Risk factors and child mortality among the Miao in Yunnan, Southwest China

Social Science & Medicine 53 (2001) 1683–1696

Risk factors and child mortality among the Miao in Yunnan,Southwest China

Peter Foggina,*, Nagib Armijo-Husseinb, Celine Marigauxa, Hui Zhuc,Zeyuan Liuc

aDepartement de geographie, Universite de Montreal, Montreal, QC, Canada H3C 3J7bEpidemiologist and health training adviser, public health consultant in Yunnan, ChinacDepartment of Neurology, Chengdu Army Kunming General Hospital, Kunming, China

Abstract

Environmental factors and the lifestyle of communities in developing countries as in the industrialized world have agreat deal to do with their health status. This study carried out among the Miao people of southeastern Yunnanprovince in Southwest China has demonstrated important links between child mortality (one indicator of health status)

and specific risk factors. These include lifestyle variables such as geographic mobility, the age of weaning and religiousbelief. In addition, the use of available health care facilities was another explanatory variable. Perhaps surprisingly, ahistory of tuberculosis seemed also to be empirically related to the presence or absence of child mortality. Although it

was impossible to show a significant statistical relationship between traditional practices and child mortality from thestudy’s database, the authors have observed qualitatively that birthing customs play an important role in explaining theperinatal component of child mortality. These various relationships shed some light on potential areas for interventionwith a view to reducing the levels of child mortality among minority peoples in China and elsewhere. # 2001 Elsevier

Science Ltd. All rights reserved.

Keywords: Miao; Hmong; Health status; Risk factors; Child mortality; Lifestyle; Environment; Health care; Cultural minorities;

Medical geography; Yunnan; China

Introduction

Over 98% of all children in industrialized countries

survive through their pre-school years, but in somepopulations of the developing world as many as 15%still die before the age of five (Azuh, 1994; Corsini &Viazzo, 1997). The purpose of this paper is to focus on

an analysis of one such population (the Miao ofsouthwest China) with the goal of elucidating riskfactors that may be related to child mortality. The

choice of this population was predicated upon the factthat it is one of the largest minorities in China, that it isa group that is known to often have marginal access to

health care, and that there was the availability ofresearch collaborators to do such a study.By far, the largest component of child mortality (i.e.,

deaths before the age of five) is its subset, infantmortality. In fact, some authors suggest that approxi-mately four-fifth of child mortality takes place amonginfants in their first year of life (WHO, 1998, p. 43;

Agha, 2000; Yassin, 2000). Although in China childmortality rates are seldom given, the more generallyused infant mortality rates (IMR) have shown a marked

improvement at the national scale in recent decades. Oneestimate given in the early 1950s was 139 deaths per 1000births (Huang et al., 1997), and notwithstanding a sharp

increase around 1960 (Becker, 1996), IMRs havesteadily declined over the past forty years (Hill &Pebley, 1989, p. 682). National estimates now rangebetween 35 and 50 deaths per thousand live births

*Corresponding author.

E-mail address: [email protected] (P. Foggin).

0277-9536/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.

PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 4 5 2 - 4

(Banister, 1992; United Nations, 1992, p. 80; WHO etal., 1998, p. 221). Infant and child mortality rates,

however, remain persistently high in some remote areasof China where minority populations have to endure lowlevels of socio-economic status. For example, in a study

which was carried out in the mid-1990s in Yunnan’sneighbouring province of Guizhou, the overall provin-cial IMR was close to 80% (compared to 68% inYunnan), and that of certain counties ran well in excess

of 100% with as high as 167% for specific ethnicminorities such as the Miao and the Buyi (Huang et al.,1997, p.1031). This is also the case for many of the

minority areas in the province of Yunnan (Li, 1987, p.197.) Given that comparable rates in eastern (andvirtually 100% Han) China are often in the 15–25%range (Li, 1987), it can readily be seen what a majorproblem this is. If anything is to be done about it, weneed first to have a better understanding of just what are

the determinants of such high infant and child mortalityrates in the more isolated and far less prosperous partsof the country.It has been shown that the health status of popula-

tions throughout the world is directly related to a certainnumber of risk factors that may or may not be points ofpractical intervention in terms of public health (Han-

cock, 1992; Matteson et al., 1998). These include lifestyle(Hetzel and McMichael, 1989), various cultural vari-ables (Gesler 1992; Bernatizik, 1970), the social and

physical environments (Cartledge, 1994; Foggin et al.,1997; Gesler et al., 1997; Lee, 1972) and the provisionand use of appropriate health care services (Mohar,1998; Kohn & White, 1976; Smith, 1982; Shannon &

