Dzampe and Takahashi ﻿
Health Economics Review(2022) 12:57
https://doi.org/10.1186/s13561-022-00406-7

Open Access

RESEARCH

Competition and quality of care
under regulated fees: evidence from Ghana
Adolf Kwadzo Dzampe1,2*   and Shingo Takahashi3

Abstract
Background: How competition affects the quality of care is still not well understood empirically because of limited
and mixed results. This study examined whether competition leads to higher or lower quality health outcomes in
Ghana.
Methods: We used administrative claims data of hypertension patients for 2017 – 2019 (36 months), and an
instrumental variable method to examine the effect of competition, measured as an increase in district doctorto-population ratio on hospital-level ambulatory care sensitive condition hospitalization and in-hospital death
rates.
Results: Overall, we found that an increase in doctor density improves the quality of care for hypertension patientsin Ghana. That is, when there are more doctors, fewer patients are hospitalized, and the risk of in-hospital deaths
decreases. This result is robust to analyses at the individual and district population levels for ambulatory care sensitive
hospitalizations rate.
Conclusions: Our findings suggest that in the presence of physician-induced demand, competition can lead to
improvement in the quality of care, possibly through improved access to healthcare and increased physician time andcontact per patient. Future health policies need to consider possible welfare benefits of induced medical services andtraining more doctors.
Keywords: Competition, Physician-induced demand, Doctor-to-population ratio, Quality of care, Hypertension,
Ambulatory care sensitive hospitalization, Ghana
Introduction
Due to information asymmetry, the health economics
literature has primarily emphasized on the possibility that faced with competition, physicians may induce
demand for their financial self-interest, which may leadto escalation in costs.1 One important issue that hasreceived less attention is how this asymmetric information affects the quality of health care. As noted byLabelle et al. [1], a more important issue is the qualityof the “induced” services. A limited but growing number of empirical studies have sought to provide insight
into whether competition leads to higher or lower quality health outcomes, but results have been mixed [2–6].
1*Correspondence: forster@iuj.ac.jp
1
Japan Development Service (JDS) PhD Fellow, Graduate Schoolfor International Development and Cooperation, Hiroshima University, 1‑5‑1
Kagamiyama, Higashi‑Hiroshima, Hiroshima 739‑8529, Japan
Full list of author information is available at the end of the article

Doctors may not be perfect agents for their patients [10]. Physicians are
partly blamed for escalating health care expenditure because they are believed
to unduly influence the use of medical services for their financial self-interest– physician-induced demand [10]. As such, much of the attention of policy
makers and health economists have been focused on health service access andcost/expenditure control [1]. For example, in Ghana, the primary concern ofgovernment has been the escalating health expenditure that has led to thecontinuous deficits over the past decade [17].

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Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Existing theoretical literature on quality competitionin healthcare markets with regulated prices predicts thatcompetition increases quality of care (when prices areabove marginal cost) [7]. For example, an increase in physician density (increased competition) should improveaccess to healthcare and increase the physician timeand contacts per patient. The increased time and contacts should enable physician to follow medical guidelines and the quality per patient should increase. Also,
increased physician density (which decreases the numberof patients per physician) should increase competition
among physicians for patients. If patients value quality
and fees are fixed, physician compete by increasing quality [8].
However, how competition affects quality of care is still
not well understood empirically [9]. There are several reasons why increase competition may lead to lower qualityof care in Ghana. In the presence of physician-induced
demand (PID), increased competition provides greater
incentives for physicians to provide medical services
that may not be beneficial (or even harmful) to patientsin their quest to defend income [10]. For example, physicians may commit more patients to hospitalization or
cut back on essential inputs such as tests to preserve theirincomes as competition increases. In the case of hypertension care in Ghana, for example, past studies showthat physicians increase the intensity of treatment when
they face increased competition, by prescribing moreclasses of antihypertensive and other medications [11]. If
this is partly motivated by financial self-interest, it could
have negative implications for quality of care.
Indeed, there is growing evidence that patients are notsatisfied with quality of care they receive from accredited
health providers [12–14]. Patients usually perceive healthservices under the NHIS as “inferior” [12, 15]. As a result,
there is wide-spread provider shopping (multiple visits
within short period of time) [16, 17]. However, one challenge with many of these studies is that they are surveys
which only reflect patients’ perceptions of quality, rather
than clinical quality. In addition, patients may not beable to observe all dimensions of quality, especially whenstudies show that the medical literacy rate in Ghana isquite low [18]. However, to our knowledge, there is noempirical evidence of the relationship between competition and quality of care in Ghana.
Thus, the goal of this paper is to add new evidence from
Ghana on whether competition leads to higher or lower
quality health outcomes. As prior studies on PID show,
an increased competition leads to PID [11, 19–21]. Thereis evidence from Ghana that, when the doctor/population ratio at the district level increases, physicians react
to this increased competition by inducing patients tovisit more [11]. Instead, the focus of this study is whether

