| A | B | C | D | E | F | G | H | I | J | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
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1 | variablename | datatype | description | optionvalues | units | source | comments | ||||||||||||||||||
2 | Country | string | The country a pregnant woman was residing in. | The Gambia', 'Kenya', 'Mozambique' | |||||||||||||||||||||
3 | f2a_participant_id | string | Unique identifier of each participant prefixed by the country code. | 220-10060 | Patient record | 220 - Gambia, 254 - Kenya, 258 - Mozambique | |||||||||||||||||||
4 | exposure_day | string | Specific day on which environmental exposure variables were assigned to the participant. This variable is used to link daily exposure estimates to each participant during preconception and pregnancy windows | ||||||||||||||||||||||
5 | Village | string | The name of the village a pregnant woman reported to be coming from prior to the health facility visit. | Patient record | 721 had the village named other which could not be used and 228 their villages could not be mapped. | ||||||||||||||||||||
6 | Village code | string | Unique identifier/code assigned to each village. | ||||||||||||||||||||||
7 | cohort | string | It refers to the group a pregnant woman belongs to in the PRECISE study. | UNS', 'WRA', 'HTN/FGR', 'IUFD/SB' | Patient record | It was assigned at Visit 1. | |||||||||||||||||||
8 | precise_dyad | string | study cohort that a participant belongs to | Precise', 'Dyad' | Patient record | ||||||||||||||||||||
9 | participant_status | string | The recruitment status of the participant throughout the study. | Lost-to-followup at postpartum', 'Lost-to-followup at birth visit', 'Lost-to-followup at visit 1', 'Withdrew at visit2', 'Withdrew at visit1', 'Lost-to-followup at visit 2', 'No withdrewal', 'Withdrew at birth visit', 'Withdrew at Postpartum visit', 'Lost-to-followup after visit 1' | Patient record | ||||||||||||||||||||
10 | Visit 1 | datetime64[ns] | The date of a participant 1st PRECISE visit at a health facility for her Antenatal Care Visit. | Patient record | |||||||||||||||||||||
11 | Facility1 | string | The name of the first health facility in each country | Patient record | |||||||||||||||||||||
12 | Facility2 | string | The name of the second healthcare facility in each country. | Patient record | |||||||||||||||||||||
13 | health_facility | string | The name of the first health facility the participant visited | Patient record | Participants with missing facilities are from the DYAD cohort. | ||||||||||||||||||||
14 | Overall GHSL Mode | float64 | It refers to the Global Human Settlement Layer (GHSL) modal value. The "Mode" value refers to the most frequently occurring classification of human settlements in a given area based on the satellite imagery and other data sources used to generate the GHSL dataset. This value is then used to classify areas into Urban, Peri Urban and Rural. | Satellite Imagery | Where there was no value for the village where the participant came from the value from the facility they went to was used. | ||||||||||||||||||||
15 | Overall Village Class | string | It is a metric used to quantify and describe the degree of urbanization in a given area. It is calculated based on the GHSL_mode and urbanisation is grouped using the following criteria. | Satellite Imagery | Where there was no value for the village where the participant came from the value from the facility they went to was used. The classification is as follows : | ||||||||||||||||||||
16 | Facility GHSL_mode | float64 | It refers to the Global Human Settlement Layer (GHSL) modal value of the Facility. This value is then used to classify areas into Urban, Peri Urban and Rural. | Satellite Imagery | |||||||||||||||||||||
17 | Urban_Index__class_Fac | string | The degree of urbanization of a Facility a pregnant woman was attended. | Satellite Imagery | |||||||||||||||||||||
18 | Village GHSL_mode | float64 | It refers to the Global Human Settlement Layer (GHSL) modal value of the village. This value is then used to classify areas into Urban, Peri Urban and Rural. | Satellite Imagery | |||||||||||||||||||||
19 | Urban_Index__class_Vil | string | The degree of urbanization of a village. | Satellite Imagery | |||||||||||||||||||||