Dever, 1974; Phillips, 1990; Stock, 1983). In addition,there is likely to be intercorrelation between differentindicators of health status} for example, it would stand

to reason that higher morbidity levels would bepredictive of higher mortality rates. Taylor has pointedout that ‘‘the risk approach [identifies] individuals,

households and communities with [such] factors andprovides services to prevent or lessen the impact ofmorbidity. [This approach] is prospective, markers are

used to predict future morbidity [and mortality]. . .’’(Taylor et al., 1990, p. 1090). This conceptual modelviews the health status of a community as beinginfluenced by ‘enabling’ or ‘predictor’ categories (La-

londe, 1974; Hancock, 1986). Here we take up oneaspect of an array of health status indicators } that ofchild mortality and several of its potential determinants

or contributing factors } ‘markers’ to use Taylor’sterm.In a somewhat different vein, Millard has suggested

that we segment the various causes of child mortalityinto three categories: the ‘proximate tier’, referring tothe immediate biomedical causes of child deaths includ-

ing malnutrition, measles and other infections; the‘intermediate tier’ referring to general living conditions

that increase the exposure of children to proximatecauses, and finally, the ‘ultimate tier’ of causes which

relate to ‘broad economic, social and cultural processesand structures that form the context of the proximateand intermediate tiers’ (Millard, 1994, p. 256). Using the

vocabulary of this conceptual framework, the studyreported on here emphasizes primarily the intermediate‘tier’ of explanation using Mahadevan’s notion of ‘life-affecting variables’ (1989, 1992, p. 50; Azuh et al., 1994,

1993), but the broader processes and general socio-economic factors obviously have to be taken intoaccount as well (Oths, 1998; Smith et al., 1982). In fact,

given this study’s basic objectives, it could even be called(in the language of Gesler) ‘reformist’ in that theultimate goal of this research is to promote the

development of better preventive health care strategies(Gesler et al., 1997; Kearns, 1993; Cornia, 1997).More specifically, the target population of the study is

a minority group in the southeastern corner of Yunnanprovince (Fig. 1). One of China’s 55 officially designated‘nationalities’, the Miao people (also well known inSoutheast Asia as the Hmong or Meo) are the fifth

largest minority in China (7.4 million according to the1990 national census) (Lariviere and Marchand, 1999).Approximately half of this population group lives in the

province of Guizhou, while the provinces of Hunan tothe east and Yunnan to the southwest each haveapproximately 15% of the total. The remaining Miao

population of China is scattered unevenly throughoutadjacent provinces (Ramsey, 1987; Lebar et al., 1964).In reality, the Miao are far from being one homo-

geneous ethnic group (Johnson, 1998). Different sub-

populations are referred to by various names dependingoften on local features } the type of traditional clothingor hair styles worn by women (Geddes, 1976) and

geographic characteristics being the most common.Almost everywhere in southern China they have tendedto be driven into the poorer upland areas by the

dominant society, usually by the expanding Hanmajority (Larteguy, 1979; Quincy, 1995). At presentthey are most often located in relatively isolated

mountain areas, practicing traditional forms of agricul-ture } although many in the Wenshan prefecture ofsoutheastern Yunnan (Fig. 1) also practice irrigated ricefarming in a limited number of valleys and on terraced

mountainsides. Quite naturally, the complexity oflanguage patterns among the Miao was a major concernin carrying out this project. Diamond has summarized

the linguistic situation this way:

Just as there is no territorial unity among the Miao,

there is no linguistic unity either. [T]here are at leastthree major Miao languages (fangyan) [. . .and]Chuan-Qian-Dian (CQD) [is a written] language

category that seems to be a repository for everythingthat does not fit into the other two. It has over two

P. Foggin et al. / Social Science & Medicine 53 (2001) 1683–16961684

million speakers living in Sichuan, Yunnan and

Guizhou. [. . .] (Diamond, 1995, p. 92).

The written form of the Miao language that is used inthe study area is Chuan-Qian-Dian (CQD)1 so obviously

it was the one used (together with Mandarin) for thelanguage of the survey questionnaires.The fieldwork took place in 1997 and 1998 in three

remote counties of southeastern Yunnan, namely

Qiubei, Wenshan and Maguan (Fig. 1). These form partof the Wenshan Zhuang-Miao Autonomous Prefecturewhich borders on the Guangxi Zhuang Autonomous

Region (Guangxi, for short) to the east and Vietnam to

the south. At the time of the last census (1990) theirpopulations were, respectively, 383,718, 373,146 and336,968 (Lu, 1998, p. 310) and, given a natural growth

rate in the order of 2% per year (Gan, 1994, p. 42), maybe estimated in 1997–1998 at the time of the survey asapproximately 450,000, 419,000 and 411,000, respec-

tively. In China there is no census breakdown by ethnicgroups, but given the numerical importance of the Miao(and following local conventional wisdom) it may be

assumed that they make up one-third of the populationof this prefecture } consequently, well over 100,000 ineach of the three counties. The two other majorpopulation groups in these counties are the Zhuang

and, as usual, the Han who make up 92% of China’soverall population but who in Wenshan prefecture formonly one of the three major sub-groups: the Miao, the

Zhuang and the Han. The latter dominate in the towns,particularly the county seats (each of which bears thesame name as its respective county), whereas the two

former groups dominate the countryside } the Miaomore so in the higher mountainous areas. As in most ofYunnan, the general topography is hilly to mountai-

nous, altitudes ranging between 1000 and 2500m, andsoils are generally poor and very difficult to cultivate.

Objectives and methods

More specifically, the objective of the overall study is

to measure and analyse the health status and risk factorsof the Miao people living in southeastern Yunnan with a

Fig. 1.