Page 2 of 13

increased competition, captured by an increase in doctor/population ratio, increases or decreases the quality ofhealth outcomes.
To measure health outcomes, we used the risk of inhospital deaths and ambulatory care sensitive conditions(ACSC) hospitalization for patients with hypertension.
We focused on hypertension because it affords doctors
a high level of discretion, and deviations from optimal
quality are more likely [19]. In Ghana, hypertension isamong the leading causes of hospital visits, and prevalence is projected to increase [22]. Under Ghana’s healthcare system, patients are free to see any health provider,and fees are regulated with no cost sharing. Intuitively,
increased competition might force physicians to increase
the quality of care to attract more patients. We present aformal illustrative model in subsequent section.
Using NHIS administrative claims data for 36 months(2017–2019), we examined the relationship between
district-level doctor/population ratio and our outcome
measures of quality; namely, the risk of in-hospital deathsand ACSC hospitalization for hypertension patients at
the hospital level. Hospitalization for ACSC, also known
as avoidable or potentially preventable hospitalization
is a common indicator of the effectiveness of primarycare [23, 24]. Hypertension and diabetes are examplesof chronic ACSC. When properly managed, these conditions do not require hospitalization. Timely access tohealthcare, medications, and patient education by physicians is known to reduce hospitalization for these conditions. Thus, increasing hospitalization rates for these
conditions are mostly considered indications of poor
quality of care [25].
We deal with the potential endogeneity of the doctor/
population ratio using an instrumental variable method,in which we use the presence of medical schools in district and metropolitan districts as instruments. Our
instruments are meant to capture the ‘attractiveness’ of alocation to doctors as a place to live and practice (we provide further justification for our instrument variables in
section three). Similar instruments were used in related
studies that dealt with endogeneity of doctor-to-population ratio [8, 19, 20, 26]. We improve the robustness of
our results by performing analyses at the individual anddistrict population levels. This is because medical services may be beneficial to individual patients, but may
not be socially optimal, and vice versa [27].
We found that increased physician density improves
the quality of hypertension care in Ghana, and this persists to the population level for ACSC hospitalizations.
That is, when there are more doctors, fewer patients are
hospitalized, and the risk of in-hospital deaths decreases.
Our findings suggest that even though competition may
incentivize health providers to act more in their financial

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

self-interest (as suggested by past studies), there is noevidence that such behaviour has negative effect on thequality of hypertension care in Ghana. Rather, access tohealthcare, and physician time and contact per patient
may have improved. This finding has implications for
future health policies. The potential benefits of training
more doctors are significant.
The rest of the paper is organized as follows: The next
section presents a brief review of the related literature.
Section three presents the methods, including an illustrative model which motivates our empirical strategy,
empirical implementation strategy, data, variables, anddescriptive statistics. Section four presents the results.
We have discussion and conclusions in section five.
Related literature

There is growing literature that examine the effect ofcompetition on quality of care. Mortality is the commonly used outcome. For example, Krakauer et al. [2]used Medicare data from the United States and foundthat physician supply has negligible effects on mortality, except for areas where physician supply levels arevery low. Another study found a negative but insignificant relationship between physician density and total
mortality rates [3]. In Germany, Sundmacher and Busse[5] found significantly lower avoidable cancer death
rates for some cancers (e.g., colon) as physician supply
increased.
Other studies have also examined the relationshipbetween physician density and process quality of care [8,28, 29]. Jürges and Pohl [8] use the degree of adherence tomedical guidelines for the management of risk factors for
cardiovascular diseases (CVD) and prevention of falls toconstruct their process quality of care measure. They use
German 2004 Survey of Health, Aging and Retirement inEurope (SHARE) data. Contrary to the prediction of their
theoretical model, they find weak and insignificant effectof physician density on quality of care. More recently,
Vallejo-Torres and Morris [28] conducted similar study
in England where they analysed the relationship between
physician supply and thirty-five process measures ofquality of care covering thirteen medical conditions.
Their results show that physician density has a significant positive effect on quality of care, with effects mostly
concentrated in indicators of care related to CVDs andarthritis.
Some previous studies also analysed ACSH rates [2, 25,
30, 31]. For example, Laditka (30) find that low physician
areas in the USA have high risk of ACSH while adequate
physician supply areas have significantly less risk. Kim
et al. [31] finds a negative association between primary

Page 3 of 13

care physician density and ACSH rate in Korea. Ricketts
et al. [25] examine the relationship between ambulatory
care sensitive condition admissions rate and structuralaccess to primary care in North Carolina. They measured
structural access by the number of primary care physicians and the presence of subsidized clinics. They findthat admission rate was highly corelated with income but
not with primary care resources. We find no studies fromAfrica (and Ghana) that directly examine the relationshipbetween physician density and quality of care.

