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Draft Report of Quantitative Risk and Benefit Assessment of Consumption of Commercial Fish, Focusing on Fetal Neurodevelopmental Effects (Measured by Verbal Development in Children) and on Coronary Heart Disease and Stroke in the General Population: Section V, Scientific Basis for Risk and Benefit Assessment

January 15, 2009

This information is distributed solely for the purpose of pre-dissemination peer and public review under applicable information quality guidelines. It has not been formally disseminated by FDA. It does not represent and should not be construed to represent any agency determination or policy.

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(a) Exposure Assessment

Amount of Fish Consumed

Table V-1 shows daily fish consumption, by population group. The consumption is provided in terms of grams per day. To place grams per day in context, we can convert it into servings per week. Serving sizes vary among individuals and there is no universal serving size. If we assume a serving size of 100 grams, it produces a range of 0.97 - 1.38 servings per week for the mean daily consumption represented along the top row of the table. If we assume a serving size of 175 grams, which is about the size of a serving in the joint FDA/EPA consumption advisory on methylmercury (2004), the range becomes 0.55 - 0.79 servings per week for the mean daily consumption. The table also indicates that 12 ounces of fish per week (i.e., about 50 grams per day) -- the consumption advisory's recommended maximum for women who are pregnant or considering getting pregnant -- represents consumption in the vicinity of the 95th percentile for women of childbearing age. (i.e., approximately five percent of women consume this much fish or more).

In addition to the results from our exposure modeling, Table V-1 provides average daily consumption taken from the 2003-2004 NHANES survey for purposes of comparison. Because our model is based in part on data from 1989-1991, Table V-1 also contains the most recent NHANES results in order to verify that our results are consistent with current consumption patterns.

Table V-1: Daily Fish Consumption (g/day); Median (5th percentile, 95th percentile)
Population Statistic Women 16-45 Women 46+ Men 16-45 Men 46+
Average 13.4 (12.7, 13.9) 15.1 (14.3, 16.1) 18.3 (17.1, 19.2) 19.0 (18.0, 20.6)
10th %tile 0.1 (0.0, 0.9) 0.2 (0.0, 1.3) 0.2 (0.0, 1.2) 0.3 (0.0, 1.7)
25th %tile 2.8 (2.0, 3.6) 3.4 (2.7, 4.3) 3.7 (2.7, 4.6) 4.6 (3.5, 5.8)
50th %tile 7.2 (6.4, 7.9) 8.4 (7.4, 9.1) 9.6 (8.3, 10.6) 10.8 (9.5, 11.9)
75th %tile 16.3 (14.9, 17.7) 18.4 (16.9, 19.6) 21.9 (19.6, 23.1) 22.7 (21.0, 24.5)
90th %tile 32.3 (29.3, 34.4) 36.4 (33.7, 39.5) 43.7 (40.1, 47.6) 44.4 (40.5, 49.5)
95th %tile 46.4 (42.1, 50.7) 53.7 (47.4, 60.5) 65.5 (58.5, 74.7) 65.1 (58.2, 75.3)
99th %tile 88.3 (74.4, 114.3) 101.5 (85.0, 128.3) 136.0 (106.8, 179.3) 131.8 (108.3, 178.4)
NHANES average for comparison 10.3 14.2 16.8 20.8

Dietary Intake of Methylmercury

Table V-2 shows the results for women of child-bearing age (16-45). Recall that the mother's hair-mercury level during pregnancy is serving as a surrogate, or biomarker, for fetal exposure.

Table V-2: Dietary MeHg from Fish (µg per day)
Population Statistic Women 16-45
Median (5th, 95th)
Average 1.4 (1.3, 1.4)
10th %tile 0.0 (0.0, 0.1)
25th %tile 0.2 (0.1, 0.3)
50th %tile 0.7 (0.6, 0.7)
75th %tile 1.6 (1.5, 1.8)
90th %tile 3.4 (3.1, 3.6)
95th %tile 4.9 (4.5, 5.5)
99th %tile 10.3 (8.1, 12.8)

Table V-3 shows the results from Table V-2 along with our conversions from dietary methylmercury from fish to blood and hair concentrations. These results in terms of hair mercury can now be used as input for the dose-response modeling.

Table V-3. Model Estimates of Blood and Hair Mercury levels in Women of Childbearing Age (16-45)
Blood Hg
(µg/L)*:
Population Percentile Hair Hg (ppm)
1.2 (1.2, 1.3) Average 0.3 (0.2, 0.3)
0.1 (0.1, 0.1) 10th Percentile 0.0 (0.0, 0.0)
0.3 (0.2, 0.3) 25th Percentile 0.0 (0.0, 0.1)
0.6 (0.5, 0.7) 50th Percentile 0.1 (0.1, 0.1)
1.5 (1.3, 1.6) 75th Percentile 0.3 (0.2, 0.3)
2.9 (2.7, 3.2) 90th Percentile 0.6 (0.5, 0.7)
4.3 (3.8, 4.8) 95th Percentile 1.0 (0.8, 1.2)
8.8 (7.4, 10.7) 99th Percentile 2.2 (1.8, 2.7)

*parts per billion

(b) Fetal Neurodevelopment

Adverse Methylmercury Contribution to the Net Effect

How the Modeling Results are Expressed: As stated previously, FDA used results from age of first talking to represent the methylmercury contribution to the net effect.  Results from age of first talking, IQ, and a battery of tests as described below were used for purposes of comparative analysis.

The age of talking and walking models estimate methylmercury's contribution to the net effect in terms of length of delays. The other two models express the methylmercury effect in terms of decrements in IQ scores. One of these (Axelrad et al., 2007) actually involved measurements of IQ from the Seychelles and New Zealand, The other (Cohen et al., 2005b), involved measurements from a battery of tests administered in the Seychelles, Faroe Islands and New Zealand, the results from which the authors presented as being IQ. Consequently, in order to compare the age of talking and walking results against the results from Axelrad et al. (2007) and Cohen et al. (2005b), we converted the delays in talking and walking into Z-Scores, which are statistical tools described below that essentially measure the size of an effect. Z-Scores facilitate the comparison of results from one model to another. They also facilitate combining results from different models into a single model. We then converted the Z-Scores into a unit of measurement that is equivalent to the size of an IQ point. We refer to this units of measurement as "IQ Size Equivalents (IQse)," since they are not really IQ points.

A Brief Explanation of Z-Scores: A Z-Score describes where a particular measurement or result (e.g., a child's weight) stands relative to other measurements or results within a group (e.g., the weights of other children in the group). Assuming that the data follow a normal distribution, a Z-Score describes how far a particular result is (above or below) from the average of all the results in the group. When a Z-Score is positive, the result exceeds the average, e.g., a child is heavier than the average weight in the group. When a Z-Score is negative, the result is below the average, e.g., a child that is lighter than the average. A positive Z-Score of exactly 1.0 means that the result exceeds the average by one standard deviation. In a normal distribution, 68 percent of all the results within a group will fall within one standard deviation of the average. A Z-Score of 1.0 typically means that a particular result is about 34 percent above the average for the group. A fraction of a Z-Score means that the result is above or below the average by that fraction of a standard deviation.

Z-Scores are used to indicate the relative size of a change in a result in a population. For example, if, as result of maternal consumption of fish containing methylmercury, a child talks slightly later or slightly sooner than otherwise would have been the case, the size of the change can be expressed as the difference between what the Z-Score would have been without any exposure to methylmercury and what it is as a consequence of that exposure. In this respect we are providing "net Z-Scores," i.e. the difference between one Z-Score and another.

Another feature of Z-Scores is that they can be used to compare results from different groups. A simple example involves two identical exam scores (e.g., two scores of 75) obtained in two different college classes. Converting each exam score to a Z-Score (which compares that exam score to the other exam scores in the class) will reveal whether they are likely to produce the same or different grades (assuming that both are graded on a curve). If one exam score produces a positive Z-Score, it means that the exam result was above the average for that class. If the other exam score produces a negative Z-Score, it means that the exam score was below average. In such a situation, the Z-Scores reveal that the grades will be different even though the exam scores were identical. If the two exam scores each produce positive Z-Scores, but one is larger than the other, the one with the larger Z-Score may result a higher grade even though both are above average.

