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District Hospital Efficiency Study
Report in Afghanistan
August 2018
District Hospitals Efficiency Study Report in Afghanistan
Page 2 of 19
CONTENTS
FIGURES ............................................................................................................... 3
TABLES.................................................................................................................. 3
Acknowledgment: .................................................................................................. 4
Executive Summary ................................................................................................ 5
Acronyms ............................................................................................................... 8
1. Introduction .................................................................................................... 9
2. Methodology ................................................................................................... 10
3. Results ............................................................................................................ 13
3.1. Description of inputs, outputs and contextual factors ................................................... 13
3.2. Efficiency Results ............................................................................................................ 14
3.3. Explanation of efficiency ................................................................................................. 15
4. Discussion and Conclusion ............................................................................. 16
5. References ...................................................................................................... 19
District Hospitals Efficiency Study Report in Afghanistan
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FIGURES
Figure 1. Efficiency scores of district hospitals by quintiles........................................................... 15
TABLES
Table 1.Description of inputs and outputs and contextual variables of district hospitals ............ 14
Table 2. Regression model to explain the hospital efficiency ........................................................ 16
District Hospitals Efficiency Study Report in Afghanistan
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EXECUTIVE SUMMARY
INTRODUCTION: The Basic Package of Health Services (BPHS) and Essential Package of Hospital
Services (EPHS) have served as cornerstones of the Afghan health system. They were designed in
2003 and 2005, respectively. BPHS is offered at six standard types of health facilities, ranging
from community outreach provided by Community Health Workers (CHWs) at health posts;
outpatient care at Health Sub Centers (HSCs), Basic Health Centers (BHCs) and Mobile Health
Teams (MHTs); and inpatient services at Comprehensive Health Centers (CHCs) and District
Hospitals (DHs). DHs are the intersection between BPHS and EPHS, and they play a critical role
in reducing maternal and child mortality in the Afghanistan health sector. Given the limited
resources for health in Afghanistan, it is important to ensure DHs are fully functioning.
This analysis was conducted to assess how efficiently the BPHS DHs are performing, potential
resources the Ministry of Public Health (MoPH) can generate if the efficiency of health service
delivery at the DHs improves, and factors determining the efficiency, in order to enhance the value
for money in DHs in using existing resources.
METHODOLOGY: This study follows a classical framework of economic analysis of efficiency
using a two-step process: in the first step, efficiency of DHs was evaluated with direct inputs and
outputs, and in the second step, econometric models were applied to explain the efficiency. To
evaluate technical efficiency of DHs we used Data Envelopment Analysis (DEA) to generate an
efficiency score for each DH. In this study, an input-oriented DEA was used with variable returns
to scale.
This study considers three sets of variables: (1) direct inputs to DHs (2) direct outputs of DHs,
and (3) potential contextual factors affecting the efficiency. The inputs that were used include: (1)
the number of clinical personnel; (2) the number of non-clinical personnel; (3) the non-personnel
recurrent expenditure; and (4) the capital expenditure. The output indicators in this analysis
were: (1) the number of admissions; and (2) the number of outpatient visits. Input and output
data as well as contextual factors were obtained from the Expenditure Management Information
System (EMIS) and Health Management Information System (HMIS) managed by the MoPH for
the year 2016 (January 1st, 2016 to December 31st, 2016). A total number of 56 DHs were included
in the analysis.
To understand the determinants of variation of efficiency at the DH level, the following indicators
were included as potential contextual factors that may influence the efficiency: (1) location of the
District Hospitals Efficiency Study Report in Afghanistan
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DH based on the province; (2) the share of supporting staff among the total number of staff; (3)
the share of capital costs among total costs and; (4) an indicator of case-mix, which is estimated
as the proportion of deaths among the total number of admissions in the hospital. These
contextual factors were developed based on availability of data in the country.
