The following shows the descriptives of blood lead levels We removed blood levels of 4 to 6 µg/dL for risk factor analysis as this is the range where there is uncertainty in the measurement of blood lead levels.
| Blood Pb Levels (mg/dL) |
Blood Pb Levels (mg/dL)
|
Blood Pb Levels (mg/dL) with 4-6 removed
|
|---|---|---|
| N = 4,3061 | N = 3,4131 | |
| Blood Pb Levels (μg/dL) | 4.7 (3.9), 3.6 (2.4, 5.7), 0.2, 42.5 | 4.7 (4.4), 3.1 (2.2, 6.4), 0.2, 42.5 |
| bloodpblevelsmgdl_5cat | ||
| <4 | 2,437 (56.60%) | 2,437 (71.40%) |
| 4-6 | 893 (20.74%) | 0 (0.00%) |
| 6-10 | 647 (15.03%) | 647 (18.96%) |
| 10-20 | 275 (6.39%) | 275 (8.06%) |
| >20 | 54 (1.25%) | 54 (1.58%) |
| 1 Mean (SD), Median (Q1, Q3), Min, Max; n (%) | ||
Map of the distribution of blood lead levels by district: This shows the average blood lead levels in each district, with darker colors indicating higher levels.
Map of the distribution of blood lead levels by PSU: This map shows the average blood lead levels in each PSU, with darker colors indicating higher levels. From each PSU, roughly 1 to 3 samples were randomly selected for blood lead level testing.
Prevalence of high blood lead levels (>6 µg/dL) is 22.6% in the whole sample and 28.6% in the sample with blood lead levels of 4 to 6 µg/dL removed.
There is a wide variation in blood lead levels across districts and PSUs, with some districts and PSUs having much higher average blood lead levels than others. It is possible that risk factors are not common across India but likely highly contextual and dependent on regional factors. Possible stratified analysis by region may be required if numbers are adequate.
The diagram below captures the overarching hypothesis for high blood levels and guides the further risk factor analysis
We removed blood levels of 4 to 6 µg/dL for risk factor analysis for all the below results
Note: Other religion types: Buddhist, Sikh, Jain, Sindhi, Indigenous, Uraon, etc.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| PSU Rural/Urban | 3,413 | 0.003 | ||||||||||
| Rural | 4.6 (4.4) | — | — | 2,344 (100%) | 1,710 (73%) | 634 (27%) | — | — | ||||
| Urban | 4.9 (4.4) | 0.28 | -0.04, 0.60 | 0.084 | 1,069 (100%) | 727 (68%) | 342 (32%) | 1.18 | 1.06, 1.32 | 0.003 | ||
| Gender | 3,413 | 0.014 | ||||||||||
| Female | 4.5 (4.2) | — | — | 1,624 (100%) | 1,192 (73%) | 432 (27%) | — | — | ||||
| Male | 4.9 (4.5) | 0.42 | 0.12, 0.71 | 0.005 | 1,789 (100%) | 1,245 (70%) | 544 (30%) | 1.14 | 1.03, 1.27 | 0.014 | ||
| Age Group | 3,413 | 0.067 | ||||||||||
| 1-2 | 5.2 (5.0) | — | — | 501 (100%) | 333 (66%) | 168 (34%) | — | — | ||||
| 2-3 | 4.5 (3.9) | -0.75 | -1.2, -0.26 | 0.003 | 807 (100%) | 581 (72%) | 226 (28%) | 0.84 | 0.71, 0.99 | 0.033 | ||
| 3-4 | 4.8 (4.6) | -0.46 | -0.93, 0.02 | 0.058 | 963 (100%) | 693 (72%) | 270 (28%) | 0.84 | 0.71, 0.98 | 0.028 | ||
| 4-5 | 4.6 (4.2) | -0.64 | -1.1, -0.18 | 0.006 | 1,142 (100%) | 830 (73%) | 312 (27%) | 0.81 | 0.70, 0.95 | 0.010 | ||
| Caste | 3,399 | <0.001 | ||||||||||
| Other Caste | 4.1 (3.6) | — | — | 677 (100%) | 521 (77%) | 156 (23%) | — | — | ||||
| SC | 4.9 (4.3) | 0.76 | 0.31, 1.2 | <0.001 | 774 (100%) | 533 (69%) | 241 (31%) | 1.35 | 1.14, 1.61 | <0.001 | ||
| ST | 3.9 (3.7) | -0.21 | -0.66, 0.25 | 0.4 | 717 (100%) | 580 (81%) | 137 (19%) | 0.83 | 0.68, 1.02 | 0.072 | ||
| OBC | 5.5 (5.1) | 1.4 | 0.96, 1.8 | <0.001 | 1,183 (100%) | 760 (64%) | 423 (36%) | 1.55 | 1.33, 1.82 | <0.001 | ||
| Not known | 4.1 (2.8) | 0.00 | -1.3, 1.3 | >0.9 | 48 (100%) | 33 (69%) | 15 (31%) | 1.36 | 0.83, 2.01 | 0.2 | ||
| Religion | 3,399 | <0.001 | ||||||||||
| Hindu | 5.1 (4.7) | — | — | 2,560 (100%) | 1,712 (67%) | 848 (33%) | — | — | ||||
| Muslim | 4.0 (3.9) | -1.2 | -1.7, -0.64 | <0.001 | 296 (100%) | 236 (80%) | 60 (20%) | 0.61 | 0.48, 0.76 | <0.001 | ||
| Christian | 3.3 (2.5) | -1.8 | -2.3, -1.3 | <0.001 | 361 (100%) | 313 (87%) | 48 (13%) | 0.40 | 0.30, 0.52 | <0.001 | ||
| Others | 2.8 (2.3) | -2.3 | -2.9, -1.6 | <0.001 | 182 (100%) | 166 (91%) | 16 (8.8%) | 0.27 | 0.16, 0.41 | <0.001 | ||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Pearson’s Chi-squared test | ||||||||||||
Findings: Urban residence, male, younger age, scheduled caste, OBC, Hindu religion. These are non-modifiable and possibly point to distal determinants rather than actual causes.