1The Chuan-Qian-Dian (CQD) Miao written language is

used exclusively in southeastern Yunnan in official Miao

publications and is taught in the Miao schools of Wenshan

Prefecture. This generalized pinyin (the official romanized

spelling of Chinese characters, and now also of this form of

written Miao) official script was used as the basis for the

bilingual (Miao/CQD-Mandarin) questionnaire that was ad-

ministered in the Wenshan Prefecture health status and risk

factors survey. The interviews were conducted in the local Miao

language using the Mandarin-CDQ bilingual questionnaire.

One of the problems that had to be dealt with was the inability

of some of the Miao surveyors to easily read the CQD script,

even though they all were able to read Han (i.e., Chinese)

characters. During the training period the local team members

(all of whom spoke one of the local Miao dialects) were

familiarized with the proper wording and meaning of each of

the items in the questionnaire. (The terms Chuan-Qian-Dian

are the pinyin version of characters that refer, respectively, to

Sichuan, Guizhou and Yunnan provinces. For further informa-

tion see Enwall, 1994.)

P. Foggin et al. / Social Science & Medicine 53 (2001) 1683–1696 1685

view to obtaining the understanding needed to developmore effective programmes of preventive health care

(Karungula, 1992; Bourdier, 1995; Azuh et al., 1994).The basic hypothesis, which deals with Millard’s‘intermediate tier’ variables (1994), is that the health

levels of a community vary in relation to (1) lifestyle andcultural variables such as nutrition, tobacco and alcoholconsumption, geographic mobility, birthing practices(Hetzel & McMichael, 1989; Blaxter, 1990), (2) the

social and physical environments (Meade, 1977; Meadeet al., 1988; Bourdier et al., 1995) and (3) the way thehealth care system is perceived and used and the degree

to which it is accessible (Phillips et al., 1990, 1983;Shannon et al., 1974). The primary basis of comparisonin this work is the natural village2 which is seen as a

relatively homogeneous spatial and social entity,although measurement was carried out at the householdand, less frequently, at the individual level. The focus in

this paper is on the results of the study as they relate toone indicator of health status: child mortality.Data were gathered through a questionnaire-based

household survey using questions reflecting all the

components of the basic hypothesis. The questionnairehad been validated and used in previous cross-culturalstudies (Kohn & White, 1977; Foggin & Aurillon, 1989;

Foggin et al., 1997), some of the questions havingnecessarily to be adapted to local cultural realities(including, of course, translation into Mandarin and the

appropriate Miao language). This was extremely im-portant given that this study is based on self-perceivedindicators of health status and related risk factors(Lonner & Berry, 1987; Arbelot, 1995). A smaller survey

was done with another Miao population to the north ofKunming, the provincial capital, and the results of thiswork helped in the validation process for the ques-

tionnaire used in southeastern Yunnan. Although thereported results are based on sample data, the targetpopulation is, in one sense, the approximately 1.1

million Miao of Yunnan province, viewed as a sub-setof the over 7.4 million Miao people living in southernChina.

Natural villages were selected on the basis of aspatially based sample design. Sampling in qualitativeresearch cannot and, some say, should not obey therequirements of random sampling theory. More im-

portantly, it should be relevant to the conceptual

framework, the research questions addressed, the‘believability’ of the findings, and last but not least, to

the criteria of both ethics and feasibility (Curtis et al.,2000, p. 1003; Berry, 1968; Perrin, 1999). In each of thethree counties the practical goal was to interview

approximately 300 households in a way as statisticallyefficient and spatially representative as possible(Fig. 2). In Qiubei (the northernmost sample county),for example, a total of 299 carefully administered

questionnaires were obtained from ten villages thatwere selected on the basis of two criteria: geographicalcoverage and varying socio-economic levels. The

medical officers3 involved in the project went with thesurvey teams to all the areas where natural villageshad been selected. In the case of Wenshan, 13 villages

were selected from six townships (xiang) some beingrelatively close to the county centre of Wenshan city,others particularly isolated. For example, a series of very

small hamlets in the mountains to the west of the countyseat was included in order to be sure of covering everytype of geographical area. When this type of settlementwas found and when the hamlets were physically and

socially very close to each other, such groupings ofhouseholds were counted together as one natural villagein order to be roughly equivalent in size to the other

natural villages. In the Maguan county sample 15natural villages were selected from six different town-ships, several of which were particularly remote (for

example, those close to the Vietnamese border). Othertownships were more central and closer to the countyseat and so a relatively complete geographic coveragewas achieved.

Bilingual (CQD Miao and Mandarin) questionnaireswere administered basically in the standardized (CQD)Miao dialect although both (Yunnan style) Mandarin

(the lingua franca) and local versions of spoken Miao }

‘White (Bai) Miao’, ‘Flowery (Hua) Miao’ or ‘Green(Qing) Miao’ } were freely used for purposes of clear

communication. Almost all the interviewers (19 inQiubei and 15 in Wenshan and Maguan, not countingthe medical officers) were Miao students from a

minorities vocational college (Qiubei) or from aminorities secondary school in Wenshan (Wenshanand Maguan). In each county, training sessions basedon the bilingual questionnaire were carried out. From a

total of 907 households interviewed, the 4377 individualswhich were identified fell into standard sample agegroups for each of the three counties as shown in Fig. 3.