Results: On average, 56 DHs had an efficiency score of 94.9 percent, ranging from 88.5 percent
to 100 percent. For those hospitals that were not 100 percent efficient, the average efficiency score
was 93.7 percent. When examining efficiency by quintile, two quintiles (22 hospitals) of DHs were
technically efficient with the score of 100 percent, whereas the remaining 3 quantiles of hospitals
had an average efficiency score of 90 percent, 93 percent, and 98 percent, respectively. This
implies that hospitals in each quintile could reduce inputs by approximately 10, 7 and 2
percentage points respectively, without reducing outputs. Given that 22 DHs have efficiency score
of 1, the fifth and fourth quintile are lumped together.
We found that case mix is an important factor associated with DH efficiency. If case mix increases
by 1 percentage point, the efficiency would be reduced by 2.5 percentage points. Additionally,
share of supporting staff may be associated with efficiency. Higher share of supporting staff was
likely associated with lower efficiency. One percentage point increase in the share of supporting
staff was associated with a 0.19 percentage point reduction in the efficiency score.
Discussion and conclusion: This study evaluates technical efficiency of DHs in Afghanistan
using EMIS and HMIS and DEA methodology. The average efficiency score at DHs is estimated
at 0.95, which is very high. We should interpret this finding with caution, as DEA measures
relative efficiency, rather than absolute efficiency. This study shows that the performance among
DHs is relatively homogenous. This is perhaps because DHs offer a standardized package of
services. Furthermore, DHs are financed by donors using target population as the main parameter
to estimate the budget.
This study also suggests that EMIS, in combination with HMIS, could be very useful for efficiency
analysis, and provide valuable information for decision making. With these data, we could further
examine the efficiency of each health facility (hospitals in this case) and better understand the
constraints associated with enhancing efficiency. With additional data on personnel expenditure,
it is likely that the unit costs per outpatient visit equivalent could be derived, after converting
hospital days into an outpatient visit equivalent. It is suggested that the EMIS data be used as a
regular source of public health expenditure for producing NHA; therefore, contributing to NHA
institutionalization through reducing data collection cost and efforts.
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In conclusion, the study findings show that 60 percent of the DHs were operating technically
inefficient. Considering the limitation of resources and the needs of people for health services at
the district level, improving of efficiency in DHs is still an important policy issue for the MoPH.
Furthermore, it is important to ensure data collected from EMIS are accurate.
District Hospitals Efficiency Study Report in Afghanistan
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ACRONYMS
BPHS Basic Package of Health services
CHC Comprehensive Health Center
CHW Community Health Worker
DH District Hospital
EMIS Expenditure Management Information System
EPHS Essential Package of Hospital Services
EU European Union
HMIS Health Management Information System
HSC Health Sub Center
IPD Inpatient department
NHA National Health Accounts
OPD Outpatient department
SEHAT System Enhancement for Health Action in Transition
SM Strengthening Mechanism
THE Total health expenditure
USAID United States Agency for International Development
WB World Bank
District Hospitals Efficiency Study Report in Afghanistan
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1. INTRODUCTION
In an effort to rebuild the country’s health system after decades of conflict, the Government of
Afghanistan began implementing the Basic Package of Health Services (BPHS) to provide a
standardized package of basic primary health care services across the country. This was
complemented by the Essential Package of Hospital Services (EPHS), which provides secondary
care to the population to increase referrals and access to hospital services.
The BPHS and EPHS, designed in the 2003 and 2005, respectively, have served as cornerstones
in establishing the Afghan health system. After decades of conflict, health indicators in
Afghanistan were extremely poor. The objective of the BPHS was to cover the majority of the
Afghan population with primary health care including maternal and newborn care, child health
and immunization, public nutrition, communicable disease treatment and control, mental health,
disability care, and a supply of essential drugs (1, 2).
BPHS is offered at six standard types of health facilities, ranging from community outreach
provided by Community Health Workers (CHWs) at health posts; outpatient care at Health Sub
Centers (HSCs) and Basic Health Centers (BHCs); Mobile Health Teams (MHTs) and; inpatient
services at Comprehensive Health Centers (CHCs) and District Hospitals (DHs).
