Traditional Medical Beliefs and theImpact of Hygiene Instruction

Daniel Bennett Syed Ali Asjad Naqvi Wolf-Peter Schmidt

March 21, 2014

Abstract

Many people throughout the world believe in traditional, unscientific causes ofdisease. These beliefs may undermine public health efforts such as hygiene campaigns,which invoke the germ theory of disease. We evaluate a novel program that triesto increase the salience of hygiene instruction by showing participants live microbesfrom their environment under a microscope. We find that this program has a largereffect on hygiene knowledge, hygiene behavior, and respondent and child health thaninstruction alone. However, we also show (consistent with Bayesian learning) that onlynon-believers in traditional medicine respond to the intervention. This pattern suggeststhat traditional beliefs contribute to the communicable disease burden in developingcountries. Finally, we show that pre-existing behavioral constraints limit the program’simpact on hygiene, illustrating a key limitation of instructional interventions.

Daniel Bennett: University of Chicago, dmbennett@uchicago.edu; Asjad Naqvi: Vienna University ofEconomics and Business, snaqvi@wu.ac.at; Wolf-Peter Schmidt: London School of Hygiene and TropicalMedicine, Wolf-Peter.Schmidt@lshtm.ac.uk. We received helpful feedback from Dan Black, Jeff Grogger,and Edward Higgins.

1 Introduction

Traditional medical beliefs are common throughout the world. Traditional medicine accounts

for 30-50 percent medical consumption in China, and up to 80 percent of consumption in

parts of Sub-Saharan Africa (WHO 2003). In South Asia, which is the focus of this study,

many people subscribe to Unani medicine, in which illnesses arise through imbalance between

humorally “hot” and “cold” elements (Anwar et al. 2012, Karmakar et al. 2012). Hakims,

who provide Unani medical care, are active in markets throughout the region (Banerjee et

al. 2004, Das and Hammer 2005).

Traditional medical beliefs may contribute to the global disease burden by discourag-

ing healthy behavior. Traditional health practices potentially crowd out modern practices

if these inputs are substitutes in the health production function or the household budget

constraint. Unani beliefs are a particular threat to the adoption of modern hygiene and san-

itation practices, which call upon the germ theory of disease. In the Unani system, eating

too much food or eating foods that are humorally hot leads to diarrhea (Nielsen et al. 2003).

A belief that humoral imbalances cause infection reduces the impetus to was hands and

maintain latrines. Traditional and modern practices sometimes clash more directly. Oral

rehydration (including breast milk) is the consensus treatment for infant diarrhea. However

Unani medicine recommends withholding breast milk from infants with diarrhea because

working in the fields makes a woman’s breast milk hot (Mull and Mull 1988).1

Microbe Literacy is a novel hygiene program that tries to make hygiene messages salient

to people with traditional medical beliefs. Despite the policy importance of hygiene instruc-

tion, policymakers have struggled to communicate hygiene messages effectively (Fewtrell et

al. 2005, Monte et al. 1997). Microbe Literacy facilitators use microscopes to show partici-

pants the microbes that are present in everyday substances from their environment, such as

standing water and buffalo dung. For many participants, who are mostly poor and illiterate,

1As another example, some traditional healers in Sub-Saharan Africa advocate ritual bloodletting as atreatment for AIDS (Hrdy 1987).

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the demonstration is the first time they have conceived of microscopic life. Instructors begin

by using magnifying glasses to demonstrate the concept of magnification. Then participants

take turns looking through a microscope while others observe on a closed-circuit television.

In a follow-up infection prevention workshop, instructors lead a discussion of hygiene best

practices, such as hand washing, latrine usage, and food safety.

We evaluate this program through a cluster-randomized trial. We offered Microbe Lit-

eracy to female participants in existing government-sponsored adult literacy classes (ALCs)

in southern Punjab Province, Pakistan. ALC students tend to be socioeconomically disad-

vantaged and lack formal schooling. The experiment includes three arms, which respectively

received the full program, only the infection prevention workshop, and no programming.

This design allows us to measure the impact of the program both absolutely and relative

to conventional hygiene instruction. We measure knowledge through several factual hygiene

questions, and gauge hygiene behavior by directly observing the personal appearance of the

respondent and her children. We also elicit the prevalence of diarrhea, fever, and cough for

the respondent and her young children.

Microbe Literacy significantly improves hygiene knowledge and behavior. The program

increases hygiene knowledge by 0.37 standard deviations (14 percent) and improves the

personal appearance of respondents by 0.29 standard deviations (8 percent). The program

has a larger effect on respondent hygiene than child hygiene, and it does not affect household

sanitation outcomes such as open defecation, which the participant does not directly control.

Microbe Literacy does not significantly reduce diarrhea, but reduces fever and cough by 44-46

percent for the respondent and by 19-34 percent for children younger than five. It generally

has a larger effect than standard hygiene instruction, although the difference is not always

significant.

To explore how traditional beliefs mediate the effect of the program, we aggregate several

indicators of adherence to traditional medicine into a traditional belief index (TBI). These

variables focus on beliefs in aspects of the hot/cold theory of disease and hypothetical and

2

actual utilization of hakims. We find that people without strong traditional beliefs learn

the most from the program. The effect on knowledge is small and insignificant for strong

adherents to traditional medicine, but is over four times larger and highly significant for

people without traditional beliefs. The result is robust if we control for the interaction

between treatment and 19 demographic and economic characteristics, which minimizes the

concern that the TBI proxies for socioeconomic status. This finding is consistent with a

Bayesian learning model in which traditional medicine adherents have more precise prior

beliefs. It also suggests that the program does not successfully reach people with strong

traditional beliefs.

Hygiene knowledge only changes behavior if recipients can overcome practical constraints

on behavior change. Hygiene is potentially inconvenient and expensive. Young women in

culturally conservative households may have little bargaining power over hygiene inputs.

We assess the relevance of these constraints by constructing a hygiene propensity score that

incorporates constraints based on economic circumstances, the lack of female empowerment,

and the physical environment.2 By interacting treatment with the propensity score and the

TBI, we show that the program primarily improves hygiene for people who are unconstrained

and do not hold traditional beliefs. We consider the relative importance of economic, social,

and practical constraints by creating separate propensity scores by category. Economic and

social factors appear to limit the impact on hygiene more than practical factors.