Because Z-Score and IQ scores are linked to standard deviation, a Z-Score can be converted to IQ (or at least to the size equivalent of IQ) and vice versa. The standard deviation for IQ scores in the population is 15 IQ points. Consequently, Z-Scores can be converted into IQ points by multiplying them by 15 (Cohen et al., 2005c).

Age of First Talking: The model estimates that without any contribution by methylmercury to the net effect, the age of first talking would range from 10.9 months through 18.8 months, with a central estimate of 15.1 months.(17) This timeframe provides a frame of reference for the size of the methylmercury contribution. The table provides a median estimate and 95 percent confidence interval for the size of the methylmercury contribution at various percentiles of U.S. exposure (the 10th percentile through the 99.9th percentile). In the simplest terms, the size of the methylmercury contribution probably falls within the range provided by the confidence intervals. The median estimate is at the midpoint of that range so that half the values in the range are above it and half are below. Since no other value in the range meets this criterion, we regard it as the "best" estimate.

Table V-4 shows that the most likely delays are less than a day through the 95th percentile of exposure. As reflected by the confidence intervals, there is a small possibility of no delay through the 50th percentile of exposure. This possibility suggests that methylmercury has a threshold of effect, i.e., that below some level of exposure methylmercury does not produce an adverse effect. At the 99th percentile of exposure the median estimate reaches a delay of slightly over two days and then jumps to slightly over four days at the 99th percentile. Exposure to methylmercury essentially doubles between the 99th and 99.9th percentiles.

We converted units of time into Z-Scores by dividing the age of talking in months by 2.76, which is the standard deviation of the age of talking data from the Seychelles. We then converted the net Z-Scores to IQ Size Equivalents in order to compare these results to the IQ modeling results from Axelrad et al. (2007) and the results from a battery of tests that Cohen et al. (2005b) calculated on an IQ scale.

When compared to the size of an IQ point in the far right column of Table V-4, the delays are all equivalent in size to a fraction of an IQ point (median estimates), although at the highest confidence limit at the 99.9th percentile of exposure, it slightly exceeds one IQ point in size.

Table V-4: Methylmercury's adverse contribution to the net effect on fetal neurodevelopment as measured by delay in age of first talking. The effects are provided as delays in both days and hours. These delays are also provided in terms of changes in both Z-Scores and "IQ size equivalents (IQse)."
Hg Dose
(ppm in maternal hair)*
Percentile of U.S. Delay in talking (days) Delay in talking (hours) Change in Z-Score Change in IQse
0.02 10th -0.0158
(-0.0277, 0.0000)
-0.3788
(-0.6647, 0.0000)
-0.0002
(-0.0003, 0.0000)
-0.0029
(-0.0050, 0.0000)
0.04 25th -0.0399
(-0.0701, 0.0000)
-0.9583
(-1.6814, 0.0000)
-0.0005
(-0.0008, 0.0000)
-0.0072
(-0.0127, 0.0000)
0.12 50th -0.1109
(-0.1946, 0.0000)
-2.6616
(-4.6695, 0.0000)
-0.0013
(-0.0023, 0.0000)
-0.0201
(-0.0352, 0.0000)
0.30 75th -0.2713
(-0.4759, -0.0520)
-6.5106
(-11.4217, -1.2487)
-0.0033
(-0.0057, -0.0006)
-0.0491
(-0.0862, -0.0094)
0.63 90th -0.5914
(-1.0224, -0.1818)
-14.1938
(-24.5364, -4.3643)
--0.0071
(-0.0123, -0.0022)
-0.1071
(-0.1852, -0.0329)
0.98 95th -0.9258
(-1.5794, -0.2816)
-22.2188
(-37.9047,-6.7589)
-0.0112
(-0.0191, -0.0034)
-0.1677
(-0.2861, -0.0510)
2.16 99th -2.0671
(-3.4954, -0.6835)
-49.6107
(-83.8904,-16.4035)
-0.0250
(-0.0422, -0.0083)
-0.3745
(-0.6332, -0.1238)
2.83 99.5th -2.7131
(-4.5905, -0.8962)
-65.1140
(-110.1711,-21.5093)
-0.0328
(0.0554, -0.0108)
-0.4915
(-0.8316, -0.1624)
4.37 99.9th -4.3902
(-7.4202, -1.4505)
-105.3653
(-178.0840, -34.8128)
-0.0530
(-0.0896, -0.0175)
-0.7953
(-1.3442, -0.2628)

* These hair levels have been calculated from our exposure assessment. They differ slightly, but not significantly, from the average hair levels in the NHANES sampling. The results of our modeling and the NHANES averages are both estimates. The NHANES results are estimates because they involve extrapolating from the NHANES survey sample to the general U.S. population. Our results are slightly lower than the NHANES results. One possible reason for the difference is that our modeling is focusing on methylmercury only while NHANES may be capturing some inorganic mercury in addition to methylmercury. Another possibility may be that our modeling screens out more of the methylmercury contribution from recreational fishing than does NHANES. NHANES is unlikely to capture unusual, localized patterns of recreational consumption but it does not actively screen out recreational consumption. Our modeling does some screening by using the NMFS data on commercial fish supplies, for example.

Age of First Walking: The model predicts that without any contribution by methylmercury to the net effect, the age of first walking would range from 6.3 months through 17.8 months, with a median estimate of 10.4 months. This timeframe provides a frame of reference for the size of the methylmercury contribution.

As with the table for age of first talking, Table V-5 provides a median estimate and a 95 percent confidence interval for the size of the methylmercury contribution at various percentiles of U.S. exposure (the 10th percentile through the 99.9th percentile). The table shows that for age of first walking, the most likely delays are less than a day through the 90th percentile of exposure. Above that, the median estimate is about a day-and-a-half at the 95th percentile, 3.5 days at the 99th percentile, and 4.6 days at the 99.5th percentile. As with age of talking, the delay nearly doubles to 7.4 days at the 99.9th percentile, commensurate with the increase in exposure between the 99.5th and the 99.9th percentiles.

When compared to the size of an IQ point in the far right column of Table V-5, the delays are equivalent in size to a fraction of an IQ point through the 99.5th percentile of exposure (median estimates), and slightly exceed one IQ point in size at the 99.9th percentile.

The confidence intervals for the age of first walking model are notably wider than they are for the age of talking model. At one end, the confidence limit is always zero, i.e., no adverse contribution to the net effect, suggestive of a possible threshold of effect for methylmercury that is above all U.S. exposures to it through the 99.9th percentile of exposure. By comparison, the age-of-talking model predicts that a possibility of no adverse contribution only exists through the 50th percentile of exposure. The reason for this difference is that the individual data points (i.e., the results from specific individuals in the study populations in the Seychelles and Iraq) include one individual with relatively low exposure but a significant delay in age of talking. This data point reduces the threshold of effect in that model. By contrast, the age of walking model does not contain a similar data point. Suffice it to say that both the age of first talking and age of first walking models predict the possibility of a threshold of effect but differ as to where it might be; and, in any case, these predictions do not reflect a median, or "best' estimate in either model.