DHs, as the main intersection between BPHS and EPHS, play a critical role in the Afghan health
sector. They are part of the referral system, which aims to reduce high maternal and early
childhood mortality rates. At the district level, the DH handles all services in the BPHS, including
the most complicated patients. Patients referred to the DH level include those requiring major
surgery under general anesthesia, X-rays, comprehensive emergency obstetric care, and male and
female sterilizations. It also offers comprehensive outpatient and inpatient care for mental health
patients and rehabilitation for persons requiring physiotherapy with referral for specialized
treatment when needed. The DH also provides a wider range than health centers of essential
drugs, treatment of severe malnutrition, renewable supplies, and laboratory services.
For over a decade, the World Bank (WB), the United States Agency for International Development
(USAID), and European Union (EU) have been supporting BPHS service delivery in the 34
provinces of Afghanistan. These donors extended their support through the System Enhancement
for Health Action in Transition (SEHAT) Project over the last three years to finance the
implementation of the BPHS through contracting out and contracting in arrangements, both in
rural and urban areas that allowed financing of health services in all 34 provinces of the country.
District Hospitals Efficiency Study Report in Afghanistan
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Still, challenges to adequate BPHS implementation remain. Ensuring cost-efficiency in service
delivery is a way to strengthen the BPHS and its reach to the Afghan population.
According to Afghanistan’s National Health Accounts (NHA) report, (3) 40 percent of the total
health expenditure (THE) occurred at the hospital level. Hospital management must dramatically
improve to ensure that these scarce resources are used in an effective and efficient manner and to
enable hospitals to function more effectively as part of the health system. A serious need for
improvement exists at all hospital levels, specifically DHs.
A recent fiscal space analysis conducted by the MoPH identified inefficiencies as a key area for
increasing fiscal space for health. (4) The fiscal space analysis qualitatively identified the potential
for efficiency gains in several areas: transparency and accountability, strategic purchasing,
procurement, hospital management and autonomy, health worker skills mix and task shifting,
public-private partnerships, alignment of spending with burden of disease, spending on primary
care, and distribution of health facilities. It was beyond the scope of the fiscal space analysis to
conduct an in-depth analysis of efficiency in the Afghanistan health system. Considering the
recent decrease in donor aid, the efficiency enhancement has become a priority task for the MoPH
to perform.
Therefore, this analysis was conducted to assess the potential resource we could generate by
improving efficiency of health services delivery at the DHs, using data from the Expenditure
Management Information System (EMIS) and Health Management Information System (HMIS).
The overall objective of the study is to provide insights into establishing a strong linkage between
EMIS and HMIS data systems, understanding the variation of performance of service delivery and
factors in determining the efficiency, and enhancing the value for money in using MoPH
resources.
2. METHODOLOGY
2.1 Assessment framework
This study followed a classical framework of economic analysis of efficiency using a two-step
process: in the first step efficiency of DHs was evaluated with direct inputs and outputs, and in
the second step econometric models were applied to explain the efficiency. (5) This study
considers three sets of variables: (1) direct inputs for DHs in delivering BPHS (2) direct outputs
of DHs delivering the BPHS, and (3) potential contextual factors affecting the efficiency.
District Hospitals Efficiency Study Report in Afghanistan
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2.2. Indicators and variables
In this study, the inputs that we used included following indicators: (1) the number of clinical
personnel; (2) the number of non-clinical personnel, including administrative staff and
supporting staff; (3) the non-personnel recurrent expenditure; and (4) the capital expenditure.
These four indicators capture the majority of the resources that were used by hospitals to provide
services. The output indicators in this analysis were: (1) the number of admissions; and (2) the
number of outpatient visits.
