneity was found to be high, a random-effect model had to be used. This results in a wider confidence interval of slopes and a more conservative upper bound quantitative summary of risk. The high heterogeneity shows that there are large differences between studies, which suggest it may not be appropriate to simply extrapolate from Taiwanese studies to the U.S.

The final product is an aggregated dose-response model in the range of empirical observation of arsenic. The best estimate of the slope factor from the meta-analysis is 3.0 × 10^{−5} (with unit of probability per microgram/kg/day), with the upper bound of 1.27 × 10^{−4}. These slope factors from the meta-analysis are lower than the ones from the EPA (1.5 × 10^{−3}) and NRC (8.85 × 10^{−4}). There clearly are large differences between the current study and the EPA/NRC results. The possible reason for the difference is because EPA/NRC conducted their study mainly based on data from Taiwan, while we used meta-analysis to combine data from several different regions.

Considering the most recent arsenic MCL (i.e. 10 μg/L), the associated bladder cancer risk (lifetime excess probability) conducted using the upper bound result of the meta-analysis is 2.29 × 10^{−5} (7.35 × 10^{−6} if the best estimate is used), which is much lower than NRC’s theoretical lifetime excess risk of bladder cancer for U.S. Populations (1.2 × 10^{−3} for female and 2.3 × 10^{−3} for male).

One shortcoming of this study is that there are only seven observational studies available for the meta-analysis. The available data makes it difficult to do further investigation, such as meta-regression to check whether an overall study result varies among subgroups (e.g. study type or location), or a sensitivity analysis to detect the robustness of the findings to different assumptions. New observational studies of arsenic, especially ones involving a case-control or cohort design, need the investment of large amounts of money and time. Even given that requirement, meta-analysis can be an appropriate tool to resolve the discrepancies among existing epidemiological data, and to produce a reasonable generalized dose-response model and its distribution of parameter values.

Dose-response analysis of relative risk of bladder cancer for arsenic intake from drinking water.

Slope (with the unit of lnRR per unit increase of exposure) of each study and the combined estimate of slope by using fix-effect model. The horizontal line of each study corresponds to its 95% confidence interval, and the size of the square reflects the weight of each study.

Slope (with the unit of lnRR per unit increase of exposure) of each study and the combined estimate of slope by using random-effect model.

Dose-response relationship of relative risk of bladder cancer for arsenic intake from drinking water by using fixed-effect and random-effect model.

Absolute Risk of Bladder Cancer at different proposed MCLs (Maximum Contaminant Levels) from meta-analysis. (Mean: the best estimation of slope factor, U_95: the upper bound estimation of slope factor)

Slope factors of bladder cancer generated from meta-analysis results.

Studies of Bladder Cancer (188)

Choiu et al., 1995 | Cohort | SW Taiwan | Age, sex, cigarette smoking | |||

<=50 | 25 | 1.0 | ||||

50–70 | 60 | 1.8 | ||||

71+ | 100 | 3.3 | ||||

| ||||||

Bates et al., 1995 | Case-Control | Utah, US | Age, sex, cigarette smoking | |||

<440 | 220 | 1.0 | ||||

440–<707 | 600 | 0.69 | ||||

707–<987 | 850 | 0.54 | ||||

>=987 | 1200 | 1.0 | ||||

| ||||||

Kurttio et al., 1999 | Case-Control | Finland | Age, sex, cigarette smoking | |||

<0.1 | 0.05 | 1.0 | ||||

0.1–0.5 | 0.3 | 1.53 | ||||

>=0.5 | 5 | 2.44 | ||||

| ||||||

Chiou et al., 2001 | Cohort | NE Taiwan | Age, sex, cigarette smoking, and duration of well water drinking | |||

<=10 | 5 | 1.0 | ||||

10.1–50 | 30 | 1.5 | ||||

50.1–100 | 75 | 2.2 | ||||

>100 | 150 | 4.8 | ||||

| ||||||

Steinmaus et al., 2003 | Case-Control | Western U.S. (California and Nevada) | Age, gender, occupation, smoking history | |||

<10 | 5 | 1.0 | ||||

10–80 | 45 | 1.04 | ||||

>80 | 120 | 0.94 | ||||

| ||||||

Moore et al., 2003 | Case-Control | Argentina & Chile | Tumor stage and grade | |||

<10 | 5 | 1.0 | ||||

10–99 | 55 | 1.46 | ||||

100–299 | 200 | 2.26 | ||||

>300 | 400 | 1.36 | ||||

| ||||||

Bates et al., 2004 | Case-Control | Argentina | Multivariate-adjusted | |||

0–50 | 25 | 1.0 | ||||

51–100 | 75 | 0.88 | ||||

101–200 | 150 | 1.02 | ||||

>200 | 300 | 0.6 |

Comparison of the results by using different models and including different studies.

7 | Fixed-effect | 0.006 | 0.006 | 0.006 |

Random-effect | 0.005 | −0.002 | 0.012 | |

6 | Fixed | 0.006 | 0.006 | 0.006 |

Random | 0.004 | −0.003 | 0.012 |

Risk of bladder cancer at different MCLs

MCL (ppb) | AR (U_95) | AR (Mean) | AR (L_95) |
---|---|---|---|

0 | 0 | 0 | 0 |

1 | 2.17E-06 | 7.21E-07 | −5.47E-06 |

3 | 6.60E-06 | 2.17E-06 | −1.59E-05 |

5 | 1.11E-05 | 3.64E-06 | −2.57E-05 |

10 | 2.29E-05 | 7.35E-06 | −4.78E-05 |

20 | 4.88E-05 | 1.50E-05 | −8.29E-05 |

50 | 1.48E-04 | 3.98E-05 | −1.41E-04 |