Table V-5: Methylmercury's adverse contribution to the net effect on fetal neurodevelopment as measured by delay in age of first walking. The effects are expressed as delays in both days and hours. These delays are also expressed in terms of changes in both Z-Scores and "IQ Size Equivalents (IQse)."
Hg Dose (ppm in maternal hair) Percentile of U.S. Delay in walking (days) Delay in walking (hours) Change in Z-Score Change in IQse
0.02 10th -0.0259
(-0.0930, 0.0000)
-0.6225
(-2.2308, 0.0000)
-0.0003
(-0.0011, 0.0000)
-0.0047
(-0.0168, 0.0000)
0.04 25th -0.0656
(-0.2368, 0.0000)
-1.5748
(-5.6830, 0.0000)
-0.00008
(-0.0029, 0.0000)
-0.0119
(-0.0429, 0.0000)
0.12 50th -0.1823
(-0.6530, 0.0000)
-4.3744
(-15.6714, 0.0000)
-0.0022
(-0.0079, 0.0000)
-0.0330
(-0.1183, 0.0000)
0.30 75th -0.4461
(-1.5908, 0.0000)
-10.7057
(-38.1797, 0.0000)
-0.0054
(-0.0192, 0.0000)
-0.0808
(-0.2882, 0.0000)
0.63 90th -0.9920
(-3.4041, 0.0000)
-23.8073
(-81.6994, 0.0000)
-0.0120
(0.0411, 0.0000)
-0.1797
(-0.6167, 0.0000)
0.98 95th -1.5640
(-5.2607, 0.0000)
-37.5360
(-126.2561, 0.0000)
-0.0189
(-0.0635, 0.0000)
-0.2833 (-0.9530, 0.0000)
2.16 99th -3.5134
(-11.5457, 0.0000)
-84.3207
(-277.0978, 0.0000)
-0.0424
(-0.1394, 0.0000)
-0.6365
(-2.0916, 0.0000)
2.83 99.5th -4.6147
(-15.1282, 0.0000)
-110.7534
(-363.0763, 0.0000)
-0.0557
(-0.1827, 0.0000)
-0.8360
(-2.7406, 0.0000)
4.37 99.9th -7.4767
(-24.2005, 0.0000)
-179.4416
(-580.8112, 0.0000)
0.0903
(-0.2923, 0.0000)
-1.3545
(-4.3841, 0.0000)

The IQ Model (Axelrad et al., 2007) and the Battery of Tests Model (Cohen et al., 2005b): The decrements estimated by the Axelrad et al. (2007) and Cohen et al. (2005b) models are shown in Table V-6. Because the results from both models were expressed as decrements in IQ points, we present them the same way. Moreover, since the results are close to each other, we show them as essentially one result.

Table V-6. IQ loss from methylmercury predicted by Axelrad et al. (2007) and Cohen et al. (2005b)
Percentile of exposure (Hg in hair):
U. S. women of child-bearing age
Change in IQ (central estimates)
10th 0.00 of an IQ point*
50th 0.02 of an IQ point
90th 0.13 of an IQ point
95th 0.20 of an IQ point
99th 0.43 of an IQ point
99.9th 0.87 of an IQ point

* This number is actually higher than zero, but is low enough to "round" to zero when only two digits to the right of the decimal point are shown.

The results from these models are close to the results from our age-of-first talking model and similar to the results from our age of first walking model in terms of size of effect. (Compare the "IQ Size Equivalents" in Tables V-4 and 5 to the results in Table V-6.) This consistency occurs despite the differences in study populations, age of children, outcome measures, and differences in the analytical approaches. It helps obviate concerns that our model has too narrow a focus relative to a broad range of potential measures of neurodevelopment, as well as the very young age of the children, could limit its ability to provide valid results.

Beneficial Fish Contribution to the Net Effect

Table V-7 reports the results from this model. The model essentially predicts the amount of improvement on the language components of the MacArthur Communicative Development Inventory at 15 months and the Denver Developmental Screening Test at 18 months as a consequence of maternal fish consumption. The table expresses these results as changes in Z-Scores. In the right column, the Z-Scores are converted to "IQ Size Equivalents." The fish consumption column, i.e., the number of grams of fish eaten per day, reflects consumption of a variety of fish over time because the model does not differentiate among types of fish from a nutritional standpoint. Each estimate of fish consumption is associated with an estimated hair-mercury level in the box to the left of it. This hair-mercury level represents what a person's exposure would be if each fish he or she ate contained 0.086 ppm of methylmercury, i.e., the average amount of methylmercury in commercial fish weighted for popularity. In this model, these hair-mercury levels are provided primarily for context since the model only measures the beneficial contribution of the fish independent of methylmercury.

As Table V-7 shows, when consumption involves a variety of fish containing, collectively, the average amount of methylmercury in commercial fish weighted by popularity, the neurodevelopmental effects predicted by the "beneficial fish effect" model are larger than the adverse effects predicted for methylmercury by the ages of first talking and walking models (median estimates) at every percentile of fish consumption and corresponding exposure to methylmercury. Even so, the beneficial effects do not exceed the size of one IQ point until consumption exceeds 44.2 grams of fish per day. Consumption beyond that amount produces benefits that are equivalent in size to just under two IQ points at 97.5 grams of fish per day; equivalent in size to 2.4 IQ points at 127 grams of fish per day; and equivalent in size to just under four IQ points at 205.7 grams of fish per day. At this highest level of fish consumption examined by the model, the upper limit of the confidence interval shows a small possibility of benefits equivalent in size to 7.5 IQ points.

Table V-7: Fish's beneficial contribution to the net effect on fetal neurodevelopment as measured by improvements in verbal scores on the MacArthur Communicative Development Inventory and the Denver Communication Test. The improvements are expressed in terms of changes in both Z-Scores and "IQ Size Equivalents (IQse)." Because the assessment did not measure the differences in beneficial contributions from species to species, these results essentially reflect eating a variety of fish over time.
Hg Dose (ppm in maternal hair)
Estimated for a Corresponding
Amount of Fish/ Day
Amount of Fish Consumed
(grams of fish/day)
Change in
Z-Score
Change in
IQse
0.02 0.8 0.0010
(0.0003, 0.0020)
0.0152
(0.0049, 0.0293)
0.04 2.0 0.0025
(0.0008, 0.0048
0.0376
(0.0121, 0.0724)
0.12 5.5 0.0069
(0.0022, 0.0133)
0.1033
(0.0333, 0.1991)
0.30 13.3 0.0168
(0.0054, 0.0324)
0.2518
(0.0812, 0.4854)
0.63 28.6 0.0360
(0.0116, 0.0694)
0.5403
(0.1741, 1.0414)
0.98 44.2 0.0557
(0.0179, 0.1073)
0.8348
(0.2691, 1.6091)
2.16 97.5 0.1229
(0.0396, 0.2369)
1.8437
(0.5943, 3.5538)
2.83 127.8 0.1610
(0.0519, 0.3104)
2.4155
(0.7786, 4.6561)
4.37 205.7 0.2600
(0.0838, 0.5013)
3.9007
(1.2573, 7.5188)

The Net Effect on Fetal Neurodevelopment from Commercial Fish: In order to estimate the net effect on fetal neurodevelopment from maternal consumption of commercial fish, we developed this model by combining the results from age of talking in Iraq and Seychelles (representing "methylmercury") with early age verbal comprehension results from the United Kingdom (representing "fish"). The results were combined by converting them all into a common metric of Z-Scores and then adding them together. We converted these Z-Scores into "IQ Size Equivalents."

"Average Commercial Fish" Results: As with the other models, we present results in a table that predicts effects at specific levels of fish consumption and methylmercury exposure (Table V-8). The disadvantage in this presentation is that, by necessity, it is limited to people who eat a variety of fish that, over time, contain both an average amount of methylmercury for commercial fish (0.086 ppm) and an average amount of nutrients that contribute to a beneficial net effect for fetal neurodevelopment.

There is also an advantage in this presentation, however, because it can estimate whether exposure to methylmercury through the 99.9th percentile of exposure from eating a lot of "average" commercial fish -- which are toward the low end of the spectrum in terms of methylmercury concentrations(18) - could result in a net adverse effect.

The results, as presented in Table V-8, are beneficial through the 99.9th percentile of exposure to methylmercury. This level of exposure requires the consumption of 205.7 grams of "average" commercial fish per day. For purposes of comparison, the current FDA/EPA consumption advisory recommends eating no more than 50 grams of fish per day. Neither the median estimates nor the confidence intervals surrounding each median estimate predict the possibility of an adverse effect.