To understand the determinants of variation of efficiency at the hospital level, the following
indicators were included as potential contextual factors that may influence the efficiency: (1)
location of hospitals based on the categories of provinces where hospitals are located; (2) the share
of support staff among the total number of staff; (3) the share of capital costs among total costs;
(4) an indicator of case-mix, which is estimated as the proportion of deaths among the total
number of admissions in the hospital. The category of provinces was based on multiple factors,
such as remoteness, and was developed by the MoPH. The share of staffing and capital
expenditure helps to examine the allocation of resources. These contextual factors were developed
based on the availability of the data in the country. All data on inputs, outputs, and contextual
factors were for the year 2016, starting from January 1st to December 31st. The reason for selection
data in 2016 is due to the fact that the data on utilization of health services was not available for
2017 in the HMIS.
2.3. Data sources
The major data sources for the expenditures such as recurrent costs and capital cost were obtained
from the EMIS that was centrally managed by the Ministry of Public Health (MoPH). The EMIS
was developed under the Health Policy Project funded by the USAID, jointly with the MoPH. (6)
EMIS collects information on expenditure from all sources, including donors, projects, and
governments for all type of expenditures that health facilities incur, including hospitals, such as
expenditure on human resources (e.g. medical staff, administrative staff and supporting staff),
capital investment (e.g. equipment and machinery and tools), and other recurrent costs (e.g.
administrative costs, maintenance, supplies, utilities, and so on). EMIS has been applied to all
NGO-run health facilities to collect expenditure data to enhance the accountability.
For data on outputs, such as the number of inpatient admissions and outpatient visits, they were
obtained from HIMS, which was also managed by the MoPH. Additionally, HMIS also provided
District Hospitals Efficiency Study Report in Afghanistan
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information on the number of different types of personnel, and information to derive contextual
factors, such as the number of deaths at hospitals. In Afghanistan, HMIS has been used for many
years, with reasonably good validity. All data were reported quarterly from health facilities to the
provincial level and were then aggregated at the higher level.
The data was first extracted into an excel file including transaction details for providing input
data. We categorized all expenditure made in NGOs management offices as management cost and
all health facilities expenditures as field cost. After detailed discussion, the consensus was made
to use the average annual exchange rate from the Afghanistan Central Bank website for each year
and apply it when converting other currencies into USD. All the expenditure amounts were
converted into USD currency using average exchange rate. Indirect cost – usually up to 10 percent
of the total project budget - is an additional fund given to each implementer based on their
expenditure. It contained both management and filed expenses. We decided to allocate
management expenditure (implementers’ main office and provincial office expenditure) to field
expenditure. The percentage share of each health facility from total expenditure to health facilities
was used as allocation factor for adding management cost to each health facility. To identify the
outlier, standardized methods were used and any health facilities expenditure beyond the range
of minus plus 3 standard deviation (99.7 percent) was considered as an outlier and dropped from
the analysis. Since, Strengthening Mechanism (SM) provinces - Kapisa, Panjsher and Parwan –
does not report using EMIS, they are excluded from the study. Therefore, the sample includes
DHs from 31 out of 34 provinces. In total 56 DHs were included in the analysis.
2.4. Analysis approach
We used Data Envelopment Analysis (DEA), a classic non-parametric approach to evaluating
technical efficiency, to estimate the technical efficiency of DHs. In this study, an input-oriented
DEA was used with variable returns to scale. The model detailed description could be found in the
book edited by Coelli et al. (7) DEA estimates an efficiency score for each of hospitals, ranging
from 0 to 1. An efficiency score of 1 means that the hospitals is fully efficient with an efficiency
score of 100 percent, while an efficiency score of 0 suggests that the hospital does not produce any
outputs.
