Social Science and Medicine 52 (2001) 267–277 Implications of health care provision on acute lower respiratory infection mortality in Bangladeshi children Mohammad Ali a , Michael Emch b, *, Fahmida Tofail a , Abdullah H. Baqui a a ICDDR,B, Centre for Health and Population Research, Mohakhali, Dhaka, Bangladesh b Department of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USA Abstract This study uses a geographic information system to evaluate the effects of health care provision on acute lower respiratory infection (ALRI) mortality in very young children in rural Bangladesh. Since 1988, an ALRI control program has been operating in a rural area of Bangladesh in an effort to decrease morbidity and mortality of children suffering from ALRI. ALRI-specific mortality data for very young children (52 years of age) were obtained from a surveillance system of the area from 1988 to 1993. The ALRI mortality data were aggregated by clusters of households called baris. In order to avoid bias in the population size of baris, spatial moving averages of ALRI-specific death rates were calculated. The relationships between ALRI death rates and several environmental and health service provision variables were measured using regression analysis. The results show that the ALRI mortality rate was 54% lower in the community-based ALRI control program area than in a comparison area where there was no intervention. Greater access to allopathic practitioners was related to lower ALRI mortality rates while access to indigenous practitioners was related to higher mortality. In conclusion, the benefit of the community-based ALRI control program, using a simple case management strategy and improved access to allopathic practitioners, should be replicated in other rural areas of Bangladesh in an effort to reduce child ALRI mortality. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Acute lower respiratory infection; Pneumonia; Spatial analysis Introduction Acute lower respiratory infection (ALRI), primarily pneumonia, is the leading cause of morbidity and mortality in very young children in Bangladesh. Approximately 25% of all deaths in children under five and 40% of the deaths in infants are associated with this disease (Baqui, Black, Arifeen, Hill, Mitra & al Sabir, 1998). Bacterial causes of severe ALRI are very common in developing countries (Shann, 1986; Shann, Gratten, Germer, Linemann, Hazlett & Payne, 1984; Mastro et al., 1993). However, little is known about the causative agents of bacterial pneumonia in these countries (WHO/ ARI/90.10, 1993). To prevent pneumonia, vaccines are routinely being used in developed countries. However, vaccines are expensive and are not commonly used in developing countries (Saha et al., 1997). Therefore, in an effort to control ALRI in the developing world, the World Health Organization (WHO) has developed a simple case management strategy where children diag- nosed with pneumonia are treated with antibiotics (WHO, 1986). In the absence of preventive measures, the WHO suggests that this simple case management strategy be practiced through community-based pro- grams (WHO/UNICEF, 1986). In 1988, an ALRI control program was initiated in a rural area of Bangladesh where a well-established community-based research program has been operating for three decades. The aim of the program is to decrease childhood ALRI morbidity and mortality through educating community health workers (CHWs). The CHWs are given training on detection, diagnosis, and E-mail address: [email protected] (M. Ali). *Corresponding author. Tel.: +1-8802-8811751; fax: +1- 8802-8826050. 0277-9536/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII:S0277-9536(00)00120-9
Transcript
Social Science and Medicine 52 (2001) 267–277
Implications of health care provision on acute lowerrespiratory infection mortality in Bangladeshi children
Mohammad Alia, Michael Emchb,*, Fahmida Tofaila, Abdullah H. Baquia
a ICDDR,B, Centre for Health and Population Research, Mohakhali, Dhaka, BangladeshbDepartment of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USA
Abstract
This study uses a geographic information system to evaluate the effects of health care provision on acute lower
respiratory infection (ALRI) mortality in very young children in rural Bangladesh. Since 1988, an ALRI controlprogram has been operating in a rural area of Bangladesh in an effort to decrease morbidity and mortality of childrensuffering from ALRI. ALRI-specific mortality data for very young children (52 years of age) were obtained from a
surveillance system of the area from 1988 to 1993. The ALRI mortality data were aggregated by clusters of householdscalled baris. In order to avoid bias in the population size of baris, spatial moving averages of ALRI-specific death rateswere calculated. The relationships between ALRI death rates and several environmental and health service provisionvariables were measured using regression analysis. The results show that the ALRI mortality rate was 54% lower in the
community-based ALRI control program area than in a comparison area where there was no intervention. Greateraccess to allopathic practitioners was related to lower ALRI mortality rates while access to indigenous practitioners wasrelated to higher mortality. In conclusion, the benefit of the community-based ALRI control program, using a simple
case management strategy and improved access to allopathic practitioners, should be replicated in other rural areas ofBangladesh in an effort to reduce child ALRI mortality. # 2000 Elsevier Science Ltd. All rights reserved.