Note: Other house ownership types: begar, quarters, encroached, landless, relatives, etc. Other Latrine types: Community latrine, present at home and not using, dry toilet, katcha, compost, no toilet, etc. Other cooking fuel types: cow dung, solar, biogas, electricity, kerosene, coal, weed, other, multiple types, etc. Other cooking/drinking water types: hand pump, rainwater, metro, tube well, share water, public tap, protected well, pipeline, chara, etc. Other water purification types: solar disinfection, let it stand, water filter, etc.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| Type of Family | 3,399 | 0.10 | ||||||||||
| Extended | 4.3 (4.0) | — | — | 428 (100%) | 322 (75%) | 106 (25%) | — | — | ||||
| Joint | 4.7 (4.6) | 0.36 | -0.16, 0.88 | 0.2 | 756 (100%) | 547 (72%) | 209 (28%) | 1.12 | 0.92, 1.37 | 0.3 | ||
| Nuclear | 4.8 (4.4) | 0.41 | -0.04, 0.87 | 0.075 | 2,215 (100%) | 1,558 (70%) | 657 (30%) | 1.20 | 1.01, 1.44 | 0.046 | ||
| Ownership of house | 3,385 | 0.7 | ||||||||||
| Own House | 4.7 (4.5) | — | — | 2,988 (100%) | 2,138 (72%) | 850 (28%) | — | — | ||||
| Rented/Other | 4.6 (3.6) | -0.10 | -0.56, 0.36 | 0.7 | 397 (100%) | 280 (71%) | 117 (29%) | 1.04 | 0.88, 1.21 | 0.7 | ||
| Type of House | 3,397 | 0.4 | ||||||||||
| Pucca | 4.6 (4.3) | — | — | 2,048 (100%) | 1,479 (72%) | 569 (28%) | — | — | ||||
| Semi-pucca | 4.7 (4.3) | 0.15 | -0.20, 0.50 | 0.4 | 855 (100%) | 598 (70%) | 257 (30%) | 1.08 | 0.95, 1.22 | 0.2 | ||
| Kutcha | 5.0 (4.8) | 0.41 | -0.02, 0.84 | 0.062 | 494 (100%) | 350 (71%) | 144 (29%) | 1.05 | 0.90, 1.22 | 0.5 | ||
| No of rooms in house | 3,388 | <0.001 | ||||||||||
| >1 | 4.6 (4.4) | — | — | 3,062 (100%) | 2,228 (73%) | 834 (27%) | — | — | ||||
| 0-1 | 5.8 (4.5) | 1.2 | 0.66, 1.7 | <0.001 | 326 (100%) | 190 (58%) | 136 (42%) | 1.53 | 1.32, 1.75 | <0.001 | ||
| Separate Kitchen | 3,396 | 0.017 | ||||||||||
| Yes | 4.5 (4.2) | — | — | 2,436 (100%) | 1,767 (73%) | 669 (27%) | — | — | ||||
| No | 5.1 (4.7) | 0.56 | 0.23, 0.89 | <0.001 | 960 (100%) | 657 (68%) | 303 (32%) | 1.15 | 1.02, 1.29 | 0.016 | ||
| Cooking fuel | 3,413 | 0.043 | ||||||||||
| Firewood | 4.8 (4.6) | — | — | 1,076 (100%) | 774 (72%) | 302 (28%) | — | — | ||||
| LPG | 4.6 (4.3) | -0.20 | -0.52, 0.12 | 0.2 | 2,268 (100%) | 1,623 (72%) | 645 (28%) | 1.01 | 0.90, 1.14 | 0.8 | ||
| Others | 6.3 (5.5) | 1.5 | 0.48, 2.6 | 0.004 | 69 (100%) | 40 (58%) | 29 (42%) | 1.50 | 1.08, 1.96 | 0.007 | ||
| Drinking Water | 3,397 | <0.001 | ||||||||||
| Bore well | 4.8 (4.8) | — | — | 911 (100%) | 658 (72%) | 253 (28%) | — | — | ||||
| Open well | 4.1 (4.1) | -0.72 | -1.4, -0.08 | 0.028 | 219 (100%) | 177 (81%) | 42 (19%) | 0.69 | 0.51, 0.91 | 0.013 | ||
| Others | 5.3 (5.2) | 0.53 | -0.21, 1.3 | 0.2 | 161 (100%) | 107 (66%) | 54 (34%) | 1.21 | 0.94, 1.52 | 0.13 | ||
| RO/Bottled/Purchased water | 5.2 (4.1) | 0.38 | -0.13, 0.90 | 0.14 | 402 (100%) | 252 (63%) | 150 (37%) | 1.34 | 1.14, 1.58 | <0.001 | ||
| Surface/Spring water | 4.1 (3.2) | -0.66 | -1.5, 0.15 | 0.11 | 130 (100%) | 95 (73%) | 35 (27%) | 0.97 | 0.70, 1.29 | 0.8 | ||
| Tap water | 4.5 (4.2) | -0.27 | -0.63, 0.09 | 0.14 | 1,488 (100%) | 1,084 (73%) | 404 (27%) | 0.98 | 0.86, 1.12 | 0.7 | ||
| Water tanker | 5.6 (4.6) | 0.77 | -0.20, 1.7 | 0.12 | 86 (100%) | 52 (60%) | 34 (40%) | 1.42 | 1.05, 1.85 | 0.014 | ||
| Cooking water | 3,395 | 0.005 | ||||||||||
| Bore well | 4.8 (4.8) | — | — | 928 (100%) | 669 (72%) | 259 (28%) | — | — | ||||
| Open well | 4.1 (4.1) | -0.67 | -1.3, -0.02 | 0.042 | 221 (100%) | 176 (80%) | 45 (20%) | 0.73 | 0.54, 0.95 | 0.028 | ||
| Others | 5.3 (5.6) | 0.51 | -0.20, 1.2 | 0.2 | 171 (100%) | 116 (68%) | 55 (32%) | 1.15 | 0.89, 1.45 | 0.2 | ||
| RO/Bottled/Purchased water | 4.8 (3.6) | 0.05 | -0.57, 0.67 | 0.9 | 244 (100%) | 160 (66%) | 84 (34%) | 1.23 | 1.00, 1.50 | 0.041 | ||
| Surface/Spring water | 4.2 (3.3) | -0.55 | -1.3, 0.25 | 0.2 | 133 (100%) | 96 (72%) | 37 (28%) | 1.00 | 0.73, 1.31 | >0.9 | ||
| Tap water | 4.6 (4.2) | -0.18 | -0.53, 0.17 | 0.3 | 1,608 (100%) | 1,153 (72%) | 455 (28%) | 1.01 | 0.89, 1.16 | 0.8 | ||
| Water tanker | 5.6 (4.6) | 0.85 | -0.10, 1.8 | 0.079 | 90 (100%) | 54 (60%) | 36 (40%) | 1.43 | 1.07, 1.85 | 0.010 | ||
| Water purification method | 3,398 | <0.001 | ||||||||||
| Water filter/ E. Purifier | 3.8 (3.9) | — | — | 245 (100%) | 199 (81%) | 46 (19%) | — | — | ||||
| Boiling | 3.7 (3.0) | -0.09 | -0.73, 0.56 | 0.8 | 603 (100%) | 482 (80%) | 121 (20%) | 1.07 | 0.79, 1.47 | 0.7 | ||
| Strain through a cloth | 5.0 (4.3) | 1.2 | 0.41, 2.0 | 0.003 | 236 (100%) | 157 (67%) | 79 (33%) | 1.78 | 1.31, 2.47 | <0.001 | ||