Methods
Model

A theoretical model which motivates our empiricalstrategy was proposed by Jürges and Pohl [8] The intuition of this illustrative model is that more physicians
should improve access to medical services (as travel
time reduces) and increase physician time and contacts
per patient. Increased time and contact should enablephysicians to better follow medical guidelines, and thequality of care per patient should increase. Additionally, increased physician density should lead to increased
competition among physicians for patients. If patientsvalue quality and fees are fixed (as in Ghana), physicians
compete by increasing their quality.
Formally, the model by Jürges and Pohl [8] is as follows: physicians control only the quality of service. Like
Ghana, prices are set by regulators. Hence, the model
assumes that physicians take prices as given and set quality to maximize their income. Cost depends on quality and increases at an increasing rate. Benefit depends
on true quality (which is observable by patients) andincreases at a decreasing rate. The model also assumes
two binding constraints: the physician must at least breakeven for every patient treated. In addition, patients havethe option of going to other hospitals or not to receive
treatment at all, which gives them some minimum benefit. Therefore, patients must receive benefits that are atleast equal to their outside option when they visit a physician. The number of patients that demand services froma particular physician depends on the benefits they provide and doctor/population ratio in the district of practice. The physician density is determined exogenously.
Increasing quality increases the number of patients,
which increases the income of physicians. On the other
hand, an exogenous increase in doctor/population ratio
decreases the number of patients per physician, which
decreases income. Thus, the optimal quality level as thedoctor/population ratio increases must increase. The
testable prediction of this model is that quality increases
with physician density.

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Empirical strategy

We are interested in testing whether competition, measured as an increased doctor/population ratio, leads toimproved quality of care. Therefore, we estimate the following model at the health provider level:

Page 4 of 1337]. This introduces a negative bias. On the other hand,
physicians may locate in health-resource-deprived areas,
where the potential need for health care is high sincepatients are fully insured, or out of ethical considerations [8, 35]. If patients in such areas are less healthy, they

Qualityit = β0 + β1 ln Doc/Popl ct + Xit II + Cct Ŵ + δm + y + αr + εit,where Qualityit is the measure of health quality outcomes for provider i in month t. We used two quality
measures; the first is ACSC hospitalization rate, calculated as the number of hospitalizations per 100 hospital
visits. The second is in-hospital death rate, calculated as
the number of in-hospital deaths per 100 hospitalizations. Our main analysis uses provider-level data (i.e.,
hospital). The computation of the ACSC hospitalization
rate uses all hospital visits for hypertension. However,
the in-hospital death rate is computed based on the inpatient visits subsample. Doc/Popl ct is the doctor/population ratio at the district level, measured as the numberof doctors per 10,000 district population. Xit is a vector
of provider-level characteristics such as mean age, gender, type, and ownership of hospital. Cct is a vector of area
and organization characteristics (contextual factors) thatcan be either time-varying or time-invariant; for example, unemployment rate, proportion of population withno education, per capita income, and hospital beds per
capita (see Table 1 for the complete list of variables).
Andersen [32] refers to these contextual factors as predisposing, enabling, and needing characteristics that areimportant determinants of health outcomes. For example, we may observe better health outcomes in areas
where there are more people insured, unemployment
rates are lower, and more medical care resources suchas hospital beds and nurses are available. [32, 33] δm andλy are the month and year fixed effects, respectively. We
also control for region-level fixed effects, αr, to counterthe concern that doctor density may be correlated with
time-invariant region characteristics. For example, doctors in regions with high doctor density may develop certain practice styles, such as providing more amenities orseeking a second opinion [34], which might directly affect
the quality of healthcare. Including region fixed effects
removes bias due to this type of endogeneity.
Even after controlling for region-level fixed effects, thedoctor/population ratio may still be endogenous becauseof time-varying omitted variables, as pointed out in previous studies. [8, 35]. For example, patients in areas withhigh doctor density may have greater access to general health knowledge about beneficial lifestyle changes
through doctors’ advice, and hence may have a lower
risk of in-hospital deaths and ACSC hospitalizations [36,(1)

will have higher ACSC hospitalizations and in-hospital
deaths, introducing a positive bias.
Therefore, our preferred estimation approach is thetwo-stage least squares (2SLS) method. Suitable instruments are variables that affect the location decisions ofphysicians but have no direct effect on our outcome
measures. We instrument the doctor/population ratiousing the presence of medical schools and the metropolitan status of the district. The argument is that starting
physicians tend to live in the district where their medical schools are located after graduation, since they would
have forged location-specific capital, so that district
mobility tends to be low [38–40]. Additionally, in Ghana,the metropolitan districts have advantages in terms ofbetter amenities (such as schools, access to electricity,
and water). This makes it an attractive location for physicians both professionally and as consumers [19, 20].
Our instruments also need to be uncorrelated withomitted factors such as health knowledge and healthresource-deprived areas. One concern with this assumption may be that patients in metropolitan areas may
have better health knowledge if people in metropolitan
areas have more education. However, our model alreadycontrols for region-level average education (proportion with no education). Another concern may be that
metropolitan areas and areas with medical schools may
have more medical care resources. However, our model
additionally controls for medical care resources, such as
hospital beds per capita and nurse/population ratio. Yetanother concern is that healthy individuals may prefer tolive in “attractive” areas. One such mechanism may bethat more educated people self-select into metropolitanarea, and educated people tend to be healthier [32, 33].
However, we already control for the average educational
level in each region. We also control for socio-economic
indicators such as unemployment rate and per capita
income to further minimize the possibility of the instrumental variables being correlated with the error term.
Thus, after controlling for these characteristics, ourinstruments would capture the “attractiveness” of theselocations to doctors as a good place to live and practice.
Because our models are over-identified, we can test theexclusion restriction assumption using the Hansen J
statistic.