20 | Exposure_Period | string | It refers to the month when a woman encountered an environmental exposure during the 9 months of pregnancy. First month (01) being the month of conception. | Calculated variable | They had missing GA dates at visit 1 | ||||||||||||||||||||
21 | NDVI | float64 | This is a combination of village and facility, where there was no village NDVI value, health facility based NDVI values where used to fill the gaps. | Satellite Imagery | |||||||||||||||||||||
22 | facility_ndvi_modis | float64 | Modis satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Health Facility level. | Satellite Imagery | |||||||||||||||||||||
23 | ERA5_LST_facility | float64 | Land Surface Temperature (LST) derived from the era5 satellite, is a measure of how hot or cold the surface of the Earth is. It was derived at health facility level. | Degrees Celsius | Satellite Imagery | ||||||||||||||||||||
24 | Precip_facility | float64 | This is precipitation at a health facility from the PERSIANN dataset is derived using satellite observations and machine learning techniques. | Millimetres (mm) | Satellite Imagery | ||||||||||||||||||||
25 | conception_date | datetime64[ns] | Refers to the day when a sperm fertilizes an egg, resulting in pregnancy. This is the moment when the embryo starts forming, marking the beginning of pregnancy. The date the participants conceived calculated from the visit1 date and the Precise GA days on visit1. | 1/1/2018 | Calculated variable based on visit 1 dates and GA days on visit 1 date | 3109 had missing GA dates at visit 1 and 600 records with missing conception date where from the dyad group | |||||||||||||||||||
26 | Longitude | float64 | This is the longitude of the village centroid a pregnant woman reported to be coming from prior to the health facility visit. | Google maps, Open Street Map, Map Carta | 2034 village was not mapped and 721 had a village named "other" , 949 Had blank village names and could not be used. | ||||||||||||||||||||
27 | Latitude | float64 | This is the Latitude of the village centroid a pregnant woman reported to be coming from prior to the health facility visit. | Google maps, Open Street Map, Map Carta | 2034 village was not mapped and 721 had a village named "other" , 949 Had blank village names and could not be used. | ||||||||||||||||||||
28 | Overall Distance1 | float64 | This is a combination of population weighted distances and google based distances between the village and facility1 , where there was no population weighted distances, google based distances where used to fill the gaps. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
29 | Overall Distance2 | float64 | This is a combination of population weighted distances and google based distances between the village and facility2 , where there was no population weighted distances, google based distances where used to fill the gaps. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
30 | Dist_to_fac_visited | float64 | This variable combines population-weighted distances and Google-based distances to the actual facility visited, with Google distances filling gaps where population-weighted data is unavailable. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
31 | Birth visit - after birth | datetime64[ns] | The date of a participants Antenatal Care visit at a health facility after giving birth . | Patient record | Some of the women don’t have a date because the visit 1 date is the day they gave birth some didn’t go to the health facility after they gave birth | ||||||||||||||||||||
32 | Weighted_distance_to_facility1 | float64 | This is a population weighted distance matrix between the village and facility1 while taking into account the size of the population in each area. This metric gives more weight to areas with higher population densities, meaning that the distance for more densely populated areas will have a greater impact on the overall average distance. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
33 | Weighted_distance_to_facility2 | float64 | This is a population weighted distance matrix between the village and facility2 giving more weight to areas with higher population densities. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
34 | meanDEM | float64 | It refers to the average height of a village above sea level. | Meters | Satellite Imagery | ||||||||||||||||||||
