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Applying WHO’s ‘workforce indicators of

staffing need’ (WISN) method to calculate

the health worker requirements for India’s

maternal and child health service guarantees

in Orissa State

Amy Hagopian,

1

* Manmath K Mohanty,

2

Abhijit Das

3

and Peter J House

4

1Department of Global Health, University of Washington, Seattle, WA, USA,2Health Resource Unit, Human Development Foundation (HDF), Bhubaneswar, Orissa, India,3Centre for Health and Social Justice, New Delhi, India and4Family Medicine, University of Washington, Seattle, WA, USA

*Corresponding author. Center for Health Workforce Studies, University of Washington, 4534 11th Av. NE, Seattle, WA 98105, USA. Tel:þ1–206–616 4989. E-mail: hagopian@u.washington.edu

Accepted 6 December 2010

Objective In one district of Orissa state, we used the World Health Organization’s

Workforce Indicators of Staffing Need (WISN) method to calculate the number of health workers required to achieve the maternal and child health ‘service guarantees’ of India’s National Rural Health Mission (NRHM). We measured the difference between this ideal number and current staffing levels.

Methods We collected census data, routine health information data and government

reports to calculate demand for maternal and child health services. By conducting 54 interviews with physicians and midwives, and six focus groups, we were able to calculate the time required to perform necessary health care tasks. We also interviewed 10 new mothers to cross-check these estimates at a global level and get assessments of quality of care.

Findings For 18 service centres of Ganjam District, we found 357 health workers in our

six cadre categories, to serve a population of 1.02 million. Total demand for the MCH services guaranteed under India’s NRHM outpaced supply for every category of health worker but one. To properly serve the study population, the health workforce supply should be enhanced by 43 additional physicians, 15 nurses and 80 nurse midwives. Those numbers probably under-estimate the need, as they assume away geographic barriers.

Conclusions Our study established time standards in minutes for each MCH activity promised

by the NRHM, which could be applied elsewhere in India by government planners and civil society advocates. Our calculations indicate significant numbers of new health workers are required to deliver the services promised by the NRHM.

Keywords Health workers, maternal and child health, rural health, health planning, health

professionals

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KEY MESSAGES

Focusing on maternal and child care services, we used the Workforce Indicators of Staffing Need (WISN) approach to measure demand and supply for particular functions for each health cadre type, using facility-level routine data and interviews with health care providers.

Time standards in minutes were generated for each maternal and child health activity promised by the National Rural Health Mission (NRHM), standards that could be applied to other districts in India.

Our findings indicate that significant numbers of new health workers are required to deliver the services promised by the NRHM.

Background

Worldwide health workforce shortages have been identified as one of the primary threats to population health in low-income countries (Chenet al. 2004; Dreeschet al. 2005; Guilbert 2006). Nations seeking to improve population health services face daunting health workforce challenges.

India’s commitment to health service improvements

India’s national government rolled out a programme in 2005 to finance changes in the health system at the state level, known as the National Rural Health Mission (NRHM) (National Rural Health Mission 2005; Deolalikaret al. 2008; Mani 2008; Sharma 2009; MOHFW 2010). The purpose of NRHM was to address the large variations in the health system and its performance from state to state, seeking to bring forward the states that had been lagging behind (Sharma 2009). The plan called for a package of service guarantees for each citizen (such as full immunization for each child and the capacity to deliver all babies in medical facilities), and created new categories of health workers and structures to deliver those services.

Means of measuring workforce needs

Various means have been established to calculate workforce shortages (Markham and Birch 1997; Daviaud and Chopra 2008), but most health workforce planning uses a combination of practitioner-to-population ratios, historical patterns and professional judgment. More sophisticated analyses may use calculations of workforce size and mix through use of case-load profiling, acuity measures, queuing theory, production func-tions, treatment care standards or a combination of factors in regression analysis (Hornby et al. 1976; Lipscomb et al. 1995; Tucker et al. 1999; Hurst 2006; Hurst et al. 2008; Musau et al. 2008; Schooet al. 2008).