| Add bleach/chlorine tablets | 6.6 (5.3) | 2.8 | 1.0, 4.6 | 0.002 | 26 (100%) | 13 (50%) | 13 (50%) | 2.66 | 1.58, 4.09 | <0.001 | ||
| None | 5.2 (4.8) | 1.4 | 0.84, 2.0 | <0.001 | 1,975 (100%) | 1,317 (67%) | 658 (33%) | 1.77 | 1.38, 2.36 | <0.001 | ||
| Others/Multiple | 3.8 (3.3) | 0.03 | -0.70, 0.76 | >0.9 | 313 (100%) | 258 (82%) | 55 (18%) | 0.94 | 0.66, 1.34 | 0.7 | ||
| Latrine type | 3,413 | <0.001 | ||||||||||
| Present and using | 4.6 (4.3) | — | — | 2,914 (100%) | 2,105 (72%) | 809 (28%) | — | — | ||||
| Others | 3.8 (3.1) | -0.81 | -1.5, -0.14 | 0.017 | 175 (100%) | 138 (79%) | 37 (21%) | 0.76 | 0.56, 1.00 | 0.068 | ||
| Open Defecation | 6.0 (5.4) | 1.4 | 0.94, 1.9 | <0.001 | 324 (100%) | 194 (60%) | 130 (40%) | 1.45 | 1.24, 1.66 | <0.001 | ||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 28 | 19 | 9 | |||||||||
| Unknown | 16 | 10 | 6 | |||||||||
| Unknown | 25 | 19 | 6 | |||||||||
| Unknown | 17 | 13 | 4 | |||||||||
| Unknown | 16 | 12 | 4 | |||||||||
| Unknown | 18 | 13 | 5 | |||||||||
| Unknown | 15 | 11 | 4 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Pearson’s Chi-squared test | ||||||||||||
Findings: Nuclear family, small houses, no separate kitchen, water from RO/bottled/water tanker, inadequate or improper water purification, and open defecation. We get some indication that water with lead contamination may be a source via ingestion. This needs to explored further in the case-control data where water has been tested for lead contamination. However, we would not know that the water tested was from which source.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| Electricity Available | 3,397 | >0.9 | ||||||||||
| No | 4.0 (3.4) | — | — | 16 (100%) | 12 (75%) | 4 (25%) | — | — | ||||
| Yes | 4.7 (4.4) | 0.70 | -1.5, 2.9 | 0.5 | 3,381 (100%) | 2,413 (71%) | 968 (29%) | 1.15 | 0.58, 3.36 | 0.8 | ||
| Electric Fan | 3,399 | 0.012 | ||||||||||
| No | 4.3 (4.4) | — | — | 377 (100%) | 290 (77%) | 87 (23%) | — | — | ||||
| Yes | 4.8 (4.4) | 0.48 | 0.01, 0.95 | 0.044 | 3,022 (100%) | 2,137 (71%) | 885 (29%) | 1.27 | 1.06, 1.55 | 0.015 | ||
| Black and White Television | 3,399 | 0.027 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,167 (100%) | 2,276 (72%) | 891 (28%) | — | — | ||||
| Yes | 5.2 (4.5) | 0.55 | -0.04, 1.1 | 0.068 | 232 (100%) | 151 (65%) | 81 (35%) | 1.24 | 1.02, 1.48 | 0.022 | ||
| Colour Television | 3,399 | 0.11 | ||||||||||
| No | 5.1 (5.1) | — | — | 1,100 (100%) | 766 (70%) | 334 (30%) | — | — | ||||
| Yes | 4.5 (4.0) | -0.60 | -0.91, -0.28 | <0.001 | 2,299 (100%) | 1,661 (72%) | 638 (28%) | 0.91 | 0.82, 1.02 | 0.11 | ||
| Radio/Transistor | 3,399 | 0.022 | ||||||||||
| No | 4.8 (4.5) | — | — | 3,152 (100%) | 2,235 (71%) | 917 (29%) | — | — | ||||
| Yes | 3.7 (2.9) | -1.0 | -1.6, -0.46 | <0.001 | 247 (100%) | 192 (78%) | 55 (22%) | 0.77 | 0.59, 0.96 | 0.029 | ||
| Pressure Cooker | 3,399 | <0.001 | ||||||||||
| No | 5.7 (5.4) | — | — | 810 (100%) | 514 (63%) | 296 (37%) | — | — | ||||
| Yes | 4.4 (4.0) | -1.3 | -1.6, -0.92 | <0.001 | 2,589 (100%) | 1,913 (74%) | 676 (26%) | 0.71 | 0.64, 0.80 | <0.001 | ||
| Cot/Bed | 3,399 | <0.001 | ||||||||||
| No | 5.5 (4.8) | — | — | 470 (100%) | 292 (62%) | 178 (38%) | — | — | ||||
| Yes | 4.6 (4.3) | -0.92 | -1.3, -0.49 | <0.001 | 2,929 (100%) | 2,135 (73%) | 794 (27%) | 0.72 | 0.63, 0.82 | <0.001 | ||
| Mattress | 3,399 | <0.001 | ||||||||||
| No | 5.9 (5.2) | — | — | 886 (100%) | 529 (60%) | 357 (40%) | — | — | ||||
| Yes | 4.3 (4.0) | -1.6 | -2.0, -1.3 | <0.001 | 2,513 (100%) | 1,898 (76%) | 615 (24%) | 0.61 | 0.55, 0.68 | <0.001 | ||
| Chair | 3,399 | <0.001 | ||||||||||
| No | 5.5 (4.6) | — | — | 347 (100%) | 212 (61%) | 135 (39%) | — | — | ||||
| Yes | 4.6 (4.4) | -0.85 | -1.3, -0.36 | <0.001 | 3,052 (100%) | 2,215 (73%) | 837 (27%) | 0.70 | 0.61, 0.82 | <0.001 | ||
| Table | 3,399 | <0.001 | ||||||||||
| No | 5.5 (4.9) | — | — | 994 (100%) | 635 (64%) | 359 (36%) | — | — | ||||
| Yes | 4.4 (4.1) | -1.1 | -1.4, -0.78 | <0.001 | 2,405 (100%) | 1,792 (75%) | 613 (25%) | 0.71 | 0.63, 0.79 | <0.001 | ||
| Mobile Phone | 3,399 | 0.054 | ||||||||||
| No | 5.1 (4.3) | — | — | 171 (100%) | 111 (65%) | 60 (35%) | — | — | ||||
| Yes | 4.7 (4.4) | -0.45 | -1.1, 0.22 | 0.2 | 3,228 (100%) | 2,316 (72%) | 912 (28%) | 0.81 | 0.66, 1.01 | 0.044 | ||
| Landline Phone | 3,399 | 0.2 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,311 (100%) | 2,359 (71%) | 952 (29%) | — | — | ||||