pneumonia, is the leading cause of morbidity andmortality in very young children in Bangladesh.Approximately 25% of all deaths in children under five
and 40% of the deaths in infants are associated with thisdisease (Baqui, Black, Arifeen, Hill, Mitra & al Sabir,1998). Bacterial causes of severe ALRI are very common
in developing countries (Shann, 1986; Shann, Gratten,Germer, Linemann, Hazlett & Payne, 1984; Mastro etal., 1993). However, little is known about the causativeagents of bacterial pneumonia in these countries (WHO/
ARI/90.10, 1993). To prevent pneumonia, vaccines are
routinely being used in developed countries. However,vaccines are expensive and are not commonly used indeveloping countries (Saha et al., 1997). Therefore, in an
effort to control ALRI in the developing world, theWorld Health Organization (WHO) has developed asimple case management strategy where children diag-
nosed with pneumonia are treated with antibiotics(WHO, 1986). In the absence of preventive measures,the WHO suggests that this simple case management
strategy be practiced through community-based pro-grams (WHO/UNICEF, 1986).In 1988, an ALRI control program was initiated in a
rural area of Bangladesh where a well-established
community-based research program has been operatingfor three decades. The aim of the program is to decreasechildhood ALRI morbidity and mortality through
educating community health workers (CHWs). TheCHWs are given training on detection, diagnosis, andE-mail address: [email protected] (M. Ali).
0277-9536/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 1 2 0 - 9
management of pneumonia cases, and mothers aretaught to recognize pneumonia and told how and where
to obtain treatment. A pneumonia ward was establishedin the existing hospital, where nasal oxygen, a broadrange of antibiotics, intravenous fluids, and 24-hour
monitoring services are available. The facility forms theendpoint of a functional chain of referral from healthworkers at the village level, to paramedics at four clinicsdispersed throughout the study area, to medical officers
based in the hospital, who evaluate and managecomplicated or severe cases (Stewart et al., 1994a).During the study period, blood and nasopharyngeal
swabs were collected and cultured at the study areahospital bacteriology lab. The focus of the program is toincrease the speed of pneumonia detection so that cases
can be evaluated and managed at an earlier stage, thus,preventing progression to severe and/or fatal disease.The goal is to effectively reduce ALRI-specific mortality
(Fauveau, Stewart, Chakraborty & Khan, 1992).In Bangladesh, modern health facilities are insufficient
in rural areas. There is only one government hospitalstaffed by nine qualified doctors for approximately
200,000 people living in the area. In this area, severaldifferent medical cultures have evolved, each withdistinctive ideologies about disease causation and the
nature of treatments. Several studies (Ashraf, Chowdh-ury & Streefland, 1982; Claquin, 1981; Feldman, 1983;Sarder & Chen, 1981) have observed that non-qualified
allopathic doctors and various indigenous practitionersconstitute the largest group of health care providers torural Bangladeshis. Parents’ choices of healers for theirchildren are very complex. The choices depend on a
great variety of conditions including the relativeproximity of the healer. Bhardwaj and Paul (1986)found that when patients exhibit acute symptoms of a
disease, they are more likely to be placed under the careof qualified doctors rather than taken to indigenousmedical practitioners. However, loss of time, when a
family is searching for medical care or if they first chooseinexperienced healers, reduces the chances that a childwill live.
