Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland

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Highest Birth Rate | Below Poverty
Population
Families and Households
Female Fertility
Female Fertility (Unmarried)
Race (Community Size)
Race (Percentage)
Ancestry (Community Size)
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian / Chaldean / SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianEuropeanFijianFilipinoFinnishFrenchFrench American IndianFrench CanadianGermanGerman RussianGhanaianGreekGuamanian / ChamorroGuatemalanGuyaneseHaitianHmongHonduranHopiHoumaHungarianIcelanderIndian (Asian)IndonesianInupiatIranianIraqiIrishIroquoisIsraeliItalianJamaicanJapaneseJordanianKenyanKiowaKoreanLaotianLatvianLebaneseLiberianLithuanianLumbeeLuxembourgerMacedonianMalaysianMalteseMarshalleseMenomineeMexicanMexican American IndianMongolianMoroccanNative HawaiianNavajoNepaleseNew ZealanderNicaraguanNigerianNorthern EuropeanNorwegianOkinawanOsageOttawaPaiutePakistaniPalestinianPanamanianParaguayanPennsylvania GermanPeruvianPimaPolishPortuguesePotawatomiPuebloPuerto RicanPuget Sound SalishRomanianRussianSalvadoranSamoanScandinavianScotch-IrishScottishSeminoleSenegaleseSerbianShoshoneSierra LeoneanSiouxSlavicSlovakSloveneSomaliSouth AfricanSouth AmericanSouth American IndianSoviet UnionSpaniardSpanishSpanish AmericanSpanish American IndianSri LankanSubsaharan AfricanSudaneseSwedishSwissSyrianTaiwaneseThaiTlingit-HaidaTohono O'OdhamTonganTrinidadian and TobagonianTsimshianTurkishU.S. Virgin IslanderUgandanUkrainianUruguayanUteVenezuelanVietnameseWelshWest IndianYakamaYaquiYugoslavianYumanYup'ikZimbabwean
Ancestry (Percentage)
AfghanAfricanAlaska NativeAlaskan AthabascanAlbanianAleutAlsatianAmericanApacheArabArapahoArgentineanArmenianAssyrian / Chaldean / SyriacAustralianAustrianBahamianBangladeshiBarbadianBasqueBelgianBelizeanBermudanBhutaneseBlackfeetBolivianBrazilianBritishBritish West IndianBulgarianBurmeseCajunCambodianCanadianCape VerdeanCarpatho RusynCelticCentral AmericanCentral American IndianCherokeeCheyenneChickasawChileanChineseChippewaChoctawColombianColvilleComancheCosta RicanCreeCreekCroatianCrowCubanCypriotCzechCzechoslovakianDanishDelawareDominicanDutchDutch West IndianEastern EuropeanEcuadorianEgyptianEnglishEstonianEthiopianEuropeanFijianFilipinoFinnishFrenchFrench American IndianFrench CanadianGermanGerman RussianGhanaianGreekGuamanian / ChamorroGuatemalanGuyaneseHaitianHmongHonduranHopiHoumaHungarianIcelanderIndian (Asian)IndonesianInupiatIranianIraqiIrishIroquoisIsraeliItalianJamaicanJapaneseJordanianKenyanKiowaKoreanLaotianLatvianLebaneseLiberianLithuanianLumbeeLuxembourgerMacedonianMalaysianMalteseMarshalleseMenomineeMexicanMexican