Introduction: The Global Burden of Disease

Let’s take a quick look at the diseases with the highest global DALY burden, based on IHME estimates, to help target our search.

The goal is to try to find relevant datasets for some of these diseases/causes, with a (partial) focus on LMICs.

Plot

Source data

1 Databases

If none of the datasets below suit you, then it’s time to get some new ones. If you decide to use a new dataset, remember to signal it so it can be added to this list.

2 Current Use of the Datasets

Before you choose a dataset, please verify who else is using it (HERE) and if it is already used 2 or 3 times, consider choosing a different one.

3 AFRICA

Febrile diseases || Burkina Faso

Differentiating causes of febrile illness in Burkina Faso: data from an accuracy study comparing gold standard culture techniques with a haemocytometry based algorithm (IMS), procalcitonin (PCT) and C-reactive protein (CRP)

Dimensions: 914 rows × 129 columns

Comments: Similar to the Diarrhea Mali study. Researchers collected clinical data from hospital patients with fever-causing illnesses, and tested the accuracy of an algorithm that uses blood metric measurements data to predict the infection cause.

Quite rich in variables. Lots of lab data though, which may not be intelligible to some audiences.

Rating: 4/5

3.1 Data

3.2 Data summary

3.3 Abstract

Different causes of acute febrile illness due to different infectious diseases (e.g. bacterial, viral malaria) may present with a similar clinical presentation. We performed a clinical diagnostic study to assess the diagnostic accuracy of a new tool - the Infection Manager System (IMS) - an algorithm which uses haemocytometric data to predict the cause of infection (e.g. bacterial, viral, malaria). The current dataset is a subset of data obtained during this study which was performed in a rural setting in Burkina Faso. The study was registered at ClinicalTrials.org under Identifier NCT02669823. All data used for the manuscript entitled “Infection Manager System (IMS) as a new hemocytometry-based bacteremia detection tool: a diagnostic accuracy study in a malaria-endemic area of Burkina Faso” are included in the current subset of data.

To test the IMS we collected clinical and demographic data from approximately 900 patients aged between 3 months and 100 years presenting with an acute febrile illness. Upon inclusion, 2-5 ml EDTA anticoagulated blood was sampled for haemocytometry, malaria diagnostics (thick- and thin blood films and RDTs) and blood culture. A nasopharyngeal swab and aliquots of residual blood and plasma were stored at -80° for retrospective analyses. 1. A viral panel on nasopharyngeal swabs 2. PCR’s for malaria, Salmonella, S. aureus, H. influenzae, S. pneumoniae on whole blood or plasma samples and 3. C-reactive protein (CRP) and procalcitonin (PCT) levels on plasma samples. Additional diagnostics such as chest X-ray, echography, urinalysis, and culture of urine, stool, pus, or cerebrospinal fluid were performed on clinical indication.

In this cohort we attempted to provide a microbiologically proven diagnosis for all patients admitted with febrile illness using gold standard methods (e.g. blood culture, malaria microscopy and PCR). We then assessed the accuracy of the novel IMS to differentiate causes of infection against these conventional diagnostic methods. We furthermore assessed the accuracy of both CRP and PCT in differentiating causes of infection and compared them to the performance of the IMS.

We found that the IMS had a higher diagnostic accuracy to detect bacteremia than PCT at a cut of value of 0.5 µg/L, and was comparable in sensitivity, but superior in specificity to CRP at a cut of value of 20 mg/L. Subanalysis among patients below the age of five showed that they had a slightly lower accuracy of IMS, PCT and CRP. Combining the IMS and CRP did not significantly improve accuracy due to the high level of overlap between CRP and the IMS. The high negative predictive value of IMS –also in non-bacteremic bacterial infections – suggests that the IMS holds promise to rationalize antimicrobial prescription in healthcare facilities where hematology analyzers are available. The relatively low specificity and PPV demonstrate that it is not (yet) suitable as a diagnostic for bacteremia.

Ebola || Sierra Leone

Deaths in Sierra Leone from ebola with metadata and geographical district information

Dimensions: 11903 rows × 8 columns

Comments: Data is from the RECON outbreak datasets.

Rating: 4.5/5

3.4 Data

Measles || Niger

Weekly reported measles cases in Niger from 1995 to 2004 at the district level, collected by the Ministry of Health of Niger

Dimensions: 418 rows × 4 columns

Comments: The full article can be found [here]{https://doi.org/10.1098/rsif.2020.0480}

Rating: 3/5

3.5 Data

Measles Data

Population Data

3.6 Abstract

Measles is a major cause of child mortality in sub-Saharan Africa. Current immunization strategies achieve low coverage in areas where transmission drivers differ substantially from those in high-income countries. A better understanding of measles transmission in areas with measles persistence will increase vaccination coverage and reduce ongoing transmission. We analysed weekly reported measles cases at the district level in Niger from 1995 to 2004 to identify underlying transmission mechanisms. We identified dominant periodicities and the associated spatial clustering patterns. We also investigated associations between reported measles cases and environmental drivers associated with human activities, particularly rainfall. The annual and 2–3-year periodicities dominated the reporting data spectrum. The annual periodicity was strong with contiguous spatial clustering, consistent with the latitudinal gradient of population density, and stable over time. The 2–3-year periodicities were weaker, unstable over time and had spatially fragmented clustering. The rainy season was associated with a lower risk of measles case reporting. The annual periodicity likely reflects seasonal agricultural labour migration, whereas the 2–3-year periodicity potentially results from multiple mechanisms such as reintroductions and vaccine coverage heterogeneity. Our findings suggest that improving vaccine coverage in seasonally mobile populations could reduce strong measles seasonality in Niger and across similar settings.

Cholera || Nigeria

Data from: Descriptive epidemiology of the 2018 cholera outbreak in Nigeria: implications for the global roadmap strategies

Dimensions: 43,996 rows × 23 columns

Comments: Outbreak linelist data. Perfect for the epidemic reporting course.

The data dictionary is not available, but we can use the data from Table 2 and 3 in their accompanying paper to figure out the code-value mapping.

Rating: 5/5

3.7 Data

3.8 Data summary

3.9 Abstract

Background

The cholera outbreak in 2018 in Nigeria reaffirms its public health threat to the country. Evidence on the current epidemiology of cholera required for the design and implementation of appropriate interventions towards attaining the global roadmap strategic goals for cholera elimination however seems lacking. Thus, this study aimed at addressing this gap by describing the epidemiology of the 2018 cholera outbreak in Nigeria.

Methods

This was a retrospective analysis of surveillance data collected between January 1st and November 19th, 2018. A cholera case was defined as an individual aged 2 years or older presenting with acute watery diarrhoea and severe dehydration or dying from acute watery diarrhoea. Descriptive analyses were performed and presented with respect to person, time and place using appropriate statistics.

Results

There were 43,996 cholera cases and 836 cholera deaths across 20 states in Nigeria during the outbreak period, with an attack rate (AR) of 127.43/100,000 population and a case fatality rate (CFR) of 1.90%. Individuals aged 15 years or older (47.76%) were the most affected age group, but the proportion of affected males and females was about the same (49.00 and 51.00% respectively). The outbreak was characterised by four distinct epidemic waves, with higher number of deaths recorded in the third and fourth waves. States from the north-west and north-east regions of the country recorded the highest ARs while those from the north-central recorded the highest CFRs.

Conclusion

The severity and wide-geographical distribution of cholera cases and deaths during the 2018 outbreak are indicative of an elevated burden, which was more notable in the northern region of the country. Overall, the findings reaffirm the strategic role of a multi-sectoral approach in the design and implementation of public health interventions aimed at preventing and controlling cholera in Nigeria.

Malaria || Nigeria

Data from: Long-lasting insecticidal net use and asymptomatic malaria parasitaemia among household members of laboratory-confirmed malaria patients attending selected health facilities in Abuja, Nigeria, 2016: a cross-sectional survey

Dimensions: 602 rows × 40 columns

Comments: Nice, straightforward survey data. Researchers tried to use survey respondent’s housing conditions to predict whether they would be positive for malaria.

Rating: 3.5/5

3.10 Data

3.11 Data summary

3.12 Abstract

Introduction

In Nigeria, malaria remains a major burden. There is the presupposition that household members could have common exposure to malaria parasite and use of long-lasting insecticidal net (LLIN) could reduce transmission. This study was conducted to identify factors associated with asymptomatic malaria parasitaemia and LLIN use among households of confirmed malaria patients in Abuja, Nigeria.