This study makes three primary contributions. We establish experimentally that a

program to increase the salience of hygiene instruction improves learning, behavior change,

and health. Existing public health studies of hygiene instruction, which have mixed results

(Davis et al. 2011, Beau De Rochars et al. 2011), do not formally examine why programs

may or may not be effective. Secondly, we show that traditional beliefs limit the acquisition

2Economic variables include housing characteristics, food deprivation, and education. Social variablesmeasure the patriarchal orientation of the household, include whether the respondent needs permission tohave female friends, whether she is a decision maker regarding the schooling of children, and whether sheinteracts with people outside of the village. Practical variables include water supply and latrine characteristicsand the presence of animals.

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of hygiene knowledge through the program. This result suggests that traditional beliefs

contribute to the global burden of disease by discouraging health behaviors. Little or no

economic research assesses the health implications of traditional beliefs, which are ubiquitous

in both rich and poor countries. Finally, we show that economic and social factors limit the

program’s impact on behavior. To improve health, informational interventions must first

improve knowledge and behavior. The lengthy causal chain may make hygiene instruction

less cost effective than more expensive sanitation and water supply projects that directly

affect behavior and health.

We proceed in Section 2 describes the context, the setting, and the intervention. Section

3 describes our empirical strategy, including the randomization and data collection. Section

4 presents results, and Section 5 concludes.

2 Context

We conducted this study in rural parts of four districts in southern Punjab Province, Pak-

istan. Although this region produces abundant wheat and cotton, residents are relatively

poor because of the unequal land distribution. The population is predominantly Sunni Mus-

lim, although people belong to several sects. Communities are culturally conservative and

practice Purdah, which limits the interaction of women outside of the village.

The National Commission for Human Development (NCHD) regularly conducts adult

literacy classes throughout the country. Classes are gender-segregated and typically take

place in the home of an affluent member of the village. The classes are free to participants,

include around 25 people per class, and aim to establish basic literacy and numeracy. Classes

meet for 90 minutes per day, six days a week, for six months. The median age for participants

in our sample is 25, with ages ranging from 15 to 60. Because more affluent people seek

formal schooling, ALC participants are generally poorer than average.

Many people in rural Pakistan hold traditional beliefs about the causes of illness. Some

of these beliefs derive from Unani medicine, which emphasizes humoral “hot” and “cold”

4

states (Mull and Mull 1988). A person achieves health through balance between hot and

cold states, which do not correspond to physical temperature. Foods and activities have hot

and cold properties. Because diarrhea is a symptom of excess heat in the body, a person

with diarrhea should avoid other heat sources and consume cold foods. A mother’s hot or

cold status is believed to influence the quality of her breast milk. Working in the field makes

a mother’s breast milk hot and less suitable as a food source. People also perceive that

consuming too much food may lead to diarrhea. Nielsen et al. (2003) find that “when eating

too much, the food could not circulate properly in the stomach, resulting in incomplete

digestion and diarrhea.” In our sample, 93 percent of respondents believe that eating hot

foods is a cause of diarrhea.

People seek care from both traditional and Western health care providers, including con-

currently for the same illness (Hunte and Sultana 1992). Traditional medicine practitioners

known as hakims advocate Unani, herbal, and Ayurvedic remedies. A common treatment is

a taveez, or amulet, which is inscribed with religious sayings. People can seek Western health

care from both public and private clinics, as well as from lady health workers (LHWs), who

travel from village to village. Anwar et al. (2012) argue that people distrust public health

care providers in Pakistan because the standards of care in the public system are so poor.

The high cost and lack of access to Western care also encourages people to seek traditional

medical treatments. 41 percent of respondents in our sample trust hakims “ a good amount”

or “a great deal.”

2.1 Description of the Intervention

Microbe Literacy (ML) is a novel hygiene-education program developed by the South Asia

Fund for Health and Education (SAFHE), an international non-profit organization. The

ML curriculum incorporates a microscope demonstration and an infection privation work-

shop, each of which requires 90 minutes of class time. Facilitators begin the microscope

demonstration by using magnifying glasses to illustrate the concept of magnification. Next

5

they explain that a microscope allows someone to see small things with even more powerful

magnification. Each workshop uses one microscope, which is connected to a monitor so that

everyone can see at once. Facilitators create slides using nearby substances likely to contain

microbes, such as standing water, buffalo dung, and spoiled food.

The infection prevention (IP) workshop takes place approximately one week after the

microscope demonstration. Instructors teach participants about the different ways that

communicable diseases can be transmitted. Participants learn good hygiene and sanitation

practices, including hand washing with soap, proper food handling and storage, and water

purification. They learn to avoid open defecation and garbage disposal and to maintain a

clean cooking area. Participants learn to treat diarrhea through oral rehydration. This cur-

riculum captures the essential components of hygiene education as it is practiced elsewhere.

Field reports suggest that the intervention leaves a strong impression on participants.

One facilitator describes a microscope workshop: “It was amazing for women to see the bac-

teria on the slides which had been sampled from their homes. Women were very astonished

to know how much bacteria live around them. They expressed that they will be careful to

avoid microbes for themselves and for their children.” A facilitator described an infection

prevention workshop: “Women said that they get sick every week, but now they will take

preventative measures and they are hopeful they will never get sick.”

Ahmad et al. (2012) conducted a pilot study of Microbe Literacy in the Swat Valley of

Pakistan. The authors report that baseline diarrhea prevalence is 52 percent among children

under age 5, which is 50 percent higher than in our sample. In this study, ML reduces

diarrhea by 65 percent and respiratory illness by 76 percent. The study employs a pre-post

methodology, which means that seasonal variation in disease conditions may confound the

results.

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2.2 Data and Sampling Methodology

We collaborated with the NCHD to implement a randomized evaluation of Microbe Literacy

in the spring of 2013. The NCHD operated 605 ALCs for women in the districts of Rahim

Yar Khan, Muzaffargarh, Bahawalnagar, and Lodhran during this period. We selected 210

adult literacy classes and concentrated on Lodhran and Rahim Yar Khan districts. We

enrolled all eligible ALC participants who were present in class for the baseline survey and

excluded participants under age 15. Surveyors conducted interviews in respondents’ homes

to avoid interference from other classmates.

The survey instrument measures the respondent’s demographic and economic charac-

teristics, health, knowledge of disease pathways, and adherence to traditional medicine. We

measure knowledge by asking respondents whether they agree or disagree with four state-

ments: “Untreated water is safe to drink”, “I can tell if my hands are dirty just by looking

at them”, ”It is safe to eat food that has been touched by flies”, and “The worst thing diar-

rhea can do is make my child uncomfortable.” The correct answer to all of these questions

is “false”, so that respondents cannot answer correctly simply by agreeing with surveyors.