After obtaining the efficiency scores from the DEA model, we used efficiency score as a dependent
variable and contextual factors as independent variables, and conducted an ordinary least square
model as the second stage of the efficiency analysis, to explain the efficiency. The regression model
was expressed as:
District Hospitals Efficiency Study Report in Afghanistan
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𝑒𝑓𝑓𝑖𝑐𝑒𝑖𝑛𝑐𝑦 = 𝛽0 + 𝐵1𝑃𝑟𝑜𝑣𝑖𝑛𝑐𝑒 + 𝛽2𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑠𝑢𝑝𝑝𝑜𝑟𝑡𝑖𝑛𝑔 𝑠𝑡𝑎𝑓𝑓
+ 𝛽3𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 + 𝛽4𝑐𝑎𝑠𝑒 𝑚𝑖𝑥 + 𝜀
Additionally, descriptive analysis was conducted for all inputs, output and contextual variables.
For continuous variable, means and standard deviations were calculated, while frequency was
calculated for categorical variables. All the statistical analyses were conducted using STATA 10,
except DEA which was implemented with R.
3. RESULTS
3.1. Description of inputs, outputs and contextual factors
In total, there were 56 DHs included in the analysis. These DHs were located in 26 provinces. The
provinces with the greatest number of the DHs were Kabul and Balkh, while a few provinces had
one DHs. Table 1 shows the detailed description of inputs and outputs used to calculate the
efficiency score of the DHs, as well as the contextual variables. On average, the capital expenditure
was $17,953 per year per hospital, while the recurrent expenditure was $141,593 per year per
hospital, with a total of $159,545. On average, the number of supporting staff and technical staff
(e.g. doctors, nurses, lab technicians) was 20.6 and 22.2, respectively. The average number of
outpatient visits and inpatient visits was 115,287 and 3,621 respectively. The share of supporting
staff as percentage of the total number of staff was estimated at 48 percent, and the share of the
capital expenditure was about 12.2 percent. As to the case mix, which was measured as the
percentage of death among admitted patients, the average value was 0.5 percent, ranging from 0
percent to 2.3 percent.
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Table 1.Description of inputs and outputs and contextual variables of district hospitals
Variables Obs Mean Std. Min Max
Capital costs 56 17953 21450 618 82469
Recurrent costs 56 141593 77091 43104 357467
Number of supporting staff 56 20.6 5.1 14.0 49.0
Number of technical staff 56 22.2 4.4 14.0 42.0
Number of OPD visits 56 115287 48456 17383 245303
Number of IPD admissions 56 3621 2669 424 12456
share of supporting staff 56 48.0% 4.6% 35.9% 61.9%
Share of capital cost 56 12.2% 15.6% 0.5% 62.3%
Case mix 56 0.5% 0.5% 0.0% 2.3%
Note: OPD denotes outpatient department; IPD inpatient department.
3.2. Efficiency Results
On average, 56 DHs had an efficiency score of 94.9 percent, ranging from 88.5 percent to 100
percent. Among the 56 DHs, 22 had an efficiency of 100 percent. For those hospitals that were not
at 100 percent efficient, the average efficiency score was 93.7 percent. To see the details of the
DH efficiency score, they were grouped in quintiles based on their efficiency score (Figure 1). Out
of the 56 DHs included in the analysis 22 (39 percent) of DHs were technically efficient with the
score of 1, whereas the remaining 3 quintiles of hospitals had average efficiency score of less than
one (90 percent, 93 percent, 98 percent). This implies that on average, these quantiles of DHs
could reduce their utilization of all inputs by approximately 10 percent, 7 percent and 2 percent
respectively, without reducing outputs or increase output given the same input. Given that a lot
of hospitals have efficiency score of 1, the fifth and fourth quintile are put together.
District Hospitals Efficiency Study Report in Afghanistan
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Figure 1. Efficiency scores of district hospitals by quintiles
3.3. Explanation of efficiency
Table 2 shows the results from the regression model to explain efficiency differences of DHs. As
most hospitals were efficient with an efficiency score of 100 percent and a high average efficiency
score of 94.9 percent, there was limited variation of efficiency as a dependent variable. The R
square of the regression model is 16.6 percent. None of the contextual variables were statistically
significant, except case mix. If case mix increases by 1 percentage point, the efficiency would be
reduced by 2.5 percentage points. Additionally, share of supporting staff may be associated with
efficiency. Higher share of supporting staff was likely associated with lower efficiency. A 1
percentage point increase in the share of supporting staff was associated with 0.19 percentage point
reduction in the efficiency score.