The Workforce Indicators of Staffing Need (WISN) method was developed by the World Health Organization (WHO) to calculate optimal allocations and deployment of staff (Shipp 1998). With WISN, the researcher calculates the staff time it would take to deliver a package of services for a given population. For example, if there are 80 000 newborns in a population each year, and it takes 7 minutes to vaccinate each 9-month-old child for measles, the staff time requirements to meet that obligation are a matter of simple calculation (80 0007 minutes). Total time required to vaccinate all chil-dren can then be divided by hours available per nurse per year to determine total nurses needed. Total nurses available can be

compared with nurses needed to estimate the surplus or gap in staffing. We chose WISN because it is ideal for use in discrete geographic areas with a specific set of services (in our case, maternal and child health). The WISN approach allowed us to measure the demand and supply for a set of functions for each health cadre type, using facility-level routine data supple-mented by interviews with health care providers. While it is time-intensive, compared with simple population ratios as an alternative, its results are reasonably precise and more helpful for planning and policy development. Similar approaches have been used in Tanzania and Kenya (Musau et al. 2008; Nyamtemaet al. 2008).

Alternatives to WISN have also been used to calculate workforce capacity. Hirschhorn and colleagues used a method to evaluate the effects of task-shifting among various health cadres for AIDS treatment in resource-limited settings (Hirschhornet al. 2006). Faulkner has written about estimating psychiatric workforce requirements based on patient needs, and offers a simple formula for making calculations: (# patients needing careamount of treatment time required)/amount of time offered per psychiatrist¼number of psychiatrists required (Faulkner 2003). Dreesch et al. (2005) have developed an approach to estimating human resource requirements based on time needed to address health deficits of the population.

Hagopian et al. (2008) published a method to model work-force needed, by workwork-force cadre, to meet HIV treatment protocols in Mozambique. An unpublished 2004 WHO model offers a ‘user guide’ to increasing access to anti-retroviral therapy (ART) by assessing health workforce needs (Zurnet al. 2006). A recent report from Uganda uses a workforce indica-tor method to calculate faculty requirements at a training institution (Kitanda 2008).

The WHO has calculated a worldwide stock of 59.2 million health workers (defined as doctors, nurses, pharmacists) and, using a minimum ratio standard, estimates a shortage of 2.4 million providers. The World Health Report 2006determined that a minimum ratio of 2.3 doctors, nurses and midwives per 1000 population was required, based on studies attempting to measure the ecological relationship between national workforce to population ratios and associated measures of vaccination coverage and attended birth rates (Guilbert 2006).

Setting for this study

This paper focuses on the district of Ganjam in the southern coastal part of Orissa State in India. Orissa State is India’s 11th largest state (of 28) by population, and is located along the Bay of Bengal on the eastern coastline. The interior of the state is

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mountainous and sparsely populated. Orissa is estimated to be the poorest state in the nation, and Government of India policy-makers have formally designated it a ‘backward state’, a status that allows it preferential treatment in various government policy schemes. In 2009, it was reported Ganjam had 243 physicians and 102 staff nurses working in its 10 hospitals and 35 primary health centres, with 57 vacant doctor positions and 8 vacancies in nursing. Primary sources of income are fishing and agriculture, attracting large numbers of migrant workers. Unemployment is relatively high. Migration patterns have been cited as a contributor to Ganjam’s status as home to the highest number of HIV cases in the state (Centre for Development Studies and Activities 2000; National AIDS Control Organization 2007; MOHFW 2009; Ganjam District Administration 2010).

Infant and maternal mortality rates in Orissa were last estimated to be 71 per 1000 live births and 358 per 100 000 live births, respectively (Office of the Registrar General 2007). The infant mortality rate for our study district of Ganjam was higher than the state average, at 86 (Rajanet al. 2008).