Methods

A cross-sectional survey was conducted from March to August 2016 in twelve health facilities selected from three area councils in Abuja, Nigeria. Participants were selected using multi-stage sampling technique. Overall, we recruited 602 participants from 107 households linked to 107 malaria patients attending the health facilities. Data on LLIN ownership, utilization, and house characteristics were collected using a semi-structured questionnaire. Blood samples of household members were examined for malaria parasitaemia using microscopy. Data were analyzed using descriptive statistics, Chi-square, and logistic regression (α = 0.05).

Results

Median age of respondents was 16.5 years (Interquartile range: 23 years); 55.0% were females. Proportions of households that owned and used at least one LLIN were 44.8% and 33.6%, respectively. Parasitaemia was detected in at least one family member of 102 (95.3%) index malaria patients. Prevalence of asymptomatic malaria parasitaemia among study participants was 421/602 (69.9%). No association was found between individual LLIN use and malaria parasitaemia (odds ratio: 0.9, 95% confidence interval (95%CI): 0.6–1.3) among study participants. Having bushes around the homes was associated with having malaria parasitaemia (adjusted OR (aOR): 2.7, 95%CI: 1.7–4.2) and less use of LLIN (aOR: 0.4, 95%CI: 0.2–0.9). Living in Kwali (aOR: 0.1, 95% CI: 0.0–0.2) was associated with less use of LLIN.

Conclusion

High prevalence of asymptomatic malaria and low use of LLIN among household members of malaria patients portend the risk of intra-household common source of malaria transmission. We recommend household health education on LLIN use and environmental management. Study to explore the role of preventive treatment of household members of confirmed malaria patient in curbing transmission is suggested. Strategies promoting LLIN use need to be intensified in Kwali.

Typhoid || Uganda

Temporal, spatial and household dynamics of typhoid fever in Kasese district, Uganda

Dimensions: 201 rows × 209 columns

Comments: Case-control study. Researchers interviewed typhoid cases and matched controls to understand predictors of illness.

The main dataset is a household survey and this is the only dataset shown here.

The Zenodo repo includes some supplementary datasets. Do take a look.

Rating: 4/5

3.13 Data

3.14 Data summary

3.15 Abstract

Typhoid fever affects 21 million people globally, 1% of whom succumb to the disease. The social, economic and public health consequences of this disease disproportionately affect people in Africa and Asia. In order to design context specific prevention strategies, we need to holistically characterise outbreaks in these settings. Here we used retrospective data (2013-2016) at national and district level to characterize temporal and spatial dynamics of typhoid fever outbreaks using time series and spatial analysis. We then selected cases matched with controls to investigate household socio-economic drivers using a conditional logistic regression model, in addition to develop a typhoid outbreak-forecasting framework. The incidence rate of typhoid fever at national and district level was ~ 160 and 60 cases per 100,000 persons per year, respectively, predominantly in urban areas. Bwera sub-county registered the highest incidence rate, followed by Kisinga, Kitholhu and Nyakiyumbu sub-counties. The male-female case ratio at district level was at 1.68 and outbreaks occurred between the 20th and 40th week (May and October) each year preceded by seven weeks of precipitation. Our forecasting framework predicts outbreaks better at the district rather than at the national level. We have identified a temporal window associated with typhoid fever outbreaks in Kasese district, which is preceded by precipitation, flooding and displacement of people. We also observed that high typhoid incidence areas also had high environmental contamination with limited water treatment. Taken together with the forecasting framework, this knowledge can inform the development of specific control and preparedness strategies at district and national levels.

Enteropathogens || Zambia

Adaptation of the small intestine to microbial enteropathogens in Zambian children with stunting

Dimensions: 463 rows × 11 columns for biomarkers dataset 282 rows × 13 columns for pathogens dataset

Comments: Perhaps a bit too technical (in a biomedical sense). For 297 children with stunting, the researchers collected biomarker data and tested them for gut pathogens, then applied a nutritional intervention, and tested the children months later. So each dataset has two rows per child, before and after intervention.

Will likely be good demonstration of the need for pivoting, joining.

Rating: 3.5/5

3.16 Data

Biomarkers dataset:

Pathogens dataset:

3.17 Data summary

3.18 Data dictionary

3.19 Abstract

Environmental enteropathy is a major contributor to growth faltering in millions of children in Africa and South Asia. We carried out a longitudinal, observational and interventional study in Lusaka, Zambia, of 297 children with stunting (aged 2–17 months at recruitment) and 46 control children who had good growth (aged 1–5 months at recruitment). Control children contributed data only at baseline. Children were provided with nutritional supplementation of daily cornmeal-soy blend, an egg and a micronutrient sprinkle, and were followed up to 24 months of age. Children whose growth did not improve over 4–6 months of nutritional supplementation were classified as having non-responsive stunting. We monitored microbial translocation from the gut lumen to the bloodstream in the cohort with non-responsive stunting (n = 108) by measuring circulating lipopolysaccharide (LPS), LPS-binding protein and soluble CD14 at baseline and when non-response was declared. We found that microbial translocation decreased with increasing age, such that LPS declined in 81 (75%) of 108 children with non-responsive stunting, despite sustained pathogen pressure and ongoing intestinal epithelial damage. We used confocal laser endomicroscopy and found that mucosal leakiness also declined with age. However, expression of brush border enzyme, nutrient transporter and mucosal barrier genes in intestinal biopsies did not change with age or correlate with biomarkers of microbial translocation. We propose that environmental enteropathy arises through adaptation to pathogen-mediated epithelial damage. Although environmental enteropathy reduces microbial translocation, it does so at the cost of impaired growth. The reduced epithelial surface area imposed by villus blunting may explain these findings.

4 ASIA

Diabetes || China

Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study

Dimensions: 211833 rows × 25 columns

Comments: Clinical information about adults at baseline was used to predict whether they developed diabetes after some years of followup.

Lots of records! Would be great for linear regression, perhaps survival analysis.

Rating: 3.5/5

4.1 Data

4.2 Data summary

4.3 Abstract

Objective Type 2 diabetes mellitus is increasing in young adults, and greater adiposity is considered a major risk factor. However, whether there is an association between obesity and diabetes and how this might be impacted by age is not clear. Therefore, we investigated the association between body mass index (BMI) and diabetes across a wide range of age groups (20–30, 30–40, 40–50, 50–60, 60–70 and ≥70 years old).

Design We performed a retrospective cohort study using healthy screening programme data.

Setting A total of 211 833 adult Chinese persons >20 years old across 32 sites and 11 cities in China (Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, Nantong) were selected for the study; these persons were free of diabetes at baseline.

Primary and secondary outcome measures Fasting plasma glucose levels were measured and information regarding the history of diabetes was collected at each visit. Diabetes was diagnosed as fasting plasma glucose ≥7.00 mmol/L and/or self-reported diabetes. Patients were censored at the date of diagnosis or the final visit, whichever came first.

Results With a median follow-up of 3.1 years, 4174 of the 211 833 participants developed diabetes, with an age-adjusted incidence rate of 7.35 per 1000 persons. The risk of incident diabetes increased proportionally with increasing baseline BMI values, with a 23% increased risk of incident diabetes with each kg/m2 increase in BMI (95% CI 1.22 to 1.24). Across all age groups, there was a linear association between BMI and the risk of incident diabetes, although there was a stronger association between BMI and incident diabetes in the younger age groups (age×BMI interaction, p<0.0001).

Conclusions An increased BMI is also independently associated with a higher risk of developing diabetes in young adults and the effects of BMI on incident diabetes were accentuated in younger adults.

HIV || India

Depressive symptoms and their sociodemographic determinants among people living with HIV/AIDS in Bangladesh: a cross-sectional study

Dimensions: 338 rows × 37 columns

Comments: Interesting survey data. HIV-positive people were surveyed about socio-demographics and their feelings of depression, with the eye to understanding the causes of depression.

Could be particularly interesting for students who want to know about the psychosocial aspects of disease.

The accompanying paper still in preprint stage. Would be a fun exercise trying to wrangle the DOCX variable dictionary into a proper machine-readable variable dictionary!