Over 50 percent of respondents believe that they can directly observe whether their hands

are dirty, and over 80 percent believe that diarrhea is not a serious health risk for children.

We sum the correct responses to create a knowledge score. Respondents correctly answer

around two out of four questions correctly in the baseline.

We measure hygiene by directly observing the personal appearance of the respondent

and her children. Surveyors measured this outcome on a three-point Likert scale, which we

standardized by providing an example image for each possible response, which we show in

Appendix Figure 1. This approach avoids relying self-reports, which are unreliable for a

socially-desirable behavior such as hygiene. Appendix Table 1 shows that personal appear-

ance is strongly correlated with other hygiene and sanitation outcomes that are available in

the baseline survey. We also show results for the presence of open defecation and garbage and

the cleanliness of the cooking area, which reflect household rather than individual behavior.

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We measure health by eliciting the incidence of diarrhea, fever, and cough for the re-

spondent and her three youngest children within the past two weeks.3 We limit the child

sample to children younger than five because young children are the most susceptible to

diarrhea and for consistency with other data sources. This limitation does not have an

important effect on our estimates. Improved hygiene may also reduce fever and cough in-

cidence. Cough indicates upper respiratory infection, while fever is the symptom of several

illnesses, including diarrheal infections. As Schmidt et al. (2011) note, diarrhea is measured

with error because respondents have varying interpretations of what constitutes diarrhea.

Fever and cough arguably contain less measurement error because the interpretation of these

symptoms is less idiosyncratic. For respondents, baseline diarrhea, cough, and fever are 13

percent, 26 percent, and 17 percent respectively. For their children younger than five, these

incidences are 34 percent, 36 percent, and 24 percent.

We conducted a follow-up survey in August and September 2013, around 14 weeks

after the intervention. The follow-up questionnaire closely resembles the baseline question-

naire but does not include some socioeconomic variables that would be unlikely to vary

over the timespan. The follow-up occurred immediately after Ramadan, during the rainy

season. Incidence of diarrhea is lower during this period. Seasonality may also affect re-

sponses to hygiene knowledge questions because some respondents may have answers that

are seasonally-specific. For instance, respondents may believe that hand contamination is

more visible during the dry season than during the rainy season.

To examine how traditional beliefs mediate the impact of the program, we develop a

traditional belief index using several baseline indicators. These variables include whether the

3Following the recommendation of Schmidt et al. (2010) and Schmidt et al. (2011), we elicit diarrhea overboth one-week and two-week horizons. The one-week horizon reduces measurement error due to lack of recall,while the two-week horizon is easier to compare to other data sources. To assess whether surveyor promptingaffected our responses, we administered two versions of the diarrhea question to random subsamples ofrespondents. In the prompted version, the surveyor provided the definition of diarrhea as three loose stoolswithin 24 hours before asking if the respondent or her children had experienced diarrhea. In the unpromptedversion, the surveyor asked if the respondent or her children had experienced any illness and only recordedresponses matching the definition of diarrhea. This distinction did not lead to meaningful differences indiarrhea prevalence.

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respondent believes that (1) eating hot foods causes diarrhea, (2) eating cold foods causes

diarrhea, (3) withholding foods and liquids is an effective treatment for diarrhea, (4) with-

holding breast milk is an effective treatment for diarrhea, and (5) other home remedies are

effective treatments for diarrhea. We also include whether the respondent (6) has consulted

a hakim within the past three months, (7) would consult a hakim if her child was having fits

(seizures), and (8) would consult a hakim if her child was having fainting spells.4 The TBI

is the unweighted sum of these outcomes, and varies from 0 to 8, with a median of 3. Figure

1 shows the frequency distribution of the TBI. As an alternative, we also define the TBI as

the first principal component of these variables.

Adherence to traditional beliefs is correlated with socioeconomic status. Appendix Table

2 shows how key variables in our analysis differ for people with above-median and below-

median values of the TBI. Non-believers in traditional medicine are younger, more literate,

and somewhat less religious. They live in somewhat nicer housing and they are more likely

to have animals. These respondents and their children have lower incidence of diarrhea,

fever, and cough, although the causal relationship between these variables is ambiguous.

Interactions with the TBI may capture these socioeconomic and health differences rather

than traditional beliefs per say. As a robustness test to address this concern, regressions with

TBI interactions also control for the interaction of treatment with all of the demographic

and economic variables in this table.

3 Empirical Approach

3.1 Study Design

Our study incorporates three treatment arms. The Microbe Literacy (ML) arm received

both the microscope demonstration and the infection prevention workshop. The Infection

Prevention (IP) arm received only the infection prevention workshop. The Control arm

4We elicited the hypothetical responses to fits and fainting spells because these morbidities have vaguecauses and have both traditional and Western explanations.

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received no hygiene education through the program. Before randomizing, we groups ALCs

into 110 geographic clusters, which we defined so that all clusters were at least 1 kilometer

apart. Based on this limitation, control ALCs are a median of 3 kilometers from the nearest

ALC in the ML arm. We stratified by grouping clusters into three diarrhea prevalence

categories and four districts, which led to a total of 12 cells containing an average of 17.5

ALCs.

Geographic proximity creates the possibility that information from the treatment may

reach control respondents. In practice, treatment contamination does not appear to be a

serious concern. The practice of Purdah severely limits the ability of females to interact with

people outside of their village. In our baseline survey, 69 percent of respondents had not

spoken to anyone outside of their village and 62 percent had not traveled outside of their

village in the past seven days. In the follow-up, only 8 percent of control respondents had

heard of an event involving microscopes. Results below do not change if we discard control

respondents who live near the treatment or are aware of the program.

We assess the validity of the randomization by comparing the baseline characteristics

of respondents by treatment status. Table 1 includes 19 demographic and economic char-

acteristics, including age, marital status, religion, assets, and labor supply. The table also

summarizes the knowledge, hygiene, health outcome variables. Columns 1 through 3 report

the baseline mean for each treatment arm. Columns 4 and 5 show p-values for the difference

between the ML and IP arms, and between the ML and C arms respectively.5 5.7 percent of

the mean comparisons in this table are statistically significant at 5 percent, which supports

the validity of the randomization.

Our stratification procedure ensures balance in health variables across treatment arms

but may yield economically meaningful baseline imbalances in other variables.6 The demo-

graphic variables in Table 1 do not vary systematically by treatment arm, however individual

5We obtain p-values by regressing each variable on treatment dummies while controlling for strata dum-mies and clustering by randomization group, which is consistent with our main estimation strategy below.