2The expression ‘natural villages’ corresponds normally to

the smallest unit of settlement, usually involving a number of

spatially concentrated extended families, and is generally used

in opposition to the notion of ‘administrative’ villages which

usually include several natural villages falling under the same

administrative and geographic place name. However, occasion-

ally (as in the case of Wenshan county) small hamlets of just a

few households were grouped together as one ‘natural village’

when they were physically close to each other.

3This research was conducted in collaboration with a team of

health personnel (three doctors and one nurse) from the

Chengdu Army Kunming General Hospital, a major health

care facility provided by the People’s Liberation Army.

Although this may surprise some, this was one of the main

reasons that such a study could be carried out in the Chinese

context.


An indication of child mortality was obtained by askingquestions of each sample household pertaining to thenumber of births and child deaths over the previous five

years. From these responses it was possible to know ifthere had been births and whether or not there had beenchild mortality in any given household during this

period.Given the categorical (often dichotomous) nature of

the resulting data, a non-parametric statistical analysis

(chi-square contingency tables and multiple logisticregression4) was conducted with a view to hypothesis

testing. Specifically, this involved checking the statisticalsignificance of an array of hypothesized relationshipsbetween various risk factors (the independent variables)

and the presence or absence of child mortality in all ofthe sample households taken together (the dependentvariable).

For the purposes of multiple logistic regressionanalysis, a dichotomous dependent variable of childmortality was created that simply indicated for all of the

sample households (N=907) whether or not there hadbeen at least one under-five child’s death in the previousfive years. The specific operational independent vari-ables that were analysed were: (1) cultural and lifestyle

risk factors } geographic mobility, birthing practices,breast feeding, weaning age, smoking, alcohol consump-tion, religious belief, social network, village location; (2)

environmental factors } socio-economic status, housingconditions, water supply; (3) perception and use of thehealth care system } use of health care facilities; and (4)

general levels of morbidity } recent illness in the family,chronic illness, tuberculosis.

Results

In order to create a variable that adequately reflectschild mortality, the first step was to discover how manyhouseholds had had births over the previous five years.

Approximately 40% of all sample households had birthsduring this 5-year period (35% in Maguan, 36% inWenshan, 50% in Qiubei); these figures are consistent

with birth rates observed for Miao peoples elsewhere(Kunstader et al., 1993). Of course, only some of the

Fig. 2. Sample village groups in Wenshan county.

4After conducting extensive hypothesis testing by chi-square

contingency table analysis, most of these associations, and

others as well, became apparent. The problem that remained

was to establish the degree and direction (positive or negative)

and the functional nature of the relationships } only a

regression model of some kind could provide this type of

insight. The tool needed to meet the requirements of our data

was multiple logistic regression. This technique is often applied

to the analysis of categorical data and can be interpreted in a

fashion similar to standard linear regression. The dependent

variable, however, must be dichotomous, even though inde-

pendent variables can be measured on an ordinal scale in as

many classification categories as necessary. The method used

was forward stepwise multiple logistic regression (the Wald and

the LR techniques gave identical results) (Norusis/SPSS, 1993;

Hosmer & Lemeshow, 1989; Freeman, 1987; Menard et al.,

1995). In Table 1 the R values are based on the Wald statistic,

which in turn corresponds for each independent variable to the

square of the regression coefficient (b) divided by the standard

error, or simply, (b/s.e.)2. The individual R values can be

understood as partial correlation coefficients (Norusis et al.,

1993, p. 5).


households reporting births also reported child mortal-ity5 (Fig. 4). Obviously, in villages where there were no

births in the previous five years, it was impossible tomake any estimate of child mortality.6 Nevertheless, ascan be seen from Fig. 4, there was some child mortalityin the majority (29) of the 38 villages in the study

sample. At the county level the average child mortalityindicator estimates ranged between 116 (Wenshan) and240% (Qiubei).7

The question naturally arose as to why infant andchild mortality rates among the Miao, as well as those ofother remote populations in China, are so high. Clearly,

isolation and lack of accessibility to modern health carefacilities are factors. For example, this explainedpartially why one of Huang et al.’s (1997) samplecounties, having much better transportation and health

care infrastructures, had substantially lower IMRs thanthe two other counties in the Guizhou study. But even

these ‘lower’ rates were very high (73% for the Han,123% for the Miao and 165% for the Buyi)! They alsonoted that the majority of deaths in the first year of achild’s life were due to respiratory diseases (1997,

p.1034) which, though not always associated with thetime of birth, are almost always related to environ-mental factors in the home environment (Shah et al.,

2000; Sowards, 1997; Foggin & Aurillon, 1989). Never-theless, the same study also found that approximately25% of all (Miao) infant deaths were associated with

birth asphyxia and neonatal tetanus, both conditionstaking place generally at about the time of birth. All thisunderscores how important birthing practices are tolevels of child and infant mortality. Consequently, given

the high estimated levels of child mortality in south-eastern Yunnan, it was thought important to inquirespecifically about delivery and birthing practices. We

were able to determine that the majority of births takeplace outside the house either with or without assistance(Fig. 5). In the case of Qiubei county over 35% of

sample households reported giving birth ‘outside withouthelp’, and comparable levels hold for the counties ofWenshan and Maguan (both approximately 30%). It

was found that well over 95% of all respondentsreported that their children were born at home in theirvillage and not at any clinic or hospital. What this meansis that both the possibility and probability of infections

(either respiratory or through neonatal tetanus) andother illnesses associated with birthing conditions (e.g.,neonatal asphyxia) leading eventually to a new-born’s

death are highly increased due to the lack of accessibilityto adequate health care facilities, or at least to trainedhealth care personnel (Davis and Lambert, 1997, p. 133).