Rating: 4/5

4.4 Data

4.5 Data summary

4.6 Data dictionary

4.7 Abstract

Background: This study aimed to determine the prevalence of depression and its associated factors among people living with HIV/AIDS in Bangladesh.

Methods: This cross-sectional study, which took place in Dhaka, Bangladesh, from July to December 2020, included 338 HIV-positive people. The method used was a simple random sampling technique. The Beck Depression Inventory assessed depression in HIV-positive people (BDI).

Results: More than 62 percent of the 338 people surveyed had severe depression, 30.5 percent had moderate depression, 5.6 percent had mild depression, and 1.8 percent had no depression at all. Age, being a man, being married, and having a low monthly income were all significant predictors of depression.

Conclusions: This study found that depressive symptoms are highly prevalent among HIV-positive patients in Bangladesh. The authors recommend that health care providers address depressive disorders for people with HIV/ AIDS comprehensively.

Diet diversity || Vietnam

Retail Diversity for Diet Diversity - Dietary Intake Data

Dimensions: 408 rows × 353 columns

Comments:

Interesting survey data. Women in Hanoi were interviewed about their food shopping, and this was used to create nutrition profiles for these women.

Should be good practice for wrangling.

Could be used in parallel with the Retail Outlet Census data and the Shopping Practices Household Survey data from the same organization.

