Human resource assessment for scaling up VL active case detection in Bangladesh, India and Nepal


Corresponding Author Eva Naznin, World Health Organization, 20 Avenue Appia, 1211 Geneva, Switzerland. Tel.: +610450542005; E-mail:



To determine whether medical staff at PHC level would have the time to take up additional activities such as 1-day fever camps for active VL case detection.


This article assessed the workload of health staff of different professional categories working at health facilities in Bangladesh, India and Nepal. Data were collected from different sites in high endemic VL areas. The study population was the health staff of government health facilities at all levels. Workload indicators of staffing need (WISN) software were adopted to carry out the analysis of staff workload and their availability in the selected health facility. The WISN difference and WISN ratio for a particular health facility were calculated from actual staffing available and calculated staffing requirement.


The results showed a mixed picture of the availability of health workers. In most settings of Bangladesh and India, physicians with or without laboratory technicians would have time for active case detection. In Nepal, this would be performed by trained nurses and paramedical personnel.


If all vacant posts were filled, active case detection could be performed more easily. The elimination programme can be scaled up with the current staffing levels in the endemic areas with some short training if and when necessary.



Déterminer si le personnel médical au niveau des SSP aurait le temps d'entreprendre des activités supplémentaires telles que des camps d'une journée de mesure de la fièvre pour le dépistage actif des cas de LV.


Cette étude a évalué la charge de travail du personnel de santé de différentes catégories professionnelles travaillant dans les établissements de santé au Bangladesh, en Inde et au Népal. Les données ont été recueillies auprès de différents sites dans les zones d'endémie élevée pour la LV. La population de l’étude était le personnel de santé des centres de santé publics à tous les niveaux. Un logiciel pour les indicateurs de charge de travail en besoins de personnel (CTBP) a été adopté pour mener à bien l'analyse de la charge de travail du personnel et leur disponibilité dans l’établissement de santé choisi. La différence de CTBP et le rapport de CTBP pour un établissement de santé particulier ont été calculés à partir de l'effectif de personnel disponible et le besoin en personnel calculé.


Les résultats ont montré une image contrastée de la disponibilité des agents de santé. Dans la plupart des sites au Bangladesh et en Inde, les médecins avec ou sans techniciens de laboratoire auraient le temps pour effectuer la détection active des cas. Au Népal, cela serait réalisé par des infirmier(e)s qualifié(e)s et par du personnel paramédical.


Si tous les postes vacants étaient pourvus, le dépistage actif pourrait être réalisé plus facilement. Le programme d’élimination peut être déployé avec les taux actuels de personnel dans les zones endémiques moyennant une certaine courte formation lorsque et si cela est nécessaire.



Determinar si el equipo médico de atención primaria tendría el tiempo de asumir actividades adicionales tales como campañas de detección de fiebre de un día como parte de la detección activa de casos de LV.


En este artículo se evalúa la carga de trabajo de los trabajadores sanitarios con diferentes categorías trabajando en los centros sanitarios en Bangladesh, India y Nepal. Se recolectaron datos de diferentes lugares en áreas con una altamente endémicas para LV. La población de estudio era el personal sanitarios de centros gubernamentales de todos los niveles. Se adaptaron los indicadores de carga de trabajo del software WISN (Workload Indicators of Staffing Needs) para llevar a cabo el análisis de la carga de trabajo y la disponibilidad del personal sanitario en el centro elegido. Se calculó la diferencia en WISN y el ratio de WISN para un centro sanitario en particular según el personal sanitario realmente disponible y el personal requerido según cálculos.


Los resultados mostraban un panorama mixto de la disponibilidad de los trabajadores sanitarios. En la mayoría de los emplazamientos de Bangladesh y los médicos hindúes con y sin técnicos de laboratorio tenían tiempo para la detección activa de casos. En Nepal lo llevarían a cabo enfermeras entrenadas y personal paramédico.


Si se ocupasen todos los puestos vacantes, sería más fácil llevar a cabo la detección activa de casos. El programa de eliminación puede llevarse a escala con los niveles actuales de recursos humanos en áreas endémicas con poco entrenamiento si fuese necesario.