| Yes | 4.2 (3.5) | -0.54 | -1.5, 0.39 | 0.3 | 88 (100%) | 68 (77%) | 20 (23%) | 0.79 | 0.51, 1.12 | 0.2 | ||
| Watch/Clock | 3,370 | 0.029 | ||||||||||
| No | 4.9 (4.6) | — | — | 1,038 (100%) | 714 (69%) | 324 (31%) | — | — | ||||
| Yes | 4.6 (4.3) | -0.28 | -0.60, 0.04 | 0.086 | 2,332 (100%) | 1,690 (72%) | 642 (28%) | 0.88 | 0.79, 0.99 | 0.028 | ||
| Washing Machine | 3,399 | <0.001 | ||||||||||
| No | 4.8 (4.5) | — | — | 2,521 (100%) | 1,760 (70%) | 761 (30%) | — | — | ||||
| Yes | 4.3 (4.2) | -0.50 | -0.83, -0.16 | 0.004 | 878 (100%) | 667 (76%) | 211 (24%) | 0.80 | 0.70, 0.91 | <0.001 | ||
| Refrigerator | 3,399 | <0.001 | ||||||||||
| No | 5.0 (4.6) | — | — | 1,779 (100%) | 1,220 (69%) | 559 (31%) | — | — | ||||
| Yes | 4.4 (4.1) | -0.62 | -0.92, -0.33 | <0.001 | 1,620 (100%) | 1,207 (75%) | 413 (25%) | 0.81 | 0.73, 0.90 | <0.001 | ||
| Computer | 3,399 | 0.022 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,195 (100%) | 2,267 (71%) | 928 (29%) | — | — | ||||
| Yes | 4.1 (3.6) | -0.60 | -1.2, 0.02 | 0.059 | 204 (100%) | 160 (78%) | 44 (22%) | 0.74 | 0.56, 0.95 | 0.029 | ||
| Internet | 3,399 | <0.001 | ||||||||||
| No | 5.0 (4.6) | — | — | 1,585 (100%) | 1,075 (68%) | 510 (32%) | — | — | ||||
| Yes | 4.5 (4.2) | -0.49 | -0.78, -0.19 | 0.001 | 1,814 (100%) | 1,352 (75%) | 462 (25%) | 0.79 | 0.71, 0.88 | <0.001 | ||
| Sewing Machine | 3,399 | 0.5 | ||||||||||
| No | 4.7 (4.4) | — | — | 2,866 (100%) | 2,040 (71%) | 826 (29%) | — | — | ||||
| Yes | 4.6 (4.3) | -0.08 | -0.48, 0.33 | 0.7 | 533 (100%) | 387 (73%) | 146 (27%) | 0.95 | 0.81, 1.10 | 0.5 | ||
| Air Conditioner/Cooler | 3,399 | 0.6 | ||||||||||
| No | 4.7 (4.3) | — | — | 2,821 (100%) | 2,019 (72%) | 802 (28%) | — | — | ||||
| Yes | 4.9 (4.7) | 0.24 | -0.15, 0.64 | 0.2 | 578 (100%) | 408 (71%) | 170 (29%) | 1.03 | 0.90, 1.18 | 0.6 | ||
| Bicycle | 3,399 | 0.7 | ||||||||||
| No | 4.7 (4.2) | — | — | 2,181 (100%) | 1,552 (71%) | 629 (29%) | — | — | ||||
| Yes | 4.7 (4.7) | 0.07 | -0.24, 0.37 | 0.7 | 1,218 (100%) | 875 (72%) | 343 (28%) | 0.98 | 0.87, 1.09 | 0.7 | ||
| Motorcycle/Scooter | 3,399 | 0.031 | ||||||||||
| No | 4.6 (4.5) | — | — | 1,603 (100%) | 1,173 (73%) | 430 (27%) | — | — | ||||
| Yes | 4.8 (4.3) | 0.16 | -0.14, 0.45 | 0.3 | 1,796 (100%) | 1,254 (70%) | 542 (30%) | 1.13 | 1.01, 1.25 | 0.031 | ||
| Car/Four Wheeler | 3,399 | <0.001 | ||||||||||
| No | 4.8 (4.5) | — | — | 2,988 (100%) | 2,087 (70%) | 901 (30%) | — | — | ||||
| Yes | 3.7 (3.6) | -1.1 | -1.6, -0.69 | <0.001 | 411 (100%) | 340 (83%) | 71 (17%) | 0.57 | 0.46, 0.71 | <0.001 | ||
| Animal Drawn Cart | 3,399 | 0.10 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,218 (100%) | 2,288 (71%) | 930 (29%) | — | — | ||||
| Yes | 4.1 (3.4) | -0.66 | -1.3, 0.00 | 0.048 | 181 (100%) | 139 (77%) | 42 (23%) | 0.80 | 0.60, 1.03 | 0.11 | ||
| Water Pump | 3,399 | 0.5 | ||||||||||
| No | 4.7 (4.3) | — | — | 2,924 (100%) | 2,082 (71%) | 842 (29%) | — | — | ||||
| Yes | 4.7 (4.8) | -0.02 | -0.44, 0.41 | >0.9 | 475 (100%) | 345 (73%) | 130 (27%) | 0.95 | 0.81, 1.11 | 0.5 | ||
| Tractor | 3,399 | 0.2 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,293 (100%) | 2,357 (72%) | 936 (28%) | — | — | ||||
| Yes | 5.0 (4.8) | 0.36 | -0.49, 1.2 | 0.4 | 106 (100%) | 70 (66%) | 36 (34%) | 1.19 | 0.89, 1.53 | 0.2 | ||
| Thresher/Seed Removal Machine | 3,399 | 0.8 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,359 (100%) | 2,399 (71%) | 960 (29%) | — | — | ||||
| Yes | 4.9 (5.1) | 0.18 | -1.2, 1.5 | 0.8 | 40 (100%) | 28 (70%) | 12 (30%) | 1.05 | 0.61, 1.59 | 0.8 | ||
| Home Theatre | 3,399 | 0.7 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,321 (100%) | 2,373 (71%) | 948 (29%) | — | — | ||||
| Yes | 4.5 (3.3) | -0.24 | -1.2, 0.75 | 0.6 | 78 (100%) | 54 (69%) | 24 (31%) | 1.08 | 0.74, 1.46 | 0.7 | ||
| Vacuum Cleaner | 3,399 | 0.059 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,333 (100%) | 2,373 (71%) | 960 (29%) | — | — | ||||
| Yes | 3.2 (2.7) | -1.5 | -2.6, -0.44 | 0.006 | 66 (100%) | 54 (82%) | 12 (18%) | 0.63 | 0.35, 1.00 | 0.080 | ||
| Microwave/Electric Oven | 3,399 | <0.001 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,307 (100%) | 2,346 (71%) | 961 (29%) | — | — | ||||
| Yes | 3.2 (3.7) | -1.5 | -2.4, -0.62 | <0.001 | 92 (100%) | 81 (88%) | 11 (12%) | 0.41 | 0.22, 0.68 | 0.002 | ||
| Treadmill/Exercise Equipment | 3,399 | 0.11 | ||||||||||
| No | 4.7 (4.4) | — | — | 3,387 (100%) | 2,421 (71%) | 966 (29%) | — | — | ||||
| Yes | 5.0 (2.6) | 0.35 | -2.1, 2.8 | 0.8 | 12 (100%) | 6 (50%) | 6 (50%) | 1.75 | 0.83, 2.68 | 0.053 | ||