Rating: 4/5

4.8 Data

4.9 Data summary

4.10 Data dictionary

Variable code Variable name Label description
I. GENERAL SURVEY INFORMATION NA NA
Cluster ID of villages 101: Doi Can - Ba Dinh 102: Giang Vo - Ba Dinh 103: Kim Ma - Ba Dinh 104:Lieu Giai - Ba Dinh 105: Ngoc Khanh - Ba Dinh 106: Ngoc Ha - Ba Dinh 107: Thanh Cong - Ba Dinh 108: Vinh Phuc - Ba Dinh 109: Lang Ha - Ba Dinh
NA NA 201: Cat Linh - Dong Da 202: Kim Lien - Dong Da 203: Phuong Mai - Dong Da 204: Thinh Quang - Dong Da 205: Tho Quan - Dong Da 206: Van Mieu - Dong Da 207: Quoc Tu Giam - Dong Da 208: Lang Thuong - Dong Da 209: Hang Bot - Dong Da 210: Nam Dong - Dong Da
hhid House Hold ID ###
idno Individual Number Mothers/women =2
Address Detail address of respondents NA
Location Name of urban district Dong Da Ba Dinh
Respondentname Women name Full name of women of reproductive age
Sex Sex of women Female = 2
DOB Date of birth of children or mothers/women dd/mm/yyyy
DOV Date of visit/survey dd/mm/yyyy
visitid ID of visit ####
visitno Number of visit times for each household -24 hour recall survey in Hanoi is coded for 1.
AgeY Age women are calculated by year(s) Women: 15-49 years old
NormalFood Normal food day 1. Yes 2. No 6. Not Applicable 7. Unknown/ Unanswered
FeastDay Feast Day 1. Yes 2. No 6. Not Applicable 7. Unknown/ Unanswered
MarketDay Market Day 1. Yes 2. No 6. Not Applicable 7. Unknown/ Unanswered
Sickday Was mother/child sick 1. Yes 2. No 6. Not Applicable 7. Unknown/ Unanswered
AppetiteDay Did sickness affect appetite 1. Yes 2. No 6. Not Applicable 7. Unknown/ Unanswered
II. PHYSICAL ACTIVITY, ANTHROPOMETRIC INFORMATION NA NA
physio Physical status 0. Normal 1. Pregnant 2. Lactating
phyactivity Physical activity level 1. Normal 2. Light physical activity 3. Hard physical activity
memo Memo status for women 0.Pre-menarche/premenopausal 1.Menstrual
pregage Pregnancy months/age for breastfed child 0. NA 1. First 3 months 2. From 3-6 months 3. Above 6 months
wt weigh of children or mothers/women (kg) ##.#
rnicode RNI code #######
V. CONSUMED FOODS FORMATION NA NA
ConsumVolumn_w Consummed amount from ingredient/food ####
DRY_w Consummed dry from ingredient/food (g) ###.##
WATER_w Consummed water from ingredient/food (g) ###.##
ENERC_KCAL_w Consummed enery from ingredient/food (Kcal) ###.#############
PROCNT_w Consummed Protein from ingredient/food (g) ###.###
FAT_w Consummed Lipid from ingredient/food (g) ###.#############
CHOCDF_w Consummed Carbohydrate from ingredient/food (g) ###.##
VA RAE_w Consummed Vitamin A Retinol Activity Equivalents from ingredient/food (µg) #####.#############
RETINOL_w Consummed Retinol from ingredient/food (µg) #####.#############
ALCAR_w Consummed α-caroten from ingredient/food (µg) ####.#
BECAR_w Consummed β-caroten from ingredient/food (µg) #####.#############
BECRY_w Consummed β-cryptoxanthin from ingredient/food (µg) ####.##
VITC_w Consummed Vitamin C from ingredient/food (mg) ###.#############
THIA_w Consummed Vitamin B1 from ingredient/food (mg) ##.#############
RIBF_w Consummed Vitamin B2 from ingredient/food (mg) #.####
VIT_B3_w Consummed Vitamin B3 from ingredient/food (mg) ##.####
VIT_B6_w Consummed Vitamin B6 from ingredient/food (mg) #.#####
VIT_B12_w Consummed Vitamin B12 from ingredient/food (µg) ##.#############
FOL_w Consummed Folate from ingredient/food (µg) ####.#############
CA_w Consummed Calcium from ingredient/food (mg) ####.#############
bio_CA_w Consummed Calcium from ingredient/food (mg) after adjustment ####.#############
FE_w Consummed Iron from ingredient/food (mg) ###.#############
bio_FE_w Consummed Iron from ingredient/food (mg) after adjustment ###.#############
FE_Heme_w Consummed Iron_Heme from ingredient/food (mg) ###.##
FE_Non-heme_w Consummed Iron_non Heme from ingredient/food (mg) ##.#############
Iron_animal_w Consummed Iron_animal from ingredient/food (mg) ###.##
Iodine_w Consummed Vitamin B2 from ingredient/food (µg) ##.#############
MG_w Consummed Magnesium from ingredient/food (mg) ###.##
NA_w Consummed Sodium from ingredient/food (mg) #####
ZN_w Consummed Zinic from ingredient/food (mg) ##.#############
enerc_PRO_w Consummed enery from ingredient/food from Protein NA
enerc_FAT_w Consummed enery from ingredient/food from Fat NA
enerc_CHOCDF_w Consummed enery from ingredient/food from Carbonhydrate NA
VIII. MINIMUM DIETARY DIVERSITY FOOD GROUP OF WOMEN (MDD_fgw) NA NA
MDD_fgw1 Consummed amount from Starchy staples ####.###
MDD_fgw2 Consummed amount from Pulses ####.###
MDD_fgw3 Consummed amount from Nuts and seeds ####.###
MDD_fgw4 Consummed amount from Dairy ####.###
MDD_fgw5 Consummed amount from Meat, poultry and fish ####.###
MDD_fgw6 Consummed amount from Eggs ####.###
MDD_fgw7 Consummed amount from Vitamin A-rich dark green leafy vegetables ####.###
MDD_fgw8 Consummed amount from Vitamin A rich fruits and vegetables ####.###
MDD_fgw9 Consummed amount from Other vegetables ####.###
MDD_fgw10 Consummed amount from Other Fruits ####.###
MDD_fgw11 Consummed amount from Insects and small protein foods ####.###
MDD_fgw12 Consummed amount from Other oils and fats ####.###
MDD_fgw13 Consummed amount from Savoury and fried snacks ####.###
MDD_fgw14 Consummed amount from Sweets ####.###
MDD_fgw15 Consummed amount from Sugar sweetened beverages ####.###
MDD_fgw16 Consummed amount from Condiments and seasonings ####.###
MDD_fgw17 Consummed amount from Other beverages and foods ####.###
WDDS Women Dietary Diversity Scores (Min=0; Max=10) #
WDDS_re - Status of women reaching WDDS (5 out of 10 food groups) - Generate from WDDS. If WDDS <5 -> WDDS_re=0; WDDS ≥5 -> WDDS_re=1 0. No 1. Yes
IX. FAO FOOD GROUP OF WOMEN/(fao_fgw) (with ending words:_w for women) NA NA
fao_fg1 Consummed amount from Foods made from grains ####.###
fao_fg2 Consummed amount from White roots and tubers and plantain ####.###
fao_fg3 Consummed amount from Pulses ####.###
fao_fg4 Consummed amount from Nuts and seeds ####.###
fao_fg5 Consummed amount from Milk and milk products ####.###
fao_fg6 Consummed amount from Organ meat ####.###
fao_fg7 Consummed amount from Meat and poultry ####.###
fao_fg8 Consummed amount from Fish and seafood ####.###
fao_fg9 Consummed amount from Eggs ####.###
fao_fg10 Consummed amount from Dark green leafy vegetables ####.###
fao_fg11 Consummed amount from Vitamin A-rich vegetables, roots and tubers ####.###
fao_fg12 Consummed amount from Vitamin A-rich fruits ####.###
fao_fg13 Consummed amount from Other vegetables ####.###
fao_fg14 Consummed amount from Other fruits ####.###
fao_fg15 Consummed amount from Insects and other small protein foods ####.###
fao_fg16 Consummed amount from Other oils and fats ####.###
fao_fg17 Consummed amount from Savoury and fried snacks ####.###
fao_fg18 Consummed amount from Sweets ####.###
fao_fg19 Consummed amount from Sugar sweetened beverages ####.###
fao_fg20 Consummed amount from Condiments and seasonings ####.###
fao_fg21 Consummed amount from Other beverages and foods ####.###
fg_flm Consummed amount from Flesh meat (Meats, Poutry, Fish) ####.###
VitA_FVW Consummed amount from Vitamin A rich Fruit and Vegetables (includes: dark green leafy) of women ####.###
X. FREQUENCY OF MEALS NA NA
NMM1 How many Main meals you ate within last 24 hour? #
NMM2 How many Snack meals you ate within last 24 hour? #
NMC1 How many Main meals your child [child name] ate within last 24 hour? #
NMC2 How many Snack meals your child [child name] ate within last 24 hour? #
XI. VARIABLES RELATED FOOD SOURCE AND PROCESSED FOOD NA NA
source1_w Number of ingredients from own production #
source2_w Number of ingredients from Supermarket ##
source3_w Number of ingredients from Convenience store ##
source4_w Number of ingredients from Grocery ##
source5_w Number of ingredients from Specialty store ##
source6_w Number of ingredients from Formal wet market ##
source7_w Number of ingredients from Street stall/vendor ##
source8_w Number of ingredients from home town/ rural area #
source9_w Number of ingredients from Gift/Present #
source_total Number of ingredients from all food sources ##
source1_vita_w Amount of vitamin A from Own production (µg) ###.##########
source2_vita_w Amount of vitamin A from Supermarket (µg) ###.##########
source3_vita_w Amount of vitamin A from Convenience store (µg) ###.##########
source4_vita_w Amount of vitamin A from Grocery (µg) ###.##########
source5_vita_w Amount of vitamin A from Specialty store (µg) ###.##########
source6_vita_w Amount of vitamin A from Formal wet market (µg) ###.##########
source7_vita_w Amount of vitamin A from Street stall/vendor (µg) ###.##########
source8_vita_w Amount of vitamin A from Home town/ rural area (µg) ###.##########
source9_vita_w Amount of vitamin A from Gift/Present (µg) ###.##########
source1_bioca_w Amount of Bio Calcium from Own production (mg) ###.##########
source2_bioca_w Amount of Bio Calcium from Supermarket (mg) ###.##########
source3_bioca_w Amount of Bio Calcium from Convenience store (mg) ###.##########
source4_bioca_w Amount of Bio Calcium from Grocery (mg) ###.##########