The objective of any health policy is to maximise the benefits gained from the use of societal resources devoted to health development. Scarcity of staff is one of the major barriers to delivering adequate healthcare services in many poor countries, and staff shortages are a growing concern particularly in rural areas and especially when locally developed health programmes have to be ‘scaled up’ to national level. ‘Scaling up’ refers to extending health intervention coverage for the benefit of larger populations and support policy and programme development at a large or national scale (Simmons et al. 2007). A scaling up programme requires human resources for health service delivery, and their unavailability poses a foremost constraint (Kurowski et al. 2003). Unfortunately, most of the international health-related development goals are set in the form of health outcome, but barely discuss health workers required to achieve pre-set goals (Kurowski et al. 2003). Unavailability of health staff could be the result of geographical imbalance in the distribution despite of adequate numbers at national level (Hongoro & McPake 2004). Another issue is optimum use of staff productivity to bridge the staffing gap (Kurowski et al. 2003), as research in Tanzania and Chad has shown: health staff provided a work volume of only 50–60% of what they were hired for. Other studies showed that the present workforce would be sufficient to scale up activities in AIDS control (Simba et al. 2004). In another context, additional nurses were needed for quality perinatal care because the existing workforce was already working under huge workload pressure (Nyamtema et al. 2008).

Visceral leishmaniasis (VL) or kala-azar (KA) is a preventable and costly illness among the poor in South-East Asia mainly in Bangladesh, Nepal and India. The delay in diagnosis and treatment of illness not only adds to the continued transmission, but adds to the costs of care seeking for households (Sharma et al. 2006). Elimination of kala-azar is the goal in Bangladesh, India and Nepal (World Health organization South East Asian Regional Office 2005). Consequently, the policy makers in these countries are interested in identifying cost-effective programme strategies to better diagnose and treat patients with KA. Recent studies showed that active case detection through a house-to-house search had the most potential for increasing case detection – almost the ‘gold standard’, but it is an expensive method (Singh et al. 2011). Subsequent studies showed the potential of the camp approach for cost-effective case detection (Hirve et al. 2010). ‘Camp approach’ means that in highly VL endemic villages, all people with chronic fever (>14 days) are invited to come at a given time to a meeting point in the village, where the spleen will be examined by a doctor, and if positive, a rapid diagnostic test (rK39) will be conducted by a laboratory technician or a trained auxiliary or nurse. If all three are positive (chronic fever, spleen, rK39), the patient will be sent for VL treatment to the local hospital. The staff requirements for organising a 1-day fever camp are as follows: one physician, one laboratory technician and one assistant (could be the driver of the vehicle). The materials needed are the rK39 tests that are provided free by the governments; the transport to the village will be done with the vehicle of the health unit or public transport and the staff needs subsistence allowance (see cost estimates of fever camps in Huda et al. 2012).

When scaling up active case detection (ACD) from a small number of study districts to all VL endemic districts in the three countries, the question arose whether this could be achieved with the existing health staff at PHC level. To answer this question, an assessment of the actual workload of existing staff was required. The human resource assessment not only reveals how much staff time is available at present to work for KA case detection and providing treatment, but also calculates how much additional staff will be needed to achieve the programme goals and helps to estimate the staff workload among health facilities even if personnel are employed in different activities (Bratt et al. 1999). Information on how health workers currently spend their time can help programme managers to determine whether it is possible to add new services within existing capacity constraints on available health worker time (Adam et al. 2005).

The primary focus of this study was to assess health staff workload for performing additional KA active case detection, using workload indicators of staffing needs (WISN). The WISN method is based on the work undertaken by health staff and is used in this context to estimate human resources availability for VL active case detection at PHC/UHC/district level. The main objective of the study was to assess the current workload of health staff of different professional categories working at the district hospital or in primary healthcare centres and health posts and to calculate the shortage or surplus of health workers to be able to scale up ACD of visceral leishmaniasis.


Study areas

In Bangladesh, the study was conducted in three highly VL endemic upazilas (subdistricts) of Mymensingh district (Gafargao, Bhaluka and Trishal); in India, in four primary healthcare centres (PHC Parsa, PHC Paroo, PHC Motipur and PHC Saraiya) in Bihar state; in Nepal, in two VL endemic districts: Sarlahi and Mahottari. The study population consisted of the health staff from health facilities of the selected districts working at DHs, PHCs and HPs level.

Sample size

In the three countries, the primary healthcare units with the highest reported annual incidence of VL cases were selected, that is, in those areas where active VL case detection will have to be conducted. Within the health facilities, it was attempted to interview and include all health staff. All staff were interviewed after signing a consent form; in most cases, several visits to the facility were necessary. The team also collected the staff charts from the facility to get the number of current posts, vacant posts and absent staff.