SD

0.356
0.246
0.871
0.083
0.045

Mission hospital

Private hospitalPrimary hospital

Secondary hospital

Teaching hospital

0.042
11.601

Proportion aged 65 over

Registered Nurses per 10,000
0.817
20.37723.1468.786
37.01641.591

Hospital beds per 1,000

Proportion no education

Real per capita Income (’000)

Unemployment rate

Proportion travel time < 30 min

Proportion population insured

Region characteristics ¶2.243

Doctors per 10,000 population

District Characteristics

0.398

Public hospital

Health Provider characteristics

Average length of stay

Gender (F = 1/M = 2)

1.263

42.00437.4278.676

23.00720.3240.831

11.738

0.043

2.152

0.034

0.061

0.905

0.243

0.383

0.374

3.006

1.256

(11.945)(9.836)(2.287)(20.608)(16.676)(0.318)(10.443)(0.012)(2.648)(0.181)(0.240)(0.293)(0.429)(0.486)(0.484)(3.919)(0.092)42.48937.7368.362

19.76021.6260.877

10.094

0.044

1.517

1.260

(12.194)(9.426)(2.235)(18.911)(17.340)(0.321)(9.060)(0.012)(2.112)(0.131)(5.311)(0.256)42.41538.1648.352

20.43621.381

0.882

10.373

1.557

1.560

2.962

1.251

62.102

0.006

Mean

(12.056)(9.367)(2.218)(19.571)(16.945)(0.319)(9.190)(2.117)(2.120)(3.530)(0.087)(4.653)(0.013)SD

62.732

0.035

Mean

42.14639.5538.538

24.852

16.8700.861

11.996

2.510

0.055

0.056

0.889

0.179

0.399

0.422

(12.408)(9.374)(2.312)(21.334)(12.422)(0.313)(10.649)(2.762)(0.227)(0.230)(0.314)(0.383)(0.490)(0.494)(0.430)(0.430)(13.944)(0.184)SD

44.32439.806

8.032

21.06520.4400.919

12.902

2.362

0.033

0.068

0.899

0.138

0.446

0.416

4.558

0.309

0.691

60.797

0.021

Mean

Inpatient(12.944)(8.547)(2.181)(20.277)(14.644)(0.297)(11.152)(2.774)(0.179)(0.252)(0.302)(0.345)(0.497)(0.493)(7.417)(0.462)(0.462)(16.661)(0.143)SD(2022) 12:57(11.799)(9.924)(2.279)(20.477)(17.059)(0.317)(10.356)(0.011)(2.668)(0.208)(0.276)(0.335)(0.430)(0.479)(0.490)(0.136)61.980

0.232

SD

0.246

(4.855)(6.993)

Mean

Male

62.026

2.157

Mean

All Visits

0.754(5.892)(15.835)SD§

Inpatient

Patient Level

FemaleMean age

Individual characteristics

In-hospital death (= 1 if YES)ACSC hospitalization (= 1 if YES)

In-hospital deaths per 1,000 population

ACSC hospitalization per 1,000 population

61.696

7.341

ACSC hospitalization per 100 visits

In-hospital deaths per 100 hospitalizations

Mean

All Visits

All Visits

Inpatient

District Level

Health Provider Level

Variable

Sample Population

Table 1 Summary statistics

Dzampe and Takahashi ﻿Health Economics Review
Page 5 of 13

0.2152,963

N(0.409)
2,093

0.083

0.100

Year 2017 figures imputed for 2018 and 2019 except for proportion population insured which has figures for all year

(0.386)

SD Standard deviation

2,486

0.182

0.212

¶

(0.411)(0.439)§Real per capita income inflation adjusted (base year: 2018)

0.261

Presence of medical school(0.276)(0.301)All Visits

All Visits

Inpatient

District Level

Health Provider Level

Metropolitan area status

Instruments

Sample Population

Table 1 (continued)

1,902

0.090

0.109

Inpatient(0.286)(0.312)

1,681,083

0.247

0.256

All Visits

Patient Level(0.432)(0.436)59,253

0.197

0.181

Inpatient(0.397)(0.385)