Even if pre-natal, perinatal and post-natal hospital careare close enough to be geographically accessible,discussions with villagers led us to the conclusion thatmany, if not most, feel they cannot afford hospital care

for financial reasons (Henderson et al., 1998). This is just

Fig. 3. Age structure of three county sample.

5Concerning the possible relationship between numbers of

births and child mortality, Palloni and Fafalimanana have

pointed out that there is ‘strong evidence supporting the

hypothesis that short birth intervals, birth order, and mother’s

age at birth have fairly powerful effects on infant and early child

mortality. . . There is remarkably unambiguous empirical

evidence of the impact of fertility on infant and child mortality

[whereas] evidence of an effect of infant and child mortality on

fertility has been stubbornly elusive’ (Palloni & Fafalimanana,

1999, p. 41–42).6With regard to Fig. 4, it should be noted that, notwith-

standing higher observed birth levels in Qiubei county, levels of

child mortality in this county are consistently much higher there

then in the other two. This would corroborate the argument

that the CMIs for the three counties calculated here are, if

anything, conservative estimates.7The child mortality indicator was obtained by dividing the

number of under-five child deaths in the past five years by the

number of births over the same period, and then multiplying by

1000 in order to obtain the number of deaths per thousand

births. However, the dependent variable of the regression model

had to be simply the presence or absence of child mortality over

the previous five years.


as true for children under five years as it is for infantsunder one year of age.When, by use of chi-square contingency table

analysis, any of the hypothesized independent variablesshowed a statistically significant association with thedependent variable, child mortality, it was included in amultiple logistic regression analysis (Table 1). Clearly

there was intercorrelation between some of the so-called‘independent’ variables used and, as a result, theseconfounding variables were excluded from the forward

stepwise regression equation. When this is the case, theyare indicated below the significant independent variablesas being of interest, even though they were not included

in the regression equation because of their redundancywith another variable (see Table 1). For example, the‘geographic mobility’ and the ‘village location’ variablesare highly correlated; consequently only one of them

was included in the multiple stepwise logistic regression,even though both are strongly associated with childmortality.

With regard to the choice of independent variables thespecific hypotheses were the following: first, thatgeographic mobility contributes to an increased risk of

child mortality (CM); second, that geographic locationplays a role in levels of CM; third, that some culturalvariables (e.g., breast feeding, age of weaning, socialnetworks, religious belief) are predictors of CM; fourth,

that socio-economic status (measured by total familyincome and by a qualitative question on the presence orabsence of ‘cash problems’) is inversely proportional to

levels of CM; fifth, that household consumption oftobacco and alcohol is linked to levels of CM; sixth, thatgeneral ‘family morbidity’ (here measured by a history

of tuberculosis-like symptoms and by functional inca-pacity in the household) leaves a family more vulnerableto higher CM; and finally, seventh, that the way familiesuse and relate to available health care services will also

have an impact on the levels of CM.Table 1 summarizes the results of this analysis

and indicates the relative contribution of the seven

Fig. 4. Birth indicator (calculated on an annual basis) for Wenshan county.

Fig. 5. Delivery and birthing conditions.


statistically significant independent variables to the

‘predicting’ of child mortality defined in this way. Theseresults are based on the use of dichotomous (and in afew cases, interval scale) variables derived from ques-

tionnaire response data (Reynolds, 1977; Freeman,1987; Menard, 1995). Clearly, only some of the riskfactor variables could, in the final analysis, be entered as

part of a descriptive and predictive model. For theothers, either the probability of error was too high(p=50.05), or as explained above, a related variablewas excluded from the regression model because of

intercorrelation between the two supposedly

‘independent’ variables. Although these ‘rejected’ vari-

ables may be valid in a conceptual way, we could notdemonstrate the presumed relationships from the datathat had been gathered. For example, we would have

expected a strong association between child mortalityand the socio-economic environment (Antonovsky &Bernstein, 1977; Hertz et al., 1994), but the only

statistical link that could be clearly established showeda low negative correlation with almost no functionalrelationship (b=�0.003) between total family incomelevels and child mortality (r=�0.093, p=0.025). Even

though this provides added predictive power to the

Table 1

Stepwise multiple logistic regression analysis

Dependent variable: Child mortality indicator (CMI) (method: forward stepwise logistic regression)