source5_bioca_w Amount of Bio Calcium from Specialty store (mg) ###.##########
source6_bioca_w Amount of Bio Calcium from Formal wet market (mg) ###.##########
source7_bioca_w Amount of Bio Calcium from Street stall/vendor (mg) ###.##########
source8_bioca_w Amount of Bio Calcium from Home town/ rural area (mg) ###.##########
source9_bioca_w Amount of Bio Calcium from Gift/Present (mg) ###.##########
source1_fe_w Amount of Iron from Own production (mg) ###.##########
source2_fe_w Amount of Iron from Supermarket (mg) ###.##########
source3_fe_w Amount of Iron from Convenience store (mg) ###.##########
source4_fe_w Amount of Iron from Grocery (mg) ###.##########
source5_fe_w Amount of Iron from Specialty store (mg) ###.##########
source6_fe_w Amount of Iron from Formal wet market (mg) ###.##########
source7_fe_w Amount of Iron from Street stall/vendor (mg) ###.##########
source8_fe_w Amount of Iron from Home town/ rural area (mg) ###.##########
source9_fe_w Amount of Iron from Gift/Present (mg) ###.##########
source1_biofe_w Amount of Bio iron from Own production (mg) ###.##########
source2_biofe_w Amount of Bio iron from Supermarket (mg) ###.##########
source3_biofe_w Amount of Bio iron from Convenience store (mg) ###.##########
source4_biofe_w Amount of Bio iron from Grocery (mg) ###.##########
source5_biofe_w Amount of Bio iron from Specialty store (mg) ###.##########
source6_biofe_w Amount of Bio iron from Formal wet market (mg) ###.##########
source7_biofe_w Amount of Bio iron from Street stall/vendor (mg) ###.##########
source8_biofe_w Amount of Bio iron from Home town/ rural area (mg) ###.##########
source9_biofe_w Amount of Bio iron from Gift/Present (mg) ###.##########
source1_zn_w Amount of Zinc from Own production (mg) ###.##########
source2_zn_w Amount of Zinc from Supermarket (mg) ###.##########
source3_zn_w Amount of Zinc from Convenience store (mg) ###.##########
source4_zn_w Amount of Zinc from Grocery (mg) ###.##########
source5_zn_w Amount of Zinc from Specialty store (mg) ###.##########
source6_zn_w Amount of Zinc from Formal wet market (mg) ###.##########
source7_zn_w Amount of Zinc from Street stall/vendor (mg) ###.##########
source8_zn_w Amount of Zinc from Home town/ rural area (mg) ###.##########
source9_zn_w Amount of Zinc from Gift/Present (mg) ###.##########
source1_biozn_w Amount of Bio zinc from Own production (mg) ###.##########
source2_biozn_w Amount of Bio zinc from Supermarket (mg) ###.##########
source3_biozn_w Amount of Bio zinc from Convenience store (mg) ###.##########
source4_biozn_w Amount of Bio zinc from Grocery (mg) ###.##########
source5_biozn_w Amount of Bio zinc from Specialty store (mg) ###.##########
source6_biozn_w Amount of Bio zinc from Formal wet market (mg) ###.##########
source7_biozn_w Amount of Bio zinc from Street stall/vendor (mg) ###.##########
source8_biozn_w Amount of Bio zinc from Home town/ rural area (mg) ###.##########
source9_biozn_w Amount of Bio zinc from Gift/Present (mg) ###.##########
source1_ener_w Amount of Energy from Own production (kcal) ###.##########
source2_ener_w Amount of Energy from Supermarket (kcal) ###.##########
source3_ener_w Amount of Energy from Convenience store (kcal) ###.##########
source4_ener_w Amount of Energy from Grocery (kcal) ###.##########
source5_ener_w Amount of Energy from Specialty store (kcal) ###.##########
source6_ener_w Amount of Energy from Formal wet market (kcal) ###.##########
source7_ener_w Amount of Energy from Street stall/vendor (kcal) ###.##########
source8_ener_w Amount of Energy from Home town/ rural area (kcal) ###.##########
source9_ener_w Amount of Energy from Gift/Present (kcal) ###.##########
source1_unpro_w Amount of Unprocessed food from Own production (g) ###.##########
source2_unpro_w Amount of Unprocessed food from Supermarket (g) ###.##########
source3_unpro_w Amount of Unprocessed food from Convenience store (g) ###.##########
source4_unpro_w Amount of Unprocessed food from Grocery (g) ###.##########
source5_unpro_w Amount of Unprocessed food from Specialty store (g) ###.##########
source6_unpro_w Amount of Unprocessed food from Formal wet market (g) ###.##########
source7_unpro_w Amount of Unprocessed food from Street stall/vendor (g) ###.##########
source8_unpro_w Amount of Unprocessed food from Home town/ rural area (g) ###.##########
source9_unpro_w Amount of Unprocessed food from Gift/Present (g) ###.##########
source1_cul_w Amount of Culinary processed food from Own production (g) ###.##########
source2_cul_w Amount of Culinary processed food from Supermarket (g) ###.##########
source3_cul_w Amount of Culinary processed food from Convenience store (g) ###.##########
source4_cul_w Amount of Culinary processed food from Grocery (g) ###.##########
source5_cul_w Amount of Culinary processed food from Specialty store (g) ###.##########
source6_cul_w Amount of Culinary processed food from Formal wet market (g) ###.##########
source7_cul_w Amount of Culinary processed food from Street stall/vendor (g) ###.##########
source8_cul_w Amount of Culinary processed food from Home town/ rural area (g) ###.##########
source9_cul_w Amount of Culinary processed food from Gift/Present (g) ###.##########
source1_processed_w Amount of Processed food from Own production (g) ###.##########
source2_processed_w Amount of Processed food from Supermarket (g) ###.##########
source3_processed_w Amount of Processed food from Convenience store (g) ###.##########
source4_processed_w Amount of Processed food from Grocery (g) ###.##########
source5_processed_w Amount of Processed food from Specialty store (g) ###.##########
source6_processed_w Amount of Processed food from Formal wet market (g) ###.##########
source7_processed_w Amount of Processed food from Street stall/vendor (g) ###.##########
source8_processed_w Amount of Processed food from Home town/ rural area (g) ###.##########
source9_processed_w Amount of Processed food from Gift/Present (g) ###.##########
source1_ultra_w Amount of Ultra-processed food from Own production (g) ###.##########
source2_ultra_w Amount of Ultra-processed food from Supermarket (g) ###.##########
source3_ultra_w Amount of Ultra-processed food from Convenience store (g) ###.##########
source4_ultra_w Amount of Ultra-processed food from Grocery (g) ###.##########
source5_ultra_w Amount of Ultra-processed food from Specialty store (g) ###.##########
source6_ultra_w Amount of Ultra-processed food from Formal wet market (g) ###.##########
source7_ultra_w Amount of Ultra-processed food from Street stall/vendor (g) ###.##########
source8_ultra_w Amount of Ultra-processed food from Home town/ rural area (g) ###.##########
source9_ultra_w Amount of Ultra-processed food from Gift/Present (g) ###.##########
source1_chocdf_w Amount of Carbonhydrate from Own production (g) ###.##########
source2_chocdf_w Amount of Carbonhydrate from Supermarket (g) ###.##########
source3_chocdf_w Amount of Carbonhydrate from Convenience store (g) ###.##########
source4_chocdf_w Amount of Carbonhydrate from Grocery (g) ###.##########
source5_chocdf_w Amount of Carbonhydrate from Specialty store (g) ###.##########
source6_chocdf_w Amount of Carbonhydrate from Formal wet market (g) ###.##########
source7_chocdf_w Amount of Carbonhydrate from Street stall/vendor (g) ###.##########
source8_chocdf_w Amount of Carbonhydrate from Home town/ rural area (g) ###.##########
source9_chocdf_w Amount of Carbonhydrate from Gift/Present (g) ###.##########
source1_procnt_w Amount of Protein from Own production (g) ###.##########
source2_procnt_w Amount of Protein from Supermarket (g) ###.##########
source3_procnt_w Amount of Protein from Convenience store (g) ###.##########
source4_procnt_w Amount of Protein from Grocery (g) ###.##########
source5_procnt_w Amount of Protein from Specialty store (g) ###.##########
source6_procnt_w Amount of Protein from Formal wet market (g) ###.##########
source7_procnt_w Amount of Protein from Street stall/vendor (g) ###.##########
source8_procnt_w Amount of Protein from Home town/ rural area (g) ###.##########
source9_procnt_w Amount of Protein from Gift/Present (g) ###.##########
source1_fat_w Amount of Fat from Own production (g) ###.##########
source2_fat_w Amount of Fat from Supermarket (g) ###.##########
source3_fat_w Amount of Fat from Convenience store (g) ###.##########
source4_fat_w Amount of Fat from Grocery (g) ###.##########
source5_fat_w Amount of Fat from Specialty store (g) ###.##########
source6_fat_w Amount of Fat from Formal wet market (g) ###.##########
source7_fat_w Amount of Fat from Street stall/vendor (g) ###.##########
source8_fat_w Amount of Fat from Home town/ rural area (g) ###.##########
source9_fat_w Amount of Fat from Gift/Present (g) ###.##########
source1_vitc_w Amount of Vitamin C from Own production (µg) ###.##########
source2_vitc_w Amount of Vitamin C from Supermarket (µg) ###.##########
source3_vitc_w Amount of Vitamin C from Convenience store (µg) ###.##########