In Bangladesh, we interviewed for the workload assessment: five physicians and one laboratory technician (MTlab) in Trishal upazila health complex (UHC), seven physicians and two MTlabs in Gaforgaon UHC and eight physicians and two MTlabs in Bhaluka UHC. In India, we interviewed two physicians in the PHC Paroo, two physicians in PHC Motipur, three physicians in PHC Saraiya and two physicians in PHC Parsa. In Nepal's Mahottari district, information on workload of health staff was obtained at the district hospital, two PHCs, two health posts and five health facilities. In Nepal's Sarlahi district, the district hospital, three PHCs, five health posts and nine health facilities were selected and health staff were interviewed.

Data collection

A set of structured questionnaires was designed to obtain data on available staff time, staff unit time spent on each activity, staff daily work routine in a week, annual workload and salary information. Data were collected through interviews, document analysis and discussions.

The information on health worker's time spent on the KA programme was collected using a structured questionnaire administered by the programme personnel asking the health worker about the time he/she was involved in various components of the KA programme. The actual payment made to these personnel was taken as cost incurred against their time contribution for participating in active VL case detection (camp activity and or index case detection). The analysis identified the number of patients with VL that can be managed by a healthcare provider in a year's time.

Ethical approval

Ethical approval was obtained from the participating research institutions, the WHO Ethical Research Committee and the KA elimination programmes in the countries.

Data collection and analysis

Using the WISN methodology (WHO 1998), the calculations followed the following procedure.

Setting objectives

The general objective was to contribute to the scaling up of active VL case detection in Bangladesh, India and Nepal through the assessment of current workload pressure on health staff in the different districts.

Choosing the basic design of the procedure to be implemented

The WHO WISN approach was adopted to analyse staff workload and availability. A group of investigators from Bangladesh, India and Nepal was trained on WISN analysis.

Setting up the implementation

After the training, the first step was to form the implementation group in each country. Group members were subdivided into two groups according to their role and responsibilities in the implementation programme. These groups were as follows: (i) the steering committee, whose functions are to set the policies for the work within the agreed objectives, to approve strategies for implementation, to agree work plans and budgets for the development, to monitor progress and to supervise (WHO 1998). The steering committee was set up in three countries including members from Government officials, WHO and WISN programme person. (ii) The task force committee, which consists of the implementation manager and his/her task force, whose function is to implement the study (WHO 1998). Teams were formed by members of government staff at the facility level, WISN programme personnel and the data collection and management group.

Mobilising commitment for applying the WISN method

Government officials were invited to a workshop to be introduced to the WISN programme. It was ensured that the members understood the benefit of workload assessment and the mechanism of the implementing process delivered through the workshop.

Data analysis

Data were analysed using WISN software (WHO 2010) and Microsoft Excel 2003. Available working time (AWT) was calculated by summing absent days (public holidays, sick leave, maternity/paternity leave, casual leave) and subtracting the total from the total working days in a year.

To define the workload and set the activity standard, information on three standards was collected. (a) Activity standard comprised the time to be spent on average on each activity (e.g. 10 min per inpatient/day, 7 min per outpatient and 20 min per emergency). The activity standard in a country is usually set by senior and knowledgeable staff with substantial experience of the work for which the standards are being set. (b) Service standard (annual statistics) included the main activities for which regular data are collected (number of inpatients, outpatients, emergencies, laboratory samples, samples tested, etc.) and the average time spent on each activity by asking the staff for time spent on each activity including the preparation of equipments. (c) The allowance standard comprised activities for which data are not collected routinely (recording, reporting, field visits, lunch times, etc.) but which occupy a health staff besides his/her main workload. The allowance standard has two subcategories: (c.i) category allowance standards for support activities (supervision and administrative which were collected for all workers in a staff category, for example, all health centre staff); and (c.ii) individual allowance standard for additional activities by certain staff categories collected for a fixed number of workers in a staff category (supervision, field visit) and represent the total days spent on each activity in a year. The calculation of various standards is given in Table 1.

Table 1. Example of service standard, category allowance standard (CAS) and individual allowance standard (taken from the pilot study in Bangladesh)
WorkloadWorkload componentService standard (min/patient)Category allowance standard (CAS)CAS% working timeIndividual allowance standard (IAS)Annual IAS
  1. AWT, available working time.