6Bruhn and McKenzie (2009) argue that rerandomization, which improves balance on all observables,does not increase the validity of the randomization but instead shifts imbalance onto unobservables.

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variables sometimes show imbalance. Although hygiene behavior and health outcomes are

generally balanced in Table 1, hygiene knowledge is notably imbalanced: ML respondents

answer around 0.2 fewer questions correctly than either IP or control respondents. The

magnitude of this imbalance may make it difficult to identify a treatment effect through a

standard cross-sectional regression.

Our regressions are based on the following specification in which i indexes the respondent

and j indexes the ALC. We estimate this specification using only data from the follow-up

round.

Yij = β1MLj + β2IPj + β3Ybij + Sj + εij (1)

ML and IP are indicators for the Microbe Literacy and Infection Prevention arms. Y bij

is the baseline value of the dependent variable, and Sj is a vector of strata dummies. If

the dependent variable is balanced in the baseline, controlling for Y bij improves precision

by absorbing variation in the dependent variable. We control for strata dummies because

random assignment occurs within strata (Kernan et al. 1999). We cluster standard errors

by randomization group.

As we discuss above, hygiene knowledge happens to be imbalanced across treatment arms

in the baseline. This imbalance interferes with the ability to detect a treatment effect if the

treatment causes arms to achieve greater balance than they had in the baseline. Because

knowledge is initially lower in the ML arm than the control arm, a specification such as

Equation (1) may fail to find a treatment effect since knowledge levels have converged by

the follow-up. We address this concern by estimating the effect on hygiene knowledge as a

difference-in-difference in addition to the specification above.

Yijt = β1POSTt ·MLj + β2POSTt · IPj (2)

+ β3MLj + β4IPj + β5POSTt + Sj + εijt

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The difference-in-difference nets out the difference in baseline levels and identifies a treatment

effect using the differential change in the outcome.7

We supplement estimates of these specifications with regressions that incorporate inter-

actions with the traditional belief index. A negative coefficient on the interaction between

MLj and TBIij indicates that the impact of ML is weaker for respondents who hold tra-

ditional beliefs. Traditional beliefs are not randomly assigned, and may be correlated with

other factors that mediate the impact of the program. We address the concern that the

TBI may proxy for socioeconomic status by controlling for the interaction of ML and IP

with a long list of socioeconomic characteristics. These characteristics include age, liter-

acy, education, marital status, household size, religious sect, religious adherence (number of

fasting days during the most recent Ramadan; number of prayers per day), house character-

istics, roof characteristics, savings, land, livestock, electricity, refrigerator and mobile phone

ownership, agricultural employment, and female labor force participation.

4 Results

4.1 The Impact of Microbe Literacy

We begin by showing the impact of Microbe Literacy on hygiene knowledge, hygiene behavior,

and health. Table 2 shows the effect of Microbe Literacy on hygiene knowledge. Panel A

shows results based on a cross-sectional regression in the follow-up period (Specification 1)

while Panel B shows results based on a difference-in-difference specification. Columns 1 and

2 estimate the impact on the raw knowledge score, which is the unweighted sum of correct

responses. Columns 3 and 4 estimate the impact on the Rasch score, which utilizes item

response theory to weight questions according to their difficulty.

Column 1 of Panel B shows our preferred specification, in which Microbe Literacy im-

7Estimates of equation (2) for hygiene and health and estimates of equation (1) for hygiene knowledgeare available from the authors. Hygiene and health estimates are not sensitive to the choice of specification.Cross-sectional difference regressions for hygiene knowledge show no treatment effect, which is consistentwith convergence of the ML arm after baseline imbalance.

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proves the knowledge score by 0.28 points. This change is 14 percent improvement over the

baseline level and equals 0.37 standard deviations in the knowledge index. Infection Pre-

vention without the microscope component increases the score by only 0.11 points, which

is statistically different from the impact of ML (p = 0.07). Column 1 of Panel A shows

that estimates are smaller and are insignificant if we use equation (1) to estimate the effect

on knowledge. This finding is consistent with the baseline imbalance for hygiene knowledge

across study arms. Columns 2 and 4 include controls for baseline demographic and economic

characteristics of respondents, as well as the baseline dependent variable. As expected, these

controls slightly increase the precision of the estimates. Panel A estimates become larger and

statistically significant. This pattern arises because controlling for the baseline dependent

variable partially addresses the baseline imbalance in the dependent variable.

Table 3 estimates the treatment effect on several hygiene outcomes. Panel A shows

estimates that exclude demographic and economic controls, while Panel B shows estimates

that include these controls. Our primary hygiene outcomes are the personal appearance

of the respondent and her children, which appear in Columns 1 and 2. Microbe Literacy

significantly improves the personal appearance of the respondent. The average score increases

by 0.17 points on a three-point scale. This effect is 8 percent of the baseline level and 0.29

standard deviations. The effect is marginally significantly different from the effect of IP, with

a p-value of 0.13 without controls and 0.10 with controls. The estimate for child hygiene is

qualitatively similar but is smaller and not statistically significant.

Columns 3-5 show estimates for other hygiene and sanitation outcomes for the house-

hold. Column 3 shows results for the surveyor observation of the absence of open defecation,

Column 4 shows results for the absence of garbage, and Column 5 shows results for the

cleanliness of the cooking area. These outcomes differ from respondent and child hygiene

in Columns 1 and 2 because they are determined jointly with the household. Whereas

a respondent plausibly has some autonomy over grooming choices that are reflected in her

appearance, other household members also influence the cleanliness or dirtiness of the house-

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hold environment. The lack of results here is consistent with our hypothesis that important

constraints limit the impact of the program on behavior.

Table 4 shows the impact on diarrhea, cough, and fever based on Equation (1). Odd

columns show results for the respondent and even columns show results for children under

age five. We distinguish between the full sample (Panel A) and the high-diarrhea subsample

(Panel B), which is defined according to the stratification. Columns 1 and 2 do not show

significant treatment effects on diarrhea. ML reduces diarrhea by 26 percent for the respon-

dent and by 7 percent for her children, but these estimates are not significant. Estimates

are larger but remain insignificant for the high-diarrhea subsample.

