WHO Malawi MSM_PSE_NUM
Annual WHO Global Health Observatory time series for Malawi covering 'MSM_PSE_NUM' (MSM_PSE_NUM).
Filename
who_malawi_msm_pse_num_MSM_PSE_NUM.csv
Preview
Schema
Coverage
Source Linked
5
Downloads
2
Views
4
Rows Parsed
826.0 B
File Size
09 May 2026
Updated
WHO Malawi MSM_PSE_NUM is a CSV dataset from World Health Organization, ingested into the Kafukufuku Data Hub with 4 parsed rows.
Annual WHO Global Health Observatory time series for Malawi covering 'MSM_PSE_NUM' (MSM_PSE_NUM). The source is catalogued as who gho indicator timeseries. Spatial granularity is national. It belongs to the who malawi health indicators collection. Geographic scope is MWI.
| Source | World Health Organization |
| Format | CSV |
| Rows Parsed | 4 |
| Country | MWI |
| Spatial Granularity | National |
| Dataset Type | Who Gho Indicator Timeseries |
Filename
who_malawi_msm_pse_num_MSM_PSE_NUM.csv
| Source | World Health Organization |
| Format | CSV |
| Filename |
who_malawi_msm_pse_num_MSM_PSE_NUM.csv
|
| Rows Parsed | 4 |
| Dataset Type | Who Gho Indicator Timeseries |
| Indicators | 0 |
| File Size | 826.0 B |
| Status | completed |
| Spatial Granularity | National |
| Hosted by Kafukufuku | Yes |
| Checksum MD5 |
382f40cfd2d029a0b127cfb29bd964cb
|
| Last Updated | 09 May 2026 |
Coverage Summary
| Country | MWI |
| Spatial Granularity | National |
| Years Covered | Not identified |
| Distinct Years | 4 |
| Districts Identified | 2 |
| Regions Identified | 2 |
District Coverage
lilongwe
zomba
Region Coverage
central
southern
2 districts were identified during normalization.
Pipeline Lineage
Raw file
Completed
CSV · who_malawi_msm_pse_num_MSM_PSE_NUM.csv
Parse
Not run
No parse job recorded
Bronze
Created
4 bronze record(s)
Silver
Reviewed
4 silver record(s)
Published
Published
616 datamart record(s)
| Latest Parse Job | Not recorded |
| Latest Crawler Job | Not linked |
| Bronze Records | 4 |
| Silver Records | 4 |
| Published Records | 616 |
| Validation Issues | 0 errors, 2 warnings, 0 info |
Published Domains
Health · 616
Terms & Citation
| Access Level | Unspecified |
| License | License not specified |
| Source URL | https://ghoapi.azureedge.net/api/MSM_PSE_NUM |
Suggested Citation
World Health Organization. WHO Malawi MSM_PSE_NUM. Kafukufuku Data Hub, coverage n.d.. Source: https://ghoapi.azureedge.net/api/MSM_PSE_NUM.
No explicit license was captured during ingestion. Reuse should be checked against the original publisher terms.
The canonical source URL points to the upstream publisher or download location.
Schema
| Field | Type | Profile | Coverage | Missing | Distinct | Examples | Notes |
|---|---|---|---|---|---|---|---|
_domain |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
health
|
Two-level field best treated as a yes/no or flag variable. |
_domain_confidence |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
1.0
|
Two-level field best treated as a yes/no or flag variable. |
comments |
Text | Categorical | 100.0% present | 0.0% | 4 |
Region: National; Methods: Triangulation and adjustment; Source: KP workbook
Region: National; Method: Projection; Source: John Hopkins
Region: Lilongwe Mzuzu Blantyre Mangochi Machinga and Zomba; Method: PLACE; Source: 2017 Global Fund Concept Note
|
Field is best read as a label/category rather than a numeric measure. |
country |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
MWI
|
Two-level field best treated as a yes/no or flag variable. |
country_code |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
MWI
|
Two-level field best treated as a yes/no or flag variable. |
dataset_name |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
WHO GHO Malawi MSM_PSE_NUM
|
Two-level field best treated as a yes/no or flag variable. |
indicator_code |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
MSM_PSE_NUM
|
Two-level field best treated as a yes/no or flag variable. |
indicator_name |
Boolean / Flag | Binary | 100.0% present | 0.0% | 1 |
MSM_PSE_NUM
|
Two-level field best treated as a yes/no or flag variable. |
value |
Integer | Categorical Numeric | 100.0% present | 0.0% | 4 |
50000.0
42600.0
3900.0
|