Purpose of this research

In 2008, the United Nations Population Fund (UNFPA) financed a programme to support civil society groups to build their capacity to evaluate the successes and shortcomings of the NRHM in meeting its service guarantee promises. The pro-gramme was carried out through a partnership between the Centre for Health and Social Justice, based in Delhi, India, and the University of Washington, based in Seattle, Washington, USA. Teams from 12 civil society health-focused organizations partnered with university faculty and India’s Centre for Health and Social Justice to provide guidance in evaluation methods, and subsequently the Indian teams were mentored through their research processes. This paper represents the results of one of those projects.

The purpose of this paper was to use the WISN method to calculate the number of health workers in one district of Orissa state that would be required to achieve the maternal and child health ‘service guarantees’ of India’s NRHM, and measure the difference between this ideal number and current staffing levels.

Methods

For this case study of health staffing levels in Ganjam District of Orissa State, we used calculation methods recommended in the WHO’s WISN guide (Shipp 1998).

We limited the focus of our analysis to maternal and child health (MCH) services, and restricted the health worker cadres to medical officers (doctors), staff nurses, lady health visitors, auxiliary nurse midwives, male health workers and laboratory technicians. Lady health visitors are a health worker category that oversees auxiliary nurse midwives. We further limited our analysis to 6 of 22 blocks in Ganjam District, and to 18 facilities within those blocks: six community health centres, six primary-care level health centres (known as ‘new’ primary health centres), and six sub health centres (known as sub-centres) that pre-date the newer category and include

limited inpatient care. These facilities were selected in a purposive manner, as they were facilities with which the organization of one of our authors (MKM) had pre-existing relationships.

We restricted our focus to MCH services in part because census data create the opportunity to support accurate calcu-lations, because specific cadres of health workers are aimed at this category of services, and because services in this area address an important Millennium Development Goal where progress has been lagging (United Nations 2010).

The steps in our analysis were relatively straightforward, although the size of the district and the multiple levels of health facilities required extensive calculations. We used inter-locking Microsoft ExcelTM spreadsheets, with separate work-sheets for each geographic level in our analysis.

Secondary data sources included the India Census (Government of India 2001), routine health information data from health units in Ganjam District, the Child Survival and Safe Motherhood (CSSM) register of health workers, the district’s health ‘program implementation plan’, and a 2005– 07 yearbook of ‘special information on health infrastructure of Orissa’. We used data abstraction tools to gather both demand and supply data from these sources.

Primary data sources included 24 interviews with physicians, staff nurses, lady health visitors and laboratory technicians. We conducted six focus groups with auxiliary nurse midwives and an additional 30 interviews with them. We also interviewed 10 mothers who had delivered babies within 3 months of the interview date, using semi-structured interview guides and pre-formed focus group questions. The purpose of these interviews and focus groups was to calibrate our staffing standards assumptions (time requirements for each service delivery task), to assess the patient contact time available for each cadre in a year (supply calculations), to assess the proportion of time spent on maternal and child care, and to gather qualitative assessments of the adequacy of staffing.

The data collection team comprised a lead PhD-level re-searcher and 10 data collectors, all associated with the Human Development Foundation, a non-government organization. He was also part of the NRHM evaluation team trained in the UNFPA-funded programme conducted by CHSJ and the UW in Delhi, June and December 2008.

Discussions with district health officials were held prior to field collection to obtain permissions. Researchers obtained informed consent from all participants in interviews and focus groups, using standard consent agreements.

Demand calculations

We calculated the total demand for services that would be generated if all the promised MCH services were delivered. To calculate demand for MCH services, we estimated the total patient visit time required (under the provisions of the NRHM) to provide all MCH services for the study population for the period April 2007 through March 2008. Several tiers of calculations were made, and are enumerated below.

What components go into an MCH package of services?