The remainder of the table shows that ML significantly reduces in incidence of cough

and fever. Cough declines by 5.7 percentage points (44 percent) for the respondent and by

4.4 percentage points (34 percent) for her children. These estimates are nearly twice as large

in the high-diarrhea subsample, which also has higher baseline prevalence of cough. In Panel

A, the effect of ML on cough is significantly larger than the effect of IP. We find similar

estimates for fever. ML reduces incidence of fever by 12 percentage points (46 percent) for

the respondent and by 5.1 percentage points (19 percent, p = 0.15) for her children.

4.2 Traditional Beliefs and the Impact on Hygiene Knowledge

This subsection investigates whether adherence to traditional beliefs medicates the impact of

Microbe Literacy. We modify our primary specifications by interacting treatment with the

baseline traditional belief scores of respondents. The sign of this interaction is theoretically

ambiguous because it depends on how Bayesian learning parameters vary by TBI. However

one possibility is that high-TBI respondents may learn less from the program because they

have more precise prior beliefs, which lead them to discount the information they receive.

Estimates for the differential impact by TBI on hygiene knowledge appear in Table 5.

Columns 1 and 2 show results based on the raw sum of traditional belief indicators, while

Columns 3 and 4 show results based on the first principal component of these variables.

14

Estimates in Column 1 indicate that someone with a TBI score of 0 increases her score by

0.71 points. Each unit increase in the TBI reduces the impact on the score by 0.16 points.

Although the treatment is randomly assigned in this regression, traditional beliefs are not

randomly assigned, and so omitted variables that are correlated with the TBI may confound

these estimates. In particular, Appendix Table 2 shows that people without traditional

beliefs have higher socioeconomic status. Columns 2 and 4 address this concern by controlling

for the interaction between Post × treatment and all of the demographic and economic

variables reported in Table 1. These controls, which are jointly significant (p < 0.0001),

only slightly attenuate the estimate, which suggests that traditional beliefs do not proxy for

socioeconomic status.

Figure 2 illustrates the differential treatment effects for people who do not hold tradi-

tional beliefs. The figure plots the ML coefficient from separate regressions by TBI value.

ML increases the knowledge scores of people with TBI values of 0 or 1 by 0.59 but increases

the scores of people with TBI values of 4 or more by just 0.13. This result suggests that the

program is unsuccessful in terms of conveying knowledge to people with traditional medical

beliefs.

Estimates for the differential effect by TBI on hygiene and health appear in Table 6.

Odd columns show results for the respondent and even columns show results for her children

younger than five. These estimates are consistent with the findings in Table 5, but they are

weaker and not statistically significant. Respondents with high TBI values improve their

appearance less in response to the program. Their health in terms of fever and cough also

does not fall as much. The insignificance of these results suggests that other factors may also

mediate hygiene and health outcomes, and thereby weaken the impact of the intervention.

We investigate this hypothesis further below.

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4.3 Propensity Score Interactions

Beahvioral constraints may prevent people from changing behavior in response to new in-

formation. These constraints are multidimensional and may vary individually. We identify

three categories of factors that plausibly influence respondent and child hygiene. We sum-

marize these variables and their contributions to the propensity score in Appendix Table

3. Economic variables include housing characteristics (the type of house and roof), whether

household members skip meals, and the respondent’s education. Gender variables include

whether the respondent needs her husband’s permission to have female friends, whether

she travels or speaks with people outside of her village, and the identity of the household

decision-maker with respect to the education of children. Physical variables include the type

of water supply, the characteristics of the latrine (including where it drains and whether it

is shared), and the presence of several types of domesticated animals.

Because most of these variables are categorical, we regression hygiene on category indi-

cators. The table shows the f-statistics and p-values for these indicators by group. Almost all

of these variables are highly significant. They jointly explain 10 percent of hygiene variation.

Our baseline estimates are based on a propensity score using OLS. However estimates based

on an ordered probit model, which is more appropriate for an ordered dependent variable,

are comparable.

Table 7 shows estimates for the interaction between treatment, the propensity score,

and the TBI. Panel A shows the straight interaction, while Panel B distinguishes between

respondents with TBI values of 0 or 1, and those with TBI values of 2 or more. Columns

1 and 2 show results for the combined propensity score, and indicate that people with

high propensity for good hygiene respond more to treatment, however this effect diminishes

as the traditional belief index increases. Panel B shows the stark result that the entire

treatment effect on hygiene arises for people with traditional belief responses of 0 or 1, which

indicates that the absence of traditional beliefs and the absence of practical constraints are

complementary for behavior change.

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Columns 3 through 8 distinguish between the categories of constraints. Economic and

(to a lesser extent) gender constraints appear to limit behavior change more than physical

constraints. We illustrate this pattern further in Figures 3, 4, and 5, which plot the responses

for high- and low-propensity respondents with different TBI values. In particular, Figure 3

shows that the hygiene response is concentrated among high-propensity respondents without

traditional beliefs.

5 Conclusion

Hygiene education may have mixed effectiveness as an anti-diarrheal intervention because

people hold traditional beliefs that are not consistent with a pathogenic model of disease

transmission. Microbe Literacy attempts to make hygiene instruction more salient by demon-

strating the existence of microbes to participants. Our estimates generally show that ML has

a larger impact on knowledge, behavior, and health than conventional hygiene education,

although these differences are not always statistically significant. Neither ML or IP has a

significant effect on diarrhea in this study, despite the focus of this intervention on improving

this outcome.

Estimates for the interaction between the program and traditional medical beliefs indi-

cate that these beliefs interfere with learning and behavior change. The lack of significant

treatment effects for adherents to traditional beliefs in Figures 2 and 3 suggest that policy-

makers must take more drastic steps to overcome the bias against hygiene education among

people with traditional beliefs. Our results suggest that the prevalence of traditional medi-

cal beliefs in the developing world may interfere with the adoption of healthy behaviors and

contribute to the tremendous disease burden in poor countries.

Finally, our estimates highlight how practical constraints may prevent behavior change

from learning interventions. Hygiene education is attractive from a policy perspective be-

cause it is inexpensive compared to water supply and sanitary investments. However a

combination of educational and infrastructural investments may be needed to achieve large

17

and sustained health improvements.