Several studies (Zaman et al., 1996; Tupasi et al.,1988; Hortal, Benitez, Contera, Etorena, Montano &Meny, 1990; de Francisco, Morris, Hall, ArmstrongSchellenberg & Greenwood, 1993; Heiskanen-Kosma,
Korppi, Jokinee & Heinonen, 1997; Muhe, Lulseged,Mason & Simoes, 1997) were conducted to identify therisk factors of ALRI diseases. Even with these sig-
nificant advances to the knowledge of this disease, ALRImortality is still very high. Understanding a disease in aspecific context requires knowledge of the environmen-
tal, social, and health resources of a particular setting. Ageographic information system (GIS) can inform ourunderstanding of health problems, policies and prac-
tices. The methodological tools of a GIS are useful forinvestigating spatial variation in health care resources
and for determining its association with adverse out-come of diseases (Gatrell, 1999). This knowledge can
inform health services planning thus facilitating betterservice delivery, which can reduce child mortality. Thisstudy uses a GIS to evaluate the effectiveness of the
aforementioned ALRI control program and other healthcare provisions in this rural Bangladeshi study area.This is accomplished by calculating the spatial variationof ALRI mortality in very young children and compar-
ing this to the variation of health care provision in thisarea.
Study area
The study area, called Matlab, is the field researcharea for the International Centre for Diarrhoeal DiseaseResearch, Bangladesh (ICDDR,B). It is 53 km southeast
of Dhaka, the capital of Bangladesh. The DhonagodaRiver flows from north to south bisecting the study areainto two approximately equal parts. A demographicsurveillance system (DSS), initiated in 1963, records all
vital events of the study area population. The surveil-lance system operates in 142 villages, which comprise a184 km2 area. The population of the area is 207,703.
Fourteen percent are children under five and 5% arechildren under two (ICDDR,B, 1996). The people of thestudy area live in clusters of patrilocal households called
baris.The DSS area is divided into two functional units, the
intervention and comparison areas. There are 70 villages
in the intervention area, which is 89 km2. The interven-tion area receives intensive maternal and child healthinterventions through a community-based program. Thearea has been divided into four blocks (A through D) to
support the CHWs in their community-based activities.In contrast, the 72 villages of the comparison areareceive only governmental health services.
The ALRI control program
The ALRI control program is operating in theintervention area of the DSS area. According to the
World Health Organization, acute respiratory infection(ARI) is classified into four categories for children aged2 months to 4 years: (1) no pneumonia but cough orcold; (2) pneumonia; (3) severe pneumonia; and (4) very
severe disease. For children below 2 months, it isclassified into three groups: no pneumonia, severepneumonia, and very severe disease (WHO/ARI/91.20,
1993). However, the classification of ARI is simplified inthe Matlab ALRI control program so that the CHWscan be easily trained. In the Matlab classification system,
an ARI case is categorized as no pneumonia, pneumo-nia, and severe pneumonia (Stewart et al., 1994a). No
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277268
pneumonia means the child has only a cough and/orlow-grade fever. Children with a respiratory rate that is
more than 50 breaths per minute, no chest retractions,and no signs of severe disease are classified aspneumonia cases. Severe pneumonia is diagnosed when
children have chest retractions or other signs of severedisease with a respiratory rate more than 50 breaths perminute. Infants below two months with any signs ofpneumonia are treated as severe cases.
The detection of ARI cases by CHWs is both active,during pre-scheduled household visits, and passive,when concerned family members bring children for
assessment. The children diagnosed as severe cases arereferred to the Matlab hospital for evaluation and care.The pneumonia cases are treated in one of the four study
area outpatient clinics. When a child has a cough orcold, the mothers are advised to give supportive care andinstructed to consult the CHWs if the children show
signs that the disease is getting worse.
Study data
The study data were obtained from three sources: theDSS, the Matlab GIS database, and a study on the
spatial distribution of health practitioners in Matlab.