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57
Page 6 of 13

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Data, variables and descriptive statistics

The main data are from the National Health Insurance
Authority’s administrative claims databases from 2017 to2019 (36 months). It contains information on the patient’s
age, date of treatment, and diagnosis, including the International Classification of Diseases (ICD-10) codes, typeof treatment, outcome of treatment (whether the patientdied in hospital or discharged), information on service
providers, among others. We identified hypertensive disease cases using the following ICD-10 codes: I10—I13,
and I15 and in a few cases based on descriptions usedinstead of the ICD-10 codes (e.g., hypertension, essential
hypertension, chronic hypertension, hypertension old—about 4% of data). For analyses at the provider level, theindividual data were aggregated.
Data on doctors and nurses were obtained from the
Ministry of Health. These data were purposely compiled for this study by the Monitoring and Evaluation
Unit. Population data were obtained from the US Census
Bureau [41] and Ghana Statistical Services (GSS). Data onsocioeconomic characteristics of the region for 2017 arefrom the Ghana Living Standard Survey Wave 7 (GLSS7)
published by GSS. We obtained data on the number ofhospital beds from the Health Sector in Ghana (Facts andFigures, 2018) published by the Ghana Health Service.
Data on the location of accredited medical schools wereobtained from the Medical and Dental Council of Ghana.
Table 1 presents the summary statistics. Our main
results are from data analysed at the provider level, which
are shown in columns (1) and (2). Column (1) showssummary statistics using all hospital visits for patientswith hypertension. The average ACSC hospitalization per
100 hospital visits was 7.3. The average age of patientswith hypertension was 62 years. There are on average 2.2
doctors per 10,000 persons. There are 25% more females
than males, and the average length stay in hospitalizationis three days. Column (2) shows the summary statistics
based on inpatient data only. An average of 2.2 in-hospital deaths out of 100 hospitalizations are recorded every
month.
Individual-level and district-level summary statistics are shown in Columns (3) to (6). On average, about
3.5% of all hypertension patients were hospitalized,
and of the patients who were hospitalized, about 2.1%
resulted in in-hospital deaths. Patients who were hospitalized were, on average, slightly younger (61 years)
than all hypertension patients who visited the hospital(63 years). (See Table 1 for summary statistics). We also
computed the correlation between the two instrumental variables. The correlation coefficient is statisticallysignificant. However, we believe that each instrument
represents separate mechanism through which it generates random variation in the endogenous variable which

Page 7 of 13

allows us to isolate the causal effect of medical school
and metropolitan areas.

Results
Table 2 presents the main results of the effect of doctor/population ratio on quality of care analysed at theprovider level. Column (1) shows the pooled ordinary
least square (OLS) result on the ACSC hospitalization
rate, which suggests that there is a negative association
between the doctor/population ratio and ACSC hospitalization rate, although it is not statistically significant.
As we discussed earlier, the doctor-to-population ratio islikely to be endogenous, even after controlling for timeand region fixed effects. Column (2) shows our 2SLS estimation results using the presence of a medical school andthe metropolitan status of the district as instruments.
We first describe some diagnostic statistics. Regarding the test for the relevance of our instruments, we canreject the null hypothesis that coefficients for our instruments are zero in the first-stage regression, with F statistics equal to 27.71. We would like to note that this far
exceeds the usual rule-of-thumb criterion that F statistics
should be greater than 10 for strong instruments [42]. We
also fail to reject the over-identifying restrictions with ap-value eqaul to 0.21 (please see Hansen’s J statistics inTable 2). Overall, the tests give us further confidence inour choice of instruments.
According to the results in Column (2), the estimatedcoefficient for the doctor/population ratio is statisticallysignificant at the one percent level. This coefficient indicates that if the doctor/population ratio increases by one
standard deviation from the mean (i.e., 2.24 to 4.91 per
10,000, or a 119% increase), the number of ACSC hospitalization per 100 visits would decrease by 4.8 (computed
as (4.04/100)*119), representing a decrease of about 65%.
The finding that an increase in doctor density decreases
the ACSC hospitalization rate is consistent with the findings from past studies [30, 31].
Column (3) shows the OLS result for the effect of thedoctor-to-population ratio on in-hospital death rate. Like
the finding for ACSC hospitalization, this result does
not show a statistically significant coefficient for doctor
density. However, when we apply 2SLS in Column (4),we again find a negative and statistically significant coefficient of -1.66 for the doctor density. That is, if the doctor/population ratio increases by one standard deviationfrom the mean (i.e., 2.15 to 4.80 per 10,000, or a 123%
increase), the number of in-hospital deaths per 100 hospitalizations would decrease by 2.04, that is, a decreaseof about 95%. In addition, we would like to note that
our instruments appear to be relevant in the first-stage
regression with F-statistics equal to 32.71, and that we
fail to reject the null hypothesis of the overidentifying

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Page 8 of 13

Table 2 Health provider level estimates of the effect of doctor-to-population ratio on quality of care
Dependent variables:

(1)

(2)

ACSC hospitalization per 100 visits

(3)

(4)

In-hospital death per 100
hospitalizations

OLS

2SLS

OLS

2SLS

ln(Doctors per 10,000 population)-1.072-4.04***

0.028-1.661***(0.791)(1.26)(0.36)(0.524)ln(Proportion aged 65 and over)

1.319

0.393

1.239

0.546

(3.077)(3.146)(1.623)(1.815)ln(Registered nurse per 10,000 population)-0.5633.478**-0.5681.701**(1.923)(1.745)(.607)(.758)

Mean age-0.579**-0.6**-0.116-0.125(0.291)(0.287)(0.097)(0.097)ln(Hospital beds per 1,000 population)

111.685

114.471

16.39513.509(75.324)(74.365)(26.005)(25.802)ln(Proportion no education)

34.67

34.2721.049-0.931(22.21)(21.799)(6.556)(6.423)ln(Real per capita income)

28.89929.584

4.536

3.609

(19.871)(19.5)(6.968)(6.91)ln(Unemployment rate)

26.89**

24.648**-1.216-2.976(12.425)(12.254)(4.431)(4.535)46.669

51.06

3.354

2.652

(42.6)(42.321)(11.406)(11.405)ln(Proportion travel time < 30 min)Mission hospital
Private hospital
GenderSecondary hospital
Tertiary hospitalln(Proportion population insured)-3.169-2.896-0.4610.026(2.66)(2.6)(.739)(.708)-2.798-3.346-1.281-1.576*(2.46)(2.651)(.789)(.915)

12.287***12.06***

13.921**14.07**(3.575)(3.509)(6.911)(6.78)

2.519

3.972

3.251*

3.942**(2.724)(2.901)(1.855)(1.856)-2.951-1.30-1.122-.527(3.146)(2.978)(2.104)(2.046)33.09129.781

6.967

7.181

(20.235)(20.365)(7.582)(7.761)-0.3-0.233ln(Average length of stay)
Hansen J statistic (p value)-1.60 (0.21)(0.225)(0.215)-1.85 (0.17)

Weakiv (F statistic)

-

27.71

-

32.71

Kleibergen-Paap rk LM (p value)-

8.09 (0.017)-

7.07 (0.029)

Standard errors are in parentheses clustered at the district level. Estimates include month, year, and region dummies. Instruments: Presence of medical school andmetropolitan status of the district. Real per capita income (base year: 2018)***

p < 0.01**

p < 0.05*

p < 0.10

restrictions with p-value equal to 0.17. Overall, ourresults support the hypothesis that competition increases
the quality of care for patients with hypertension. We
found that fewer patients were hospitalized, and the inhospital death rate reduced as doctor density increased,
an indication of improved quality of care [24].

Robustness checkWe estimate two additional sets of models to test therobustness of our results to different units of analysis
and to evaluate whether medical services are beneficial atboth the individual and population levels. This is because
not only is there a conflict between individual healthDzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Page 9 of 13

Table 3 District population level estimates of the effect of doctor-to-population ratio on quality of care
Dependent variables:ln(Doctors per 10,000 population)ln(Proportion aged 65 and over)ln(Registered nurse per 10,000 population)
Mean ageln(Hospital beds per 1,000 population)ln(Proportion no education)ln(Real per capita income)ln(Unemployment rate)
Genderln(Proportion travel time < 30 min)ln(Proportion population insured)ln(Average length of stay)

(1)

(2)

(3)

(4)

ACSC hospitalization per 1,000 population

In-hospital deaths per 1,000
population

OLS

2SLS

OLS

2SLS

-0.003-0.163**0.0002-0.004(0.027)(0.069)(0.001)(0.003)

0.113

0.119

-0.003-0.003(0.108)(0.125)(0.007)(0.007)

0.098**

0.313***

0.003

0.008**(0.038)(0.12)(0.002)(0.004)0.003*

0.003

0.000

0.000

(0.002)(0.002)(0.000)(0.000)3.049**

3.308**

0.022

0.026

(1.265)(1.582)(0.053)(0.051)

1.11**

1.088**

0.004

0.002

(0.451)(0.469)(0.012)(0.011)0.724**

0.761*

0.002

0.002

(0.336)(0.417)(0.013)(0.012)

0.697**

0.598*-0.004-0.007(0.324)(0.317)(0.019)(0.018)-0.288***-0.274***-0.007*-0.006(0.056)(0.057)(0.003)(0.004)2.516***

2.795***

0.028

0.032

(0.866)(0.95)(0.023)(0.024)-0.271-0.346

0.017

0.016

(0.378)(0.48)(0.016)(0.016)--0.00020.0001(0.0007)(0.0006)

Hansen J statistic (p-value)-

0.513 (0.47)-

2.77 (0.10)

Weakiv (F statistic)