18

Table 1: Baseline Characteristics by Treatment Status

Mean P-valueML IP C ML − IP ML − C(1) (2) (3) (4) (5)

Demographic CharacteristicsAge 26.0 27.7 28.1 0.36 0.21Illiterate 0.25 0.24 0.38 0.95 0.04Any schooling 0.28 0.13 0.11 0.15 0.03Married 0.57 0.63 0.63 0.37 0.31Household size 6.6 6.5 6.4 0.86 0.68Barailvi sect 0.78 0.81 0.74 0.78 0.81Hadis sect 0.14 0.16 0.24 0.79 0.35Ramadan fasting days 12.7 14.4 15.4 0.21 0.04Prays at least once per day 0.75 0.73 0.76 0.81 0.95

Economic CharacteristicsImproved roof 0.83 0.70 0.70 0.28 0.13Bedrooms 1.9 1.9 1.9 0.97 0.86Any savings 0.11 0.09 0.12 0.58 0.84Land (acres) 4.0 4.1 3.3 0.93 0.71Animals 0.55 0.66 0.62 0.34 0.55Works outside the home 0.29 0.40 0.36 0.27 0.48Electricity 0.98 0.94 0.93 0.30 0.11Refrigerator 0.25 0.29 0.23 0.67 0.85Mobile phone 0.83 0.84 0.87 0.92 0.34Agriculture 0.46 0.39 0.37 0.65 0.50

Outcome VariablesKnowledge score 1.93 2.13 2.18 0.15 0.04Appearance (respondent) 2.35 2.37 2.36 0.58 0.61Appearance (children) 2.01 2.18 2.15 0.15 0.53Diarrhea (respondent) 0.12 0.15 0.14 0.45 0.72Diarrhea (children < 5) 0.31 0.35 0.36 0.55 0.23Cough (respondent) 0.18 0.17 0.15 0.55 0.09Cough (children < 5) 0.23 0.22 0.26 0.94 0.95Fever (respondent) 0.24 0.27 0.25 0.61 0.55Fever (children < 5) 0.34 0.34 0.40 0.74 0.24

Note: We obtain p-values from an OLS regression with standard errors clustered by randomization groupthat conditions on strata indicators.

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Table 2: The Impact of Microbe Literacy on Hygiene Knowledge

Hygiene KnowledgeRaw Score Rasch

(1) (2) (3) (4)

Panel A: Simple DifferenceMicrobe Literacy 0.044 0.094∗∗ 0.045 0.089∗∗

(0.057) (0.044) (0.057) (0.045)

Infection Prevention 0.031 0.039 0.031 0.041(0.059) (0.048) (0.059) (0.048)

ML − IP (p-value) 0.82 0.25 0.82 0.32

Panel B: Difference-in-DifferencePost × Microbe Literacy 0.28∗∗∗ 0.26∗∗∗ 0.29∗∗∗ 0.26∗∗∗

(0.086) (0.079) (0.087) (0.080)

Post × Infection Prevention 0.11 0.085 0.11 0.085(0.091) (0.083) (0.091) (0.083)

Post × ML − Post × IP (p-value) 0.07 0.06 0.07 0.06

Control for:Baseline dependent variable - Yes - YesBaseline characteristics - Yes - Yes

Note: Standard errors are clustered by randomization unit and are robust to heteroskedasticity.All regressions control for strata indicators. N = 3704 in Panel A and N = 7516 in Panel B. ForColumns 1 and 2, we compute the unweighted sum of correct responses. For Columns 3 and 4,we aggregate according to the Rasch IRT model. * p < 0.1, ** p < 0.05, *** p < 0.01

20

Table 3: The Impact of Microbe Literacy on Personal Hygiene

Personal Appearance CleanlinessRespondent Children Defecation Garbage Kitchen

(1) (2) (3) (4) (5)

Panel A: Without ControlsMicrobe Literacy 0.17∗∗ 0.10 0.067 0.0048 -0.012

(0.066) (0.063) (0.090) (0.068) (0.043)

Infection Prevention 0.053 0.093 0.017 0.047 0.047(0.055) (0.058) (0.091) (0.070) (0.040)

ML − IP (p-value) 0.13 0.89 0.54 0.50 0.10

Panel B: With ControlsMicrobe Literacy 0.16∗∗ 0.093 0.032 -0.015 -0.0079

(0.062) (0.058) (0.084) (0.057) (0.039)

Infection Prevention 0.047 0.073 -0.015 0.044 0.015(0.050) (0.051) (0.087) (0.062) (0.034)

ML − IP (p-value) 0.10 0.70 0.54 0.31 0.53

Dependent variable mean 2.33 1.96 1.93 1.94 2.14Observations 3930 2890 3930 3930 3925

Note: standard errors appear in parentheses. Standard errors are clustered by randomization group and are robust toheteroskedasticity. All regressions control for strata indicators. Regressions in Panel B control for baseline values of thedependent variable and all variables in Table 1. * p < 0.1, ** p < 0.05, *** p < 0.01.

21

Table 4: The Impact on Respondent and Child Health

Dependent variable: Diarrhea Cough FeverSample: R C R C R C

(1) (2) (3) (4) (5) (6)

Panel A: Without ControlsMicrobe Literacy -0.029 -0.015 -0.057∗∗∗ -0.044∗∗ -0.12∗∗∗ -0.051

(0.022) (0.033) (0.017) (0.020) (0.037) (0.035)

Infection Prevention -0.0053 0.013 0.0092 -0.0054 -0.070∗ -0.053(0.024) (0.032) (0.021) (0.022) (0.038) (0.035)

ML − IP (p-value) 0.24 0.39 0.0006 0.06 0.13 0.94

Panel B: With ControlsMicrobe Literacy -0.026 -0.0099 -0.047∗∗∗ -0.045∗∗ -0.11∗∗∗ -0.049

(0.020) (0.031) (0.015) (0.019) (0.033) (0.031)

Infection Prevention -0.0056 0.016 0.0096 -0.0071 -0.065∗ -0.057∗

(0.022) (0.031) (0.019) (0.022) (0.034) (0.034)

ML − IP (p-value) 0.28 0.41 0.001 0.06 0.16 0.79

Dependent variable mean 0.11 0.21 0.13 0.13 0.26 0.27Observations 3836 2619 3836 2619 3836 2619

Note: standard errors appear in parentheses. Standard errors are clustered by randomization group and are robust toheteroskedasticity. Odd columns show results for the adult respondent and even columns show results for her children underage 5. Regressions in Panel B control for the baseline values of the dependent variable and all variables in Table 1. * p < 0.1,** p < 0.05, *** p < 0.01.