Demographic surveillance system
In the study area, a demographic surveillance system(DSS), initiated in 1966, records all vital demographic
events of the study area population. Since implementingthe system, this longitudinal population database hasbeen providing support to conduct health, epidemiolo-gical, and population studies and to evaluate health
service programs. The ALRI-specific mortality datawere collected from the DSS for the period 1988 to 1993.Children under two years old were chosen as the study
population because they comprise 85% of all ALRI-specific deaths, and as such the age-specific data wereextracted from the DSS database. The cause of death
was determined through verbal autopsy, which involvesinterviewing relatives of the deceased. The term ‘‘verbalautopsy’’, first proposed by the Narangwal Project in
India (Kielmann, DeSweemer, Parker & Taylor, 1983),refers to a method of retrospective interviewing ofindividuals who have witnessed a death and can describewhat happened preceding death. In the Matlab system,
the relatives were asked about signs and symptoms ofthe deceased and health workers recorded this informa-tion on a verbal autopsy form. The field procedures and
methods for detecting deaths and assessing the causesare described extensively elsewhere (Fauveau, Wojty-niak, Chowdhury & Sarder, 1991). The medical assis-
tants of the program were trained to assign the causes inbroad categories. For all deaths, one underlying disease
is cited as the primary cause, and several symptomspreceding death are described in the reports so they can
be used for future investigation. The classification of thecauses of death was derived from the InternationalStatistical Classification of Diseases, Injuries and Causes
of Death (WHO, 1977), and was adjusted according tothe reporting system of the DSS.Population and in-migration data for the study period
(1988–93) were obtained from the DSS. These data sets
were used to compare ALRI death rates at differentlocations with migration rates and population densities.These two variables were considered to be a proxy for
exposure. The average mid-year population of the studypopulation was 187,709 and the cumulative number ofin-migrants for the study period was 33,195. The DSS
collects data for many demographic variables, includingdate of birth and data of in- and out-migration into thestudy area. This allows detailed age-specific population
statistics to be calculated. There were a total of 78,272children under two years old at risk during the studyperiod. This figure reflects date of death, out-migrationdates, as well as graduation from the study population
because of a 2nd birthday. The total number of ALRIdeaths during the study period was 791 for childrenunder two.
Matlab GIS database
The spatial data for this study were obtained from aMatlab GIS database, which was created by the authorsof this study, to facilitate spatial analysis in health and
population research (Emch, 1999). The spatial featuresin the database include the Matlab hospital, treatmentcenters, village boundaries, rivers, canals, a flood-control embankment, religious institutions, schools,
roads, and baris. These vector GIS data were derivedfrom 1:10,000 scale base maps and a global positioningsystem survey to update the maps. The baris are
identified by the DSS census number within the struc-ture of the GIS database. This allows attribute datato be linked to the spatial database, thus, disease
incidence data can be linked to specific bari locations(Fig. 1).
Health practitioner study
Data on the spatial distribution of health practitionerswere collected through a survey conducted by the
CHWs. The practitioners were categorized into qualifieddoctors, unqualified allopaths, indigenous health practi-tioners (ayurvedic, unani, and homeopath), midwives
(dias), and private pharmacists according to theirqualifications and the type of service they provide. Thelocations of the practitioners were incorporated into the
GIS database. The practitioners were classified into twogroups for this study: allopaths (qualified or unqualified)
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277 269
and indigenous practitioners. It is hypothesized that thelocation of practitioners is an important variable in
health-seeking behavior. Only those practitioners whoreside inside the study area were considered in thisstudy. Six qualified allopathic doctors, 144 unqualified
allopathic doctors, and 448 indigenous practitioners livein the study area.
Methods
GIS methods
The Matlab GIS database is maintained in both the
ArcInfo and Atlas GIS software systems. The databasewas converted from its original vector format to a
Fig. 1. Matlab study area.
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277270
grid-based system called Idrisi. Idrisi was used in theanalytical stage of this project. A raster cell size of 30 by
30 m was chosen because the average size of a bari isapproximately 900 m2. Each feature (study area bound-ary, rivers, ALRI treatment centers, and baris) was
converted into a separate raster image. There were 7032out of 7541 pixels that included baris. Some of the bariswithin 30 m of each other were aggregated into a singlepixel, however, the corresponding attribute data of these
baris were also aggregated. The baris were the unit ofanalysis in this study. Children under two years old livedin a total of 5564 baris during the study period. Since the
DSS provided individual level data, the study data wereaggregated by raster grid cell.