Benefits tend to increase as both fish consumption and exposure to methylmercury increase. The benefits are the size of a fraction of an IQ point through the 95th percentile of exposure to methylmercury (involving the consumption of 44.2 grams of fish per day), but then increases to the size of about 1.5 IQ points at the 99th percentile of exposure (involving the consumption of about 98 grams of fish per day), and to about the size of three IQ points at the 99.9th percentile of exposure (involving 205.7 grams of fish per day). At this highest level the model also predicts a low possibility that the benefit could be as high as about 6.8 IQ points (the highest confidence limit of the confidence interval surrounding the median estimate). Note that these predicted benefits are all slightly lower than those predicted for the beneficial contribution from fish. We attribute the difference to the adverse contribution of methylmercury to the net effect.

Table V-8: The net effect on fetal neurodevelopment from eating commercial fish that, collectively, contain an average amount of methylmercury and an average amount of beneficial nutrients. Eating a variety of commercial fish over time should achieve this outcome. The results are expressed in terms of changes in both Z-Scores and "IQ Size Equivalents (IQse)."
Hg Dose (ppm in maternal hair)
Estimated for a Corresponding
Amount of Fish/ Day
Amount of Fish Consumed
(grams of fish/day)
Change in Z-Score Change in IQse
0.02 0.8 0.008
(0.0001, 0.0018)
0.0126
(0.0018, 0.0268)
0.04 2.0 0.0021
(0.0003, 0.0044)
0.0310
(0.0043, 0.0660)
0.12 5.5 0.0057
(0.0008, 0.0121)
0.0851
(0.0115, 0.1813)
0.30 13.3 0.0137
(0.0018, 0.0293)
0.2054
(0.0276, 0.4389)
0.63 28.6 0.0292
(0.0037, 0.0627)
0.4373
(0.0561, 0.9405)
0.98 44.2 0.0448
(0.0058, 0.0969)
0.6714
(0.0866, 1.4531)
2.16 97.5 0.0985
(0.0121, 0.2139)
1.4778
(0.1816, 3.2090)
2.83 127.8 0.1291
(0.0156, 0.2803)
1.9361
(0.2345, 4.2044)
4.37 205.7 0.2078
(0.0253, 0.4526)
3.1177
(0.3788, 6.7895)

"Baseline" Results: We also modeled results from actual U.S. consumption of commercial fish by women of childbearing age, including diets involving fish that are both lower and higher in methylmercury. Because this version of the model involves fish that vary substantially in the amount of methylmercury they contain, we could not equate any particular level of exposure to methylmercury to a corresponding amount of fish per day or vice versa. Consequently, we present these results in terms of percentiles of the population that are likely to experience a particular effect, without associating these percentiles to specific levels of exposure or consumption. Table V-9 arrays these percentiles from adverse (lower population percentiles) to beneficial (higher population percentiles).

The effects are presented as changes in Z-Score and "IQ Size Equivalents." In summary, the model estimates that one-tenth of one percent of the population is likely to experience an adverse effect and that most of the remainder of the population is likely to experience a beneficial effect (although some may experience no effect one way or the other). These are the median estimates of effect. The confidence intervals surrounding these estimates include a small possibility of no adverse effect for anyone but also a small possibility of an adverse effect through 10 percent of the population. It is this probability of a net adverse effect for a small segment of the population that differentiates the "baseline" results from results involving identical exposures from methylmercury but only from the consumption of "average commercial fish," e.g., from eating a variety of commercial fish over time.

Table V-9: The net effect on fetal neurodevelopment on a population basis as a result of "baseline" consumption of commercial of fish, i.e., what women of childbearing age actually eat (as of about 2005). The population percentiles are arrayed from most adverse net effect (at the top) to most beneficial net effect (at the bottom). The results are expressed in terms of changes in both Z-Scores and "IQ Size Equivalents (IQse)."
Population Percentile Change in Z-Score Change in IQse
0.1 Percentile -0.003
(-0.046, 0.000)
-0.04
(-0.69, 0.000)
0.2 Percentile -0.000
(-0.028, 0.000)
-0.00
(-0.41, 0.00)
0.3 Percentile 0.000
(-0.024, 0.000)
0.00
(-0.36, 0.01)
0.4 Percentile 0.000
(-0.019, 0.000)
0.00
(-0.29, 0.01)
0.5 Percentile 0.000
(-0.018, 0.000)
0.00
(-0.27, 0.01)
1st Percentile 0.000
(-0.011, 0.001)
0.00
(-0.17, 0.01)
5th Percentile 0.001
(-0.002, 0.003)
0.02
(-0.03, 0.04)
10th Percentile 0.002
(-0.001, 0.005)
0.03,
(-0.01, 0.08)
25th Percentile 0.004
(0.000, 0.010)
0.06
(0.00, 0.15)
50th Percentile 0.009
(0.001, 0.021)
0.14
(0.02, 0.31)
75th Percentile 0.020
(0.003, 0.045)
0.30
(0.05, 0.67)
90th Percentile 0.039
(0.007, 0.082)
0.58
(0.11, 1.23)
95th Percentile 0.055
(0.011, 0.118)
0.82
(0.17, 1.77)
99th Percentile 0.105
(0.022, 0.226)
1.58
(0.33, 3.39)
99.5th Percentile 0.140
(0.028, 0.299)
2.10
(0.42, 4.49)
99.9th Percentile 0.221
(0.048, 0.540)
3.32
(0.72, 8.09)
Most Adverse -0.027
(-0.138, 0.000)
-0.41
(-2.07,0.00)
Most Beneficial 0.311
(0.088, 0.788)
4.67
(1.32, 11.82)

"What-If" Scenarios: We modeled several "what-if" scenarios in addition to the recent "baseline" in order to predict how changes in fish consumption by women of childbearing age could affect their children's neurodevelopment.

The results are presented as population shifts above or below the "baseline." For purposes of these "what if" scenarios, we calculated the average individual effect on neurodevelopment for all children at the "baseline" as compared to what the average effect would be if their mothers ate no fish and were essentially exposed to no methylmercury during pregnancy. The "baseline" represents an average improvement in Z-Score of 0.017 (equivalent in size to an average improvement of 0.255 of an IQ point) from maternal fish consumption during pregnancy as compared to maternal consumption of no fish.(19) A change against the "baseline" is an increase or decrease in this average individual effect.

A summary of the results is presented in Table V-10.

First "What If" Scenario: Women of Childbearing Age Limit Their Consumption to 12 Ounces a Week. Under this scenario, women who consume 12 ounces or less of fish per week would not alter the amount or types of fish they eat. Those who are eating more than 12 ounces per week would reduce their consumption to exactly 12 ounces but would not change the types of fish they eat. (The third and fourth scenarios involve changes in types of fish.)

On an overall national basis, the average change against baseline is predicted to be a loss per child of 0.001 Z-Score (equivalent to the size of 0.015 of an IQ point) even though most children would not be affected one way or another (because roughly 95 percent of pregnant women do not eat over 12 ounces of fish per week). The change against "baseline" reflects the reduction in fish consumption by roughly five percent of pregnant women. (However, children whose mothers had to reduce their consumption of fish that were high in methylmercury could experience an improvement.) Again, an average for all children shows how this scenario would affect the national average relative to the "baseline."

Second "What If" Scenario: Women of Childbearing Age All Consume 12 Ounces a Week. Under this scenario, all women of childbearing age eat exactly 12 ounces of commercial fish per week. This scenario would require changes in consumption by most people. Twelve ounces of fish per week is about 40 pounds per year while per capita fish consumption is only around 16 pounds per year. Most people would have to increase their fish consumption substantially in order to maintain 12 ounces per week. Only a small minority  (about five percent) would have to reduce consumption.

On an overall national basis, the predicted average change against "baseline" is a neurodevelopmental improvement per child of 0.038 Z-Score (equivalent to the size of 0.57 of an IQ point). This is the greatest average per-child gain in all of our scenarios due to the substantial national increase in fish consumption that would be needed for most people to achieve 12 ounces per week.

Children born to mothers who had to increase their fish consumption (most children) would generally experience increased benefits. However, if their mothers increased their fish consumption by eating a lot of fish that were relatively high in methylmercury , their benefits could be decreased to the point where the net effect for them could become adverse.

For children whose mothers had to reduce consumption down to 12 ounces per week (a minority), the model predicts they would generally experience a reduction in benefits. However, if their mothers' reduced fish consumption involved eating less fish that were relatively high in methylmercury, an opposite result could occur.  