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Table 5: Traditional Belief Interactions for Hygiene Knowledge

Hygiene Knowledge (Raw Score)(1) (2) (3) (4)

Post × Microbe Literacy 0.71∗∗∗ 1.42∗∗∗ 0.29∗∗∗ 1.12∗∗∗

(0.22) (0.42) (0.086) (0.40)

Post × Microbe Literacy × TBI -0.16∗∗ -0.14∗∗ -0.085∗ -0.057(0.069) (0.068) (0.045) (0.046)

Demographic and economic interactions - Yes - YesTraditional Belief Index Raw Raw PC PCObservations 7516 7516 7516 7516

Note: standard errors appear in parentheses. Standard errors are clustered by randomization group and are robust toheteroskedasticity. All regressions include ML, IP , and the level and interactions with TBI. Even columns controlfor the interaction of all demographic and economic characteristics with ML and IP . Columns 1 and 2 define the TBIaccording to the raw score, while Columns 3 and 4 define the TBI according to the first principal component.

23

Table 6: Traditional Belief Interactions for Hygiene and Health

Appearance Diarrhea Fever CoughR C R C R C R C(1) (2) (3) (4) (5) (6) (7) (8)

Microbe Literacy 0.29∗∗ 0.14 -0.038 -0.053 -0.15∗ -0.073 -0.11∗∗∗ -0.11∗

(0.13) (0.13) (0.039) (0.069) (0.080) (0.086) (0.038) (0.055)

Microbe Literacy × TBI -0.052 -0.018 0.0031 0.014 0.010 0.0077 0.021 0.024(0.039) (0.040) (0.016) (0.022) (0.022) (0.025) (0.013) (0.017)

Observations 3614 2651 3614 2472 3614 2472 3614 2472

Note: standard errors appear in parentheses. Standard errors are clustered by randomization group and are robust to heteroskedasticity. Thetraditional belief index (TBI) is the unadjusted sum of responses. Odd columns refer to the respondent and even columns refer to the children.

24

Table 7: Propensity Score Interactions for Respondent and Child Hygiene

Dependent variable: Personal Appearance of:R C R C R C R C

Propensity score: Combined Economic Gender Physical(1) (2) (3) (4) (5) (6) (7) (8)

Panel A: PS and TBI InteractionsMicrobe Literacy × PS 0.88∗∗ 0.54 1.57∗∗ 2.05∗∗ 1.31 0.34 -0.26 -0.019

(0.44) (0.46) (0.65) (0.85) (0.81) (0.73) (0.75) (0.65)

Microbe Literacy × PS × TBI -0.22 -0.13 -0.38∗ -0.59∗∗ -0.35 -0.013 0.043 -0.027(0.13) (0.12) (0.21) (0.26) (0.26) (0.22) (0.23) (0.18)

Panel B: PS Interactions for High/Low TBI

Microbe Literacy × PS × [TBI ≥ 2] 0.12 0.14 0.16 0.010 0.13 0.22 -0.013 0.092(0.20) (0.24) (0.28) (0.48) (0.28) (0.40) (0.37) (0.31)

Microbe Literacy × PS × [TBI < 2] 1.07∗∗∗ 0.43 1.66∗∗∗ 1.46∗∗ 1.41∗ 0.81 0.15 -0.31(0.38) (0.39) (0.58) (0.59) (0.73) (0.67) (0.59) (0.50)

Observations 3930 2890 3930 2890 3930 2890 3930 2890

Note: standard errors appear in parentheses. Standard errors are clustered by randomization group and are robust to heteroskedasticity. Odd columnsrefer to the respondent and even columns refer to her children.

25

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8

Perc

en

t

Traditional Belief Index

Figure 1: The Frequency Distribution of the Traditional Belief Index

26

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

≤1 2 3 ≥4

Diff

ere

nc

e-in

-Diff

ere

nc

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oe

ffic

ien

t

Traditional Belief Index

Figure 2: The Impact of ML on Hygiene Knowledge by TBI

27

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

≤1 2 3 ≥4

Mic

rob

e L

itera

cy

Co

eff

icie

nt

Traditional Belief Index

High Economic PS Low Economic PS

Figure 3: The Impact of ML on Respondent Appearance by TBI and Economic PS

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-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

≤1 2 3 ≥4

Mic

rob

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itera

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Co

eff

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Traditional Belief Index

High Gender PS Low Gender PS

Figure 4: The Impact of ML on Respondent Appearance by TBI and Gender PS

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-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

≤1 2 3 ≥4

Mic

rob

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itera

cy

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eff

icie

nt

Traditional Belief Index

High Physical PS Low Physical PS

Figure 5: The Impact of ML on Respondent Appearance by TBI and Physical PS

30

Appendix Table 1: The Correlation Between Personal Appearance and Other Hygiene Measures

Absence of Absence of Cleanliness Breastfeeds Hand Rinse Hand RinseDefecation Garbage of Kitchen Exclusively Turbidity E. coli

(1) (2) (3) (4) (5) (6)

Appearance of Respondent 0.32∗∗∗ 0.29∗∗∗ 0.28∗∗∗ 0.038 -0.18∗∗∗ -22.5∗

(0.029) (0.024) (0.017) (0.042) (0.036) (11.5)

Appearance of Children 0.10∗∗∗ 0.048∗∗ 0.20∗∗∗ 0.072∗ -0.12∗∗∗ -9.01(0.029) (0.024) (0.017) (0.042) (0.037) (11.8)

Observations 3288 3288 3288 669 1650 1650R2 0.08 0.08 0.25 0.01 0.05 0.01

Note: standard errors appear in parentheses. Regressions use only baseline data.

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Appendix Table 2: Baseline Characteristics of Respondents by TBI

Baseline Mean P-valueTBI < 2 TBI ≥ 2 (1) − (2)

(1) (2) (3)

Demographic CharacteristicsAge 24.2 26.7 0.00Illiterate 0.13 0.26 0.00Any schooling 0.09 0.11 0.34Married 0.45 0.57 0.00Household size 6.8 7.0 0.15Barailvi sect 0.91 0.83 0.00Hadis sect 0.08 0.15 0.00Ramadan fasting days 12.5 12.2 0.66Prays at least once per day 0.58 0.71 0.00

Economic CharacteristicsImproved roof 0.90 0.82 0.00Bedrooms 2.19 2.16 0.73Any savings 0.05 0.13 0.00Land (acres) 3.4 3.4 0.84Animals 0.72 0.66 0.08Works outside the home 0.36 0.33 0.61Electricity 0.93 0.93 0.93Refrigerator 0.31 0.26 0.04Mobile phone 0.90 0.84 0.00Agriculture 0.59 0.45 0.00

Outcome VariablesKnowledge index 1.92 2.12 0.08Appearance (respondent) 2.30 2.38 0.02Appearance (children) 2.10 2.15 0.13Diarrhea (respondent) 0.05 0.16 0.00Diarrhea (children < 5) 0.28 0.35 0.00Cough (respondent) 0.09 0.19 0.00Cough (children < 5) 0.19 0.25 0.02Fever (respondent) 0.13 0.29 0.00Fever (children < 5) 0.28 0.38 0.00

Note: we obtain p-values from an OLS regression with standard errors that are clusteredby randomization group.