source4_vitc_w Amount of Vitamin C from Grocery (µg) ###.##########
source5_vitc_w Amount of Vitamin C from Specialty store (µg) ###.##########
source6_vitc_w Amount of Vitamin C from Formal wet market (µg) ###.##########
source7_vitc_w Amount of Vitamin C from Street stall/vendor (µg) ###.##########
source8_vitc_w Amount of Vitamin C from Home town/ rural area (µg) ###.##########
source9_vitc_w Amount of Vitamin C from Gift/Present (µg) ###.##########
source1_thia_w Amount of Vitamin B1 from Own production (µg) ###.##########
source2_thia_w Amount of Vitamin B1 from Supermarket (µg) ###.##########
source3_thia_w Amount of Vitamin B1 from Convenience store (µg) ###.##########
source4_thia_w Amount of Vitamin B1 from Grocery (µg) ###.##########
source5_thia_w Amount of Vitamin B1 from Specialty store (µg) ###.##########
source6_thia_w Amount of Vitamin B1 from Formal wet market (µg) ###.##########
source7_thia_w Amount of Vitamin B1 from Street stall/vendor (µg) ###.##########
source8_thia_w Amount of Vitamin B1 from Home town/ rural area (µg) ###.##########
source9_thia_w Amount of Vitamin B1 from Gift/Present (µg) ###.##########
source1_ribf_w Amount of Vitamin B2 from Own production (µg) ###.##########
source2_ribf_w Amount of Vitamin B2 from Supermarket (µg) ###.##########
source3_ribf_w Amount of Vitamin B2 from Convenience store (µg) ###.##########
source4_ribf_w Amount of Vitamin B2 from Grocery (µg) ###.##########
source5_ribf_w Amount of Vitamin B2 from Specialty store (µg) ###.##########
source6_ribf_w Amount of Vitamin B2 from Formal wet market (µg) ###.##########
source7_ribf_w Amount of Vitamin B2 from Street stall/vendor (µg) ###.##########
source8_ribf_w Amount of Vitamin B2 from Home town/ rural area (µg) ###.##########
source9_ribf_w Amount of Vitamin B2 from Gift/Present (µg) ###.##########
source1_vitb3_w Amount of Vitamin B3 from Own production (µg) ###.##########
source2_vitb3_w Amount of Vitamin B3 from Supermarket (µg) ###.##########
source3_vitb3_w Amount of Vitamin B3 from Convenience store (µg) ###.##########
source4_vitb3_w Amount of Vitamin B3 from Grocery (µg) ###.##########
source5_vitb3_w Amount of Vitamin B3 from Specialty store (µg) ###.##########
source6_vitb3_w Amount of Vitamin B3 from Formal wet market (µg) ###.##########
source7_vitb3_w Amount of Vitamin B3 from Street stall/vendor (µg) ###.##########
source8_vitb3_w Amount of Vitamin B3 from Home town/ rural area (µg) ###.##########
source9_vitb3_w Amount of Vitamin B3 from Gift/Present (µg) ###.##########
source1_vitb6_w Amount of Vitamin B6 from Own production (µg) ###.##########
source2_vitb6_w Amount of Vitamin B6 from Supermarket (µg) ###.##########
source3_vitb6_w Amount of Vitamin B6 from Convenience store (µg) ###.##########
source4_vitb6_w Amount of Vitamin B6 from Grocery (µg) ###.##########
source5_vitb6_w Amount of Vitamin B6 from Specialty store (µg) ###.##########
source6_vitb6_w Amount of Vitamin B6 from Formal wet market (µg) ###.##########
source7_vitb6_w Amount of Vitamin B6 from Street stall/vendor (µg) ###.##########
source8_vitb6_w Amount of Vitamin B6 from Home town/ rural area (µg) ###.##########
source9_vitb6_w Amount of Vitamin B6 from Gift/Present (µg) ###.##########
source1_vitb12_w Amount of Vitamin B12 from Own production (µg) ###.##########
source2_vitb12_w Amount of Vitamin B12 from Supermarket (µg) ###.##########
source3_vitb12_w Amount of Vitamin B12 from Convenience store (µg) ###.##########
source4_vitb12_w Amount of Vitamin B12 from Grocery (µg) ###.##########
source5_vitb12_w Amount of Vitamin B12 from Specialty store (µg) ###.##########
source6_vitb12_w Amount of Vitamin B12 from Formal wet market (µg) ###.##########
source7_vitb12_w Amount of Vitamin B12 from Street stall/vendor (µg) ###.##########
source8_vitb12_w Amount of Vitamin B12 from Home town/ rural area (µg) ###.##########
source9_vitb12_w Amount of Vitamin B12 from Gift/Present (µg) ###.##########
source1_fol_w Amount of Folic from Own production (µg) ###.##########
source2_fol_w Amount of Folic from Supermarket (µg) ###.##########
source3_fol_w Amount of Folic from Convenience store (µg) ###.##########
source4_fol_w Amount of Folic from Grocery (µg) ###.##########
source5_fol_w Amount of Folic from Specialty store (µg) ###.##########
source6_fol_w Amount of Folic from Formal wet market (µg) ###.##########
source7_fol_w Amount of Folic from Street stall/vendor (µg) ###.##########
source8_fol_w Amount of Folic from Home town/ rural area (µg) ###.##########
source9_fol_w Amount of Folic from Gift/Present (µg) ###.##########
source1_mg_w Amount of Magie from Own production (mg) ###.##########
source2_mg_w Amount of Magie from Supermarket (mg) ###.##########
source3_mg_w Amount of Magie from Convenience store (mg) ###.##########
source4_mg_w Amount of Magie from Grocery (mg) ###.##########
source5_mg_w Amount of Magie from Specialty store (mg) ###.##########
source6_mg_w Amount of Magie from Formal wet market (mg) ###.##########
source7_mg_w Amount of Magie from Street stall/vendor (mg) ###.##########
source8_mg_w Amount of Magie from Home town/ rural area (mg) ###.##########
source9_mg_w Amount of Magie from Gift/Present (mg) ###.##########
source1_na_w Amount of Sodium from Own production (mg) ###.##########
source2_na_w Amount of Sodium from Supermarket (mg) ###.##########
source3_na_w Amount of Sodium from Convenience store (mg) ###.##########
source4_na_w Amount of Sodium from Grocery (mg) ###.##########
source5_na_w Amount of Sodium from Specialty store (mg) ###.##########
source6_na_w Amount of Sodium from Formal wet market (mg) ###.##########
source7_na_w Amount of Sodium from Street stall/vendor (mg) ###.##########
source8_na_w Amount of Sodium from Home town/ rural area (mg) ###.##########
source9_na_w Amount of Sodium from Gift/Present (mg) ###.##########
source1_iodine_w Amount of Iodine from Own production (µg) ###.##########
source2_iodine_w Amount of Iodine from Supermarket (µg) ###.##########
source3_iodine_w Amount of Iodine from Convenience store (µg) ###.##########
source4_iodine_w Amount of Iodine from Grocery (µg) ###.##########
source5_iodine_w Amount of Iodine from Specialty store (µg) ###.##########
source6_iodine_w Amount of Iodine from Formal wet market (µg) ###.##########
source7_iodine_w Amount of Iodine from Street stall/vendor (µg) ###.##########
source8_iodine_w Amount of Iodine from Home town/ rural area (µg) ###.##########
source9_iodine_w Amount of Iodine from Gift/Present (µg) ###.##########
source1_ca_w Amount of Calcium from Own production (mg) ###.##########
source2_ca_w Amount of Calcium from Supermarket (mg) ###.##########
source3_ca_w Amount of Calcium from Convenience store (mg) ###.##########
source4_ca_w Amount of Calcium from Grocery (mg) ###.##########
source5_ca_w Amount of Calcium from Specialty store (mg) ###.##########
source6_ca_w Amount of Calcium from Formal wet market (mg) ###.##########
source7_ca_w Amount of Calcium from Street stall/vendor (mg) ###.##########
source8_ca_w Amount of Calcium from Home town/ rural area (mg) ###.##########
source9_ca_w Amount of Calcium from Gift/Present (mg) ###.##########
source1_ener_procnt_w Amount of Energy from Protein of Own production food (kcal) ###.##########
source2_ener_procnt_w Amount of Energy from Protein of Convenience store food (kcal) ###.##########
source3_ener_procnt_w Amount of Energy from Protein of Grocery food (kcal) ###.##########
source4_ener_procnt_w Amount of Energy from Protein of Grocery food (kcal) ###.##########
source5_ener_procnt_w Amount of Energy from Protein of Specialty store food (kcal) ###.##########
source6_ener_procnt_w Amount of Energy from Protein of Formal wet market food (kcal) ###.##########
source7_ener_procnt_w Amount of Energy from Protein of Street stall/vendor food (kcal) ###.##########
source8_ener_procnt_w Amount of Energy from Protein of Home town/ rural food (kcal) ###.##########
source9_ener_procnt_w Amount of Energy from Protein of Gift/Present food (kcal) ###.##########
source1_ener_fat_w Amount of Energy from Fat of Own production food (kcal) ###.##########
source2_ener_fat_w Amount of Energy from Fat of Convenience store food (kcal) ###.##########
source3_ener_fat_w Amount of Energy from Fat of Grocery food (kcal) ###.##########
source4_ener_fat_w Amount of Energy from Fat of Grocery food (kcal) ###.##########
source5_ener_fat_w Amount of Energy from Fat of Specialty store food (kcal) ###.##########
source6_ener_fat_w Amount of Energy from Fat of Formal wet market food (kcal) ###.##########
source7_ener_fat_w Amount of Energy from Fat of Street stall/vendor food (kcal) ###.##########
source8_ener_fat_w Amount of Energy from Fat of Home town/ rural food (kcal) ###.##########
source9_ener_fat_w Amount of Energy from Fat of Gift/Present food (kcal) ###.##########
source1_ener_chocdf_w Amount of Energy from Carbonhydrate of Own production food (kcal) ###.##########
source2_ener_chocdf_w Amount of Energy from Carbonhydrate of Convenience store food (kcal) ###.##########