| Type of PDS/Ration Card | 3,395 | 0.019 | ||||||||||
| APL | 4.4 (4.5) | — | — | 720 (100%) | 542 (75%) | 178 (25%) | — | — | ||||
| No card | 4.8 (4.3) | 0.38 | -0.08, 0.85 | 0.11 | 652 (100%) | 469 (72%) | 183 (28%) | 1.14 | 0.95, 1.36 | 0.2 | ||
| BPL/AAY/TPDS | 4.8 (4.4) | 0.40 | 0.03, 0.78 | 0.035 | 2,023 (100%) | 1,412 (70%) | 611 (30%) | 1.22 | 1.06, 1.42 | 0.006 | ||
| Any Woman Enrolled in a Self-Help Group | 3,381 | 0.3 | ||||||||||
| No | 4.6 (4.3) | — | — | 2,344 (100%) | 1,687 (72%) | 657 (28%) | — | — | ||||
| Yes | 4.8 (4.5) | 0.20 | -0.12, 0.52 | 0.2 | 1,037 (100%) | 727 (70%) | 310 (30%) | 1.07 | 0.95, 1.19 | 0.3 | ||
| Any Household Member with Disability | 3,393 | 0.7 | ||||||||||
| No | 4.6 (4.2) | — | — | 2,526 (100%) | 1,808 (72%) | 718 (28%) | — | — | ||||
| Yes | 4.9 (4.8) | 0.25 | -0.09, 0.59 | 0.14 | 867 (100%) | 614 (71%) | 253 (29%) | 1.03 | 0.91, 1.16 | 0.7 | ||
| Household Covered by Insurance | 3,396 | 0.009 | ||||||||||
| No | 4.9 (4.4) | — | — | 1,581 (100%) | 1,095 (69%) | 486 (31%) | — | — | ||||
| Yes | 4.5 (4.4) | -0.32 | -0.62, -0.02 | 0.034 | 1,815 (100%) | 1,331 (73%) | 484 (27%) | 0.87 | 0.78, 0.96 | 0.009 | ||
| Household Owns Agricultural Land | 3,324 | <0.001 | ||||||||||
| No | 4.5 (4.2) | — | — | 2,336 (100%) | 1,715 (73%) | 621 (27%) | — | — | ||||
| Yes | 5.1 (4.7) | 0.59 | 0.27, 0.92 | <0.001 | 988 (100%) | 661 (67%) | 327 (33%) | 1.25 | 1.11, 1.39 | <0.001 | ||
| Owns Livestock | 3,273 | 0.2 | ||||||||||
| No | 4.6 (4.1) | — | — | 2,453 (100%) | 1,765 (72%) | 688 (28%) | — | — | ||||
| Yes | 5.0 (5.0) | 0.38 | 0.03, 0.72 | 0.033 | 820 (100%) | 571 (70%) | 249 (30%) | 1.08 | 0.96, 1.22 | 0.2 | ||
| Cows/Bulls/Buffaloes/Yaks | 748 | >0.9 | ||||||||||
| No | 5.2 (5.8) | — | — | 131 (100%) | 91 (69%) | 40 (31%) | — | — | ||||
| Yes | 5.0 (5.0) | -0.20 | -1.2, 0.76 | 0.7 | 617 (100%) | 427 (69%) | 190 (31%) | 1.01 | 0.77, 1.36 | >0.9 | ||
| Camels | 569 | >0.9 | ||||||||||
| No | 4.8 (4.9) | — | — | 566 (100%) | 406 (72%) | 160 (28%) | — | — | ||||
| Yes | 4.3 (2.2) | -0.48 | -6.0, 5.0 | 0.9 | 3 (100%) | 2 (67%) | 1 (33%) | 1.18 | 0.24, 5.87 | 0.8 | ||
| Horses/Donkeys/Mules | 565 | 0.5 | ||||||||||
| No | 4.8 (4.8) | — | — | 563 (100%) | 404 (72%) | 159 (28%) | — | — | ||||
| Yes | 6.0 (3.0) | 1.2 | -5.5, 7.9 | 0.7 | 2 (100%) | 1 (50%) | 1 (50%) | 1.77 | 0.44, 7.12 | 0.4 | ||
| Goats/Sheep | 659 | 0.077 | ||||||||||
| No | 4.6 (4.4) | — | — | 366 (100%) | 268 (73%) | 98 (27%) | — | — | ||||
| Yes | 5.3 (5.4) | 0.76 | 0.01, 1.5 | 0.046 | 293 (100%) | 196 (67%) | 97 (33%) | 1.24 | 0.98, 1.57 | 0.077 | ||
| Pigs | 577 | 0.2 | ||||||||||
| No | 4.9 (4.9) | — | — | 524 (100%) | 370 (71%) | 154 (29%) | — | — | ||||
| Yes | 4.4 (5.5) | -0.53 | -1.9, 0.87 | 0.5 | 53 (100%) | 42 (79%) | 11 (21%) | 0.71 | 0.38, 1.14 | 0.2 | ||
| Chickens/Ducks | 645 | 0.019 | ||||||||||
| No | 5.1 (4.8) | — | — | 379 (100%) | 257 (68%) | 122 (32%) | — | — | ||||
| Yes | 4.6 (5.2) | -0.51 | -1.3, 0.27 | 0.2 | 266 (100%) | 203 (76%) | 63 (24%) | 0.74 | 0.56, 0.95 | 0.021 | ||
| Wealth Index | 3,399 | <0.001 | ||||||||||
| Highest | 4.2 (4.1) | — | — | 641 (100%) | 499 (78%) | 142 (22%) | — | — | ||||
| Higher | 4.6 (4.2) | 0.37 | -0.10, 0.84 | 0.12 | 680 (100%) | 493 (73%) | 187 (28%) | 1.24 | 1.03, 1.50 | 0.025 | ||
| Middle | 4.3 (3.6) | 0.08 | -0.39, 0.56 | 0.7 | 659 (100%) | 494 (75%) | 165 (25%) | 1.13 | 0.93, 1.38 | 0.2 | ||
| Lower | 4.5 (4.1) | 0.35 | -0.11, 0.81 | 0.14 | 718 (100%) | 510 (71%) | 208 (29%) | 1.31 | 1.09, 1.58 | 0.004 | ||
| Lowest | 5.9 (5.4) | 1.7 | 1.2, 2.2 | <0.001 | 701 (100%) | 431 (61%) | 270 (39%) | 1.74 | 1.47, 2.07 | <0.001 | ||
| Unknown | 16 | 12 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 43 | 33 | 10 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| Unknown | 18 | 14 | 4 | |||||||||
| Unknown | 32 | 23 | 9 | |||||||||
| Unknown | 20 | 15 | 5 | |||||||||
| Unknown | 17 | 11 | 6 | |||||||||
| Unknown | 89 | 61 | 28 | |||||||||
| Unknown | 140 | 101 | 39 | |||||||||
| Unknown | 2,665 | 1,919 | 746 | |||||||||
| Unknown | 2,844 | 2,029 | 815 | |||||||||
| Unknown | 2,848 | 2,032 | 816 | |||||||||
| Unknown | 2,754 | 1,973 | 781 | |||||||||
| Unknown | 2,836 | 2,025 | 811 | |||||||||
| Unknown | 2,768 | 1,977 | 791 | |||||||||
| Unknown | 14 | 10 | 4 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Fisher’s exact test; Pearson’s Chi-squared test | ||||||||||||
Findings: Largely ownership of assets is generally associated with lower blood lead levels, except for a few such as agricultural land and livestock.