Main health service activityInpatient5    
Support activityAdministration 1 h daily[(1 × AWT)/yearly working hours] × 100  
Reporting0.5 h monthly
Meetings2 h monthly
Additional activitySupervision   4 h twice a year8 h/year

Standard workload of health staff

Standard workload is calculated using the data on AWT and activity standard (i.e. time spent on each activity). Standard workload can be expressed as AWT in a year/sum of unit time. AWT in a year is calculated by subtracting total absent days from total working days in a year. ‘Sum of unit times’ in a year is calculated by unit time to complete a specific activity, for example, total time spent on each patient. Two types of allowance factors were calculated from allowance standards to determine overall staff requirement. The category allowance factor: CAF = 1/[1−(total% CAS/100)] covers the activities performed by all members of a health worker group. Therefore, CAF is a multiplier in determining the total required health staff. The individual allowance factor (IAF) is annual IAS/AWT in a year. All calculations (AWT, unit time) were done in the same time unit, for example, hour or minutes. An example of calculating workloads and staff requirements is provided in Table 2. All were entered into the WISN software to determine the staff requirement, surplus or shortages in each facility. Comparing the findings with existing staff determined the staff surplus and shortages in each category of staff in a facility.

Table 2. Workload assessment at Trishal upazila health complex, Bangladesh. (a) Main health service activities, (b) Support activities, (c) Additional activities
StaffWorkload componentActivity standardUnitAnnual statistics (2009)Standard workloadRequired staff
Medical officerInpatient5Min/patient2543219770.12
Outpatient3Min/patient61 205366281.67
Patient with visceral leishmaniasis (VL)10Min/patient1279109880.12
StaffWorkload componentWorkloadUnitActivity standard
Medical officerAdministrative26Hour/month0.17
Field visit8Day/year0.03
Total category allowance standard (CAS)0.21
Category allowance factor (CAF)1.27
Additional activitiesNo. of staffWorkloadUnitActivity standard
Field visit14Hour/year0
Total individual allowance standard (IAS)0.12

Determining overall staff requirements in a year

The steps in determining staff surplus/shortages comprise the following:

  • Divide the workload of most recent service statistics (e.g. total number of inpatient in a year) by standard workloads
  • Add together calculated staff requirements of all service components
  • Multiply by category allowance factor
  • Add IAFs

Calculating the physician requirement in Trishal UHC, Bangladesh, would look like this:

  • Total required staff based on standard workload for main health services activities: 2.68
  • Multiply with category allowance factor: 2.68 × 1.27 = 3.4
  • Adding IAF: 3.4 + 0.12 = 3.52

Thus, the required physician for Trishal UHC is 3.52.

In the same way, the calculation for other health facilities was done in three countries. Following the example of physician requirement in Trishal UHC in Bangladesh, requirement for health facility staff was calculated for Bangladesh, India and Nepal in Tables 2-4, respectively.

Table 3. Standard workload of health staff and determined overall staff requirements in a year in Parsa PHC, India. (a) Main health service activities, (b) Support activities, (c) Additional activities
StaffWorkload componentActivity standardUnitAnnual statisticsStandard workloadRequired staff
Medical officer (MO)Outpatient (OPD)3Min/patient8670340 882.82.12
Inpatient (IPD)7Min/patient1073717521.20.61
StaffComponentWorkloadUnitActivity standard
Medical officer (MO)Administrative2Hours/day0.29
Field visit4Days/month0.17
Total category allowance standard (CAS)0.69
Category allowance factor (CAF)3.23
Additional activitiesNo. of staffWorkloadUnitActivity Standard
Total individual allowance standard (IAS)0.18
Table 4. Standard workload of health staff and overall staff requirements in a year in Jaleshwor DH, Nepal. (a) Main health service activities, (b) Support activities, (c) Additional activities
StaffWorkload componentActivity StandardUnitAnnual statisticsStandard workloadRequired staff
Outpatients8Min/patient28 61412388.952.31
Inpatient Discharge4.5Min/patient129522024.80.06
Workload componentWorkloadUnitActivity standard
Clinical meeting3Hours/month0.02
General administration2.5Hours/week0.06
Monthly recording and reporting4Hours/month0.02
Staff meeting17Hours/year0.01
Total category allowance standard (CAS)0.11
Category allowance factor (CAF)1.12
Additional activitiesNo. of staffWorkloadUnitActivity standard
Duty roster preparation11Hours/month0.01
Field visit18Days/year0.03
General administration11Hours/day0.14
Meeting with FCHV14Days/year0.02
Monthly recording and reporting15Hours/month0.03
Monthly reporting to DHO11Days/month0.04
Total individual allowance standard (IAS)0.27