Spatial filtering
Spatial filtering is commonly used to enhance satellite
imagery for visual interpretation but the method canalso be used to ‘smooth’ data and to compute variousenvironmental variables relating to space. Smoothingdata means that outlier values in a spatial data set are
suppressed, and their numbers are adjusted to valuessimilar to surrounding values through some type ofaveraging. The amount of ‘smoothing’ of the data
depends on the size of the ‘moving window’ to be used inthe filtering process. When a larger window size is usedlocal level characteristics are obscured. A smaller
window size focuses on local level variation in thesurface. In this study, several variables were calculatedby using spatial filters. The outcome variable for this
study, the ALRI morality rate, was calculated by using afiltering technique called the spatial moving averagerate. Rushton (1998) holds that health data are betterdescribed by methods that assume that disease rates are
spatially continuous, which can be achieved by comput-ing the spatial moving average rate. In the study area,the size of the population at risk varies from one bari to
another and such variation in the data may influencedisease outcomes. The variations in the population sizewere adjusted by computing a spatial moving average
rate, which yielded a spatially smoothed data set.A seven by seven pixel, moving window was used to
smooth the ALRI data. The total number of cases andpopulation at risk within the window were summed and
the ratio of cases to population yielded the movingaverage mortality rate for the bari. The mathematicalexpression for computing the spatial moving average
within a raster data processing system is defined by:
mi ¼Pr
j¼1 cj � kjPrj¼1 nj � kj
� 1000 fori ¼ j
where: mi=moving average ALRI mortality rate for
pixel i; cj=number of ALRI-specific deaths (52 years)at pixel j; nj=number of children (52 years) at pixel j;
kj=kernel value (unitary) of cell j of the movingwindow; r=number of cells in the moving window.
Population density and in-migration rates
Human settlement and migration flows are nothomogeneously distributed in the study area. The shapeand size of census-based units are markedly varied.
Therefore, estimating density of phenomena by census-based units could poorly describe a distribution. Thus,population and in-migration densities were both calcu-
lated using a spatial filtering method. The mathematicalexpression for computing the density in a raster cellarray is:
yi ¼1
ei
Xrj¼1
vj � kj for i ¼ j
where: yi=density of the phenomena for pixel i;vj=attribute value of pixel j of the phenomena;
kj=kernel value (unitary) of cell j of the movingwindow; ei=correction term of the boundary effect forpixel i.
A seven by seven pixel window was used in the spatialfiltering process. The correction term (ei) was used to getthe estimates in unit area, which is defined by:
ei ¼1
r
Xrj¼1
vj � kj
ei=correction term of the boundary for pixel i;
vj=attribute value of pixel j; kj=kernel value (unitary)of cell j of the moving window; r=number of cells of themoving window.The vj was set to ‘1’ for the pixels inside the study area
and ‘0’ for the pixels outside.
Health practitioner–population ratio
The health practitioner to population ratio for eachbari was estimated based on the number of practitioners
within one square mile of baris. The ratio was calculatedfor both allopathic doctors and indigenous practitioners.A window size of 55 by 55 pixels (approximately 1 mile2)
was used. The computational expression is given by
oi ¼Pr
j¼1 dj � kj
eiPr
j¼1 pj � kj� 1000
where: oi=health practitioner-population ratio for pixel
i; dj=number of practitioners at pixel j; pj=number ofpeople at pixel j.
Cost distance to the nearest ALRI treatment center
Cost distance is a measure of the effort it takes to
move over a surface. Movement in space incurs a cost(e.g., time or money), which is a function of frictions and
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277 271
forces that impede or facilitate movement. A large riverand many canals flow through the study area impeding
normal movement of people. Linear distance is thereforean imprecise measure of access to treatment centers andhealth facilities. These barriers were considered when
modeling the time it takes people to reach the nearesthealth facility. Roads usually accelerate movement,however, since roads are not well developed in the studyarea and vehicles are not available, they were not
considered in calculating the cost distance.The raster GIS software package, Idrisi, was used to
calculate the cost distance. The cost distance is measured
by calculating the minimum number of cells that mustbe traversed to move from that cell to the nearest sourcetarget. In this study, rivers and canals were considered to
be barriers to movement. A cost of 1 was assigned toground surfaces and a cost of 5 was assigned to riversand canals. This implies that movement through water
takes 5 times as long as other areas. This estimateconsiders the time it takes to wait for ferry service on asmall rowboat as well as the time it takes to cross thewater bodies.
Statistical methods
Simple and multiple linear regression analysis was
used to estimate the strength of relationships betweenthe aforementioned factors and ALRI mortality.