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Appendix Table 3: Elements of the Propensity Score

Personal AppearanceR C(1) (2)

Economic variablesHousing characteristics 44.0 15.1

(0.00) (0.00)

Household skips meals 1.81 0.92(0.16) (0.40)

Respondent education 2.14 2.72(0.05) (0.01)

Gender variablesPermission to have friends 7.82 18.01

(0.00) (0.00)

Interaction beyond village 5.58 2.30(0.00) (0.04)

Education decision-making 3.86 3.68(0.00) (0.00)

Physical variablesWater supply characteristics 4.12 6.08

(0.00) (0.00)

Latrine characteristics 9.39 15.7(0.00) (0.00)

Presence of animals 10.44 8.65(0.00) (0.00)

Observations 4068 3293R2 0.10 0.11

Note: the table reports F statistics and p-values (in parentheses) fromlinear semi-parametric regressions using baseline data.

33

     

   

Appendix Figure 1: Photographs Corresponding to Personal Appearance Categories

34

References

Ahmad, Fayyez, Uzair Ahmad, Bilal Aziz, Edward Higgins, Ian King, AsjadNaqvi, and Iqbal Rehman, “Preliminary Results on Use of Microscopes to ReduceRates of Diarrhea and Respiratory Illness in Children Under Five of Non-Literate Par-ents in Swat Valley, Pakistan,” August 2012. Unpublished manuscript.

Anwar, M., J. Green, and P. Norris, “Health-Seeking Behaviour in Pakistan: a Narra-tive Review of the Existing Literature,” Public Health, 2012, 126, 507–517.

Banerjee, Abhijit, Angus Deaton, and Esther Duflo, “Wealth, Health, and HealthServices in Rural Rajasthan,” American Economic Review, May 2004, 94 (2), 326–330.

Beau De Rochars, Valery, Julie Tipret, Molly Patrick, Lara Jacobson, KamilBarbour, David Berendes, Diana Bensyl, Cathie Frazier, Jean Domercant,Roodly Archer, Thierry Roels, Jordan Tappero, and Thomas Handzel,“Knowledge, Attitudes, and Practices Related to Treatment and Prevention of Cholera,Haiti, 2010,” Emerging Infectious Diseases, November 2011, 17 (11), 2158–2161.

Bruhn, Miriam and David McKenzie, “In Pursuit of Balance: Randomization in Prac-tice in Development Field Experiments,” American Economic Journal: Applied Eco-nomics, 2009, 1 (4), 200–232.

Das, Jishnu and Jeffrey Hammer, “Which doctor? Combining Vignettes and itemresponse to measure clinical competence,” Journal of Development Economics, 2005,78, 348–383.

Davis, Jennifer, Amy Pickering, Kirsten Rogers, Simon Mamuya, and AlexandriaBoehm, “The Effects of Informational Interventions on Household Water Management,Hygiene Behaviors, Stored Drinking Water Quality, and Hand Contamination in Peri-Urban Tanzania,” American Journal of Tropical Medicine and Hygiene, February 2011,84 (2), 184–191.

Fewtrell, L, RB Kaufmann, D Kay, W Enanoria, L Haller, and JM Colford,“Water, Sanitation, and Hygiene Interventions to Reduce Diarrhoea in Less DevelopedCountries: a Systematic Review and Meta-Analysis,” Lancet Infectious Diseases, 2005,5 (1), 42–52.

Hrdy, Daniel, “Cultural practices contributing to the transmission of human immunodefi-ciency virus in Africa,” Reviews of Infectious Diseases, 1987, 9 (6), 1109–19.

Hunte, Pamela and Farhat Sultana, “Health-Seeking Behavior and the Meaning ofMedications in Balochistan, Pakistan,” Social Science and Medicine, 1992, 34 (12),1385–1397.

Karmakar, Palash, Muhammad Mazharul Islam, Md. Golam Kibria, Moham-mad Salim Hossain, and Mohammad Mafruhi Sattar, “Prevalence, belief andawareness of preferring traditional health care system in urban and rural people of

35

Noakhali district, Bangladesh,” International Current Pharmaceutical Journal, 2012, 1(9), 229–234.

Kernan, Walter, Catherine Viscoli, Robert Makuch, Lawrence Brass, and RalphHorwitz, “Stratified Randomization for Clinical Trials,” Journal of Clinical Epidemi-ology, January 1999, 52 (1), 19–26.

Monte, Cristina, Ann Ashworth, Marilyn Nations, A. Lima, A. Barreto, andSharon Huttly, “Designing Educational Messages to Improve Weaning Food HygienePractices of Families Living in Poverty,” Social Science and Medicine, 1997, 44 (10),1453–64.

Mull, J. Dennis and Dorothy Mull, “Mothers’ Concepts of Childhood Diarrhea in RuralPakistan: What ORT Program Planners Should Know,” Social Science and Medicine,1988, 27 (1), 53–67.

Nielsen, Melanie, Anneke Hoogvorst, Flemming Konradsen, Muhammed Mu-dasser, and Wim van der Hoek, “Causes of Childhood Diarrhea as Perceived byMothers in the Punjab, Pakistan,” Southeast Asian Journal of Tropical Medicine andPublic Health, June 2003, 34 (2), 343–351.

Schmidt, Wolf-Peter, Benjamin Arnold, Sophie Boisson, Bernd Genser, StephenLuby, Mauricio Barreto, Thomas Clasen, and Sandy Cainrcross, “Epidemiolog-ical Methods in Diarrhoea Studies - an Update,” International Journal of Epidemiology,December 2011, 40 (6), 1678–92.

, Bernd Genser, Mauricio Barreto, Thomas Clasen, Stephen Luby, SandyCainrcross, and Zaid Chalbi, “Sampling Strategies to Measure the Prevalence ofCommon Recurrent Infections in Longitudinal Studies,” Emerging Themes in Epidemi-ology, August 2010, 7 (15), 1–13.

World Health Organization, “Traditional Medicine,” 2003. Fact Sheet No. 134.

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