American IndianMongolianMoroccanNative HawaiianNavajoNepaleseNew ZealanderNicaraguanNigerianNorthern EuropeanNorwegianOkinawanOsageOttawaPaiutePakistaniPalestinianPanamanianParaguayanPennsylvania GermanPeruvianPimaPolishPortuguesePotawatomiPuebloPuerto RicanPuget Sound SalishRomanianRussianSalvadoranSamoanScandinavianScotch-IrishScottishSeminoleSenegaleseSerbianShoshoneSierra LeoneanSiouxSlavicSlovakSloveneSomaliSouth AfricanSouth AmericanSouth American IndianSoviet UnionSpaniardSpanishSpanish AmericanSpanish American IndianSri LankanSubsaharan AfricanSudaneseSwedishSwissSyrianTaiwaneseThaiTlingit-HaidaTohono O'OdhamTonganTrinidadian and TobagonianTsimshianTurkishU.S. Virgin IslanderUgandanUkrainianUruguayanUteVenezuelanVietnameseWelshWest IndianYakamaYaquiYugoslavianYumanYup'ikZimbabwean
Immigrant Origin (Total)
AfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaAzoresBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma / MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabwe
Immigrant Origin (Percentage)
AfghanistanAfricaAlbaniaArgentinaArmeniaAsiaAustraliaAustriaAzoresBahamasBangladeshBarbadosBelarusBelgiumBelizeBoliviaBosnia and HerzegovinaBrazilBulgariaBurma / MyanmarCabo VerdeCambodiaCameroonCanadaCaribbeanCentral AmericaChileChinaColombiaCongoCosta RicaCroatiaCubaCzechoslovakiaDenmarkDominicaDominican RepublicEastern AfricaEastern AsiaEastern EuropeEcuadorEgyptEl SalvadorEnglandEritreaEthiopiaEuropeFijiFranceGermanyGhanaGreeceGrenadaGuatemalaGuyanaHaitiHondurasHong KongHungaryIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKoreaKuwaitLaosLatin AmericaLatviaLebanonLiberiaLithuaniaMalaysiaMexicoMicronesiaMiddle AfricaMoldovaMoroccoNepalNetherlandsNicaraguaNigeriaNorth MacedoniaNorthern AfricaNorthern EuropeNorwayOceaniaPakistanPanamaPeruPhilippinesPolandPortugalRomaniaRussiaSaudi ArabiaScotlandSenegalSerbiaSierra LeoneSingaporeSomaliaSouth AfricaSouth AmericaSouth Central AsiaSouth Eastern AsiaSouthern EuropeSpainSri LankaSt. Vincent and the GrenadinesSudanSwedenSwitzerlandSyriaTaiwanThailandTrinidad and TobagoTurkeyUgandaUkraineUruguayUzbekistanVenezuelaVietnamWest IndiesWestern AfricaWestern AsiaWestern EuropeYemenZaireZimbabwe
Income
Income (Families)
Income (Households)
Poverty
Poverty (Families)
Unemployment
Employment Occupations
Employment Industries
Employer Class
Commute Time
Commute Means
School Enrollment
Education by Degree Field
Occupancy
Finances
Physical Characteristics
Maryland
Compare Zip Codes
Comparison Subject