This needs to be explored further in the future case-control data where livestock and feed/water may be additionally tested for lead contamination. There is a clear dose-response type relationship between wealth index and blood lead levels, with lower income being associated with higher blood lead levels. This may be due to poorer living conditions, lack of access to cleaner water, and higher exposure to lead-containing products among lower-income households.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| Anemia status | 3,413 | <0.001 | ||||||||||
| No Anemia | 4.5 (4.1) | — | — | 2,213 (100%) | 1,624 (73%) | 589 (27%) | — | — | ||||
| Any Anemia | 5.1 (4.8) | 0.68 | 0.38, 0.99 | <0.001 | 1,200 (100%) | 813 (68%) | 387 (32%) | 1.21 | 1.09, 1.35 | <0.001 | ||
| Anaemia Status (Any, New) | 3,413 | <0.001 | ||||||||||
| No Anemia | 4.5 (4.1) | — | — | 2,213 (100%) | 1,624 (73%) | 589 (27%) | — | — | ||||
| Mild Anemia | 4.9 (4.7) | 0.49 | 0.12, 0.87 | 0.010 | 685 (100%) | 479 (70%) | 206 (30%) | 1.13 | 0.99, 1.29 | 0.073 | ||
| Moder+Sev Anemia | 5.4 (4.9) | 0.94 | 0.52, 1.4 | <0.001 | 515 (100%) | 334 (65%) | 181 (35%) | 1.32 | 1.15, 1.51 | <0.001 | ||
| Iron Deficiency Status | 741 | 0.001 | ||||||||||
| No Iron Deficiency | 4.2 (3.7) | — | — | 571 (100%) | 434 (76%) | 137 (24%) | — | — | ||||
| Iron Deficiency (ID) | 5.5 (4.9) | 1.3 | 0.56, 1.9 | <0.001 | 170 (100%) | 108 (64%) | 62 (36%) | 1.52 | 1.18, 1.93 | <0.001 | ||
| Iron Deficiency (Adjusted) | 739 | 0.8 | ||||||||||
| Iron Deficiency (Adj) | 4.1 (3.3) | — | — | 59 (100%) | 44 (75%) | 15 (25%) | — | — | ||||
| No Iron Deficiency (Adj) | 4.5 (4.1) | 0.43 | -0.65, 1.5 | 0.4 | 680 (100%) | 496 (73%) | 184 (27%) | 1.06 | 0.71, 1.77 | 0.8 | ||
| Anaemia Adjusted for Iron | 739 | 0.066 | ||||||||||
| No Anemia + Iron Deficiency | 3.2 (2.2) | — | — | 33 (100%) | 29 (88%) | 4 (12%) | — | — | ||||
| Anemia + No Iron Deficiency | 4.3 (3.7) | 1.1 | -0.37, 2.6 | 0.14 | 223 (100%) | 166 (74%) | 57 (26%) | 2.11 | 0.95, 6.62 | 0.12 | ||
| No Anemia + No Iron Deficiency | 4.7 (4.3) | 1.4 | -0.01, 2.9 | 0.051 | 457 (100%) | 330 (72%) | 127 (28%) | 2.29 | 1.05, 7.13 | 0.081 | ||
| Anemia + Iron Deficiency | 5.3 (4.0) | 2.0 | -0.06, 4.1 | 0.057 | 26 (100%) | 15 (58%) | 11 (42%) | 3.49 | 1.37, 11.5 | 0.017 | ||
| Vitamin B12 Status | 708 | 0.8 | ||||||||||
| Normal (≥203 pg/mL) | 4.5 (4.0) | — | — | 441 (100%) | 324 (73%) | 117 (27%) | — | — | ||||
| Deficient (<203 pg/mL) | 4.6 (4.4) | 0.08 | -0.55, 0.71 | 0.8 | 267 (100%) | 194 (73%) | 73 (27%) | 1.03 | 0.80, 1.32 | 0.8 | ||
| Serum Folate Status | 707 | 0.4 | ||||||||||
| Normal (≥4 ng/mL) | 4.5 (4.1) | — | — | 612 (100%) | 445 (73%) | 167 (27%) | — | — | ||||
| Deficient (<4 ng/mL) | 4.3 (4.1) | -0.25 | -1.1, 0.65 | 0.6 | 95 (100%) | 73 (77%) | 22 (23%) | 0.85 | 0.56, 1.22 | 0.4 | ||
| Vitamin D Status | 705 | 0.3 | ||||||||||
| Normal (≥12 ng/mL) | 4.5 (3.8) | — | — | 576 (100%) | 426 (74%) | 150 (26%) | — | — | ||||
| Deficient (<12 ng/mL) | 4.9 (5.3) | 0.43 | -0.36, 1.2 | 0.3 | 129 (100%) | 89 (69%) | 40 (31%) | 1.19 | 0.87, 1.57 | 0.2 | ||
| Unknown | 2,672 | 1,895 | 777 | |||||||||
| Unknown | 2,674 | 1,897 | 777 | |||||||||
| Unknown | 2,674 | 1,897 | 777 | |||||||||
| Unknown | 2,705 | 1,919 | 786 | |||||||||
| Unknown | 2,706 | 1,919 | 787 | |||||||||
| Unknown | 2,708 | 1,922 | 786 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Pearson’s Chi-squared test | ||||||||||||
| Correlation between Blood micronutrient and Blood Pb levels | ||
| Micronutrient | Correlation Coefficient (r) | P-value |
|---|---|---|
| ferritin_clean | -0.110 | 0.0026 |
| log_ferritin | -0.164 | 0.0000 |
| ferritin_adj | 0.035 | 0.3445 |
| log_ferritin_adj | 0.045 | 0.2251 |
| folate_ng_ml | 0.053 | 0.1614 |
| rbc_folate_ng_ml | -0.038 | 0.3336 |
| b12_pg_ml | -0.069 | 0.0665 |
| vitd_ng_ml | 0.029 | 0.4373 |
Findings: Anemia and iron deficiency are clearly associated with higher blood lead levels. This supports our hypothesis that this leads to increased absorption of lead in the gut. But it is also possible that lead exposure contributes to these deficiencies. In addition, the continuous variables of blood levels of ferritin (not adjusted ferritin) and vitamin B12 have a weak negative correlation with blood lead but others like folate and vitamin D are not associated.
These indicators of longer term nutritional status and micronutrient deficiency are more relevant for lead absorption. In simple words, lower intake of micronutrients in diet over a longer period leads to lower blood levels of these micronutrients which leads to higher absorption of lead in the gut and higher blood lead levels.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| CRP Status | 748 | 0.040 | ||||||||||
| Inflammation | 3.6 (2.9) | — | — | 58 (100%) | 49 (84%) | 9 (16%) | — | — | ||||
| No Inflammation | 4.6 (4.1) | 1.0 | -0.08, 2.1 | 0.069 | 690 (100%) | 497 (72%) | 193 (28%) | 1.80 | 1.05, 3.63 | 0.059 | ||
| AGP Status | 746 | 0.2 | ||||||||||
| Inflammation | 4.0 (3.2) | — | — | 95 (100%) | 74 (78%) | 21 (22%) | — | — | ||||
| No Inflammation | 4.6 (4.2) | 0.59 | -0.29, 1.5 | 0.2 | 651 (100%) | 470 (72%) | 181 (28%) | 1.26 | 0.87, 1.94 | 0.3 | ||
| Inflammation Status | 747 | 0.050 | ||||||||||
| Inflammation | 3.8 (3.0) | — | — | 117 (100%) | 94 (80%) | 23 (20%) | — | — | ||||
| No Inflammation | 4.7 (4.2) | 0.90 | 0.10, 1.7 | 0.027 | 630 (100%) | 451 (72%) | 179 (28%) | 1.45 | 1.01, 2.20 | 0.062 | ||
| Unknown | 2,665 | 1,891 | 774 | |||||||||
| Unknown | 2,667 | 1,893 | 774 | |||||||||
| Unknown | 2,666 | 1,892 | 774 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Pearson’s Chi-squared test | ||||||||||||
| Correlation between Inflammation markers and Blood Pb levels | ||
| Inflammation markers | Correlation Coefficient (r) | P-value |
|---|---|---|
| crphs_clean | -0.067 | 0.0662 |
| agp_clean | -0.081 | 0.0272 |
| log_crp | -0.098 | 0.0081 |
| log_agp | -0.080 | 0.0281 |
Findings: Generally lack of inflammation is associated with higher blood lead levels. This may be because inflammation leads to sequestration of lead in the liver and other tissues, leading to lower blood lead levels. The biological plausibility of this association needs to be assessed further. Whether to use these variables to adjust for the relationship between blood micronutrients and blood lead levels needs to be assessed.