90.1%
93.2%
98.2%
100.0%
84%
86%
88%
90%
92%
94%
96%
98%
100%
1 2 3 4&5
Eff
icie
ncy
Quintile
District Hospitals Efficiency Study Report in Afghanistan
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Table 2. Regression model to explain the hospital efficiency
Variables Coef. Std. Err. T P>t [95% Conf. Interval]
Province group 1 Reference
Province group 2 -0.002 0.018 -0.120 0.906 -0.038 0.034
Province group 3 0.000 0.018 0.020 0.982 -0.035 0.036
Province group 4 0.026 0.018 1.460 0.152 -0.010 0.062 Share of supporting staff -0.195 0.126 -1.550 0.128 -0.447 0.058 Share of capital costs 0.028 0.037 0.750 0.455 -0.047 0.103
Case mix -2.492 1.191 -2.090 0.042 -4.887 -0.098
Constant 1.057 0.063 16.750 0.000 0.930 1.184
4. DISCUSSION AND CONCLUSION
This study evaluates technical efficiency of DHs in Afghanistan using DEA methodology, and
shows that the average efficiency score at DHs is 0.95, which is relatively high. From the total of
56 district hospitals included in this study, 22 (39 percent) were technically efficient. The average
efficiency score of the other 3 quintiles of 34 (61 percent) hospitals was 0.94, which means the
respective hospitals can produce the same amounts by saving 6 percent of their inputs. These
three quintiles of hospitals also have different efficiency scores that varies from 0.90 to 0.98.
Considering the efficiency scores of these three quintiles of hospitals, they can produce the same
amount of services by saving 10 percent, 7 percent and 2 percent of inputs, using existing efficient
peers.
DEA measures relative efficiency, rather than absolute efficiency. This study shows that the
performance among DHs is relatively homogenous. This is perhaps due to the fact that DHs offer
a standardized package of services and the financing of DHs is mostly from donors using coverage
population as the main parameter to estimate the budget, which results in relatively homogenous
performance. Such a result, in some degree, is consistent with the claim that “DHs show less
variation in costs across the study hospitals.” (8) Further investigation is needed to better
understand the efficiency of DHs.
A high average efficiency score does not mean that the efficiency in DHs cannot be improved.
Previous study shows that the bed occupancy rate at EPHS hospitals was estimated at 72 percent,
and the unit costs for outpatient visits remain substantially varied in EPHS hospitals, ranging
from $1.31 to $4.53.(8) Similarly, the cost per bed per day varies from $13 to $38. Such variation
District Hospitals Efficiency Study Report in Afghanistan
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suggests that there is still much room for improvement for some of the health facilities. It should
be noted that the unit cost analysis is different from the efficiency analysis because the efficiency
analysis considers economy of scale while the unit cost analysis does not. Thus, it is not possible
to compare the results from the efficiency analysis to those from the unit cost analysis directly.
However, the unit cost analysis does provide useful information on scope of improving efficiency;
the unit cost varies substantially after controlling for variables that cannot be easily changed (e.g.
location).
The regression analysis showed that case mix is associated with the efficiency of DHs, which is
expected. The efficiency is lower in the hospitals with more severe patients. In this study, we use
the proportion of deaths among admitted patients as a proxy of severity of illness of patients in
hospitals. We assume that the hospital with high case fatality hosts more severe patients. The
severe patients would need more resources to treat, and result in relatively higher treatment costs.
Therefore, the efficiency is reduced.
The study also pointed out that the share of supporting staff may impact the hospitals efficiency.
The higher share of supporting staff may be associated with lower efficiency. At DHs in
Afghanistan, the proportion of non-technical staff represents 50 percent of the total number of
staff, ranging from 35.8 percent to 61.9 percent. The managers of BPHS should examine the
necessity of some non-technical positions to ensure that everyone hired in the hospital could
contribute to the provision of quality health services to patients. DHs with higher proportion of
non-technical staff should pay more attention to address this concern.