To calculate demand, we catalogued all the services required to provide comprehensive MCH services, including pregnancy

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registration, antenatal visits, routine injections, laboratory tests, admission examinations, institutional delivery of all babies, management of complications of delivery, newborn assessment, cord clamping, occasional resuscitation, post-partum care, home visits post-delivery, well-child care (including immunizations). Additionally, NRHM promises family planning, management of childhood illnesses, adolescent care, school-based disease con-trol, water quality monitoring, home visits, community needs assessments and generating vital records.

How much time does it take to deliver each service unit to one individual?

We calculated normative time-based ‘activity standards’ using a combination of two sources: (1) the service standards in the WISN methods guide (Shipp 1998), based on observations in Tanzania, Papua New Guinea, Kenya and Hong Kong, and (2) estimates of the time required to deliver a variety of MCH services, made by health providers in interviews and focus groups we conducted in Ganjam District (Kolehmainen-Aitken et al. 1990). For example, the basic laboratory tests for pregnant women each require an amount of time: urine test, 10 minutes; stool test, 10 minutes; Hb blood test, 15 minutes; sputum test, 1 hour; HIV test, 1 hour. Medical doctors were assigned 15 minutes to supervise a delivery, assuming they were on site and available, and 1 hour per day to attend to critical care patients. Staff nurses were assigned 1.5 hours for assisting or conducting deliveries. Estimates of administrative activity time were also made for each cadre of health worker.

What is the total health worker time required to serve this population, by cadre type?

The time requirements for each MCH service were multiplied by the relevant population size, to derive the total time required to serve the population by provider type. For some services, each person in the MCH population would be expected to receive the service (pregnancy registration), while for others we made estimates of how many would be expected to need the service (about 25% of children might need an assessment of whether pneumonia is present). The latter estimates were made by review of records at the clinics we observed.

In our case, an anchor figure in our calculations was the mid-year population estimate for 2007–08, which lists 3.5 million residents in Ganjam District and 1.02 million in our study area. In our study area, there were 79 460 children aged between 0 and 1, which we used as an estimate of births that year. There were 1362 infant deaths registered, and four maternal deaths. A 10% miscarriage was estimated, so we multiplied births by 1.1 to estimate pregnancies.

Supply calculations

We calculated the available supply of human resources for MCH care in the 18 health centres selected for this study. We used staff employed and on the job for our supply estimates, excluding vacant positions that might have been sanctioned but were unfilled.

Available work time each year

For each health worker type, we estimated the number of hours worked per year (from our interview data). We multiplied days

worked by work hours per day, and subtracted holidays, time away for training, and sick and vacation leave. The number of annual work hours for each cadre totalled 1872, except laboratory workers who work considerably more hours (2384). Staffing numbers were collected from the facilities in the study. Numbers of staff were multiplied by hours on the job to determine total service hours available for MCH care in the 18 centres.

When health workers had broad responsibilities extending beyond MCH care, we estimated their time spent on MCH as a proportion of their total effort. Physicians were estimated to spend 30% on MCH care, laboratory technicians 10%, staff nurses 40%, lady health visitors 70%, male health workers 40% and female health workers 70%.

Gap and surplus calculations

We subtracted total demand hours from total supply hours to derive the gap or surplus hours available by category of health worker in the 18 centres in the study. We then divided these hours of gap or surplus by the service hours per year a full-time practitioner would be expected to work, to determine the number of health workers required to fill the gap or who were in excess supply. The third step was to calculate the ratio of total supply hours over demand hours to estimate a comparable magnitude of the gap or surplus. Ratios of 1 imply perfect balance, while ratios below 1 indicate gaps and above 1 indicate surplus.