Map of Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland

0.0
1,000.0
Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland Map

Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland

Zip Code Births / 1,000 Women vs State vs National
1. 20667 1,000.0 52.0 (+948)#1 53.0 (+947)#28
2. 21634 630.0 52.0 (+578)#2 53.0 (+577)#117
3. 20758 414.0 52.0 (+362)#3 53.0 (+361)#244
4. 20687 413.0 52.0 (+361)#4 53.0 (+360)#246
5. 20608 405.0 52.0 (+353)#5 53.0 (+352)#257
6. 21524 351.0 52.0 (+299)#6 53.0 (+298)#340
7. 21156 333.0 52.0 (+281)#7 53.0 (+280)#368
8. 21714 255.0 52.0 (+203)#8 53.0 (+202)#636
9. 21733 234.0 52.0 (+182)#9 53.0 (+181)#747
10. 20611 219.0 52.0 (+167)#10 53.0 (+166)#843
11. 20670 214.0 52.0 (+162)#11 53.0 (+161)#864
12. 20674 204.0 52.0 (+152)#12 53.0 (+151)#935
13. 21071 197.0 52.0 (+145)#13 53.0 (+144)#1,021
14. 21869 190.0 52.0 (+138)#14 53.0 (+137)#1,096
15. 20711 186.0 52.0 (+134)#15 53.0 (+133)#1,142
16. 20645 183.0 52.0 (+131)#16 53.0 (+130)#1,185
17. 21622 176.0 52.0 (+124)#17 53.0 (+123)#1,272
18. 20762 171.0 52.0 (+119)#18 53.0 (+118)#1,347
19. 21542 170.0 52.0 (+118)#19 53.0 (+117)#1,376
20. 21798 141.0 52.0 (+89.0)#20 53.0 (+88.0)#1,968
21. 20650 140.0 52.0 (+88.0)#21 53.0 (+87.0)#1,982
22. 21636 140.0 52.0 (+88.0)#22 53.0 (+87.0)#1,998
23. 21155 134.0 52.0 (+82.0)#23 53.0 (+81.0)#2,196
24. 21851 132.0 52.0 (+80.0)#24 53.0 (+79.0)#2,253
25. 20860 132.0 52.0 (+80.0)#25 53.0 (+79.0)#2,262
26. 21647 132.0 52.0 (+80.0)#26 53.0 (+79.0)#2,276
27. 20781 130.0 52.0 (+78.0)#27 53.0 (+77.0)#2,305
28. 20710 128.0 52.0 (+76.0)#28 53.0 (+75.0)#2,391
29. 21034 127.0 52.0 (+75.0)#29 53.0 (+74.0)#2,440
30. 21082 127.0 52.0 (+75.0)#30 53.0 (+74.0)#2,460
31. 20637 123.0 52.0 (+71.0)#31 53.0 (+70.0)#2,606
32. 20602 118.0 52.0 (+66.0)#32 53.0 (+65.0)#2,803
33. 20779 117.0 52.0 (+65.0)#33 53.0 (+64.0)#2,900
34. 20896 116.0 52.0 (+64.0)#34 53.0 (+63.0)#2,944
35. 20759 115.0 52.0 (+63.0)#35 53.0 (+62.0)#2,972
36. 20751 115.0 52.0 (+63.0)#36 53.0 (+62.0)#2,988
37. 21853 113.0 52.0 (+61.0)#37 53.0 (+60.0)#3,076
38. 21402 110.0 52.0 (+58.0)#38 53.0 (+57.0)#3,280
39. 20754 107.0 52.0 (+55.0)#39 53.0 (+54.0)#3,437
40. 21227 106.0 52.0 (+54.0)#40 53.0 (+53.0)#3,504
41. 21520 106.0 52.0 (+54.0)#41 53.0 (+53.0)#3,540
42. 21601 105.0 52.0 (+53.0)#42 53.0 (+52.0)#3,568
43. 21850 104.0 52.0 (+52.0)#43 53.0 (+51.0)#3,665
44. 21650 103.0 52.0 (+51.0)#44 53.0 (+50.0)#3,784
45. 21562 100.0 52.0 (+48.0)#45 53.0 (+47.0)#3,956
46. 21837 99.0 52.0 (+47.0)#46 53.0 (+46.0)#4,056
47. 20664 98.0 52.0 (+46.0)#47 53.0 (+45.0)#4,139
48. 21561 97.0 52.0 (+45.0)#48 53.0 (+44.0)#4,239
49. 21713 95.0 52.0 (+43.0)#49 53.0 (+42.0)#4,394
50. 21084 95.0 52.0 (+43.0)#50 53.0 (+42.0)#4,404
51. 21162 95.0 52.0 (+43.0)#51 53.0 (+42.0)#4,416
52. 20877 93.0 52.0 (+41.0)#52 53.0 (+40.0)#4,565
53. 21009 92.0 52.0 (+40.0)#53 53.0 (+39.0)#4,654
54. 21758 92.0 52.0 (+40.0)#54 53.0 (+39.0)#4,689
55. 21625 92.0 52.0 (+40.0)#55 53.0 (+39.0)#4,707
56. 20706 90.0 52.0 (+38.0)#56 53.0 (+37.0)#4,853