source3_ener_chocdf_w Amount of Energy from Carbonhydrate of Grocery food (kcal) ###.##########
source4_ener_chocdf_w Amount of Energy from Carbonhydrate of Grocery food (kcal) ###.##########
source5_ener_chocdf_w Amount of Energy from Carbonhydrate of Specialty store food (kcal) ###.##########
source6_ener_chocdf_w Amount of Energy from Carbonhydrate of Formal wet market food (kcal) ###.##########
source7_ener_chocdf_w Amount of Energy from Carbonhydrate of Street stall/vendor food (kcal) ###.##########
source8_ener_chocdf_w Amount of Energy from Carbonhydrate of Home town/ rural food (kcal) ###.##########
source9_ener_chocdf_w Amount of Energy from Carbonhydrate of Gift/Present food (kcal) ###.##########
NOTE: - fsource1 (traditional formal and informal retail outlets) includes source5, source6 and source7 - fsource2 (Modern retail outlets) includes source source2, source3 and source4 - fsource3 (Own Production – not dependant on local retail outlets, policy intervention) includes source source1, source8 and source9 NA NA
fsource1_procnt_w Amount of Protein from Traditional formal and informal retail outlets (g) ###.##########
fsource2_procnt_w Amount of Protein from Modern retail outlets (g) ###.##########
fsource3_procnt_w Amount of Protein from Own Production – not dependant on local retail outlets or policy intervention (g) ###.##########
fsource_procnt_w Amount of Protein from all food sources (g) ###.##########
fsource1_chocdf_w Amount of Carbonhydrate from Traditional formal and informal retail outlets (g) ###.##########
fsource2_chocdf_w Amount of Carbonhydrate from Modern retail outlets (g) ###.##########
fsource3_chocdf_w Amount of Carbonhydrate from Own Production – not dependant on local retail outlets or policy intervention (g) ###.##########
fsource_chocdf_w Amount of Carbonhydrate from all food sources (g) ###.##########
fsource1_fat_w Amount of Fat from Traditional formal and informal retail outlets (g) ###.##########
fsource2_fat_w Amount of Fat from Modern retail outlets (g) ###.##########
fsource3_fat_w Amount of Fat from Own Production – not dependant on local retail outlets or policy intervention (g) ###.##########
fsource_fat_w Amount of Fat from all food sources (g) ###.##########
fsource1_vitc_w Amount of Vitamin C from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_vitc_w Amount of Vitamin C from Modern retail outlets (µg) ###.##########
fsource3_vitc_w Amount of Vitamin C from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_vitc_w Amount of Vitamin C from all food sources (µg) ###.##########
fsource1_vitb3_w Amount of Vitamin B3 from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_vitb3_w Amount of Vitamin B3 from Modern retail outlets (µg) ###.##########
fsource3_vitb3_w Amount of Vitamin B3 from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_vitb3_w Amount of Vitamin B3 from all food sources (µg) ###.##########
fsource1_vitb6_w Amount of Vitamin B6 from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_vitb6_w Amount of Vitamin B6 from Modern retail outlets (µg) ###.##########
fsource3_vitb6_w Amount of Vitamin B6 from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_vitb6_w Amount of Vitamin B6 from all food sources (µg) ###.##########
fsource1_fol_w Amount of Folic from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_fol_w Amount of Folic from Modern retail outlets (µg) ###.##########
fsource3_fol_w Amount of Folic from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_fol_w Amount of Folic from all food sources (µg) ###.##########
fsource1_mg_w Amount of Magie from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_mg_w Amount of Magie from Modern retail outlets (mg) ###.##########
fsource3_mg_w Amount of Magie from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_mg_w Amount of Magie from all food sources (mg) ###.##########
fsource1_na_w Amount of Sodium from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_na_w Amount of Sodium from Modern retail outlets (mg) ###.##########
fsource3_na_w Amount of Sodium from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_na_w Amount of Sodium from all food sources (mg) ###.##########
fsource1_iodine_w Amount of Iodine from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_iodine_w Amount of Iodine from Modern retail outlets (mg) ###.##########
fsource3_iodine_w Amount of Iodine from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_iodine_w Amount of Iodine from all food sources (mg) ###.##########
fsource1_thia_w Amount of Vitamin B1 from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_thia_w Amount of Vitamin B1 from Modern retail outlets (µg) ###.##########
fsource3_thia_w Amount of Vitamin B1 from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_thia_w Amount of Vitamin B1 from all food sources intervention (µg) ###.##########
fsource1_ribf_w Amount of Vitamin B2 from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_ribf_w Amount of Vitamin B2 from Modern retail outlets (µg) ###.##########
fsource3_ribf_w Amount of Vitamin B2 from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_ribf_w Amount of Vitamin B2 from all food sources (µg) ###.##########
fsource1_vitb12_w Amount of Vitamin B12 from Traditional formal and informal retail outlets (µg) ###.##########
fsource2_vitb12_w Amount of Vitamin B12 from Modern retail outlets (µg) ###.##########
fsource3_vitb12_w Amount of Vitamin B12 from Own Production – not dependant on local retail outlets or policy intervention (µg) ###.##########
fsource_vitb12_w Amount of Vitamin B12 from all food sources (µg) ###.##########
fsource1_fe_w Amount of Iron from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_fe_w Amount of Iron from Modern retail outlets (mg) ###.##########
fsource3_fe_w Amount of Iron from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_fe_w Amount of Iron from all food sources (mg) ###.##########
fsource1_zn_w Amount of Zinc from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_zn_w Amount of Zinc from Modern retail outlets (mg) ###.##########
fsource3_zn_w Amount of Zinc from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_zn_w Amount of Zinc from all food sources (mg) ###.##########
fsource1_ca_w Amount of Calcium from Traditional formal and informal retail outlets (mg) ###.##########
fsource2_ca_w Amount of Calcium from Modern retail outlets (mg) ###.##########
fsource3_ca_w Amount of Calcium from Own Production – not dependant on local retail outlets or policy intervention (mg) ###.##########
fsource_ca_w Amount of Calcium from all food sources (mg) ###.##########
fsource1_ener_chocdf_w Amount of Energy from Carbonhydrate of food at Traditional formal and informal retail outlets (kcal) ###.##########
fsource2_ener_chocdf_w Amount of Energy from Carbonhydrate of food at Modern retail outlets (kcal) ###.##########
fsource3_ener_chocdf_w Amount of Energy from Carbonhydrate of food at Own Production – not dependant on local retail outlets or policy intervention (kcal) ###.##########
fsource_ener__chocdf_w Amount of Energy from Carbonhydrate of food at all food sources (kcal) ###.##########
fsource1_ener_procnt_w Amount of Energy from Protein of food at Traditional formal and informal retail outlets (kcal) ###.##########
fsource2_ener_procnt_w Amount of Energy from Protein of food at Modern retail outlets (kcal) ###.##########
fsource3_ener_procnt_w Amount of Energy from Protein of food at Own Production – not dependant on local retail outlets or policy intervention (kcal) ###.##########
fsource_ener_procnt_w Amount of Energy from Protein of food at all food sources (kcal) ###.##########
fsource1_ener_fat_w Amount of Energy from Fat of food at Traditional formal and informal retail outlets (kcal) ###.##########
fsource2_ener_fat_w Amount of Energy from Fat of food at Modern retail outlets (kcal) ###.##########
fsource3_ener_fat_w Amount of Energy from Fat of food at Own Production – not dependant on local retail outlets or policy intervention (kcal) ###.##########
fsource_ener_fat_w Amount of Energy from Fat of food at all food sources (kcal) ###.##########
Unpro_ener_w Amount of Energy from Unprocessed food (kcal) ###.##########
Cul_ener_w Amount of Energy from Culinary processed food (kcal) ###.##########
Process_ener_w Amount of Energy from Processed food (kcal) ###.##########
Ultra_ener_w Amount of Energy from Ultra processed food (kcal) ###.##########
Unpro_fat_w Amount of Fat from Unprocessed food (g) ###.##########
Cul_fat_w Amount of Fat from Culinary processed food (g) ###.##########
Process_fat_w Amount of Fat from Processed food (g) ###.##########
Ultra_fat_w Amount of Fat from Ultra processed food (g) ###.##########
Unpro_cons_w Amount of total Food Consumed from Unprocessed food (g) ###.##########
Cul_cons_w Amount of total Food Consumed from Culinary processed food (g) ###.##########
Process_cons_w Amount of total Food Consumed from Processed food (g) ###.##########
Ultra_cons_w Amount of total Food Consumed from Ultra processed food (g) ###.##########