Results

We estimated population and health workforce supply for MCH care in 18 service centres of Ganjam District in Orissa State, India (see Table 1). The number of health workers totalled 357, to serve a population of children aged 0–1 of 24 642 and a total population of 1.02 million people. The largest MCH health worker cadre was nurse midwives, while the smallest was laboratory technicians. There were 91 male health workers, 45 medical officers, 21 staff nurses and 16 lady health visitors. Demand calculations, as described in the methods section, were made by (1) adding the time required to conduct each task in a comprehensive package of MCH services, and (2) multiplying it by the appropriate population figures. Supply calculations were made by multiplying the currently available staff hours in each category of worker by the portion of their time devoted to MCH activities. In Table 2, we illustrate the demand and supply calculations for one category of health worker (auxiliary nurse midwives) for each of the 18 service areas, and produce the gap figures for each. We then show the ratio of staff supply to the staff required, to produce a figure that illustrates the magnitude of the shortage. For example, in the Kodala health facility, the population served would require 49 571 hours of service. The 23 nurse midwives on staff, however, can only offer 30 139 hours of care, and so the gap is 19 432 hours. To staff those hours, 10 more midwives would be required, resulting in a WISN ratio of 0.61 (calculated by dividing supply by demand, or 30 139 by 49 571).

In Table 3, we illustrate our conclusion that the total demand for the MCH services guaranteed under India’s NRHM outpaced

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supply for every category of health worker, with the exception of lady health visitors. To properly serve the study population of 1.02 million residents in the 18 Ganjam service areas in our study, the health workforce supply should be enhanced by 43 additional physicians, 15 nurses, 9 laboratory workers, 80 nurse midwives and 37 male health workers. Those numbers probably under-estimate the need, as they are added across all 18 service areas, which is unrealistic in that it assumes no geographic barriers. The only over-staffed category is lady health visitors, who number about 2.4 more than needed across the 18 areas.

When the calculations are done by service centre, the Badagada Block upgraded primary health centre has the largest shortfall of staff, requiring an additional 13 FTEs to meet the minimum service guarantees in the NRHM. The WISN ratio for that centre is 0.52, indicating the supply of hours of care is only slightly better than half of that required. The smallest gaps in FTE shortage were found in the smallest facilities, where only one health staffer on duty comprised a minimum fixed service, but in most cases almost a full second FTE would be required to meet the demand.

Discussion

Our investigation explored the capacity of Ganjam District health facilities to meet service guarantees promised by India’s NRHM. We used a modified WHO WISN method, focusing only on maternal and child care services for the study population of 1.02 million people. We referred to the service guarantees in the NRHM to create lists of services each type of practitioner was expected to provide, and gathered primary data to determine the time required to deliver each service to the full population. Total demand for MCH care was calculated using population data for our service area. Total supply for MCH care was calculated using secondary staffing data supplied by the clinics in our study area.

Our findings indicate significant shortages in staffing required to meet the service guarantees of the NRHM. If state and national policy makers hope to deliver on NRHM promises, significant improvements in staffing levels for all cadres are required to serve mothers and children well. The 11 laboratory workers serving the million-plus population in our study are particularly stressed (with a ratio of 11% of staff required to meet demands), but doctors are the next most serious shortage

Table 1 Population and maternal and child health (MCH) related staff positions by cadre in selected service areas of Ganjam District, Orissa State, India, 2007–08

Health facility Total population 2007–08 Infant population (0–1 years) ANM/Health worker (female) Health worker (male) Lady health visitors Staff nurse Laboratory technician Medical officer District level Ganjam District 3 485 100 79 460 480 319 70 94 66 234 Block PHC/CHC level Patrapur Block PHC 130 361 2868 24 14 4 3 1 7