Identification of risk areas
Non-linear spatial interpolation methods assume thatvalues of a variable that are close together in space arelikely to be similar. One interpolation method, kriging,
was used to identify ALRI mortality risk areas so thatrates would not have to be linked to census-basedboundaries (Collins, 1998). Kriging is an interpolation
method for which estimates are unbiased and have aknown minimum variance (Oliver & Webster, 1990).The interpolated value of the ALRI mortality rate at
any grid node (Gj) was computed as the weighted
average of the data point values by:
Gj ¼Xni¼1
lijZi
where: lij is the weight associated with the ith data valuewhen computing Gj.
The closer a data point is to a grid node, the moreweight it carries in determining the ALRI mortality rate(Z) at a particular grid node. The sum of all the
weighting factors used to calculate a grid node value isequal to 1. The Zi is the surface value at the ith datapoint, and n is the number of data points used to
interpolate at each node. After the data were inter-polated using surface analysis software called Surfer, theresulting ALRI mortality surface was used to classifyALRI mortality risk within the study area.
Results
Table 1 summarizes the variables that were calculated
for the entire study area. There were approximately nineALRI-specific deaths per 1000 children under two yearsold per year in the study area. There was an average of
0.69 allopathic practitioners and an average of 2.1indigenous practitioners per 1000 people within 1 mile2
around baris. The average population size of a bari was
34 and the average population density was 3880 peopleper km2. The mean number of in-migrations was 5.79per bari in the six year study period which is a density of732 new people per km2. The cost distance values shown
in Table 1 are modelled relative time costs for a personto reach the nearest ALRI treatment facility, thus, theunits are arbitrary.
Table 2 shows differences for several variablesbetween the ALRI control program and comparisonareas. The ALRI infant mortality rate was 11.42 per
1000 in the comparison area and only 6.42 per 1000 inthe intervention area. Since all treatment centers are inthe program area, the mean cost distance is higher in the
comparison area. However, there were not marked
Table 1
Study variable descriptive statistics
Variables Mean Standard deviation Minimum value Maximum value
Average ALRI death rate (per 1000 children per year) 9.12 14.93 0 182.15
Allopathic practitioner/population ratio (per 1000 people) 0.70 0.76 0 4.45
Indigenous practitioner/population ratio (per 1000 people) 2.10 1.72 0 8.99
Bari population 33.77 29.68 1 444.00
Population density around baris (per km2) 3880 2628 22.6 22471
Number of in-migrants to baris from 1988–93 5.79 6.98 0 114.00
In-migration around baris from 1988–93 (per km2) 732 673 0 7506
Cost distance to the nearest ALRI treatment center 138.25 91.34 1.41 396.64
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277272
differences between the intervention and comparison
areas for the population and migration statistics.Table 3 displays the results of the simple linear
regression analysis. People living in the intervention areahad significantly lower ALRI mortality rates than those
living in the comparison area. The allopathic practi-tioner/population ratio was negatively associated withALRI mortality implying that greater access to allo-
paths decreases childhood ALRI mortality. However,greater access to indigenous practitioners did not havean influence on the mortality rate. The total population
within baris and the population density around bariswere both positively related to ALRI mortality rate.There was also a positive relationship between distancefrom an ALRI treatment center and mortality. This
result could have been affected by the spatial allocation
of the treatment centers since they are all located in the
intervention area.The multiple stepwise regression model results are
presented in Table 4. The cost distance to the nearesttreatment center, which was found to be significantly
related to ALRI mortality in the simple regressionmodel, was not retained in the final equation. This isprobably due to its collinearity with the comparison area