Third "What If" Scenario: Women of Childbearing Age Limit Their Consumption to 12 Ounces a Week of "Low Methylmercury Fish": As a modification to the first scenario, we estimated the impact if women of child-bearing age were to limit their weekly consumption to no more than 12 ounces of fish that are low in methylmercury.(20) Those who already eat 12 ounces or less of fish per week would continue to eat the same amount but would only eat fish that are low in methylmercury. Those who already eat over 12 ounces of fish per week would reduce to exactly 12 ounces and would eat only fish that are low in methylmercury. This scenario is more protective than the current FDA/EPA consumption advice because the advice allows consumption of all commercial species that average below 0.73 ppm (the average for king mackerel, one of the four commercial species that should be avoided during pregnancy per the consumption advice).

On an overall national basis, the predicted average change against "baseline" would be a loss per child of 0.0004 Z-Score (equivalent to the size of 0.006 of an IQ point). The reductions in fish consumption within the population would produce losses that exceeded the gains from all the switches to fish that are low in methylmercury. Most commercial fish, including most the more popular species, are toward the low end of the spectrum in terms of methylmercury concentration, in that they contain from 5 - 10 times less methylmercury than the highest commercial species on average. Switching to fish that are low in methylmercury would not involve substantial changes in exposure to methylmercury for most people. On the other hand, the switch to fish that are low in methylmercury produces an average loss against "baseline" that is slightly smaller than the loss in the first scenario, in which women of childbearing age do not exceed 12 ounces of fish per week but eat any fish regardless of methylmercury content.

Specifically, children born to mothers who did not have to reduce their fish consumption or change the types of fish they ate would be unaffected. The model predicts that children born to mothers who did not have to reduce their fish consumption but did have to change at least some of the types of fish they ate would likely experience a benefit.

For children whose mothers had to reduce their fish consumption but did not have to change the types of fish they ate, the model predicts they would generally experience reduced benefits. Children whose mothers had to reduce their fish consumption and had to change the types of fish they ate could experience either reduced or an increased benefits depending upon the nature of the change.

Fourth "What If" Scenario: Women of Childbearing Age Eat Only "Low Methylmercury Fish" with No Limit on Consumption: This scenario enables a comparison of the 12 ounce per week limitation on fish consumption in the previous scenario against no limitation on consumption. In both scenarios, women of childbearing age are limited to fish that are low in methylmercury.

The only change against "baseline" in this scenario is a reduction in the concentrations of methylmercury in fish consumed by some people. Otherwise, the scenario is identical to the "baseline." On an overall national basis, the predicted average change against "baseline" is a gain per child of 0.0012 Z-Score (equivalent to the size of 0.018 of an IQ point). This predicted gain derives from reduced exposures to methylmercury experienced by children whose mothers had to change at least some of the types of fish they ate.

Table V-10: "What If" Scenarios for Fetal Neurodevelopment. The results are presented as changes in overall population effects above or below a baseline.
Scenario Change in Z-Score Change Expressed as IQ Size Equivalence
Baseline:

The effect on fetal neurodevelopment from recent fish consumption and the resulting exposure to methylmercury by women of childbearing age.

Z-Score: 0.017 (0.002, 0.037)

(Average Z-Score for children is 0.017 higher than it would be if women of childbearing age ate no fish.)

Average improvement over eating no fish is equivalent to the size of 0.225 of an IQ point (0.03, 0.555)
1st Scenario:

Women of child-bearing age eat no more than 12 oz. of fish per week

Average Z-Score loses 0.0010 (-0.0001, -0.0036) from baseline. Average loss is equivalent to the size of 0.0105 of an IQ point (-0.0015, -0.054)
2nd Scenario:

Women of child-bearing age eat exactly 12 ounces of fish per week

Average Z-Score gains 0.038 (0.008, 0.076) over baseline. Average improvement is equivalent to the size of 0.57 of an IQ point (0.12, 1.17)
3rd Scenario:

Women of child-bearing age eat no more than 12 oz. of "low MeHg" fish per week

Average Z-Score loses 0.0004 (-0.0010, 0.0025) from baseline. Average loss is equivalent to the size of 0.006 of an IQ point (-0.015, 0.0375)
4th Scenario:

Women of child-bearing age eat only "low MeHg" fish with no limit on consumption

Average Z-Score gains 0.0012 (0.0002, 0.0018) over baseline. Average improvement is equivalent to the size of 0.018 IQ points (0.003, 0.027)

(c) Fatal Coronary Heart Disease (CHD)

We present results from two models, which we refer to as the "CHD meta-analysis model" and the "CHD pooled analysis model" as described in Section IV.

Baseline Results:

The "CHD meta-analysis model" defines a linear relationship between fish consumption and CHD death in which every additional 20 grams of fish per day, on average, leads to seven percent lower risk of CHD mortality (He, et al., 2004a). No model uncertainty is included in this analysis.

In Table V-11, the bottom two rows reflect the "CHD meta-analysis model's" estimates for the median change in CHD death rate and the median number of deaths due to current levels of fish consumption. Negative numbers in the fourth row indicate reductions in death rates due to fish consumption. The differences in the subpopulations reflect the differences in the overall rates in each subpopulation as well as differences in the amount of fish consumed. Results from the "CHD meta-analysis model" can also be found in the second column of Table AB-6 in Appendix B.

Table V-11: CHD deaths - Current Rates and "CHD Meta-Analysis Model" Results
Population Characteristic Women 16-45 Women 46+ Men 16-45 Men 46+
Number of people of this age in US (2001)(21) 64,349,357 56,417,619 66,229,773 48,713,395
Annual rate of CHD death 0.14 per 10,000 38 per 10,000 1.3 per 10,000 51 per 10,000
Annual deaths per year from CHD 901 214,387 8,610 248,438
Median Change in CHD death rate due to fish consumption: Meta-analysis model -0.007 per 10,000 -2.2 per 10,000 -0.09 per 10,000 -3.7 per 10,000
CHD deaths averted attributable to current fish consumption: Meta-analysis model 43 12,498 589 18,104

Table V-12 shows the "CHD meta-analysis" model's predictions for how different levels of fish consumption can affect the annual frequency of death from CHD in each subpopulation. Within each subpopulation eating more fish reduces the frequency of death from CHD and vice versa. Because the model is predicting the effect of eating a variety of fish on fatal CHD, the table does not include estimates for exposure to methylmercury at each percentile of fish exposure. For those who want to match the fish consumption percentiles in Table V-12 with methylmercury levels that have been estimated for women of childbearing age, see Table V-3.

Note also that we do not attempt to match frequency of fatal CHD with consumption of any particular type of fish (e.g., oily vs. non-oily). As stated previously, we did not model the specific qualities of fish that could reduce the risk. All the data that met our inclusion criteria for this assessment derive from the consumption of "fish" without differentiation as to species.

Table V-12: Annual Frequency of Death from CHD Based on Amounts of Fish Consumed Using "CHD Meta-Analysis Model"
Fish Consumption Percentile Women 16-45 Women 46+ Men 16-45 Men 46+
10th 0.142 in 10,000 40.2 in 10,000 1.350 in 10,000 54.4 in 10,000
25th 0.141 in 10,000 39.7 in 10,000 1.33 in 10,000 53.6 in 10,000
50th 0.139 in 10,000 39.0 in 10,000 1.30 in 10,000 52.4 in 10,000
75th 0.134 in 10,000 37.6 in 10,000 1.25 in 10,000 50.2 in 10,000
90th 0.126 in 10,000 35.1 in 10,000 1.14 in 10,000 46.0 in 10,000
95th 0.119 in 10,000 32.6 in 10,000 1.04 in 10,000 42.1 in 10,000
99th 0.098 in 10,000 25.9 in 10,000 0.71 in 10,000 29.4 in 10,000

Fish consumption percentiles are based on the median estimates of fish consumption presented in Table 2-3

Table V-13 provides outcomes from both the "CHD meta-analysis model" and the "CHD pooled analysis model," including both the central estimates and the confidence intervals surrounding each central estimate. The first two rows provide the results from the "CHD meta-analysis model." The central estimate is the median estimate (50th percentile) and the confidence intervals are the 5th and 95th percentile estimates. The last two rows of Table V-13 provide the results for these estimates from the "CHD pooled analysis model." These results in terms of death rate are shown in Table AB-6 in Appendix B.