-

15.80

-

11.52

Kleibergen-Paap rk LM (p-value)-

5.81 (0.05)-

5.08 (0.07)

Standard errors are in parentheses clustered at the district level. Estimates include month, year, and region dummies. Instruments: presence of medical school andmetropolitan status of district
Real per capita income (base year: 2018)***

p < 0.01**

p < 0.05*

p < 0.10

and population health in terms of resource allocation[27], but also because past studies have produced mixed
results depending on the unit of analysis [3, 8].
First, we aggregate the data to the district level insteadof the hospital level and re-estimate Eq. (1), where theoutcome variables are ACSC hospitalization per 1,000
district population and in-hospital deaths per 1,000 district population. Table 3 column (1) shows statisticallyinsignificant negative association between the doctor/
population ratio and ACSC hospitalization rate. However, the 2SLS results in column (2) shows statisticallysignificant negative relationship between the doctor/

population ratio and ACSC hospitalization rate, consistent with our main result. That is, if doctor/population
ratio increases by one standard deviation from the mean(i.e., 1.52 to 3.63 per 10,000, an increase of 139%), ACSC
hospitalization per 1,000 population would decrease by
0.23 (or 99%).
Column (3) shows the results of the effect of doctor/
population ratio on in-hospital mortality rate, whichsuggests a positive but statistically insignificant positive association. The 2SLS results in column (4) shows
statistically insignificant negative relationship between
the doctor/population ratio and in-hospital mortality

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Page 10 of 13

Table 4 Individual level estimates of the effect of doctor-to-population ratio on quality of care
Dependent variable:

(1)

(2)

Probability of ACSC hospitalization

(3)

(4)

Probability of in-hospital deaths

OLS

2SLS

OLS

2SLS

ln(Doctors per 10,000 population)-0.009***-0.016***0.0004-0.016*(0.002)(0.004)(0.004)(0.009)ln(Registered nurses per 10,000

0.014***

0.023***-0.005

0.015

Population)(0.003)(0.006)(0.008)(0.012)Age

0.0004***0.0007***0.0003***0.0003***(0.000)(0.000)(0.000)(0.000)ln(Hospital beds per 1,000

0.057

0.052

0.103

0.13

population)(0.118)(0.125)(0.131)(0.13)ln(Proportion no education)

0.081**

0.072**

0.011

0.000

(0.031)(0.033)(0.038)(0.037)ln(Real per capita income)

0.005

0.003

0.025

0.031

(0.03)(0.032)(0.037)(0.038)ln(Unemployment rate)

0.063**

0.057**-0.025-0.036(0.026)(0.027)(0.029)(0.03)ln(Proportion travel time

0.101

0.093

0.037

0.044

< 30 min)(0.091)(0.091)(0.066)(0.064)

Mission hospital

0.003

0.006

-0.0060.000(0.005)(0.005)(0.007)(0.006)

Private hospital-0.005-0.0070.002-0.004(0.005)(0.005)(0.012)(0.014)Male

0.013***

0.013***

0.01***

0.01***(0.002)(0.002)(0.002)(0.002)

Secondary hospital

0.014***.017***.059**.065**(0.004)(0.004)(0.025)(0.027)

Tertiary hospitalln(Proportion population insured)-.01-0.005-0.0030.001(0.015)(0.016)(0.01)(0.01)

0.002

0.004

0.024

0.021

(0.033)(0.036)(0.045)(0.048)

0.004

0.004

ln(Average length of stay)
Hansen J statistic (p-value)-

0.001 (0.97)(0.003)(0.003)-5.26 (0.02)

Weakiv (F statistic)

-

21.21

-

35.04

Kleibergen-Paap rk LM (p-value)-

5.07 (0.07)-

5.23 (0.07)

Standard errors are in parentheses clustered at the district level. Estimates include month, year, and region dummies. Instruments: Presence of medical school andmetropolitan status of the district***p < 0.01, ** p < 0.05, * p < 0.10

rate. Some past studies also found statistically insignificant negative relationship between doctor density andmortality rate [2, 3]. Note that the instruments pass thetests of relevance (F statistic = 11.52) and overidentifying restrictions (p-value = 0.10).
Finally, we also estimate Eq. (1) at the individual level,
where the outcome measures of quality are the probability of ACSC hospitalization (equal to 1 if the patient
was hospitalized and 0 otherwise) and the probabilityof in-hospital death (equal to 1 if the patient died duringhospitalization and 0 otherwise). Table 4 column (1) showsa statistically significant negative association between
physician density and the probability of ACSH. The 2SLS
results from column (2) also show that the probability of
ACSC hospitalization decreases with increasing doctor/
population ratio. Column (3) shows a negligible and statistically insignificant positive association between the doctor density and the probability of in-hospital deaths. The
2SLS results in column (4) however shows that the probability of in-hospital deaths decreases as doctor density

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

increases. Note that all the statistical tests on the instruments in these models turned out as expected, except forcolumn (4) (see bottom of Table 4). The results of thismodel should be interpreted with caution.
Our estimates may seem rather large in magnitude.
However, given the situation in Ghana, they are notunreasonable estimates because in areas where doctors are lacking, one doctor sees, on average, over 4,500
patients. As such, a small increase in the number ofdoctors will reduce the burden of each doctor dramatically. It may be the case that, due to insufficient healthcare in general, Ghana is still facing increasing returns
to scale to number of doctors.