This study also suggests that EMIS, in combination with HMIS, could be very useful for efficiency
analysis, and provide valuable information for decision making. With these data, we could further
examine the efficiency of each health facility (hospitals in this case) to better understand the
constraints for better efficiency. With additional data on expenditure of personnel, it is likely that
the unit costs per outpatient visit equivalent could be derived, after converting hospital days into
outpatient visits equivalent. It is reported that an outpatient visit is equivalent to 0.32 hospital
days. (9) It is suggested that the EMIS data be used for conducting NHA as a regular source, in
order to minimize the cost of data collection for NHA. To use EMIS for future analyses, it is critical
to ensure that the data collected from EMIS is accurate and comprehensive.
Several limitations of this study should be acknowledged. First, the EMIS collects capital
expenditure, but it does not provide information on duration of capital expenditure. Thus, it is
different to annualize the capital expenditure for detailed costing analysis. Second, the EMIS does
District Hospitals Efficiency Study Report in Afghanistan
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not collect information on existing equipment and facilities (e.g. building). The inputs of this
analysis are underestimated. However, this is the same for all DHs included in the analysis; hence,
the potential bias from this underestimation is minimized. Next, the expenditure incurred in this
period may not necessarily be used for service delivery in the same period. For example, some
hospitals may procure medicine for 2017 at the end of 2016. Such lag effect may result in bias.
Lastly, for HMIS, the study lumped all types of inpatient admissions and outpatient visits,
respectively. Inpatient admissions for different reasons are not always homogenous. Similarly, it
is true for all types of outpatient visits. In DEA, all different types of admissions are treated
equally, as are all types of outpatient visits.
In conclusion the study findings show that 60 percent of the DHs were operating technically less
efficient. Considering the limitation of resources and the needs of people for health services in
district level, improving of efficiency in DHs remains an important policy issue for the MoPH.
Efficiency Analysis Report
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5. REFERENCES
1. Loevinsohn B, Sayed GD. Lessons from the health sector in Afghanistan: how progress can be made in challenging circumstances. JAMA. 2008;300(6):724-6. Epub 2008/08/14. 2. Newbrander W, Ickx P, Feroz F, Stanekzai H. Afghanistan's basic package of health services: its development and effects on rebuilding the health system. Glob Public Health. 2014;9 Suppl 1:S6-28. Epub 2014/05/29. 3. Ministry of Public Health. National Health Accounts, Afghanistan. Kabul: Ministry of Public Health, Afghanistan, 2016. 4. Ministry of Public Health. Potential avenues increase government investment in health in Afghanistan: Fiscal space analysis. Kabul: Ministry of Public Health, Afghanistan, 2016. 5. Zeng W, Shepard DS, Chilingerian J, Avila-Figueroa C. How much can we gain from improved efficiency? An examination of performance of national HIV/AIDS programs and its determinants in low- and middle-income countries. BMC Health Serv Res. 2012;12:74. Epub 2012/03/27. 6. Ministry of Public Health. The Expenditure Managment Information System: Moving toward greater transparency, improved planning, and harmonized reporting. Kabul: Ministry of Public Health, Afghanistan; 2015; Available from: https://www.healthpolicyproject.com/pubs/419_PolicyBriefEMISenglish.pdf. 7. Coelli TJ, Rao P, O’Donnell CJ, Battese GE. An introduction to efficiency and productivity analysis. New York, NY: Springer Science+Business Media, Inc. ; 2005. 8. Ministry of Public Health. Cost analysis of Afghanistan’s Essential Package of Hospital Services (EPHS). Kabul: Ministry of Public Health, Afghanistan, 2013. 9. Shepard DS, Hodgkin D, Anthony Y. Analysis of hospital costs: A manual for managers. Geneva: The World Health Organization; 1998.