B Standard error Wald Significance R

Geographic mobilitya �0.688 0.167 16.903 b � 0.207

Health carec �0.816 0.342 5.686 d � 0.103

History of tuberculosise 0.635 0.208 9.288 f 0.145

Religious beliefg 0.470 0.184 6.534 d 0.114

Age of weaningh 0.982 0.348 7.978 f 0.131

Weak social networki 0.443 0.170 6.756 f 0.117

Total family incomej �0.003 0.002 5.026 d �0 .093

Constant: �1.057 0.749 0.426 b }

(N=647) (Correct predictions of model over observed outcomes=92.5%)

Excluded (but related to CMI); Functional incapacityk (x CMI, Chi-square, p=0.011, n=890)

(p50.05 in logistic regression) Cash problemsl (x CMI, Chi-square, p=0.030, n=884)

Smokingm (x CMI, Chi-square, p=0.044, n=753)

Village locationn (x CMI, Chi-square, p=0.000, n=904)

aThe variable used as a measure of geographic mobility comes from the question: ‘Has anyone in this household travelled away (at

least overnight) from your village sometime in the past month?’bHighly significant (p=40.001).cThe health care variable refers to a question regarding whether anyone from a given household had visited a clinic or been visited by

a health care provider during the previous two weeks.dSignificant (p=40.05).eAfter describing some basic symptoms of tuberculosis the respondent was asked if she thought there was any active case of TB in

the household: a) at present, and b) in the past. Only responses to the second part of the question (TB in the past) turned out to be

statistically associated with levels of child mortality.fVery significant (p=40.01).gThis variable refers to the responses to the question: ‘Do you believe prayers to be important when a member of the family is ill?’hBreast feeding is almost universally practiced but the age of weaning varies considerably. This variable refers to the question on the

usual age of weaning in each household (in months). It was constructed by making two categories: above or below 24 months.iThe somewhat expansive term, ‘weak social network’, is used to denote the ‘yes’ or ‘no’ responses to the question: ‘Do you

frequently feel lonely?’ It is based on the assumption that should the respondent, who is usually the leading woman in the household, be

frequently lonely, this would be indicative of a somewhat weak social network.jTotal family income was an ordinal variable based on four response categories of income ranging from low to relatively high, by

local standards.kThis refers to whether there had been some member of the sample household that had been prevented by illness over the past three

months from carrying out his/her usual activities.lThis refers to one of a series of typical problem areas that a sample household may or may not have had to face in the preceding

year (e.g., cash problems, health problems, animal problems).mThe variable ‘smoking’ refers to the presence or absence of smokers in the sample household.n In order to explore the effect of location, the thirty-eight sample villages were categorized into ten geographically concentrated

groups (Kearns & Joseph, 1993) and the fact of belonging to one village grouping rather than to another was referred to as ‘village

location’.


equation, the low beta (b) value indicates that we cannotexpect from this link that a change in family income will

be accompanied by a corresponding change in childmortality.Although only seven of the operational risk factor

variables were significantly able to predict the presenceof child mortality, this is an effective model (its correctpredictions compared to observed outcomes when all theindependent variables are taken together =92.5%; see

Norusis, 1993, p. 8). What can be seen from the reportedlogistic regression equation (Table 1) is that thefollowing variables did show significant levels of

association with child mortality (defined simply as itspresence or absence): (1) geographic mobility (travellingaway from the village, p=40.001), (2) past family

history of tuberculosis (p=0.002), (3) religious belief (theimportance of prayers to being healthy, p=0.010), 4) ageof weaning (p=0.004), (5) weak social networks (mea-

sured by asking about loneliness, p=0.009), (6) use ofhealth care services (visits to the local health clinic in thepreceding two weeks, p=0.017), and finally, (7) totalfamily income (p=0.025) which was discussed above.

Clearly, certain elements of our basic hypothesis havebeen confirmed through this analytical process, whereasthe role of other variables will require further investiga-

tion.Even though not shown through the regression model,

several other important relationships were nevertheless

also observed. For example, looking at the impact ofliving conditions on child mortality, neither of thehypothesized physical environmental risk factors (i.e.,housing and water quality) could be included in the

multiple regression analysis given the objective criteriaused. However, the variable measuring whether peoplefound their house ‘comfortable’ or not was found to be

related to child mortality when only those householdsthat had actually had births over the previous five yearswere included (p=0.019; n=359). An inverse associa-

tion observed in the contingency table indicated that themore people enjoyed their house as being ‘comfortable’,

the less likely there was to be child mortality. Interest-ingly, on this particular question the responses were not

mutually exclusive, so that a respondent could reply inthe affirmative to ‘too hot’, ‘too cold’, ‘too damp’, ‘toodraughty’ or ‘too crowded’, and still claim to find her

home ‘comfortable’. As a result it seems difficult toknow exactly what this variable captures. It might,however, imply that the question regarding ‘comfort’was measuring something other than housing quality

(for example, possibly a family’s level of expectations).Furthermore, on the assumption that housing quality isrelated to economic well being, the significant relation-

ship observed between total family income and childmortality (Table 1) adds to the argument that sinceincome is a prerequisite of adequate housing, both are

related to health status.Housing quality was also related to tuberculosis,

another health status indicator } in fact, tuberculosis-

type symptoms were reported in 26 of the 38 samplevillages. However, it was the responses to a questionabout a past history of tuberculosis (9.2% affirmative;n=906; see Fig. 6) that seemed consistently to be related

in some way to the presence or absence of childmortality (b=0.635; r=0.145; p=0.002). This wouldseem to indicate that some forms of morbidity, such as a

history of tuberculosis in the household, are functionallyrelated to levels of child mortality.