57. 20912 90.0 52.0 (+38.0)#57 53.0 (+37.0)#4,864
58. 20755 90.0 52.0 (+38.0)#58 53.0 (+37.0)#4,891
59. 20623 90.0 52.0 (+38.0)#59 53.0 (+37.0)#4,924
60. 20880 90.0 52.0 (+38.0)#60 53.0 (+37.0)#4,948
61. 20636 89.0 52.0 (+37.0)#61 53.0 (+36.0)#4,978
62. 20677 87.0 52.0 (+35.0)#62 53.0 (+34.0)#5,267
63. 21639 85.0 52.0 (+33.0)#63 53.0 (+32.0)#5,487
64. 21530 83.0 52.0 (+31.0)#64 53.0 (+30.0)#5,779
65. 21074 82.0 52.0 (+30.0)#65 53.0 (+29.0)#5,861
66. 20695 82.0 52.0 (+30.0)#66 53.0 (+29.0)#5,873
67. 21220 80.0 52.0 (+28.0)#67 53.0 (+27.0)#6,144
68. 20785 80.0 52.0 (+28.0)#68 53.0 (+27.0)#6,146
69. 21032 80.0 52.0 (+28.0)#69 53.0 (+27.0)#6,194
70. 21144 79.0 52.0 (+27.0)#70 53.0 (+26.0)#6,295
71. 20871 79.0 52.0 (+27.0)#71 53.0 (+26.0)#6,308
72. 21001 79.0 52.0 (+27.0)#72 53.0 (+26.0)#6,311
73. 20794 79.0 52.0 (+27.0)#73 53.0 (+26.0)#6,341
74. 21750 79.0 52.0 (+27.0)#74 53.0 (+26.0)#6,392
75. 21742 78.0 52.0 (+26.0)#75 53.0 (+25.0)#6,462
76. 20879 78.0 52.0 (+26.0)#76 53.0 (+25.0)#6,473
77. 21826 78.0 52.0 (+26.0)#77 53.0 (+25.0)#6,528
78. 21874 77.0 52.0 (+25.0)#78 53.0 (+24.0)#6,721
79. 21754 76.0 52.0 (+24.0)#79 53.0 (+23.0)#6,871
80. 21757 76.0 52.0 (+24.0)#80 53.0 (+23.0)#6,906
81. 21774 75.0 52.0 (+23.0)#81 53.0 (+22.0)#7,005
82. 21849 75.0 52.0 (+23.0)#82 53.0 (+22.0)#7,066
83. 21631 75.0 52.0 (+23.0)#83 53.0 (+22.0)#7,075
84. 20904 74.0 52.0 (+22.0)#84 53.0 (+21.0)#7,135
85. 21842 74.0 52.0 (+22.0)#85 53.0 (+21.0)#7,212
86. 21610 74.0 52.0 (+22.0)#86 53.0 (+21.0)#7,320
87. 20707 73.0 52.0 (+21.0)#87 53.0 (+20.0)#7,351
88. 20866 73.0 52.0 (+21.0)#88 53.0 (+20.0)#7,400
89. 21237 72.0 52.0 (+20.0)#89 53.0 (+19.0)#7,589
90. 20837 72.0 52.0 (+20.0)#90 53.0 (+19.0)#7,667
91. 20722 72.0 52.0 (+20.0)#91 53.0 (+19.0)#7,672
92. 21791 72.0 52.0 (+20.0)#92 53.0 (+19.0)#7,682
93. 21205 71.0 52.0 (+19.0)#93 53.0 (+18.0)#7,843
94. 20906 70.0 52.0 (+18.0)#94 53.0 (+17.0)#7,979
95. 20886 70.0 52.0 (+18.0)#95 53.0 (+17.0)#7,999
96. 21704 70.0 52.0 (+18.0)#96 53.0 (+17.0)#8,032
97. 21054 70.0 52.0 (+18.0)#97 53.0 (+17.0)#8,041
98. 21532 70.0 52.0 (+18.0)#98 53.0 (+17.0)#8,049
99. 21632 70.0 52.0 (+18.0)#99 53.0 (+17.0)#8,072
100. 21521 70.0 52.0 (+18.0)#100 53.0 (+17.0)#8,131

Common Questions

What are the Top 10 Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland?
Top 10 Zip Codes with the Most Births per 1,000 Women Below Poverty Level in Maryland are:
#1
1,000.0
#2
630.0
#3
414.0
#4
413.0
#5
405.0
#6
351.0
#7
333.0
#8
255.0
#9
234.0
#10
219.0
What zip code has the Most Births per 1,000 Women Below Poverty Level in Maryland?
20667 has the Most Births per 1,000 Women Below Poverty Level in Maryland with 1,000.0.
What is the Number of Births per 1,000 Women Below Poverty Level in the State of Maryland?
Number of Births per 1,000 Women Below Poverty Level in Maryland is 52.0.
What is the Number of Births per 1,000 Women Below Poverty Level in the United States?
Number of Births per 1,000 Women Below Poverty Level in the United States is 53.0.