Summary of WISN interpretation

The WISN ratio shows the amount of pressure each staff category is undergoing to cope with the annual workload and describes under- and overstaffing in the particular health facility. The WISN difference and WISN ratio for a particular health facility are calculated from actual staffing available and calculated staffing requirement (WISN difference = actual – calculated). This illustrates the level of shortage or surplus (WISN ratio = actual/calculated). If the WISN ratio is 1.00, then the calculated staff is just sufficient to carry the workload of that particular health facility as per the professional standards. If the WISN ratio is <1.00, staff is insufficient to meet the workload of that particular health facility according to professional standards. If the WISN ratio is >1.00, staff is more than sufficient to meet the workload.


The workload indicators per health institution are shown in Tables 5-7 for the three countries. The results show the calculated required staff for optimum staffing, the differences between the actual and the calculated required staffing (shortage and surplus) and the workload pressure on health workers per category as expressed in the WISN ratio. For Bangladesh, Table 5 shows more than a sufficient number of physicians in Trishal and Bhaluka UHC (WISN ratio >1). But there is a shortage of physicians in Gaforgaon UHC (WISN ratio <1). Existing MTlab is more than sufficient in all UHCs based on their annual workload (WISN ratio >1).

Table 5. Workload indicators of staffing needs (WISN) in three upazila health complexes of Bangladesh
Name of upazila health complex (a)Type of staff (b)Existing staff (excluding consultant) (c)Calculated requirement (according to annual workload) (d)Difference in staff (existing – required) (c−d)WISN ratio (c/d)
  1. MTlab, laboratory technician.

Table 6. Result of the workload indicators of staffing needs (WISN) in India
Name of primary health complex (a)Type of staff (b)Existing staff (excluding consultant) (c)Calculated requirement (according to annual workload) (d)Difference in staff (existing – required) (c – d)WISN ratio (c/d)
  1. MTlab, laboratory technician; MOIC, medical officer in charge.

Physician – MOIC11.05−0.050.95
Physician – MOIC21.070.931.87
SARAIYAPhysician – 210.280.723.57
Physician – 310.310.693.23
Physician – MOIC10.440.562.27
Table 7. Required health staff per category and WISN ratio of health staff in Nepal
DistrictName of health facilityType of staffExisting staff (excluding consultant)Calculated requirement (according to annual workload)Difference in staff (existing – required)WISN ratio
  1. WISN, workload indicators of staffing needs; HP, health post; PHCC, primary health care centre.

MahottariJaleshwor DHPhysician43.230.771.24
Nursing staff86.671.331.20
Paramedical staff63.302.701.82
Laboratory staff24.52−2.520.44
Radiology staff21.550.451.29
Gausala PHCCPhysician12.27−1.270.44
Nursing staff35.23−2.230.57
Paramedical staff43.800.201.05
Laboratory staff12.99−1.990.33
Samsi PHCCNursing staff21.600.401.25
Paramedical staff32.490.511.20
Laboratory staff11.40−0.400.71
Ekdara HPNursing staff21.370.631.46
Paramedical staff32.820.181.06
Pipara HPNursing staff43.720.281.08
Paramedical staff32.240.761.34
SarlahiMalangwa DHPhysician42.401.601.67
Nursing staff55.33−0.330.94
Paramedical staff55.12−0.120.98
Laboratory staff22.90−0.900.69
Radiology staff20.811.192.47
Barathwa PHCCNursing staff33.78−0.780.79
Paramedical staff43.970.031.01
Laboratory staff10.350.652.86
Haripur PHCCPhysician11.27−0.270.79
Nursing staff32.010.991.49
Paramedical staff21.220.781.64
Laboratory staff10.850.151.18
Lalbandi PHCCNursing staff33.14−0.140.96
Paramedical staff44.04−0.040.99
Laboratory staff11.36−0.360.74
Bhaktipur HPNursing staff11.16−0.160.86
Paramedical staff33.21−0.210.93
Gangapur HPNursing staff11.26−0.260.79
Paramedical staff32.440.561.23
Ishworpur HPNursing staff10.990.011.01
Paramedical staff32.130.871.41
Salyanpur HPNursing staff11.43−0.430.7
Paramedical staff32.600.401.15
Sasapur HPNursing staff10.970.031.03
Paramedical staff32.850.151.05