Discussion and conclusions
We examined the relationship between competition andquality of care in Ghana’s healthcare system where fees
are regulated, and patients are fully insured with no cost
sharing. We used administrative claims data and an instrumental variable method. We performed analyses at thehospital level, and at the individual and district levels toimprove the robustness of our results. Overall, we foundthat an increase in doctor density improves the quality ofcare for hypertensive patients in Ghana, which is consistent with previous findings [6, 30, 31]. Our findings suggestthat even though competition may incentivize providers to
act more in their financial self-interest in Ghana [11], there
is no evidence that such behaviour has a negative effect on
the quality of hypertension care. On the contrary, we findthat competition leads to improved quality of care.
Where we measure quality of care as ACSC hospitalization rate, we find that doctor density has statisticallysignificant positive effects at all levels of analyses. Our
results suggest that ACSC hospitalization may be highly
sensitive to improvements in primary care and that theavailability of doctors could play a significant role in
reducing ACSC hospitalization for hypertension care in
Ghana. Our finding that increased doctor density leads
to improved quality of care is consistent with past studies [30, 31]. On the other hand, when we measure quality of care as the risk of in-hospital deaths, we find mixed
results depending on the level of analysis. We find statistically significant positive effect of doctor density on
in-hospital death rate at the provider level. We failed tofind any reliable effects at the individual and district levels, even though the positive effect remained. Some paststudies have also found statistically insignificant and negligible effects [2, 3].
One possible mechanism for the positive effect of doctor density on quality of care is that as physician density
increases, the number of patients per physician decreases
and doctors are able to spend more time per patient (andbetter adhere to medical guidelines). More doctors in a

Page 11 of 13

district also improves timely access to healthcare, leading
to improvement in patient health outcomes. An important implication of our finding is that there is positive
return to training more doctors in Ghana. Our findings,
together with previous studies from the PID literature[11], suggests that medical services are at levels that the
fully informed patient would not have chosen to consume
but were found to be clinically beneficial to the patient.
It is, however, argued that the “consequences” or healthoutcomes of induced services are more important than
inducement per se [1].
A limitation of this study is that improvements in thequality of hypertension care may not necessarily imply
improvement in quality for all diseases when doctor density
increases. Generalization of the entire healthcare system
is not possible. However, hypertension is one of the leading causes of hospital visits in Ghana and is projected toincrease [22]. As such, this study may be relevant to policymakers. Additionally, we used only two measures of quality of care. However, it is possible that patients’ evaluationof quality may be in dimensions other than those evaluated
here. For example, patients may evaluate quality based on
reception by health staff or the timeliness of services at
the hospital. Indeed, studies have noted such divergence
between technical and perceived quality of care in Ghana[12]. More comprehensive measures that include patientoriented quality indicators, such as waiting time andexperiences with health staff, could be the focus of future
research. However, our quality indicators are widely usedmeasures, especially ACSH, which is a widely acceptedindicator of the effectiveness of primary care [23]. Future
health policies need to prioritise the training of doctors.
Abbreviations
NHIS: National health insurance scheme; G-DRG: Ghana diagnosis relatedgroups; NHIA: National health insurance authority; ACSC: Ambulatory caresensitive conditions; PID: Physician induced demand; GSS: Ghana statisticalservice.
Acknowledgements
The authors wish to thank the management of National Health Insurance
Authority and the Monitoring and Evaluation Unit of the Ministry of Health,Ghana for their assistance with data.
Authors’ contributions
Conception or design of the work (AKD, ST); Data collection (AKD); Data analysis and interpretation (AKD, ST); Drafting the article (AKD); Critical revision of
the article (AKD, ST); All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
The data that support the findings of this study are available from NHIA, but
restrictions apply to the availability of these data, which were used underlicense for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permissionof NHIA.

Dzampe and Takahashi ﻿Health Economics Review(2022) 12:57

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Japan Development Service (JDS) PhD Fellow, Graduate School for International Development and Cooperation, Hiroshima University, 1‑5‑1 Kagamiyama, Higashi‑Hiroshima, Hiroshima 739‑8529, Japan. 2 Claims Processing
Centre, National Health Insurance Authority, PMB Ministries, Accra, Ghana.
3
Associate Professor, Graduate School for International Development
and Cooperation, Hiroshima University, 1‑5‑1 Kagamiyama, Higashi‑Hiroshima,
Hiroshima 739‑8529, Japan.
Received: 7 March 2022 Accepted: 1 November 2022

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