Discussion

The model depicted in Table 1 is without question

descriptive and clearly is also predictive of the presenceor absence of child mortality among the Miao ofsoutheastern Yunnan. Paradoxically, the sample popu-

lation on which the model is based has an age structure(Fig. 3) that seems to be somewhat anomalous whencompared to age pyramids typical of other developingareas. However, although the two youngest age

groups appear to be under-represented, they actually

Fig. 6. Past family history of tuberculosis-like symptoms.


correspond to China’s overall national trend (Banister,1997, p. 345; WHO, 1996). Fully aware then of the

problem of potential sample bias as well as that of theintractable problem of small numbers, it is with greatcaution that we have made estimates by village

(studiously avoiding the use of the term rates which, ofcourse, can have an entirely different connotation) and itis with even more carefulness that we come to theinterpretation of these results.

We have already seen that only one of the twogeographic variables (mobility and village location)was retained in the regression model. However,

both of them are useful in the explanation of theoverall variation of child mortality in the studyarea (Shannon & Pyles, 1993; Kearns & Joseph, 1993).

The expression ‘geographic mobility’ refers to thefact that some member or members of a householdleft their village for extended times, regardless of

what the reason might be. That the relationshipbetween this factor and child mortality is strongerthan that for any other single independent variable(b=�0.688; r=�0.207; p=40.001) is indeed

noteworthy. Given the negative functional relationship,the initial interpretation was that the more villagehouseholds are in contact with the outside world and

all its influences, the lower the probability they willexperience the premature death of their young children.Certainly, with the knowledge of what is going on

beyond the immediate confines of their village there ismore likelihood of breaking away from old and some-times unhelpful traditions surrounding child birth. Inaddition, this type of geographic mobility will often

make families more aware of the health care options thatare available to them. However, an alternate explana-tion of this relationship might be that as people move

out to get additional income the levels of child mortalitywill decrease. The fact that there is a statisticalrelationship between geographic mobility as defined

and total family income (p=0.001, N=900) wouldsuggest that this may be a more reliable interpretationthan the more intuitive approach mentioned above. In

addition, this variable may well be a surrogate for stillother factors that contribute to the lowering of childmortality.On an economic level, a significant association was

observed linking village location with cash problems(p=0.030; n=884) } this simply means that althoughthese kinds of problems, although geographically wide-

spread, vary significantly from one area to another.Clearly, this observation will have important ramifica-tions in the context of integrated rural development in

which health and health care must be major components(Hussein, 1982; WHO, 1997; WHO et al., 1998). Wehave already seen how another socio-economic variable

(total family income) was significantly related to childmortality (Table 1).

As noted earlier, the average Miao child mortalityindicator values by county ranged between a ‘low’ of

116% (Wenshan) and a high of 240% (Qiubei). To theextent that this is reflective of the entire Miao popula-tion of the prefecture, these are unacceptably high levels

by any standard and, as an indicator of health status,point out the inadequate health levels of the Miao in thisregion. It should be born in mind that although onlyabout 40% of the sample households (n=363) had

experienced a birth or births during the previous five-year period, in the logistic regression analysis all thosehouseholds where there had been no births (and,

consequently, no child deaths) during this period werealso classified as not experiencing child mortality. Hadall the sample households indeed had births over the

five-year period, the number of under-five child deathswould certainly have been higher than the number ofcases reported here. So what we have in the dichot-

omous dependent variable (the presence or absence ofchild mortality by households) is undoubtedly a veryconservative estimate of the problem.It was expected that lifestyle risk factors would at least

partly account for these high levels of child mortality.For example, it is fairly widely accepted that breastfeeding practices have a significant bearing on children’s

health status and mortality levels (Huffman & Lam-phere, 1984; Raisler et al., 1999). Probably the mainreason our data did not demonstrate this relationship is

the fact that breast feeding is almost universallypracticed among the Miao so that, to all intents andpurposes, it could not be considered a discriminatingfactor. However, the age of weaning (i.e., below or

above 2 years of age) appears to be a reasonably strongpredictor of child mortality (b=0.982; r=0.13;p=0.005; see Table 1). Intuitively, one might think that

breast feeding over longer periods would lead to lowerchild mortality (and also lower birth rates), but thepositive association between the two variables seemed to

indicate the opposite. What the contingency tableindicated was that households where children areweaned after two years of age have a greater probability

of child mortality than those where weaning takes placeat a younger age. Of course, no variable acts in isolationfrom all the other factors involved; for example, theimpact of social networks on child mortality needs to be

further explored. We get a glimpse of its importance bynoting the fact that loneliness (labelled ‘weak socialnetwork’ in the logistic regression model) is also a strong

