WHO Nepal survey catalog formalizes occupation coding in a national health data set
WHO’s World Health Survey Plus Nepal 2023 includes a main work and occupation variable that classifies respondents into a tightly defined set of occupational groups. The item, labeled q0409b: main work/occupation, appears in the household data file and is recorded as a discrete numeric variable with values from 1 to 12, plus codes for other and don’t know. In practical terms, it gives the survey a standardized way to describe the kinds of work people do, which is essential for interpreting health information in a population study.
The coding scheme is broad enough to capture the major labor sectors but specific enough to preserve occupational detail. It separates managers, professionals, technicians, clerical workers, personal care workers, sales and service workers, trades and assembly workers, manual laborers, armed forces personnel, and respondents whose jobs fall outside the standard list. That structure matters because occupation is not just a demographic label; it is often a proxy for exposure patterns, workload, income stability, social position, and access to health services.
One line in the data dictionary stands out: valid responses were listed as zero, while 6,485 observations were marked invalid for this field in the file view. In a catalog context, that is not a population estimate and should not be mistaken for prevalence. It does, however, show that the variable is being presented through metadata rather than through a results table, and the caution attached to the figures is explicit. These counts describe cases in the file, not the broader Nepali population.
A workplace variable with direct public health relevance
Occupation often sits at the center of epidemiology because it helps connect social conditions to health outcomes. A professional working in health and social services, for example, may face a different mix of stressors, schedule demands, and infection exposure than a trades worker, a clerical employee, or a street vendor. The same is true for people in manual work, where physical strain and injury risk can be central concerns, and for those in customer-facing roles, where time pressure and economic instability may shape health behavior.
This survey item organizes those realities into a common framework. Health researchers can use it to compare patterns across job groups, assess inequities, and examine whether disease burden clusters in particular sectors of work. Without a variable like this, occupational analysis becomes fragmented, and the relationship between employment and health is harder to measure in a consistent way.
The list of response options also reveals the survey’s attention to sector distinctions. Category 1 covers managers in health and social services, while category 2 captures managers in sectors outside health and social services. The same split appears for professionals and for technicians or associate professionals. By drawing those lines, the questionnaire does more than name occupations; it preserves the difference between health-sector work and other forms of employment, which can be epidemiologically meaningful.
Standardized categories improve comparability across surveys
The use of a coded occupation variable makes the Nepal 2023 survey more useful for later analysis and comparison. Public health datasets depend on consistency, and occupation is one of those variables that can easily become messy if answers are recorded in free text alone. A standardized coding structure reduces ambiguity, improves machine readability, and allows researchers to group workers by function rather than by inconsistent narrative descriptions.
The inclusion of categories such as armed forces, other, and don’t know is also important. Real-world surveys rarely encounter perfectly clean data. Some participants do not fit neatly into preset job classifications, and some cannot identify their work in the terms the survey expects. Those response options help preserve the integrity of the data set instead of forcing incomplete answers into misleading categories.
The variable is part of the household data file WHS+NPLHHData, one of two files listed in the Nepal 2023 archive. A second file, WHS+NPLINDData, is also available in the catalog. The presence of a household file and an individual file suggests a layered survey architecture, although the occupation variable itself is specifically tied to the household file entry shown in the catalog view.
Metadata, not results, define the clinical value here
This entry does not present disease rates, treatment outcomes, or risk estimates. Its value lies in the architecture of the survey instrument itself. For clinicians, epidemiologists, and health-system analysts, that architecture determines what can later be measured, compared, and interpreted. Occupation can influence nearly every major health domain, from injury and cardiovascular stress to access barriers and long-term chronic disease patterns.
A clean occupation variable also supports downstream stratification by social position. Occupational class often correlates with education, income, and job security, and those factors can modify health behavior and health service use. In a national survey, such a variable may be used to identify whether manual laborers, service workers, or professionals carry different burdens of illness or access different forms of care. The survey does not make those claims on its own, but it creates the data structure needed to test them.
The catalog also records practical metadata: the reference ID is DDI-NPL-WHO-WHSPLUS-2023-V01, the producer is Professor Abhinav Vaidya, the study was created on Apr 24, 2026, and last modified on Oct 21, 2026. Page views and downloads are listed as well, reflecting use of the archive rather than any clinical endpoint. Those details help situate the data set within the WHO Multi-Country Studies Data Archive, where the focus is on documentation, access, and reproducibility.
Why this occupation field will matter for later analysis
On its own, q0409b is a survey variable. In analysis, it becomes a tool for measuring how work shapes health. The difference between a person employed in health and social services and a person working in manual labor can be clinically relevant, especially when researchers are trying to understand exposure, functional limitation, or service access in a national sample.
The response code structure is simple, but the implications are broad. A survey that can distinguish clerical support from trades work, sales and service from personal care work, and health-sector employment from non-health-sector employment can support far more precise public health analysis than one that records only “employed” or “unemployed.” Such granularity is especially important in countries where labor patterns are diverse and where occupational risk may intersect with geography, sex, and socioeconomic position.
The most concrete limitation is also the most obvious one: this catalog page provides metadata, not analytic findings. It defines how occupation is coded in World Health Survey Plus Nepal 2023, but it does not yet answer what those codes mean for morbidity, mortality, or service use. Those questions will depend on how the microdata are analyzed, and on whether the occupational categories are linked to specific health outcomes in future reporting.