Because the two approaches are based largely on the same data, their central estimates, i.e., their median estimates, are close to one another. Both models produce central estimates of annual deaths averted ranging from a low of 43 deaths averted ("CHD meta-analysis model" for women 16-45) to a high of nearly 23,000 deaths averted ("CHD pooled analysis model" for men 46+), depending on subpopulation.

Wider confidence intervals means that the "CHD pooled analysis model" predicts a wider range of possible outcomes, including a small possibility of some increase in risk of CHD death in the U.S. population due to fish consumption than does the "CHD meta-analysis model." As explained below, the range of possibilities within the confidence intervals indicate a much greater likelihood that deaths are being averted than being caused by fish consumption.

The small possibility of increased risk from fish estimated by the "CHD pooled analysis model" can stem from several possible reasons, one of which could be methylmercury in the fish. For example, some people in the United States could be experiencing circumstances similar to those in eastern Finland, where risk from CHD was high. That population ate a lot of lean lake fish that were low in nutrients such as omega-3 fatty acids and selenium. Their entire diet appeared to be low in these nutrients (Salonen et al., 1995). Others reasons could include how the fish was prepared (e.g., fried vs baked); and fish serving as a surrogate for other risk factors.

Note that the terminology differs between Tables V-11 and V-13. For example, Table IV-13 lists "deaths averted" due to current fish consumption because the "CHD meta-analysis model" does not include any possibility of deaths caused by fish consumption. However, Table IV-11 refers to "change in number of deaths due to fish consumed" because the "CHD pooled analysis model" includes some possibility of death attributed to fish consumption.

Table V-13. Median results with Confidence Intervals (5th and 95th percentiles) for Effect on CHD Death Rate from Current Levels of Fish Consumption, as predicted by the "Meta-Analysis" and "Pooled Analysis" Models
Population Characteristic Women 16-45 Women 46+ Men 16-45 Men 46+
Median Change in CHD Death Rate Due to Fish Consumed: Meta-Analysis Model -0.007 per 10,000
(-0.013 per 10,000, -0.001 per 10,000)
-2.2 per 10,000
(-4.3 per 10,000, -0.4 per 10,000)
-0.09 per 10,000
(-0.17 per 10,000, -0.002 per 10,000)
-3.7 per 10,000
(-7.2 per 10,000, -0.7 per 10,000)
Change in Number of Deaths due to Fish Consumed: Meta-Analysis Model -43
(-86, -9)
-12,498
(-24,158, -2,274)
-589
(-1,134, -106)
-18,104
(-35,151, -3,211)
Median Change in CHD Death Rate Due to Fish Consumed: Pooled Analysis Model -0.011 per 10,000
(-0.22 per 10,000, 0.026 per 10,000)
-2.8 per 10,000
(-42 per 10,000, 9.4 per 10,000)
-0.11 per 10,000
(-1.2 per 10,000, 0.34 per 10,000)
-4.7 per 10,000
(-88 per 10,000, 6.5 per 10,000)
Change in Number of Deaths due to Fish Consumed: Pooled Analysis Model -69
(-1400, 169)
-15,906
(-237,298, 52,076)
-728
(-8,080, 2261)
-22,922
(-428,305, 31,837)

The way to read this table is as follows: for women of child-bearing age, the "CHD meta-analysis model's" central estimate is that 43 CHD deaths are prevented annually due to current levels of fish consumption (although at the 5th and 95th percentile confidence intervals the estimated number of deaths averted are as high as 86 or as low as nine). Since the current annual number of deaths is 901 per year, the number of deaths if there were no fish consumption is estimated to be 901 plus 43, or 944, per the central estimate.

The "CHD pooled analysis model" produces a central estimate of 69 deaths averted due to fish consumption in this age group, although this number could be as high as 1,400 (at the 5th percentile confidence interval). It also estimates that at the 95th percentile confidence interval, up to 169 CHD deaths could be caused by fish consumption. However, the bulk of the probability distribution is less than zero, so it is more likely than not (85 percent) that increased fish consumption leads to a decrease in CHD death.

"What-If" Scenarios:

First "What If" Scenario: Women of Childbearing Age Limit Their Consumption to 12 Ounces a Week. In this scenario, women of childbearing age who are consuming 12 ounces or less of fish per week do not change their consumption but women of childbearing age who are consuming more than 12 ounces reduce their consumption to 12 ounces. This reduction is long term and does not occur solely during pregnancy. Because the models predict that a decrease in consumption causes an increase in risk, the most likely outcome would be an overall increase in the number of deaths from CHD for this population. Such an increase would be small, however, because the only people who would be eating less fish would be relatively young women. The median estimate for the "CHD meta-analysis model" is an increase of 4.6 deaths per year while the median increase for the "CHD pooled analysis model" is actually zero, but the upper confidence limit is 8.2 deaths.

NOTE: We also modeled this scenario for fetal neurodevelopment and for stroke.

Table V-14: Change in CHD Annual Deaths (vs. Baseline) Resulting from a Consumption Limit of 12 oz Per Week by Women of Childbearing Age
Population Group CHD Death Cases Per -Year
Pooled Analysis Model
CHD Death Cases Per Year
Meta-Analysis Model
Women 16-45 An increase of 0.0 (-2.4, 8.2) Deaths per year An increase of 4.6 (0.8, 10) Deaths per year

The primary values are the median cases per year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Second "What If" Scenario: Women of Childbearing Age All Consume 12 Ounces a Week. In the next scenario, all women of child-bearing age consume exactly 12 ounces of fish. As with the first scenario, this consumption is not just during pregnancy. Because most women of childbearing age consume much less than 12 ounces per week (22) the majority of women in this age group would have to increase their consumption substantially under this scenario. Only a small minority would have to decrease consumption down to 12 ounces per week. The most likely overall impact of this scenario would be a decrease in the number of deaths from CHD in women of childbearing age.

NOTE: We also modeled this scenario for fetal neurodevelopment and for stroke.

Table V-15: Change in CHD Annual Deaths (vs. Baseline) Resulting from Consuming Exactly 12 oz per Week by Women of Childbearing Age
Population Group CHD Death Cases Per Year Pooled Analysis Model CHD Death Cases Per Year Meta-Analysis Model
Women 16-45 A decrease of 88 (-187, -627)deaths per year A decrease of 108 (-21, -230)deaths per year

The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Third and Fourth "What If" Scenarios: Men and Older Women Reduce Their Fish Consumption. One of the questions surrounding the FDA/EPA consumer advisory has been whether it would affect fish consumption throughout the population even though the target audience is limited to certain women of childbearing age and young children. The next two scenarios examine the potential impact of reductions in fish consumption by (1) young men; (2) older men; (3) older women. In one scenario, the number of fish consumers in each of these subpopulations decreases by one percent (i.e., one percent of fish eaters stop consuming all fish). In the second scenario, there is a 10 percent reduction in the amount of fish consumed by all fish consumers in these subpopulations.

The "CHD meta-analysis model" predicts that a one percent reduction in the number of consumers that eat fish would be 130 additional deaths per year among for older women, 235 additional deaths per year among older men, and six additional deaths per year among young men. The "CHD pooled analysis" model's estimates are similar, although the confidence intervals are wider and include decreases in deaths per year. The predicted impact from an across the board reduction of 10 percent in the amount of fish consumed is substantially greater with 1,250 additional deaths per year among older women, 1,810 additional deaths per year among older men, and 59 additional deaths per year among young men in the "CHD meta-analysis model." Again, the "CHD pooled analysis model's" estimates are similar but with wider confidence intervals.