dummy variable. Table 5 separates the regression resultsfor each of the study variables by intervention andcomparison area. The table shows that the childhood
ALRI mortality rate in the comparison area isinfluenced by a number of factors, whereas, in theintervention area these factors do not influence mortal-ity. Table 6 shows the results of the multiple regression
analysis for the comparison area. Greater access to
Table 2
Descriptive statistics for intervention and comparison areas
Variables ALRI control program area Comparison area
Mean (Standard deviation) Mean (Standard deviation)
Average ALRI death rate (per 1000 children per year) 6.42 (14.43) 11.82 (14.94)
Allopathic practitioner/population ratio (per 1000 people) 0.89 (0.86) 0.50 (0.59)
Indigenous practitioner/population ratio (per 1000 people) 2.64 (1.76) 1.56 (1.49)
Bari population 34.71 (28.62) 32.76 (30.68)
Population density around baris (per km2) 3638 (2538) 4123 (2694)
Number of in-migrants to baris from 1988–93 5.74 (6.76) 5.84 (7.19)
In-migration around baris from 1988–93 (per km2) 679 (740) 785 (593)
Cost distance to the nearest ALRI treatment center 68.60 (34.23) 207.89 (76.24)
Table 3
Results of the simple regression analysis, Matlab study area, dependent variable: moving average ALRI death rate of children under 2
years of age, 1988–93
Factors Constant Coefficient Standard error t Significance t
Comparison area 6.420 5.395 0.394 13.70 0.000
Allopathic practitioner/population ratio 10.103 ÿ1.414 0.263 ÿ5.371 0.000
Indigenous practitioner/population ratio 9.296 ÿ8.476E-02 0.117 ÿ0.727 0.467
Bari population 8.360 2.186E-02 0.007 3.256 0.001
Population density around baris 7.658 8.539E-03 0.002 4.954 0.000
In-migration to bari 8.956 2.796E-02 0.029 0.975 0.330
In-migration around baris 8.789 1.018E-02 0.007 1.509 0.131
Cost distance to the nearest treatment center 7.579 1.113E-02 0.002 5.089 0.000
Table 4
Results of the multiple regression analysis, Matlab study area, dependent variable: moving average ALRI death rate of the children
under 2 years of age, 1988–93
Factors b Standard error t Significance t
Comparison area 5.190 0.407 12.753 0.000
Allopathic practitioner/population ratio ÿ0.462 0.267 ÿ1.729 0.084
Bari population 1.848E-02 0.007 2.663 0.008
Population density around baris 5.098E-03 0.002 2.859 0.004
(Constant) 5.327 0.487 10.948 0.000
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277 273
allopaths results in lower mortality and greater access toindigenous practitioners results in higher mortality ratesin the comparison area. The distance to ALRI treatmentfacilities was negatively related to mortality rates in the
comparison area. As there are no ALRI treatmentfacilities in the comparison area, the relationship of thedistance to the nearest facilities could have been
confounded by other factors not included in this study.Fig. 2 is the risk map that was created through the
geo-statistical analysis of the ALRI mortality rates. To
create this map, the threshold for high-risk areas was setto 21 deaths per 1000 children per year. This thresholdvalue was chosen using the following methods. Approxi-
mately two-thirds of the pixels within the ALRI surfacethat was created by kriging had ALRI mortality rates ofzero. The remaining area was then divided into twoequal areas based on mortality values within the kriged
surface. The mortality rate separating these two equalarea classes was 21. Fig. 2 supports the results obtainedfrom the statisticalanalysis. There are fewer high-risk
areas in the intervention area, especially in Blocks C andD, than in the comparison area.
Discussion and conclusions
This study has established the effectiveness of com-munity-based programs in reducing ALRI mortality.
Mortality was 54% lower in the intervention area thanin the comparison area. A simple case managementstrategy for pneumonia can reduce childhood ALRImortality in rural Bangladesh. Such programs have also
been found to be effective in other places (Mtango &Neuvians, 1986; Khan, Addiss & Rizwan-Ullah, 1990;Bang et al., 1990; Lye, Nair, Choo, Kaur & Lai, 1996).
An intensive health intervention program can eliminatethe effects of other risk factors for ALRI mortality. Theinsignificant relationship between ALRI mortality and
socioeconomic factors such as population density andin-migration rates in the intervention area supports thisfinding.