Badagada Block UGPHC 153 770 3518 20 15 1 4 1 7

Polasara Block UGPHC 152 665 4105 21 14 2 3 2 7

Buguda Block PHC 138 494 3445 21 13 3 3 2 7 Kodala CHC 119 807 3276 23 8 2 4 1 5 Jagannathprasad Block PHC 138 191 3151 26 9 2 4 1 6 PHC new level Baranga PHC New 24 818 546 6 2 0 0 1 1 Goudagotha PHC New 13 535 395 3 1 0 0 1 1 Karchuli PHC New 19 895 461 5 4 1 0 0 1 Beguniapada PHC New 29 690 719 7 3 1 0 0 1 Rahada PHC New 18 328 418 5 2 0 0 1 1 Baragaon PHC New 35 663 813 6 3 0 0 0 1 Sub-centre (SC) level Goudagotha SC 8125 185 1 1 Konkorada SC 5583 118 1 0 Biranchipur SC 7248 181 1 1 Buguda-II SC 6666 175 1 0 Kodala-II SC 5723 123 1 1 Khamarpali SC 6829 145 1 0

Total for study area 1 015 391 24 642 173 91 16 21 11 45

Notes: ANM¼Auxiliary nurse midwife; PHC¼primary health centre; CHC¼community health centre; UGPHC¼upgraded primary health centre.

Source: Staffing data were gathered in visits to the facilities named, using a data collection team in Ganjam District, 2008.

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category, with a 24% ratio. Staff nurses are not far behind, at 36%. We acknowledge Ganjam has authorized the hiring of 57 additional doctors beyond current staffing levels, but these positions remain vacant (MOHFW 2009).

There are limitations to this study. We did not directly calculate time standards of the MCH activities through time/ motion observation, but instead derived time estimates from conversations with practitioners. Actual time requirements could be different from the estimates of practitioners, for a variety of reasons. We conducted the study in an area limited to 18 clinics, with a population limited to a million, and for MCH services only. Other scopes of observation may have generated different conclusions. There may be activities involved in MCH care that we did not capture in our lists, which would understate the gap in service availability.

A strength of the study is that the research team comprised local people trained in a tested method being promulgated by the WHO. Limiting our scope of analysis to MCH services allowed a realistic and objective measure of demand, as births in the study area are routinely and relatively accurately recorded. Supply data were also relatively reliable, as there were good records of existing staff in the service areas we visited.

In the presence of government promises to deliver a min-imum package of services, the WISN approach offers a methodology to calculate the expected demand such a package of services should generate. The method then allows calcula-tions of supply based on current staffing levels, and simple subtraction creates information about the gap that exists between promises and delivery capacity. Since the method is

Table 2 Demand, supply and calculations of gaps and surpluses for nurse midwife time only to provide maternal and child health (MCH) services in selected areas of Ganjam District, Orissa State, India, 2007–08

Health facility No. of midwives Total demand in hours for MCH services Total supply of MCH activity hours for all health worker types Gap or surplus in hours (dc) No. of additional staff required to meet need (e/hours per year per FTE employee)

WISN ratio*: ratio of staff supply/required (d/c) (a) (b) (c) (d) (e) (f) (g) District level Ganjam District 480 1 100 496 628 992 471 504 251.87 0.57 Block PHC/CHC level Patrapur Block PHC 24 48 452 31 450 17 002 9.08 0.65

Badagada Block UGPHC 20 50 018 26 208 23 810 12.72 0.52

Polasara Block UGPHC 21 51 886 27 518 24 368 13.02 0.53

Buguda Block PHC 21 47 960 27 518 20 442 10.92 0.57 Kodala CHC 23 49 571 30 139 19 432 10.38 0.61 Jagannathprasad Block PHC 26 52 750 34 070 18 679 9.98 0.65 PHC new level Baranga PHC New 6 11 096 7862 3233 1.73 0.71 Goudagotha PHC New 3 6274 3931 2342 1.25 0.63 Karchuli PHC New 5 9282 6552 2730 1.46 0.71 Beguniapada PHC New 7 13 433 9173 4260 2.28 0.68 Rahada PHC New 5 9026 6552 2474 1.32 0.73 Baragaon PHC New 6 12 685 7862 4822 2.58 0.62 Sub-centre (SC) level Goudagotha SC 1 2408 1310 1098 0.59 0.54 Konkorada SC 1 2010 1310 700 0.37 0.65 Biranchipur SC 1 2385 1310 1074 0.57 0.55 Buguda-II SC 1 2349 1310 1039 0.55 0.56 Kodala-II SC 1 2040 1310 729 0.39 0.64 Khamarpali SC 1 2171 1310 860 0.46 0.60

Total for study area 173 375 796 226 695 149 094 80 0.60

Source: Staffing data (for supply figures) were gathered in visits to the facilities named, using a data collection team in Ganjam District, 2008. Demand data were calculated by assigning time standards to each activity expected to be delivered in a guaranteed package of MCH services, and multiplying by the relevant population figures in the service area.

