District Hospital Efficiency Study Report in...

District Hospital Efficiency Study

Report in Afghanistan

August 2018

District Hospitals Efficiency Study Report in Afghanistan

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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


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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


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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


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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.


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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


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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.


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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.


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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


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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:


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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.


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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


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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


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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


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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.

https://www.healthpolicyproject.com/pubs/419_PolicyBriefEMISenglish.pdf

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