For India, Table 6 shows shortages of physicians in Parsa PHC. The PHC has no laboratory technologist. PHC Paroo has more than enough physicians but a shortage of physician-MOIC. PHC Motipur has too few physicians and not enough physician-MOICs. PHC Saraiya has more physicians than required.

For Nepal, Table 7 shows that the Gausala PHCC from the Mahottari district has the highest workload among all the health facilities under the study. Lalbandi PHCC has the highest workload among the PHCCs in Sarlahi district. Among the HPs, the Bhaktipur HP from the Sarlahi has the highest workload. The physician in the DHs exceeds the optimum requirement for the present workload.


Determining the existing staff workload through WISN can help to understand the workload pressure in health facilities of the endemic districts. Thus, existing staff and/or surplus staff hired by the national programme can be involved to scale up ACD strategies. In this case, ACD workload can be included as an additional activity of a particular health staff in parallel with their main activities. ACD identifies new VL cases in the community and refers patients to hospitals to start early treatment. Early treatment reduces morbidity, mortality, transmission of disease and will help in VL elimination. Our study offers and elucidates current staffing levels, how staff cope with the current workload and whether they can handle the additional workload that is expected to be introduced with the ACD using the camp approach with the VL elimination programme.

The study had some limitations. Firstly, the WISN method itself depends partly on the annual service records. Hence, if record keeping (health management information system) is not well maintained, the results produced by the WISN method will not accurately reflect the workload of the health staff. However, in this study, the information was collected from each health facility from the persons in charge to ensure high data quality. Secondly, all staff present in the health centres during data collection were interviewed; however, any absent staff – fortunately very few – could not be included. Thirdly, the information was collected in a limited number of districts from participating countries and cannot be extrapolated to other districts. The WISN method is a facility-based workload calculation tool. Furthermore, the disease patterns, service use rates, existing staff numbers, etc. can alter the result. Fourthly, the data were gathered from the government hospitals and cannot be generalised for other health facilities such as private health institutions or NGO-run health centres. Finally, the data were dependent on the accuracy of reporting, which in some cases might not reflect reality; however, care was taken to use other sources of information for completing and validating the respondents' information. In spite of these limitations, the study results seem to reflect quite well the situation in the study districts.

The study showed variations in the levels of staffing in three countries and different facilities in each country. In Bangladesh in the three UHCs, physician time and even more laboratory technician time would be available for doing additional work such as active VL case detection using the camp approach. They would be able to conduct several 1-day camps with one physician, one laboratory technician and an additional assistant such as an assistant nurse or even the trained driver of the vehicle. In most places of India (except for Parsa and Motipur), the situation was similar for physicians: They would have time for conducting 1-day fever camps. In contrast, laboratory technicians were only under exceptional circumstances available but their work (rapid diagnostic tests) could be either done by the physician or by a trained nurse or assistant. In Nepal, the ACD camps should be done by trained nurses and paramedical staff, while physicians and laboratory staff were either absent or overloaded with work. The estimations also showed that the expected additional workload with 1-day fever camps for ACD as well as the treatment of the cases may not add a high workload, which could not be covered by the existing staff. Thus, the scaling up of the VL elimination programme using active case detection methods can be managed with the current staffing levels in the endemic areas with some short training if and when necessary.


This work is a part of the multicentre project of KA active case detection coordinated by Research and Training in Tropical Diseases of the World Health Organization (WHO/TDR), Geneva. This study is a collaborative undertaking of the International Centre For Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Rajendra Memorial Research Institute of Medical Sciences (RMRI), Banaras Hindu University (BHU), B. P. Koirala Institute of Health Sciences (BPKIHS) in Dharan and Tribhuvan University, Institute of Medicine (TU IOM) in Maharajgunj, Nepal. The authors wish to thank Dr Riitta-Liisa Kolehmainen-Aitken of the Andalusian School of Public Health (EASP) for her initial support in transferring the WISN study methodology to the three-country study teams.