| Characteristic |
Blood Pb Levels (µg/dL)
|
Mean Difference
|
Distribution by Blood Pb Categories
|
Prevalence Ratio (<4 vs. >6)
|
||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,4131 | N | Mean Difference | 95% CI | p-value | Overall N = 3,4132 |
<4 N = 2,4372 |
>6 N = 9762 |
p-value3 | PR | 95% CI | p-value | |
| Stunting status | 2,787 | <0.001 | ||||||||||
| No Stunting | 4.5 (4.1) | — | — | 1,982 (100%) | 1,438 (73%) | 544 (27%) | — | — | ||||
| Stunting | 5.1 (4.7) | 0.57 | 0.15, 0.99 | 0.008 | 530 (100%) | 352 (66%) | 178 (34%) | 1.22 | 1.06, 1.40 | 0.005 | ||
| Severe Stunting | 6.0 (5.5) | 1.5 | 0.95, 2.1 | <0.001 | 275 (100%) | 167 (61%) | 108 (39%) | 1.43 | 1.21, 1.67 | <0.001 | ||
| Wasting status | 2,761 | 0.6 | ||||||||||
| No Wasting | 4.7 (4.3) | — | — | 2,384 (100%) | 1,682 (71%) | 702 (29%) | — | — | ||||
| Wasting | 5.1 (5.0) | 0.36 | -0.16, 0.89 | 0.2 | 305 (100%) | 209 (69%) | 96 (31%) | 1.07 | 0.89, 1.27 | 0.5 | ||
| Severe Wasting | 4.9 (4.0) | 0.23 | -0.80, 1.3 | 0.7 | 72 (100%) | 48 (67%) | 24 (33%) | 1.13 | 0.78, 1.53 | 0.5 | ||
| Underweight status | 2,807 | <0.001 | ||||||||||
| No Underweight | 4.5 (4.2) | — | — | 2,105 (100%) | 1,520 (72%) | 585 (28%) | — | — | ||||
| Underweight | 5.4 (5.0) | 0.90 | 0.47, 1.3 | <0.001 | 519 (100%) | 342 (66%) | 177 (34%) | 1.23 | 1.07, 1.40 | 0.004 | ||
| Severe Underweight | 5.6 (4.8) | 1.1 | 0.40, 1.7 | 0.002 | 183 (100%) | 111 (61%) | 72 (39%) | 1.42 | 1.15, 1.70 | <0.001 | ||
| MUAC Group | 2,815 | 0.064 | ||||||||||
| Normal | 4.7 (4.4) | — | — | 2,753 (100%) | 1,942 (71%) | 811 (29%) | — | — | ||||
| severewasting | 5.0 (3.6) | 0.27 | -0.84, 1.4 | 0.6 | 62 (100%) | 37 (60%) | 25 (40%) | 1.37 | 0.97, 1.80 | 0.046 | ||
| Unknown | 626 | 480 | 146 | |||||||||
| Unknown | 652 | 498 | 154 | |||||||||
| Unknown | 606 | 464 | 142 | |||||||||
| Unknown | 598 | 458 | 140 | |||||||||
| 1 Blood Pb Levels (μg/dL): Mean (SD) | ||||||||||||
| 2 n (%) | ||||||||||||
| 3 Pearson’s Chi-squared test | ||||||||||||
Findings: Stunting, and underweight which are chronic malnutrition indicators are associated with higher blood lead levels. This may be because malnutrition leads to increased absorption of lead in the gut. But it is also possible that lead exposure contributes to malnutrition. The direction of causality cannot be determined from this cross-sectional analysis.
| Correlation between Nutritional Variables and Blood Pb Levels | ||
| Nutritional Variable | Correlation Coefficient (r) | P-value |
|---|---|---|
| Energy Intake (kcal/day) | -0.058 | 0.0032 |
| Protein Intake (g/day) | -0.092 | 0.0000 |
| Carbohydrate Intake (g/day) | -0.039 | 0.0482 |
| Total Fat Intake (g/day) | -0.054 | 0.0055 |
| Iron Intake (mg/day) | -0.081 | 0.0000 |
| Zinc Intake (mg/day) | -0.091 | 0.0000 |
| Calcium Intake (mg/day) | -0.084 | 0.0000 |
| Thiamine (B1) Intake (mg/day) | -0.089 | 0.0000 |
| Riboflavin (B2) Intake (mg/day) | -0.086 | 0.0000 |
| Niacin (B3) Intake (mg/day) | -0.064 | 0.0011 |
| Total Folate (B9) Intake (ug/day) | -0.105 | 0.0000 |
| Vitamin C Intake (mg/day) | -0.051 | 0.0089 |
| Vitamin A Intake (ug/day) | -0.061 | 0.0018 |
| Vitamin B12 Intake (ug/day) | -0.010 | 0.5955 |
| Birth Weight (grams) | 0.041 | 0.4723 |
| Logistic Regression of Nutritional Variables on Blood Pb Level Categories | ||||
| Blood Parameter | Odds Ratio (per 10-unit increase) | 95% CI Lower | 95% CI Upper | P-value |
|---|---|---|---|---|
| Energy Intake (kcal/day) | 0.998 | 0.996 | 0.999 | 0.0110 |
| Protein Intake (g/day) | 0.889 | 0.841 | 0.939 | 0.0000 |
| Carbohydrate Intake (g/day) | 0.989 | 0.977 | 0.999 | 0.0467 |
| Total Fat Intake (g/day) | 0.959 | 0.915 | 1.003 | 0.0708 |
| Iron Intake (mg/day) | 0.612 | 0.491 | 0.758 | 0.0000 |
| Zinc Intake (mg/day) | 0.381 | 0.252 | 0.570 | 0.0000 |
| Calcium Intake (mg/day) | 0.993 | 0.989 | 0.997 | 0.0004 |
| Thiamine (B1) Intake (mg/day) | 0.002 | 0.000 | 0.027 | 0.0000 |
| Riboflavin (B2) Intake (mg/day) | 0.001 | 0.000 | 0.028 | 0.0000 |
| Niacin (B3) Intake (mg/day) | 0.698 | 0.531 | 0.905 | 0.0084 |
| Total Folate (B9) Intake (ug/day) | 0.963 | 0.948 | 0.976 | 0.0000 |
| Vitamin C Intake (mg/day) | 0.965 | 0.937 | 0.993 | 0.0163 |
| Vitamin A Intake (ug/day) | 0.992 | 0.986 | 0.998 | 0.0137 |
| Vitamin B12 Intake (ug/day) | 0.871 | 0.132 | 2.323 | 0.8087 |
| Birth Weight (grams) | 1.000 | 0.996 | 1.005 | 0.9624 |
Findings: There is a weak negative correlation between intake of macro and micro- nutrients in diet and blood lead levels. We find corroborative evidence in the fact that iron deficiency and anemia also had similar association as in Part 2D. However, these are measured at a single time point and may not be the best indicators of long term nutritional status which is more relevant for lead absorption. The direction of causality cannot be determined from this cross-sectional analysis.