Table V-16: Change in CHD Annual Deaths (vs. Baseline) Resulting from a One Percent Reduction the Number of Men and Older Women Who Consume Fish
Population Group CHD Death Cases Per Year Pooled Analysis Model CHD Death Cases Per Year Meta-Analysis Model
Women 46+ An increase of 161 (-559, 2,644)deaths per year An increase of 130 (20, 286)deaths per year
Men 15-45 An increase of 8.0 (-23.3, 88)deaths per year An increase of 6.1 (0.9, 14)deaths per year
Men 46+ An increase of 264 (-385, 5,168)deaths per year An increase of 191 (29, 422)deaths per year

Estimates of increased rates of CHD Death resulting from decreased number of fish consumers in three population groups. The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Table V-17: Change in CHD Annual Deaths (vs. Baseline) Resulting from a 10 Percent Decrease in the Amount of Fish Consumed by Men and Older Women
Population Group CHD Death Cases Per Year Pooled Analysis Model CHD Death Cases Per Year Meta-Analysis Model
Women 46+ An increase of 656 (-1,937, 7,674) deaths per year An increase of 1,250 (227, 2,416) deaths per year
Men 15-45 An increase of 30 (-97, 248)deaths per year An increase of 59 (11, 113)deaths per year
Men 46+ An increase of 1,432 (-1,721, 10,749) deaths per year An increase of 1,810 (321, 3,515)deaths per year

Estimates of increased rates of CHD Death resulting from decreased number of fish consumers in three population groups. The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Fifth "What If" Scenario: 50 Percent Increase In Fish Consumption by Everyone. This scenario examines the health impact of a 50 percent increase in fish consumption by all subpopulations. The models predict decreases in CHD death from such increases in consumption.

Table V-18: Change in CHD Annual Deaths (vs. Baseline) Resulting from a 50 Percent Increase in the Amount of Fish Consumed by all Population Groups
Population Group CHD Death Pooled Analysis Model CHD Death Meta-Analysis Model
Women 16-45 A decrease of 11, (-27, 175) deaths per year A decrease of 22 (4, 43) deaths per year
Women 46+ A decrease of 2,306 (-7,154, 29,691) deaths per year A decrease of 6,249 (1,137, 12,079) deaths per year
Men 15-45 A decrease of 124 (-382, 926) deaths per year A decrease of 294 (53, 567) deaths per year
Men 46+ A decrease of 5,243 (-6,888, 48,5154) deaths per year A decrease of 9,052 (1,606, 17,576) deaths per year

The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

(d) Fatal Stroke

We present results from two models, which we refer to as the "stroke meta-analysis model" and the "stroke pooled analysis model" as described in Section IV.

Baseline Results

In Table V-19, the first three rows show the population numbers, rates and estimated number of stroke deaths for the four subpopulations. The fourth row shows the decrease in the death rate for each subpopulation that can be attributed to current fish consumption. As with CHD, the decreases are based on the amounts of fish consumed by each sub-population. There is only one dose-response function that is used for all four sub-populations. The fifth row shows the number of deaths averted annually for each subpopulation due to fish consumption per the "stroke meta-analysis model." .

Table V-19: Stroke deaths - Current Rates and "Stroke Meta-Analysis Model" Results
Population Characteristic Women 16-45 Women 46+ Men 16-45 Men 46+
Number of people of this age in US (2001)(23) 64,349,357 56,417,619 66,229,773 48,713,395
Annual rate of stroke death 0.25 per 10,000 18 per 10,000 0.24 per 10,000 13 per 10,000
Average baseline deaths per year from stroke 1,551 101,549 1,560 63,911
Median stroke death rate decrease due to fish consumption ["Stroke Meta-Analysis Model"] 0.030 per 10,000 2.2 per 10,000 0.032 per 10,000 1.8 per 10,000
Stroke lives saved (deaths averted) attributable to current fish consumption ["Stroke Meta-Analysis Model"] 192 12,570 213 8,609

For women aged 16-45, the "stroke meta-analysis model" estimates that 192 stroke deaths per year are averted due to fish consumption while 12,570 stroke deaths are averted for women over age 45 due to fish consumption. Two hundred thirteen stroke deaths per year are averted for men aged 16-45 due to fish consumption while 8,609 stroke deaths per year are averted due to fish consumption by men over the age of 45.

These figures reflect the median, i.e., central estimates. In order to show the full range of uncertainty in these estimates and to present the results from the "stroke pooled analysis model" Table V-20 shows the fifth and 95th confidence intervals in addition to the central estimates. These results are also shown in Table AB-7 in Appendix B.

Both models predict that reduction in risk of fatal stroke is the most likely outcome from fish consumption. Where these models differ most notably is in the size of the confidence intervals. The "stroke pooled analysis model" produced confidence intervals that are considerably greater than those produced by the "stroke meta-analysis model." Larger confidence intervals mean a wider range of possible outcomes; consequently, the "stroke pooled analysis model" also predicts a small possibility that fish consumption can increase the risk of stroke, as revealed by the results of the 95th percentile confidence intervals in Table V-20.(24) There is an 87 percent probability that fish consumption is averting deaths rather than causing them, however.

To read Table V-20: As noted earlier, for women aged 16-45, the "stroke meta-analysis model" predicts that 192 stroke deaths are averted per year due to fish consumption. The confidence intervals around that median estimate indicate that as many as 359 or as little as 58 deaths may be averted by fish consumption.

For this same subpopulation, the "stroke pooled analysis model's" central estimate is that 238 stroke deaths are being averted annually for this subpopulation from fish consumption but at the fifth percentile confidence interval it also estimates that fish consumption could be averting up to 1,995 deaths while at the 95th percentile of the confidence interval it predicts that fish consumption may be causing 97 stroke deaths.

Table V-20. Median results with Confidence Intervals (5th and 95th percentiles) for Effect on Stroke Death Rate from Fish Consumption, as Predicted by "Stroke Meta-Analysis" and "Stroke Pooled Analysis" Models
Population Characteristic Women 16-45 Women 46+ Men 16-45 Men 46+
Stroke Death Rate Change: Meta-Analysis Model -0.030 per 10,000
(-0.056 per 0,000, -0.009 per 0,000)
-2.2 per 10,000
(-4.3 per 10,000, -0.7 per 10,000)
-0.032 per 10,000
(-0.061 per 10,000, -0.011 per 10,000)
-1.8 per 10,000
(-3.4 per 10,000, -0.7 per 10,000)
Change in Number of Deaths due to Fish Consumed: Meta-Analysis Model -192
(-359, -58)
-12,570
(-23359, -3,994)
-213
(-402, -75)
-8,609
(-16,380, -3,233)
Stroke Death Rate Change: Pooled Analysis Model -0.037 per 10,000
(-0.15 per 10,000, 0.016 per 10,000)
-2.2 per 10,000
(-27 per 10,000, 1.4 per 10,000)
-0.04 per 10,000
(-0.31 per 10,000, 0.03 per 10,000)
-2.4 per 10,000
(-19 per 10,000, 0.9 per 10,000)
Change in Number of Deaths due to Fish Consumed: Pooled Analysis Model -238
(-1,995, 97)
-12,693
(-154,932, 7,938)
-294
(-2,054, 203)
-11,923
(-95,049, 4,558)

"What-If" Scenarios

First "What If" Scenario: Women of Childbearing Age Limit Their Consumption to 12 Ounces a Week. In the first scenario, women of childbearing age who are consuming 12 ounces or less of fish per week do not change their consumption but women of childbearing age who are consuming more than that reduce their consumption to 12 ounces. This reduction does not occur only during pregnancy.(25) Because the models predict that a decrease in consumption is likely to increase risk, the most likely change would be an overall increase in the number of deaths from stroke in this population. Such an increase would be small, however, because the only people who would be eating less fish would be relatively young women. The median estimate for the "stroke meta-analysis model" is an increase of 4.0 deaths per year while the median increase for the "stroke pooled analysis model" is actually zero.

