Today's Messages (on)
| Unanswered Messages (off)
|
Forum: Biomarkers
|
|
Topic: BMI of males
|
| Re: BMI of males [message #29102 is a reply to message #15913] |
Wed, 24 April 2024 02:08 |
sjkirabo
Messages: 2
Registered: April 2024
|
Member |
|
|
Liz-DHS wrote on Fri, 05 October 2018 14:39Dear User,
The standard recode variables for Height and Weight in men are:
HB0, HB1, HB2, HB3, HB4, HB5, HB6, HB11, HB12, HB12A, HB12B, HB13, HB32, HB33, HB35, HB40, HB41, HB50, HB51, HB52, HB53, HB55, HB56, HB57, HB58, HB60, HB61, HB62, HB63, HB64, HB65, HB66, HB67, HB68, HB69, HB70
Thank you!
I am trying to use data for Tanzania(2022), Lesotho(2014), Gabon(2019-21), and Benin(2017-18) and cannot find these variables for height and weight of males, I couldn't find the BMI too in any of the datasets.
|
|
|
|
|
Forum: Reproductive Health
|
|
Topic: Replicating Table 9.7: Timing of first postnatal checkup for the mother
|
| Re: Replicating Table 9.7: Timing of first postnatal checkup for the mother [message #29107 is a reply to message #12178] |
Wed, 24 April 2024 05:50 |
doria786
Messages: 1
Registered: April 2024
|
Member |
|
|
When it comes to generating the time_mother variable, your conditions for categorizing the timing of postnatal care appear to be logically structured. However, one potential issue could be with the use of the & operator without enclosing the conditions in parentheses. In Stata, logical conditions combined with & should be enclosed in parentheses to ensure they are evaluated correctly.
** WEIGHT VARIABLE
gen weight = v005/1000000
** SURVEY SET
gen psu = v021
gen strata = v022
svyset psu [pw = weight], strata(strata)
When it comes to generating the time_mother variable, your conditions for categorizing the timing of postnatal care appear to be logically structured. However, one potential issue could be with the use of the & operator without enclosing the conditions in parentheses. In Stata, logical conditions combined with & should be enclosed in parentheses to ensure they are evaluated correctly.
Additionally, the replace command for time_mother = . if bidx > 1 might be excluding some cases that should be included in the analysis. Make sure that this condition aligns with the methodology used in the PDHS report.
For the tabulation commands, it's crucial to ensure that the format(%4.1f) option is used consistently to match the report's percentage format. Also, check if the row option is necessary for your analysis, as it changes the way percentages are calculated. If you continue to face discrepancies, I recommend comparing your results with the PDHS report's methodology section or checking for any updates or errata related to Table 9.7. You can find the PDHS 2012-13 report here for reference.
|
|
|
|
|
Forum: Weighting data
|
|
Topic: HR Files
|
| HR Files [message #29108] |
Wed, 24 April 2024 11:36 |
Lubana
Messages: 2
Registered: January 2024
|
Member |
|
|
Hello,
I am a new DHS data user and I am not familiar with programming languages. Therefore, for my analysis, I am using ArcGIS Pro. My apologies for any inconvenience this may cause.
I am conducting a social vulnerability assessment to climate change impacts using DHS data for Kenya. I am encountering some difficulties in calculating DHS indicators. The following DHS datasets are being utilized for the analysis: KEHR8BSD.zip, KEGC8AFL.zip, and sdr_subnational_boundaries.shp.
I attempted to calculate the percentage of various indicators, such as the percentage of female-headed households (HHs), households with a population aged 65 and over, households with a population aged 13 to 18 years, literate households, households with access to improved water and sanitation, and the wealth index.
It's worth noting that the Humanitarian Data Exchange (HDE) contains the percentage of a range of demographic and socioeconomic indicators at the subnational administrative level 1 for Kenya, derived from DHS 2022 data.
However, upon completing the calculations, I noticed discrepancies between the results obtained from GIS analysis and the percentages provided on the HDE website. I am unsure why these differences exist. I did not apply any weight to my dataset because the process was confusing to me. Despite watching videos and reading resources from the DHS website, I still lack clarity on this matter.