‘predictor’ of child mortality. Exactly why this should beso is very hard to say, except that isolated mothers (andhence, presumably the ones most likely to feel ‘lonely’)

are less likely to have the needed assistance both ingiving birth and in looking after young children.With regard to a link between birthing practices and

the perinatal component of child mortality (which wewere not able to demonstrate as a significant variable),


empirical evidence in the relevant literature usuallyaffirms such a relationship (Pison et al., 1993; Karungula

et al., 1992, 1979; Azuh et al., 1994). For instance,Sandhya concluded from his study that ‘‘infantmortality [is] higher when the delivery care was

traditional than when it was modern’’ (Sandhya, 1991,p. 96). In the parallel study in Guizhou province referredto earlier, neonatal tetanus and other infections wereresponsible for a large proportion of infant deaths

close to the time of birth among the Miao of thatparticular region (i.e., where infant mortality ratesranged from 123 to 167 deaths per thousand births)

(Huang et al., 1997, p. 1033). Child mortality ratestend to be approximately 25% higher than infantmortality rates (IMR), the lion’s share of child deaths

taking place during the first year of life. Thus, takingthis differential into account, the child mortalitylevels observed in the three Yunnan counties of this

study appear to be consistent with the equivalentIMRs observed in Guizhou. Given the importancein Huang et al.’s work of the link between neonataltetanus (as well as other infections) and the high infant

mortality rates observed, it would be logical to concludethat some traditional birthing practices are also a majorcontributing factor to high infant mortality among the

Miao of Southwest China. Our field observations alsolead to the qualitative conclusion that there is, in fact, arelationship between birthing practices (described in

Fig. 5) and child mortality in the study area, much thesame as the one discussed above for neighbouringGuizhou province.At still another level, the Miao, who are a religious

people, look on prayers as an important factor in health,particularly when they are faced with the stark realitiesof their children’s deaths (r=0.14; p=40.01; see Table

1). This aspect is far more important in a traditionalsociety such as that of the Miao than is the case in thewestern world (King et al., 1999). It is significant that a

statistical link was observed between this independentvariable (belief in prayer for overcoming sickness) andthe frequency of use of the local health clinic (r=0.10

p=40.01). This is a highly plausible outcome indicatingthat both the belief in prayer and the use of availablehealth care facilities represent a combined strategy forfacing illness in the household (McCormik, Shapiro, &

Dudakis Horn, 1979). Indeed, this parallel relationshippoints in the same direction as that observed betweenbelief in prayer for health and child mortality levels.

In addition to these risk factors, there are severe localproblems such as the almost universal lack of availableclean drinking water (Perz, 1997) and, perhaps more

importantly, the widespread lack of insistence on boilingwater before consuming it (Black, 1984; Warner, 1997).It is well known that under such conditions diarrhoea

claims many infants’ lives (Taylor et al., 1990; WHOet al., 1998). In the Guizhou study already cited, for

example, it was responsible for 9.2% (Han), 15.2%(Miao) and 13.2% (Buyi) of all causes of death among

infants (Huang et al., 1997, p. 1034). Furthermore,conditions of endemic poverty leading to the lack ofadequate food during prolonged periods (particularly

before the new harvest) create conditions of severemalnutrition in certain villages. Their resistance thusreduced, children become increasingly vulnerable, oftenending up by succumbing to various respiratory,

diarrhoeal or other infectious diseases (Foster, 1984;WHO et al., 1998).Finally, various types of intervention can be envisaged

to alleviate the problem of poor community healthlevels (measured here by high CMI values), not theleast of which are educational and mother-and-child

health care programmes (Williams et al., 1994; Wallaceet al., 1988; Cleland & van Ginneken, 1988; WHO et al.,1997). However, having addressed the question of

just what are the risk factors that either contributeto or accompany high child mortality among theMiao, it is evident that we still have more questionsthan answers. Clearly, more work is required in the

field focusing on factors related specifically to thehealth status and environmental conditions of Miaochildren. Nevertheless, enough has become clear

through this and other recent studies (Huang et al.,1997; Oths et al., 1998; Foggin et al., 1997), a part ofwhich has been presented here, to provide the basis for

the promotion of health among the Miao and othersimilar populations. Some of these potentially helpfulfindings relate explicitly to certain basic needs such asreducing the general level of morbidity through im-

munization (e.g., to combat the prevalence of tubercu-losis) and socio-economic development seeking thereduction of poverty, to name two areas of concern.

Other more qualitative observations from this workhave to do with the ubiquitous need for potable watersupplies, better educational facilities and for the

improvement of supportive social networks.

Conclusion

To sum up, this study carried out among the Miao ofsoutheastern Yunnan province in Southwest China hasdemonstrated important links between child mortalityand a number of specific risk factors. These include

lifestyle variables such as geographic mobility, age ofweaning, and religious belief. In addition, the use ofavailable health care facilities was another explanatory

variable. Somewhat unexpectedly, a history of tubercu-losis-like symptoms (seen as a general indicator ofmorbidity) seemed to be empirically related to the

presence or absence of child mortality. These relation-ships shed some light on potential areas for intervention


with a view to reducing the levels of child mortalityamong the Miao people of southern China.

Acknowledgements

This research was funded by a grant (97–0111) fromthe Social Sciences and Humanities Research Council ofCanada, SSHRCC.

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Risk factors and child mortality among the Miao in Yunnan, Southwest China