Table V-21: Change in Annual Stroke Deaths (vs. Baseline) Resulting from a Consumption Limit of 12 oz Per Week by Women of Childbearing Age
Population Group Stroke Death Cases Per -Year Pooled Analysis Model CHD Death Cases Per Year Meta-Analysis Model
Women 15-45 An increase of 0.0 (-2.0, 5.1) deaths per year An increase of 4.0 (-8.1, 15.7) deaths per year

The primary values are the median cases per year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Second "What If" Scenario: Women of Childbearing Age All Consume 12 Ounces a Week. In this scenario, all women of child-bearing age consume exactly 12 ounces of fish. As with the first scenario, this consumption does not just occur during pregnancy. Because most women consume much less than 12 ounces per week, the majority of women in this age group would have to increase their fish consumption substantially. Only a small minority would have to decrease consumption down to 12 ounces per week. The models predict that the most likely overall impact of this scenario would be a decrease in the number of deaths from stroke in women of childbearing age.

Table V-22: Change in Annual Stoke Deaths (vs. Baseline) Resulting from Consuming Exactly 12 oz Per Week by Women of Childbearing Age
Population Group Stroke Death Cases Per -Year Pooled Analysis Model CHD Death Cases Per Year Meta-Analysis Model
Women 15-45 A decrease of 250 (-258, 810) deaths per year A decrease of 143 (-129, 417) deaths per year

The primary values are the median cases per year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Third and Fourth "What If" Scenarios: Men and Older Women Reduce Their Fish Consumption. These scenarios model the impact of decreased consumption of fish by: (1) young men; (2) older men; and (3) older women, none of whom are targeted by the current FDA/EPA consumer advisory. In one scenario, there is a one percent reduction in the number of fish consumers (i.e., one percent of fish eaters stop consuming all fish). In the other scenario, all fish consumers in these subpopulations reduce their fish consumption by 10 percent. For both scenarios, the models predict that most likely result would be a decrease in deaths averted in each of these subpopulations.

Table V-23: Change in Annual Stroke Deaths (vs. Baseline) Resulting from a One Percent Reduction in the Number of Men and Older Women Who Consume Fish
Population Group Stroke Death Cases Per Year Pooled Analysis Model Stroke Death Cases Per Year Meta-Analysis Model
Women 46+ An increase in 144 (-84, 1,823) deaths per year An increase in 134 (40, 260) deaths per year
Men 15-45 An increase in 2.9 (-1.8, 23) deaths per year An increase in 2.34 (-0.7, 4.5) deaths per year
Men 46+ An increase in 134 (-45, 1,028) deaths per year An increase in 94 (29, 193) deaths per year

Estimates of increased rates of stroke death resulting from decreased number of fish consumers in three population groups. The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Table V-24: Change in Annual Stroke Deaths (vs. Baseline) Resulting from a 10 Percent Decrease in the Amount of Fish Consumed by Men and Older Women
Population Group Stroke Death Cases Per Year Pooled Analysis Model Stroke Death Cases Per Year Meta-Analysis Model
Women 46+ An increase in 751 (-688, 3,193)deaths per year An increase in 542 (-122, 1240)deaths per year
Men 15-45 An increase in 13.9 (-13.1, 47)deaths per year An increase in 9.0 (-3.7, 23)deaths per year
Men 46+ An increase in 575 (-419, 1,862)deaths per year An increase in 353 (-214, 913)deaths per year

Estimates of increased rates of stroke death resulting from decreased number of fish consumers in three population groups. The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.

Fifth "What If" Scenario: 50 Percent Increase In Fish Consumption by Everyone. In this scenario, all subpopulations increase their fish consumption by 50 percent. The models predict that most likely result would be an increase in deaths averted in each subpopulation.

Table V-25: Change in Annual Stroke Deaths (vs. Baseline) Resulting from a 50 Percent Increase in the Amount of Fish Consumed by All Population Groups
Population Group Stroke Death Cases Per Year Pooled Analysis Model Stroke Death Cases Per Year Meta-Analysis Model
Women 15-45 A decrease of 52 (-39, 193) deaths per year A decrease of 35 (-14, 84) deaths per year
Women 46+ A decrease of 2,899 (-1,725, 10,889) A decrease of 2,198 (-1,411, 5,678)
Men 15-45 A decrease of 53 (-57, 168) deaths per year A decrease of 40 (-38, 104) deaths per year
Men 46+ A decrease of 2,151 (-1,810, 6,549) deaths per year A decrease of 1,507 (-1,691, 4,074) deaths per year

Estimates of decreased rates of stroke death resulting from decreased number of fish consumers in three population groups. The primary values are the median cases per person-year of the uncertainty distribution, the 5th and 95th percentiles given as confidence intervals.


Notes

(16) Note that these are mercury levels in the mothers, not in the children. The dose-response data that are available on effects on the fetus are in terms of mothers' levels of mercury, not infants' levels. Therefore the conversion from what's in the mother to what's in the infant is part of the dose-response function and does not have to be estimated.
(17) This estimate was calculated from data from the Seychelles Islands. We would expect an estimate for the U.S. population to differ somewhat, but not substantially. We made the estimate to provide a sense for how the delays predicted by the model compare to the total length of time that it takes a child to first talk.
(18) As stated previously, the "average" commercial fish weighted for popularity contains about an order of magnitude less methylmercury than the commercial species with the highest concentrations of methylmercury on average.
(19) This is so even though at the "baseline," a small fraction of the population will probably experience a net adverse effect. Because the overwhelming majority of people will experience a beneficial effect, the overall population average at the "baseline" is beneficial.
(20) For purposes of this scenario, we used 0.12 ppm to represent fis that are low in methylmercury. This concentration is slightly higher than the average for all commercial fish weighted for popularity. Table AA-2 in Appendix A provides a list of species with their average mercury concentrations.
(21) (U.S. Census Bureau, 2001)
(22) In recent survey research by FDA, median fish consumption for the non-pregnant women of childbearing age in the survey was 2.97 ounces per week while median fish consumption for the pregnant women surveyed was 1.89 ounces per week (Choiniére, C.J., Timbo, B., Street, D., Trumbo, P., Fein, S., Fish Consumption by Women of Childbearing Age, Pregnant Women, and Mothers of Infants. Presented at the International Association for Food Protection Annual Meeting, Columbus, Ohio, August 3-6, 2008.) As a caveat, the study sample was from a nationally distributed consumer panel that was not representative of the whole U.S. population. The sample size was large, however, with 1,500 women in each of these groups.
(23) (U.S. Census Bureau, 2001)
(24) There are several possible explanations for this latter prediction, none of them mutually exclusive. First, fish consumption might increase the risk of hemorrhagic stroke. This possibility dervies from a study in which Greenland Eskimos acids were found to have a higher risk of fatal hemorrhagic stroke than Danish whites (Kristensen 1983). A difference between the two groups was the higher intake of omega-3 fatty acids by the Greenland Eskimos. However, average levels of omega-3 in these Eskimos were as much as 100 time higher than average levels in the United States; consequently, the risk, if any, to U.S. residents may be low (Iso et al., 2001). Moreover, the data from the eight studies that we used in our risk assessment did not show a significant increase in risk of hemorrhagic stoke from increased fish consumption. The He et al. meta-analysis reported finding no significant association between hemorrhagic stroke and fish intake (He et al., 2004b, p. 1539).
Second, the manner of cooking could increase the risk. One study showed a decrease in risk when fish were baked or broiled but an increase in risk when fish were fried (Mozaffarian et al., 2005).
Third, risk assessment outcomes can be sensitive to variations in the data. Of the nine studies that provided the data for the risk assessment, four found no statistically significant association one way or another between fish consumption and stroke, four found that fish consumption reduced the risk of total stroke, three found that fish consumption reduced the risk of ischemic stroke, and three found no association between fish consumption and risk of hemorrhagic stroke one way or another. However, as reported above, one study found an increased risk from fried fish. Another study that found no overall association between fish consumption and stroke did find that the highest levels of fish consumption in its study group were associated with the highest incidence of stroke.
Finally, it is important to recognize that even where a study finds an overall decrease in risk, inevitably some members of the study population still have strokes that are modeled as being "due to" fish consumption even though the cause may involve other risk factors.
(25) Where a reduction in fish consumption occurs only during pregnancy, we would expect that most of the stroke benefit from higher fish consumption before and after pregnancy would still be accrued over a lifetime.

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