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Forum: Biomarkers
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Topic: Difficulty in finding blood pressure measurement values for male
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Forum: Merging data files
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Topic: Merging data files
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| Merging data files [message #30058] |
Tue, 17 September 2024 17:56 |
Elsha
Messages: 3
Registered: September 2024
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Member |
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Hi,
I am working on merging injury data with IR, MR, and PR files. Can you help me please.
I had merged injury file with PR 1:1 in STATA by generating a new variable. for instance for injury data: gen ID_new = hhid + "_" + string(haiidx) + "_" + string(hv003)
However, on merging the dataset on self-harm shows 72 counts instead of 86 (originally in injury dataset). Why is that so?
Please help me!
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Forum: Water and Sanitation
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Topic: Improved water and sanitation variable definitions
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Forum: Other countries
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Topic: Côte d'Ivoire 2021
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| Côte d'Ivoire 2021 [message #30048] |
Mon, 16 September 2024 12:44 |
lillo?S
Messages: 25
Registered: December 2015
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Member |
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I am using the latest DHS dataset for Côte d'Ivoire and am specifically interested in the data on wasting (i.e., variable hw72) from the KR file (CIKR81FL.DTA). I have noticed that the wasting information is missing for 5,767 children and ".a" for 57 children. Could you please provide some insights into the missingness patterns for this variable? Thank you very much!
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Forum: Weighting data
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Topic: Query on Cluster-Level Modeling with DHS Data and Sampling Weights
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| Query on Cluster-Level Modeling with DHS Data and Sampling Weights [message #30047] |
Mon, 16 September 2024 04:21 |
sayianka
Messages: 1
Registered: September 2024
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Member |
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Greetings DHS Forum,
I'm working on modeling the prevalence of health outcomes (e.g., diarrhea) as a function of covariates such as literacy and wealth index using DHS data. My approach focuses on cluster-level analysis (v001) with the goal of producing modeled map surfaces similar to the geospatial map surfaces DHS creates for certain indicators.
My current process is as follows:
1. I compute cluster-level proportions for variables (e.g., proportion of women uneducated, proportion from "poor" wealth index) using the weighted mean approach as per DHS guidelines, and sum the total number of children in the cluster eligible (total_children) and the number of "successes" (num_sick_children):
ddply(dhs_dataset, ~v001, summarise, mean = weighted.mean(x = my_variable, w = v005))
2. For model building, I'm considering a GLM structure like this:
glm(cbind(num_sick_children, total_children - num_sick_children) ~ prop_poor + prop_illiterate,
data = my_dhs_data, family = "binomial")
My questions are:
1. In building cluster-level models, how should I utilize the sampling weights (v005)?
- Should I use the unique v005 per cluster?
- Should I use the total v005 in the cluster?
- Or should cluster-level models not use the v005 weighting variable at all?
2. I've noted a quote from a DHS expert in post #9779 in response to #9772, and a related #6672:
"If you calculate a cluster-level mean, proportion, standard deviation, etc., it will be the same whether or not you use weights. However, for analyses that include the clusters as units, you do need to save the total weight for the cluster."
How does this apply to my GLM approach? Should I be incorporating cluster weights, and if so, how?
Thank you in advance for your guidance.
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Forum: Mortality
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Topic: Case-crossover analysis with DHS
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| Case-crossover analysis with DHS [message #30031] |
Fri, 13 September 2024 09:29 |
paull
Messages: 3
Registered: December 2023
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Member |
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Hello,
I am using the DHS data from 5 countries (Uganda, Kenya, Tanzania, Burundi and Rwanda) to model the effect of temperature om neonatal mortality. Since the cycles 7 and 8 include the day of death for neonates, it's thus possible for me to calculate the date of death. I have created a time-stratified case-crossover with distributed lag non-linear models (from the dlnm package by Antonio Gasparrini) which uses a conditional logistic regression model. Unfortunately, the clogit in R does not work with complex survey designs. The other challenge is that because of the design of the case-crossover, I have to only include cases (i.e.those with b5=0 and b6<128 (ie 0-27 days). This introduces additional challenges since it's not possible to do sub-group analyses (the subpopulations would require creating control cases and lags). I have seen several studies using the DHS data and the time-stratified case-crossover to model the effects of temperature on health outcomes. I am requesting if there is a workaround this. I am posting links of articles that used the DHS and the cco for your reference. https://www.sciencedirect.com/science/article/pii/S016041202 1005274
https://www.nature.com/articles/s41467-024-49890-x#:~:text=H eat%2Drelated%20deaths%20accounted%2C%20on,4.1%25)%20(See%20Supplementary%20Fig.
On (un)related issues, I failed to convert the dates of birth for Ethiopia DHS (in the Ethiopian Calendar) to the Gregorian Calendar because the climate data used is in the Gregorian Calendar. How would I correctly convert the Ethiopian dates to the Gregorian Calendar from b1, b17, and b2.
NB: I am using the KR file. I hope that's the correct one to use.
I am happy to provide the R code used for the case-crossover.
Thank you so much.
Paul
[Updated on: Fri, 13 September 2024 09:43] Report message to a moderator |
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Forum: General
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Topic: Inquire about variables
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| Inquire about variables [message #30025] |
Thu, 12 September 2024 17:07 |
Noorulain
Messages: 2
Registered: August 2024
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Member |
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using the Pakistan Demographic and Health Survey (PDHS) 2017-2018 dataset.
I would like to request clarification on the specific variables related to breastfeeding:
1) Could you kindly indicate which variable corresponds to children who have ever been breastfed in last 5 years?
2) Additionally, I am looking for the variable that specifically refers to children from the most recent birth who have ever been breastfed in last 5 years.
3) Does V208 include both live births and stillbirths, or does it only count live births?
NOTE:
The outcome/Dependent variable of my Analysis is Early initiation of breastfeeding coded by M34_1
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