35 | PM2_5 | float64 | It is village level fine particulate matter, or particles in the air with a diameter of 2.5 micrometers or less | Micrograms per cubic meter (µg/m³) | Calculated variable | ||||||||||||||||||||
36 | K_mean | float64 | It is the amount of Potasium nutrients in the soil of a village boundary. | Parts Per Million (ppm) | Satellite Imagery | ||||||||||||||||||||
37 | N_mean | float64 | It is the amount of soil nitrogen within a village boundary. | Parts Per Million (ppm) | Satellite Imagery | ||||||||||||||||||||
38 | P_mean | float64 | It is the amount of Phosphorus nutrients in the soil of a village boundary. | Parts Per Million (ppm) | Satellite Imagery | ||||||||||||||||||||
39 | T2MWET_max | float64 | Maximum Wet bulb temperature at 2 m | Kelvins | Satellite Imagery | ||||||||||||||||||||
40 | T2MWET_mean | float64 | Mean Wet bulb temperature at 2 m | Kelvins | Satellite Imagery | ||||||||||||||||||||
41 | T2MWET_min | float64 | Minimum Wet bulb temperature at 2 m | Kelvins | Satellite Imagery | ||||||||||||||||||||
42 | Google Distance to Facility 1 | float64 | This is a google based distance between a village where a woman is coming from and facility1. It doesn't take into account the population weights and was calculated using google distance matrix. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
43 | Google Travel Time to Facility 1 | float64 | It is the time it takes to travel from a village centroid to health facility1 based on Googles Distance Matrix | Calculated variable | |||||||||||||||||||||
44 | Google Distance to Facility 2 | float64 | This is a google based distance between a village where a woman is coming from and facility2. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
45 | Google Travel Time to Facility 2 | float64 | It is the time it takes to travel from a village centroid to health facility2 based on Googles Distance Matrix | Calculated variable | |||||||||||||||||||||
46 | Time_to_fac_visited | float64 | Time taken to travel from a village to the health facility they visited during the study based on Googles Distance. | Minutes | Calculated variable | ||||||||||||||||||||
47 | Visit 2 | datetime64[ns] | The date of a participant 2nd PRECISE visit at a health facility for her Antenatal Care Visit. | Patient record | 18583 don’t have a visit two date because the visit 1 date is the day they gave birth and 596 are from the dyad group. | ||||||||||||||||||||
48 | facility_ndvi_landsat8 | float64 | Landsat 8 satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Health Facility level. | Satellite Imagery | |||||||||||||||||||||
49 | ERA5_LST_village | float64 | Village Land Surface Temperature (LST) derived from the era5 satellite, is a measure of how hot or cold the village was. | Degrees Celsius | Satellite Imagery | ||||||||||||||||||||
50 | Precip_village | float64 | Village precipitation data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) dataset is derived using satellite observations and machine learning techniques. | Millimetres (mm) | Satellite Imagery | ||||||||||||||||||||
51 | vill_ndvi_modis | float64 | Modis satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Village level. | Satellite Imagery | |||||||||||||||||||||
52 | facility_ndvi_landsat7 | float64 | Landsat 7 satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Health Facility level. | Satellite Imagery | |||||||||||||||||||||
53 | BirthVisit_BeforeBirth_Date | datetime64[ns] | The date of a participants Antenatal Care visit at a health facility before giving birth . | Patient record | 22228 don’t have a birth visit date because the visit 1 date is the day they gave birth and 600 missing are from the dyad group. | ||||||||||||||||||||
54 | vill_ndvi_landsat7 | float64 | Landsat 7 satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Village level. | Satellite Imagery | |||||||||||||||||||||
55 | Postpartum Visit | datetime64[ns] | It is the date when a participant visited a health facility 6 weeks after giving birth. | Patient record | |||||||||||||||||||||
56 | vill_ndvi_landsat8 | float64 | Landsat 8 satellite derived Normalised Difference Vegetation Index (NDVI) is a vegetation index used as a polution proxy for PM2.5 and NO2. It was calculated at Village level. | Satellite Imagery | |||||||||||||||||||||
57 | Facility3 | string | The name of the third healthcare facility in each Gambia. | Patient record | Only Gambia had a Third healthcare facility. | ||||||||||||||||||||