The lower ALRI morality rate in the intervention areacan largely be attributed to the effectiveness of thecommunity-based program. The results of the analysispresented in Table 4 show no socioeconomic influences
on disease mortality in the ALRI control program area,suggesting that if an intensive program is in placeincluding the existence of accessible health centers,
exogenous factors become less important. This studyfound that greater access to allopathic practitioners,even though they have not been properly trained,
significantly decreases ALRI mortality rates in theabsence of an intensive health intervention program.The role of these professionals cannot be underestimatedin settings where the majority of people do not have
access to qualified allopathic doctors (Claquin, 1981). It
Table 5
Results of the simple regression analysis by area of the Matlab study area, dependent variable: moving average ALRI death rate of the
children under 2 years of age, 1988–93
Factors ALRI control project area Comparison area
b t b t
Allopathic practitioner/population ratio 0.119 0.373 ÿ1.951 ÿ4.056**aIndigenous practitioner/population ratio 8.322E-02 0.536 0.975 5.142**
Bari population 5.973E-03 0.630 4.142E-02 4.499**
Population density around baris 7.896E-06 0.003 1.216E-02 5.123**
In-migration to bari ÿ2.290E-02 ÿ0.566 6.777E-02 1.721
In-migration around baris ÿ9.184E-03 ÿ1.096 2.207E-02 2.039*
Cost distance to the nearest treatment center 2.689E-03 0.336 ÿ3.325E-02 ÿ9.079**
a*p50.05, **p50.01.
Table 6
Results of the multiple regression analysis of the comparison area of Matlab study area, dependent variable: moving average of ALRI
death rate of the children under 2 years of Age, 1988-93
Factors b Std Error t Significance t
Allopathic practitioner/population ratio ÿ1.274 0.475 ÿ2.682 0.007
Indigenous practitioner/population ratio 1.050 0.188 5.587 0.000
Population density around baris 1.223E-02 0.002 5.256 0.000
Cost distance to the nearest treatment center ÿ3.532E-02 0.004 ÿ9.698 0.000
(Constant) 15.943 0.950 16.783 0.000
M. Ali et al. / Social Science and Medicine 52 (2001) 267–277274
has been shown that when there are not enough qualifieddoctors, unqualified allopathic practitioners are some-what successful in reducing childhood ALRI mortality.
Conversely, the greater the access to indigenous practi-tioners, the higher the ALRI mortality rate. Thissuggests that parents of severely ill children might be
seeking the services of indigenous practitioners. Manypneumonia deaths occur because patients seek theservices of poorly trained providers (Gove & Pelto,
1994). More research on health care seeking behavior isessential to protect children from improper treatmentand to ensure greater access to trained health providers.Indigenous practitioners might also be trained on how
to manage severe ALRI in an effort to reduce thenumber of deaths from ALRI (Agarwal, Bhatia &Agarwal, 1993).
Stewart, Parker, Chakraborty and Begum (1994b)observed that mothers can recognize pneumonia,labored breathing, chest retractions, lethargy, and an
inability to eat as signs of severe disease which needs tobe treated outside the home. Clear guidelines should beestablished for these mothers so that they can recognize
symptoms, understand severity, and know when to seekcare. These ALRI case management guidelines canreduce case fatality markedly (Baqui et al., 1998; Zamanet al., 1996; Denno, Bentsi-Enchill, Mock & Adelson,
1994).This study suggests that a GIS can be a useful tool for
health services planning. A GIS can help assess the
needs for health services by analyzing the density ofsettlement in conjunction with the health care services
that are available in a particular area. In addition tointegrating population and health services data, thespatial variation of environmental characteristics can be
linked with disease mortality data. It is beyond the scopeof this study to try to determine what those factorsmight be. However, this technology would be useful fordetermining what factors account for the observed
spatial pattern of ALRI risk areas in this area.In conclusion, the benefit of the community-based
ALRI control program, using a simple case management
strategy and improved access to allopathic practitioners,should be replicated in other rural areas of Bangladeshin an effort to reduce child ALRI mortality. Also, the
needs of heath care services should be assessed spatiallyso that there is an appropriate allocation of the healthcare services. The government of Bangladesh has
already started a nationwide ARI program and casemanagement is the cornerstone of that program. Thenational program can benefit from the lessons learned inthis study.
Acknowledgements
This research was funded by ICDDR,B: Centre for
Health and Population Research which is supported bycountries and agencies which share its concern for thehealth problems of developing countries. Current
donors providing unrestricted support include: the aidagencies of the Governments of Australia, Bangladesh,Belgium, Canada, Japan, Kingdom of Saudi Arabia, the
Netherlands, Sweden, Sri Lanka, Switzerland, theUnited Kingdom and the United States of America;international organizations include United NationsChildren’s Fund (UNICEF).
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