Therefore, I have several questions:
--Why am I getting different results?
--Do I need to apply weight to calculate the percentage of households with access to improved water and sanitation?
--If weight application is necessary, how can I accomplish this in GIS software?
I would greatly appreciate your assistance with this matter. I have attached a document outlining the steps I followed to join DHS Census Data with DHS Spatial Data, aggregate at the subnational administrative level 1, and calculate two indicators as examples (i.e., the percentage of households with access to improved water and the percentage of households with access to improved sanitation). This document also includes results obtained from ArcGIS Pro and the HDE website. I have also attached excel files data downloaded from HDE website for the year 2022 for your convenience.
Any help will be highly appreciated. I need it urgently. Please let me know if you need any other clarification.
Thank you.
|
|
|
|
|
Forum: General
|
|
Topic: Region Variable v024 (Haiti)
|
| Re: Region Variable v024 (Haiti) [message #29106 is a reply to message #28921] |
Wed, 24 April 2024 04:42 |
ueber
Messages: 2
Registered: March 2024
|
Member |
|
|
thank you very much for your answer!
We're do I find these specific codes for every survey, because I further need them for the 2000, 2005, and 2015 survey and they used different numbers, therefore I expect to have a different distribution of codes?
Thank you very much in advance!
|
|
|
|
|
Topic: Agricultural Land ownership: Treatment of Zero values
|
|
|
|
Forum: Domestic Violence
|
|
Topic: 2016 disability, partner behaviors, and intimate partner violence report
|
| Re: 2016 disability, partner behaviors, and intimate partner violence report [message #29110 is a reply to message #29094] |
Wed, 24 April 2024 16:17 |
Bridgette-DHS
Messages: 3039
Registered: February 2013
|
Senior Member |
|
|
Following is a response from Senior DHS staff member, Tom Pullum:
It appears that the disability data in the NFHS-5 were included in the HR file but not in the PR file. I have prepared a Stata program that puts them into a pseudo PR file, with identifiers hv001 hv002 hvidx. You can run this program and then merge the data into whatever other files you are using. Only about 25,000 individuals were reported to have a disability, a small fraction of the sample, but I hope you will be able to do something with them. There has been little analysis of the disability data in this survey.
|
|
|
|
|
Forum: Mortality
|
|
Topic: Working through Odds Ratios
|
| Re: Working through Odds Ratios [message #29111 is a reply to message #29078] |
Wed, 24 April 2024 17:29 |
Janet-DHS
Messages: 698
Registered: April 2022
|
Senior Member |
|
|
Following is a response from DHS staff member, Tom Pullum:
If you search the forum you will find many related exchanges. For your purposes you can ignore b6, and just use b7, which is months of age at death for children who died. However, for ages 2+ years, the months are constructed as years x 12. Thus, for "2 years" the number of months is "24". Then the only codes should be 36, 48, 60, 72, etc. I don't know where a value of "335" would have come from....
The NMR, IMR, CMR. U5MR are rates for synthetic cohorts, following life table procedures. Except for the factor of 1000, the IMR is 1q0, the CMR is 4q1, and the U5MR is 5q0. Rates refer to aggregates and can only be calculated for an aggregate. You are apparently looking for an individual-level analog. You can certainly construct binary (0/1) variables corresponding to age at death. For example you could initialize d_nmr=0, d_imr=0, d_cmr=0, and d_u5mr=0 and then construct d_nmr=1 if b7=1, d_imr=1 if b7<12, d_cmr=1 if 1<=b7<60, and d_u5mr=1 if b7<60. The only problem is how you then deal with censoring,
The DHS approach can calculate the U5MR for deaths during the past 5 years, because of the synthetic cohort approach, but children with full exposure to the risk of death in the past 5 years were actually born 5-9 years ago. Please look at the Guide to DHS Statistics, DHS reports on mortality, or other literature. The bottom line is that there is no easy way to translate rates for aggregates into individual-level indicators, which is what you want to do.
|
|
|
|
Current Time: Wed Apr 24 19:37:00 Coordinated Universal Time 2024
|
The DHS Program User Forum
Members
Search
Help
Register
Login
Home