58 | Overall Distance3 | float64 | This is a combination of population weighted distances and google based distances between the village and facility3 , where there was no population weighted distances, google based distances where used to fill the gaps. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
59 | Google Distance to Facility 3 | float64 | It is the time it takes to travel from a village centroid to health facility3 based on Google's Distance Matrix | Kilometers (km) | Calculated variable | ||||||||||||||||||||
60 | Google Travel Time to Facility 3 | float64 | It is the time it takes to travel from a village centroid to health facility3 based on Google's Distance Matrix | Minutes | Calculated variable | ||||||||||||||||||||
61 | Weighted_distance_to_facility3 | float64 | This is a population weighted distance matrix between the village and facility3 giving more weight to areas with higher population densities. | Kilometers (km) | Calculated variable | ||||||||||||||||||||
62 | f2a_precise_id | string | Unique identifier for each participant/dyad used for linkage across data sources. | ||||||||||||||||||||||
63 | Time of Disease visit | datetime64[ns] | Date and time when the disease visit was recorded. | Patient record | |||||||||||||||||||||
64 | WRND | float64 | Weighted Road Network Density.The density of roads within a specific area, used as a proxy for traffic-related pollution. | km/km² | |||||||||||||||||||||
65 | EuclidianM | float64 | The Euclidean distance from each Village to the nearest main road, used as a proxy for traffic-related pollution exposure. | ||||||||||||||||||||||
66 | EuclidianH | float64 | The Euclidean distance from each village to the nearest highway, used as a proxy for traffic-related pollution exposure | ||||||||||||||||||||||
67 | Isolation | float64 | Refers to the distance from a village to major roads that can be accessed using public Transport. | ||||||||||||||||||||||
68 | f3_year_of_birth | float64 | In what year were you born? | 1983 | |||||||||||||||||||||
69 | f3_month_of_birth | float64 | In what month were you born? (month) | 6 | |||||||||||||||||||||
70 | f3_live_with_alone | float64 | Do you currently live alone? (yes, no) | 1-Yes, 0- No | |||||||||||||||||||||
71 | f3_number_of_people_live_with | float64 | How many members does the household have? | 1, 2, 3, 4, 5, 6, 7, 8, 9 or more | |||||||||||||||||||||
72 | f3_marital_status | float64 | What is your current marital status? (Never married (or single), co-habiting, currently married (and only wife), currently married (one of two or more wives), separated, divorced, widowed) | 1-Never married (or single), 2- co-habiting, 3- currently married (and only wife), 4 - currently married (one of two or more wives), 5 - separated, 6- divorced, 7- widowed | |||||||||||||||||||||
73 | f3_fuel_source_for_lightning | float64 | What is the primary fuel source your household uses for lighting? | 1 - None/low-voltage electricity from grid (legal or illegal connection), | |||||||||||||||||||||
74 | f3_source_drinking_water | float64 | What is the main source of drinking water used by members of your household? | 1-Piped into dwelling/piped into yard or plot | |||||||||||||||||||||
75 | f3_source_water_cooking | float64 | What is the main source of water used by members of your household for other purposes such as cooking and handwashing? (If unclear, probe to identify the place from which members of this household most often collect water for other purposes) | 1-Piped into dwelling/piped into yard or plot | |||||||||||||||||||||
76 | f3_water_source_location | float64 | Where is that water source located? | 1- In own dwelling, in own yard or plot, 2- elsewhere | |||||||||||||||||||||
77 | f3_time_get_water | float64 | How long does it take for members of your household to go there, get water and come back? | 1 - Members do not collect, number of minutes, 2-don’t know | |||||||||||||||||||||
78 | f3_toilet_facility | float64 | What kind of toilet facility do members of your household usually use? | 1-Flush or pour flush toilet, 2-Ventilated improved pit latrine, 3-Pit latrine with slab,4- Pit latrine without slab, 5-open pit, 6-Composting toilet, 7-Bucket, 8-Hanging toilet or hanging latrine, 9-No facility or bush or field, 10-Other (specify) | |||||||||||||||||||||