Numeric values appear to encode groups or ordered categories. |
year |
Year | Temporal | 100.0% present | 0.0% | 4 |
2022
2017
2016
|
Interpreted as a reporting period rather than a measure. |
Sample Rows (4)
| Low | High | Year | Value | Country | Comments | Datasource | Country_Code | Dataset_Name | Display_Value | Indicator_Code | Indicator_Name |
|---|---|---|---|---|---|---|---|---|---|---|---|
| - | - | 2022 | 50000.0 | MWI | Region: National; Methods: Triangulation and adjustment; Source: KP workbook | - | MWI | WHO GHO Malawi MSM_PSE_NUM | - | MSM_PSE_NUM | MSM_PSE_NUM |
| - | - | 2017 | 42600.0 | MWI | Region: National; Method: Projection; Source: John Hopkins | - | MWI | WHO GHO Malawi MSM_PSE_NUM | - | MSM_PSE_NUM | MSM_PSE_NUM |
| - | - | 2016 | 3900.0 | MWI | Region: Lilongwe Mzuzu Blantyre Mangochi Machinga and Zomba; Method: PLACE; Source: 2017 Global Fund Concept Note | - | MWI | WHO GHO Malawi MSM_PSE_NUM | - | MSM_PSE_NUM | MSM_PSE_NUM |
| - | - | 2014 | 23600.0 | MWI | Region: Mzuzu Lilongwe Mangochi Blantyre Mulanje Nkhata-Bay and Chikwawa Districts; Method: Wisdom of the crowd and unique object identifier methods | - | MWI | WHO GHO Malawi MSM_PSE_NUM | - | MSM_PSE_NUM | MSM_PSE_NUM |
Data Quality Summary
| Rows Analysed | 4 |
| Columns With Data | 10 |
| Cell Completeness | 71.4% |
| Continuous Columns | 0 |
| Categorical Columns | 10 |
| Identifier-Like Columns | 3 |
| Temporal Columns | 1 |
| District Recognition | 50.0% recognised · 2 unresolved rows |
| Domain Recognition | 100.0% classified · 0 unresolved rows |
| Duplicate Rows | 0 |
| Mixed-Type Columns | 0 |
| Empty Columns | 4 · low, high, datasource, display_value |
These checks summarize coverage, consistency, and variable structure from parsed rows. Identifier-like columns and year/code fields are treated as categorical so averages are not shown where they would be misleading. Malawi-specific district/domain recognition rates are included to show how much of the file the pipeline can place and classify reliably.
Categorical Variables
| Column | Profile | Coverage | Distinct | Most Common Share | Top Values | Interpretation |
|---|---|---|---|---|---|---|
_domain |
Binary | 100.0% present | 1 | 100.0% |
health - 4 (100.0%)
|
Two-level field; best read as a flag or yes/no split. |
country |
Binary | 100.0% present | 1 | 100.0% |
MWI - 4 (100.0%)
|
Two-level field; best read as a flag or yes/no split. |
dataset_name |
Binary | 100.0% present | 1 | 100.0% |
WHO GHO Malawi MSM_PSE_NUM - 4 (100.0%)
|
Two-level field; best read as a flag or yes/no split. |
indicator_name |
Binary | 100.0% present | 1 | 100.0% |
MSM_PSE_NUM - 4 (100.0%)
|
Two-level field; best read as a flag or yes/no split. |
comments |
Categorical | 100.0% present | 4 | 25.0% |
Region: Lilongwe Mzuzu Blantyre Mangochi Machinga and Zomba; Method: PLACE; Source: 2017 Global Fund Concept Note - 1 (25.0%)
Region: Mzuzu Lilongwe Mangochi Blantyre Mulanje Nkhata-Bay and Chikwawa Districts; Method: Wisdom of the crowd and unique object identifier methods - 1 (25.0%)
Region: National; Method: Projection; Source: John Hopkins - 1 (25.0%)
Region: National; Methods: Triangulation and adjustment; Source: KP workbook - 1 (25.0%)
|
Nominal field summarised by the most frequent values. |
value |
Categorical | 100.0% present | 4 | 25.0% |
23600.0 - 1 (25.0%)
3900.0 - 1 (25.0%)
42600.0 - 1 (25.0%)
50000.0 - 1 (25.0%)
|
Nominal field summarised by the most frequent values. |
_domain_confidence |
Identifier | 100.0% present | 1 | 100.0% |
1.0 - 4 (100.0%)
|
High-cardinality identifier-like field; category frequencies are less informative. |
country_code |
Identifier | 100.0% present | 1 | 100.0% |
MWI - 4 (100.0%)
|
High-cardinality identifier-like field; category frequencies are less informative. |
indicator_code |
Identifier | 100.0% present | 1 | 100.0% |
MSM_PSE_NUM - 4 (100.0%)
|
High-cardinality identifier-like field; category frequencies are less informative. |
year |
Temporal | 100.0% present | 4 | 25.0% |
2014 - 1 (25.0%)
2016 - 1 (25.0%)
2017 - 1 (25.0%)
2022 - 1 (25.0%)
|
Time-like field; values are better read as reporting periods than categories. |