Notes: *When supply meets demand, the WISN number is 1.0; when demand exceeds supply, the WISN score falls below 1.0. Severity of shortage or surplus can be measured by the distance from 1.0.

PHC¼primary health centre; CHC¼community health centre; UGPHC¼upgraded primary health centre.

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built on task aggregation, it allows for easy task substitution between cadres when making comparisons of human resources policy or planning assumptions.

Government health planners can use WISN data to consider the magnitude of staffing increases that would be needed to meet service guarantees, with specific staffing information by practitioner cadre. Using salary and training cost data, planners can generate budgets required to fill the gaps. Previous studies of this type have led other jurisdictions to understand how to measure the gap between a policy commitment to an essential package of services and the resources available on the ground, at both local levels and for entire continents (Hossain 1999; Ensoret al. 2002; Kinfu et al. 2009).

Our study generated time standards in minutes for each MCH activity promised by the NRHM. These standards could now be applied to other districts in India (time standards are available from the authors upon request). WISN, then, can provide a useful tool for civil society advocates who seek to hold governments accountable for their health service guarantees.

India is exporting doctors to countries around the world, while its own distribution of physicians within country is inadequate to meet its promises to the population (Mullan 2006). The right to health is universally acknowledged by the WHO (Hagopian 2007; Mills et al. 2008), and India has taken an important step in adopting the NRHM plan. Significant numbers of new health workers are required to deliver on that plan.

Funding

The primary source of support for this project was from the United Nations Population Fund, grant number IND7R21C. We also had support from the University of Washington Population Leadership Program, funded by the Bill and Melinda Gates Foundation and the Packard Foundation. The

authors also had support from their employers: AH by the University of Washington Department of Global Health, MKM by Human Development Foundation in Orissa State, AD by The Center for Health and Social Justice (including support from the Ford Foundation) and PJH by the University of Washington School of Medicine.

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Table 3 Demand, supply and gap/surplus by health worker type for maternal and child health (MCH) in selected service areas of Ganjam District, Orissa State, India, 2007–08

Staff category No. of workers in our study area devoted to MCH care Total demand in hours for MCH services Total supply of MCH activity hours for all health worker types Gap or surplus in hours (dc) No. of additional staff required to meet need (e/hours worked per year per worker)

WISN ratio*: ratio of staff supply/required (d/c) (a) (b) (c) (d) (e) (f) (g) Doctors 45 104 859 25 272 79 587 42.51 0.24 Staff nurses 21 43 535 15 725 27 810 14.86 0.36

Auxiliary nurse midwives 173 375 739 226 699 149 040 79.62 0.60

Lady health visitors 16 19 055 23 587 4532 2.42 1.24

Laboratory staff 11 23 257 2622 20 635 8.66 0.11

Male health workers 91 137 729 68 141 69 588 37.17 0.49

Total 357 704 174 362 046 342 128 180.39 0.51

Source: Staffing data (for supply figures) were gathered in visits to the facilities named, using a data collection team in Ganjam District, 2008. Demand data were calculated by assigning time standards to each activity expected to be delivered in a guaranteed package of MCH services, and multiplying by the relevant population figures in the service area.

Notes: *When supply meets demand, the WISN number is 1.0; when demand exceeds supply, the WISN score falls below 1.0. Severity of shortage or surplus can be measured by the distance from 1.0.

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