79 | f3_toilet_facility_location | float64 | Location of the toilet facility (e.g., in own dwelling/yard, elsewhere). | ||||||||||||||||||||||
80 | f3_toilet_share_facility | float64 | Do you share this facility with others who are not members of your household? | 1-Yes, 0-No | |||||||||||||||||||||
81 | f3_neighbor_help_pregnancy_problem | float64 | Are there neighbours or other families in your community who would help your household if there were pregnancy related problems? | 1-Yes, 0-No,99-don't know | |||||||||||||||||||||
82 | f3_decision_maker_money | float64 | Who makes the decisions about money in your household? | 1-husband,2- male partner,3- father,4- father-in-law,5- mother, 6-mother-in-law,7- brother, 8-brother-in-law,9- sister, 10-sister-in-law,11- reproductive age woman,99- don’t know | |||||||||||||||||||||
83 | f3_woman_has_money_for_transport | float64 | Do you have access to money/sufficient funds to arrange transport to seek care at the nearest facility? | 1-Yes, 0-No,99-don't know | |||||||||||||||||||||
84 | age_enrolment | float64 | Participant age at enrolment | ||||||||||||||||||||||
85 | Ethnicity | string | What is your ethnicity? | Caucasian (Origins in Europe, Middle East,North Africa [Arabic origins],Western Russia [including Afghanistan and South Russia] and Hispanics of European origin), Black African (Origins in any of the original peoples of Africa), in The Gambia Mandinka, Wollof, Sarahule, Jola, Fula, Other – specify, in Kenya Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Meru, Mijikenda/Swahili, Somali, Taita/Taveta, Turkana, Samburu, Other – specify, and in Moz Makonde, Yao, Makhuwa, Shona, Tsonga (Ronga, Changane, Tswa), Chope, Bitonga, Chuwabo, Sena, Nyungwe, Other – specify), Asian (Origins in the Indian sub-continent [e.g., India, Pakistan, Bangladesh and Sri Lanka], or in the Far East and Southeast Asia [e.g., China, Japan, Korea, Philippines, Thailand, Eastern Russia]), Other (textbox, includes origins not represented above, such as Inuit, Maori, Australian Aborigine, North and South American Native Peoples, Hispanics of Caribbean, Central & South American origin, and Pacific Islanders)] | |||||||||||||||||||||
86 | religion | string | What is your religion? | ||||||||||||||||||||||
87 | occupation | string | What kind of work do you do? | housewife, student, professional, factory, large-scale agriculture, market trader, construction, other(specify) | |||||||||||||||||||||
88 | marital_status | string | What is your current marital status? | Never married (or single), co-habiting, currently married (and only wife), currently married (one of two or more wives), separated, divorced, widowed | |||||||||||||||||||||
89 | PPI_score | float64 | Household Poverty Probability Index Score | ||||||||||||||||||||||
90 | extreme_poverty_line | float64 | % likelihood below the USAID-extreme poverty line | ||||||||||||||||||||||
91 | poverty_line | float64 | % likelihood below the poverty line | ||||||||||||||||||||||
92 | PPI_scoreX | string | Household Poverty Probability Index Score | ||||||||||||||||||||||
93 | f3_decision_maker_pregnancy | float64 | Who makes decisions about money related to pregnancy and pregnancy care? | 1-husband, 2-male partner,3- father, 4-father-in-law,5- mother,6- mother-in-law,7- brother,8- brother-in-law,9- sister,10- sister-in-law,11- reproductive age woman,99- don’t know | |||||||||||||||||||||
94 | f3_community_help_pregnancy_problem | float64 | Is there a community group or organisation (formal or informal) that offers help to women who have problems during pregnancy? | 1-Yes, 0-No,99-don't know | |||||||||||||||||||||
95 | f3_live_with_partner | float64 | Do you currently live with a partner? (yes, no) | 1-Yes, 0-No | |||||||||||||||||||||
96 | f3_live_with_parents | float64 | Do you currently live with a partner? (yes, no) | 1-Yes, 0-No | |||||||||||||||||||||
97 | f3_live_with_in_law | float64 | Do you currently live with parents-in-law? (yes, no) | 1-Yes, 0-No | |||||||||||||||||||||
98 | f3_live_with_relatives | float64 | Do you currently live with other relatives? (yes, no) | 1-Yes, 0-No | |||||||||||||||||||||
99 | f3_live_with_friends | float64 | Do you currently live with friends? (yes, no) | 1-Yes, 0-No | |||||||||||||||||||||
100 | f3_live_with_children | float64 | Do you currently live with your own children? (yes, no) | 1-Yes, 0-No |