4.11 Abstract

(This is not an abstract. Found it on their page here: https://www.wur.nl/en/project/Retail-Diversity-for-Dietary-Diversity-RD4DD.htm)

Nutrition insecurity among a growing number of urban poor in modernizing Southeast Asian metropolises is a critical issue. Serving to enlarge the capacity of local authorities in planning and implementing all-inclusive food-safe and nutrition-sensitive food retailing infrastructures, our proposed research seeks to answer the question ‘why do the urban poor eat the food they do’, in the context of transformations in the food retail environment and the organization of daily life. We want to understand in what way progressing retail modernization and restructuration policies impact the diet diversity and quality of the urban poor that depend on daily food shopping (purchasing foods on a day-to-day basis) often due to irregular and fluctuating daily income levels due to the nature of employment.

Serving as a case in point for similar developments in SEA, our research focuses on Hanoi, the capital of lower-middle income country (LMIC) Vietnam, listed among the world’s fastest growing economies. Our research specifically focuses on women, since nutrient deficiencies are particularly prevalent among women of reproductive age. Women are often the primary decision maker and mostly responsible for food purchases, meal preparation and household food allocation. They are thus key-actors in understanding and addressing nutrition vulnerability.

Households were randomly selected from the field sites, where women were asked to recall all the foods and drinks they consumed the previous day, and specifying where those foods were sourced from. An adapted quantiative 24hour recall methodology was applied

5 SOUTH AMERICA

Road injuries || Colombia

Loss of years of healthy life due to road incidents of motorcyclists in the city of Medellin, 2012 to 2015

Dimensions: 428 rows × 24 columns for deaths data

45018 rows × 23 columns for clinic injury data

87971 rows × 14 columns for injury data from police records

Comments:

Very high-quality group of datasets. Records of deaths and injuries due to motorcycle accidents.

Quite detailed, very large. Nice for time series analysis. It is all in Spanish, so may need translation. Associated paper here

Rating: 5/5

5.1 Data

Deaths dataset:

Injuries from clinic records:

The actual dataset is 45,018 rows, but we sample 500 here.

Injuries from police records:

The actual dataset is 87,971 rows, but we sample 500 here.

5.2 Data summary

Deaths dataset:

Injuries from clinic records:

Injuries from police records:

5.3 Data dictionary

Please see Zenodo record for the data dictionary: https://zenodo.org/record/4836304

5.4 Abstract

Objective

Determine the loss of years of healthy life due to road incidents of motorcyclists in the city of Medellin from 2012 to 2015.

Methods

Descriptive study with data on health care of injured motorcyclists and deaths adjusted with the Preston and Coale method, and OPS proportional distribution for the period 2012–2015. The years of life lost due to premature death (YLLs), years lived with disability (YLDs), and the disability-adjusted life years (DALYs) were calculated according to the new methodology designed for that purpose.

Results

The loss of years of healthy life due to road incidents of motorcyclists in the four-year period was 80,046 DALYs (823.8 per 100,000 inhabitants), with a higher proportion in men (81.3% and a ratio of 5 to 1 compared to women); the YLDs was 66.6% with marked differences in favor of men. There was nearly a 38% difference in the ages of 15 to 19 as well as a 19% difference from 30 to 49, compared to women. Premature death (YLLs) contributed to 33.4% of DALYs, with significant presentation in the above-mentioned age groups.

Conclusions

The greatest loss of years of healthy life due to road incidents of motorcyclists in Medellin was due to non-fatal injuries and was concentrated in young men. If the trend of motorcycle road incidents continues, both local and national road safety plans will fail to accomplish the expected results, especially among motorcycle users.

Yellow Fever || Brasil

Spatiotemporal dataset of Yellow Fever cases in Brazil

Dimensions: 100 rows × 19 columns

Comments: The full article can be found here. For the full data, go to the figshare

Rating: 4/5

5.5 Data

5.6 Abstract

Background: Yellow fever virus is a mosquito-borne flavivirus that persists in an enzoonotic cycle in non-human primates (NHPs) in Brazil, causing disease in humans through spillover events. Yellow fever (YF) re-emerged in the early 2000s, spreading from the Amazon River basin towards the previously considered low-risk, southeastern region of the country. Previous methods mapping YF spillover risk do not incorporate the temporal dynamics and ecological context of the disease, and are therefore unable to predict seasonality in spatial risk across Brazil. We present the results of a bagged logistic regression predicting the propensity for YF spillover per municipality (administrative sub-district) in Brazil from environmental and demographic covariates aggregated by month. Ecological context was incorporated by creating National and Regional models of spillover dynamics, where the Regional model consisted of two separate models determined by the regions’ NHP reservoir species richness (high vs low).

Results: Of the 5560 municipalities, 82 reported YF cases from 2001 to 2013. Model accuracy was high for the National and low reservoir richness (LRR) models (AUC = 0.80), while the high reservoir richness (HRR) model accuracy was lower (AUC = 0.63). The National model predicted consistently high spillover risk in the Amazon, while the Regional model predicted strong seasonality in spillover risk. Within the Regional model, seasonality of spillover risk in the HRR region was asynchronous to the LRR region. However, the observed seasonality of spillover risk in the LRR Regional model mirrored the national model predictions.

Conclusions: The predicted risk of YF spillover varies with space and time. Seasonal trends differ between regions indicating, at times, spillover risk can be higher in the urban coastal regions than the Amazon River basin which is counterintuitive based on current YF risk maps. Understanding the spatio-temporal patterns of YF spillover risk could better inform allocation of public health services.

6 Multiple LMICs

Diarrhea || Mali & Bangladesh

External validation of a mobile clinical decision support system for diarrhea etiology prediction in children: A multicenter study in Bangladesh and Mali

Dimensions: 150 rows × 37 columns for survey dataset

Comments: Researchers tested the accuracy of an algorithm in predicting the cause of diarrhea in kids. Main dataset contains the clinical info about the diarrhea cases.

Seems useful for cleaning, joining, plotting. In this document, we only show the Mali data, but a twin dataset from Bangladesh is included in the study, and could be nice for student practice.

Rating: 4/5

6.1 Data

Survey dataset:

mRNA test dataset:

Weather dataset:

6.2 Data summary

Survey data:

6.3 Abstract

Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.

Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application (‘App’) for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used a previously derived and internally validated model consisting of patient-specific (‘present patient’) clinical variables (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) as well as location-specific viral diarrhea seasonality curves. The performance of additional models using the ‘present patient’ data combined with other external data sources including location-specific climate, data, recent patient data, and historical population-based prevalence were also evaluated in secondary analysis. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.

Results: Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The present patient+ viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α = -0.393 (-0.455–0.331) and calibration slope β = 1.287 (1.207-1.367). By site, the present patient+ recent patient model performed best in Mali with an AUC of 0.783 (0.705-0.86); the present patient+ viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595-0.825).

Conclusions: The App accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the App’s potential use in diagnostic and antimicrobial stewardship are underway.

MERS || Middle East and Africa

Spatial data of MERS in countries of the Middle East and Africa. Several datasets are compiled together from different articles.

Dimensions: (Reeves et al.) 861 rows × 41 columns | (Ramshaw et al.) 1196 rows x 12 columns | (Unknown source) 1741 rows x 46 columns

Comments: The full article of Reeves et al. can be found here and the full article for Ramshaw et al. can be found here. Variable metadata for both datasets are also available following the DOI of these articles.

Rating: 4.5/5

6.4 Data

Reeves et al. Data

Ramshaw et al. Data

Unknown source Data

6.5 Abstract

Reeves et al. Abstract

Background: Middle Eastern respiratory syndrome coronavirus (MERS-CoV) has spread rapidly across much of the Middle East, but no quantitative mapping of transmission risk has been developed to date. Moreover, details of the transmission cycle of the virus remain unclear, particularly regarding the role of camels as a reservoir host for human infections.

Methods: We present a first analysis of the environmental circumstances under which MERS-CoV cases have occurred in the Middle East, covering all case occurrences through May 2015, using ecological niche modeling approaches to map transmission risk. We compare the environmental breadth of conditions under which cases have reported camel contacts with that of the broader population of all cases, to assess whether camel-associated cases occur under a more restricted set of environmental circumstances.

Results: We documented geographic and environmental distributions of MERS-CoV cases across the Middle East, and offer preliminary mapping of transmission risk. We confirm the idea that climatic dimensions of camel-associated cases are more constrained and less variable than the broader suite of case occurrences; hence, camel exposure may be a key limiting element in MERS-CoV transmission.

Conclusion: This study offers a first detailed geographic and environmental analysis of MERS-CoV distributions across the Middle East. Results indicated that camel-exposed cases occur under a narrower suite of environmental conditions than non-camel-exposed cases, suggesting perhaps a key role for camels in the transmission of the disease, and perhaps a narrower area of risk for ‘primary,’ camel-derived cases of MERS.

Ramshaw et al. Abstract

As a World Health Organization Research and Development Blueprint priority pathogen, there is a need to better understand the geographic distribution of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and its potential to infect mammals and humans. This database documents cases of MERS-CoV globally, with specific attention paid to zoonotic transmission. An initial literature search was conducted in PubMed, Web of Science, and Scopus; after screening articles according to the inclusion/exclusion criteria, a total of 208 sources were selected for extraction and geo-positioning. Each MERS-CoV occurrence was assigned one of the following classifications based upon published contextual information: index, unspecified, secondary, mammal, environmental, or imported. In total, this database is comprised of 861 unique geo-positioned MERS-CoV occurrences. The purpose of this article is to share a collated MERS-CoV database and extraction protocol that can be utilized in future mapping efforts for both MERS-CoV and other infectious diseases. More broadly, it may also provide useful data for the development of targeted MERS-CoV surveillance, which would prove invaluable in preventing future zoonotic spillover.