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| Correlation between Blood Parameters and Blood Pb Levels | ||
| Nutritional Variable | Correlation Coefficient (r) | P-value |
|---|---|---|
| wbc | 0.094 | 0.0000 |
| lym_perc | 0.035 | 0.0414 |
| mono_perc | -0.033 | 0.0554 |
| gran_perc | -0.025 | 0.1434 |
| lym_mm | 0.111 | 0.0000 |
| mono_mm | 0.024 | 0.1636 |
| gra_mm | 0.037 | 0.0320 |
| rbc | 0.120 | 0.0000 |
| hgb | -0.090 | 0.0000 |
| hct | -0.056 | 0.0010 |
| mcv | -0.163 | 0.0000 |
| mch | -0.116 | 0.0000 |
| mchc | -0.044 | 0.0096 |
| rdwcv | 0.130 | 0.0000 |
| rdwsd | -0.044 | 0.0094 |
| plt | 0.143 | 0.0000 |
| tht | 0.130 | 0.0000 |
| mpv | -0.060 | 0.0005 |
| pdw | -0.112 | 0.0000 |
| eag | 0.074 | 0.5479 |
| Logistic Regression of Blood parameter on Blood Pb Level Categories | ||||
| Blood Parameter | Odds Ratio (per 10-unit increase) | 95% CI Lower | 95% CI Upper | P-value |
|---|---|---|---|---|
| wbc | 1.89 | 1.49 | 2.41 | 0.00 |
| lym_perc | 1.10 | 1.04 | 1.18 | 0.00 |
| mono_perc | 0.75 | 0.58 | 0.95 | 0.02 |
| gran_perc | 0.93 | 0.87 | 0.99 | 0.01 |
| lym_mm | 4.30 | 2.86 | 6.50 | 0.00 |
| mono_mm | 2.92 | 0.47 | 17.46 | 0.24 |
| gra_mm | 1.27 | 0.91 | 1.77 | 0.16 |
| rbc | 71.03 | 19.04 | 269.73 | 0.00 |
| hgb | 0.32 | 0.20 | 0.51 | 0.00 |
| hct | 0.79 | 0.67 | 0.93 | 0.01 |
| mcv | 0.72 | 0.66 | 0.78 | 0.00 |
| mch | 0.52 | 0.43 | 0.63 | 0.00 |
| mchc | 0.67 | 0.48 | 0.90 | 0.01 |
| rdwcv | 3.40 | 2.38 | 4.87 | 0.00 |
| rdwsd | 0.84 | 0.71 | 0.98 | 0.03 |
| plt | 1.03 | 1.02 | 1.04 | 0.00 |
| tht | 63492439165171.31 | 15558399243.04 | 289524393857163328.00 | 0.00 |
| mpv | 0.25 | 0.14 | 0.45 | 0.00 |
| pdw | 0.51 | 0.42 | 0.62 | 0.00 |
| eag | 22.28 | 0.00 | 8984057.05 | 0.63 |
Findings: Complete blood count parameters such as hemoglobin, hematocrit, red blood cell count, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, platelet count and white blood cell count are all negatively correlated with blood lead levels. These are possible consequences of high blood lead levels.
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High level causal diagram for high blood levels (Simplified)
We selected four groups of modifiable risk factors for adjusted analysis based on the literature and our unadjusted analysis: 1) drinking water source, 2) micronutrient deficiency, 3) malnutrition status, and 4) diet poor in micronutrients. We ran a separate regression model for each of the exposure variables in these groups adjusted for a set of variables based on the causal diagram shown above. The more complex diagram with all the variables is not shown here.
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Drinking Water | |||
| Bore well | — | — | |
| Open well | 0.67 | 0.46, 0.96 | 0.035 |
| Others | 1.32 | 0.91, 1.89 | 0.13 |
| RO/Bottled/Purchased water | 1.81 | 1.39, 2.37 | <0.001 |
| Surface/Spring water | 0.92 | 0.60, 1.39 | 0.7 |
| Tap water | 1.02 | 0.84, 1.24 | 0.8 |
| Water tanker | 1.67 | 1.04, 2.65 | 0.032 |
| 1 Adjusted for type of house, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
The underlying hypothesis is that lead present in drinking water is a cause of high blood lead levels and it needs to be adjusted for type of house, rural/urban, and wealth index. The adjusted analysis is in line with the bivariate analysis and supports the hypothesis that RO/bottled water and water tanker as a drinking water source could be a cause of high blood lead levels. This needs to be explored further in the case-control data where water has been tested for lead contamination.
The underlying hypothesis is that micronutrient deficiency (such as iron, Vitamin B12, folate, Vitamin D, Anemia) in the body cause compensatory increase in lead absorption in the gut even though lead present in water, diet and environment may be similar across the population. They need to be adjusted for caste, religion, gender, rural/urban, and wealth index. Here, we could only find that Iron deficiency and anemia were associated but not the categorised forms of vitamins D, B12 and folate.
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Stunting status | |||
| No Stunting | — | — | |
| Stunting | 1.42 | 0.86, 2.31 | 0.2 |
| Severe Stunting | 0.95 | 0.40, 2.08 | 0.9 |
| 1 Adjusted for anemia, iron deficiency, B12 status, serum folate status, Vit D status, age group, type of house, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Wasting status | |||
| No Wasting | — | — | |
| Wasting | 1.05 | 0.59, 1.83 | 0.9 |
| Severe Wasting | 1.42 | 0.36, 4.92 | 0.6 |
| 1 Adjusted for anemia, iron deficiency, B12 status, serum folate status, Vit D status, age group, type of house, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Underweight status | |||
| No Underweight | — | — | |
| Underweight | 1.03 | 0.59, 1.77 | >0.9 |
| Severe Underweight | 2.29 | 1.02, 5.09 | 0.040 |
| 1 Adjusted for anemia, iron deficiency, B12 status, serum folate status, Vit D status, age group, type of house, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| MUAC Group | |||
| Normal | — | — | |
| severewasting | 3.77 | 0.42, 34.6 | 0.2 |
| 1 Adjusted for anemia, iron deficiency, B12 status, serum folate status, Vit D status, age group, type of house, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
The underlying hypothesis is the same as above but the cause here is malnutrition status (stunting, wasting, underweight, MUAC) instead of micronutrient deficiency. Each malnutrition status variable is adjusted for anemia, iron deficiency, B12 status, serum folate status, Vit D status, age group, type of house, rural/urban, and wealth index. We find that only underweight status was associated with higher blood lead levels but wasting/stunting and MUAC (chronic malnutrition) were not.
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Iron Intake (mg/day) | 0.96 | 0.94, 0.98 | <0.001 |
| 1 Adjusted for gender, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Zinc Intake (mg/day) | 0.92 | 0.88, 0.96 | <0.001 |
| 1 Adjusted for gender, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Calcium Intake (mg/day) | 1.00 | 1.00, 1.00 | 0.012 |
| 1 Adjusted for gender, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
| Characteristic | OR1 | 95% CI | p-value |
|---|---|---|---|
| Vitamin C Intake (mg/day) | 1.00 | 0.99, 1.00 | 0.043 |
| 1 Adjusted for gender, rural/urban, and wealth index | |||
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
The underlying hypothesis is that a diet poor in micronutrients (iron, zinc, calcium, Vitamin C) causes micronutrient deficiency and malnutrition which in turn causes high blood lead levels due to compensatory absorption. Each diet micronutrient variable is adjusted for gender, rural/urban, and wealth index. We find that higher iron and zinc intake (not calcium or Vitamn C) associated with lower blood lead levels in the adjusted analysis.
Next, we will do analysis of the case-control data to see if environmental and food related lead content can explain the high blood lead levels in children.